Information about Asia and the Pacific Asia y el Pacífico
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Chapter 6. Inflation and Income Inequality in China and India: Is Food Inflation Different?

Author(s):
Paul Cashin, and Rahul Anand
Published Date:
February 2016
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Author(s)
James P. Walsh and Jiangyan Yu 

Rapid growth in developing economies has led to an important decline in poverty both at the national level and, owing to the large size of China and India where growth has been especially rapid, globally. However, in many emerging markets, income inequality has risen as more open and market-oriented economies increased profits and potential wages, particularly for skilled labor. At the same time, rapid growth has pushed up commodity prices around the globe, raising questions about whether a seemingly inexorable rise in food prices is aggravating the problems faced by the world’s poor.

Although inflation is often seen as aggravating poverty and worsening income distribution, the distinction between food and nonfood inflation bears examining. Higher food prices can hurt the well-being of many poor people, particularly in urban areas, but may benefit producers, thereby reducing poverty among some in rural areas. Based on datasets of food and nonfood prices available at the global level (described in Annex 6.1), as well as subnationally for Chinese provinces and Indian states, the analysis in this chapter attempts to distinguish between the effect of food and nonfood inflation on changes in income inequality.

Inflation and Poverty

The relationship between inflation on the one hand, and poverty and income inequality on the other, remains unsettled in the literature, though many find that inflation generally worsens inequality. Romer and Romer (1999) look at the incomes of the poor and show that both in the United States and globally, higher inflation when accompanying economic growth can support the incomes of the poor in the short term. But in the long term, by adding to economic uncertainty, it can depress both average incomes and the incomes of the poor. Easterly and Fischer (2000) looked at a very large sample of household survey data across a wide range of countries and found that the poor were more likely than the rich to cite inflation as a problem, and that inflation tended to worsen the assessment of their own well-being more than it did for the rich. Various household-level studies on countries including Brazil, India, and the Philippines also found that higher inflation leads to a lower share of income held by the poorest share of the population.

How Does Inflation Hurt the Poor?

Various channels are posited through which inflation might hurt incomes of the poor more than the rich, such as their ability to borrow and smooth consumption, deposit cash in banks or buy bonds with a return that can exceed inflation, or their greater likelihood of owning a house and being insulated from rents. These channels might exist in any country, but some are likely to be more prevalent in developed economies, and others, such as the inadequate indexation of social benefits, are unlikely to be significant in developing economies. There are various channels by which inflation might disproportionately affect the poor. Economies of scale and barriers to entry in financial services can reduce the access of the poor to inflation hedges that the rich can access; the relatively competitive labor market for unskilled labor in developing economies reduces the bargaining power of poor workers; and storage technology (home storage for buying quantities of goods for later use and the ability to freeze perishable foods, for example) can help lock in prices for goods consumed later. Households can also hedge by allocating their portfolios between cash, which rapidly loses value, and consumption goods, which may lose value less quickly. Middle-income households confronted with rising inflation might therefore bring forward purchases of clothing, appliances, houseware, or other products in their consumption basket. But the consumption basket of poor households is disproportionately focused on food, which cannot really be brought forward because of its perishability. This same dependence on cash balances can also hurt the power of poor households in negotiating prices or wages, compared to rich households.

Another strand of the literature looks specifically at food prices and the poor. Rising food prices are likely to raise the incomes of food producers. This could compensate for lower incomes that would accrue to artisans or other households in rural areas, but only if rural households that do not own land or are net purchasers of food are relatively few. Deaton (1989) uses a nonparametric analysis of the effect of higher rice prices across different regions of Thailand, and shows that higher food prices can benefit many groups in society, though middle-class food producers benefit the most overall. Ravallion (2000) looks at the interrelationship over more than 30 years between food prices, poverty, and wages in India to analyze whether agricultural reform helps or hurts the poor. While corroborating other work that shows inflation reduces rural expenditure, he notes that once agricultural output and overall inflation are taken into account, food prices do not appear to have an independent effect on real wages. So although households may take an immediate hit when food prices rise, rising rural productivity will, in the longer term, affect both food producers and the wages of rural laborers, which would reduce rural income inequality. The effect of higher food prices on income distribution can thus be neutral if wages for laborers adjust sufficiently.

Other studies suggest that wages may not be so flexible. Numerous studies present evidence suggesting that wages may not fully adjust to higher food prices, in which case the poor suffer more, given the higher share of food in their consumption basket. Overall, the distributional impact will depend on the extent to which households are net producers or consumers of food. And both first- and second-round effects can matter. Incomes for nonfood-producing households could rise if the greater income accruing to food-producing households trickles down to other households via greater economic activity.

Urban and Rural Differences

In countries with significant rural-urban migration, such as China or India, higher rural wages (from higher food prices) relative to urban ones (also from higher food prices) affect household migration decisions. A shift in relative prices toward food, which constitutes a very large share of the consumption basket for the very poor, could have large effects on these decisions by discouraging marginal households in rural areas from sending workers to cities, or encouraging newly impoverished urban workers to return to the countryside. As these urban workers move home to rural areas, they remove the poorest segment of urban society from the cities, and, all else being equal, reduce income inequality in cities there. This movement mitigates the effect of higher food prices on income distribution (though obviously not mitigating its effect on individual households). Conversely, if higher food prices encourage the landless or other net food purchasers in rural areas to take advantage of the better wage opportunities cities afford, the net effect on rural areas could be the opposite. That is, those with relatively high and stable incomes are unlikely to leave, meaning that the rural poor who migrate are likely to be poorer and that their movement to the cities can thus reduce income inequality there.1

Given the evidence that headline inflation in many cases exacerbates income inequality, while rising food prices may have a more moderate or even benign impact, the likely upshot is that nonfood inflation must be particularly damaging to the poor. If higher food prices in rural areas pass through to wages, (that is, if wages in rural areas are elastic to food price increases), then food inflation should be less harmful, or possibly beneficial, in its effect on income inequality in rural areas. The relationship could also hold in urban areas, but given that more rural inhabitants are likely to be involved in agriculture, the relationship is likely to be stronger in rural areas. Nonfood inflation, on the other hand, should widen income inequality in both urban and rural areas.

How Do Other Factors Matter?

Nonfood inflation could thus be strongly correlated with worsening income inequality once other factors known to mitigate worsening income inequality, such as education and average income growth, have been taken into account. For food inflation, the relationship is less clear.

At the international level, food inflation should immiserate the poor in food-importing countries, while it could reduce inequality in food-exporting countries. Because most countries both import and export some food, and because the incomes of producers of different types of food can differ greatly, the aggregate relationship may not be clear just from the balance of trade. If food inflation adds to income inequality at the international level, by raising inequality in some countries by more than it reduces it elsewhere, then, on average, it is likely that wages in general are not particularly responsive to food prices. A link between higher food inflation and declining income inequality would most likely imply that the wages for the rural poor across the world are elastic to food prices. On the other hand, if food prices have little effect in aggregate, or if food inflation tends to reduce income inequality, then these wage effects must be present and could be quite large. As a first pass at this question, the following section uses a large dataset of food and nonfood inflation across a wide range of countries to assess how these price changes affect income inequality across countries and across time. The analysis is then extended to Chinese provinces, for which appropriate data are available, to see whether these effects are also visible at a national level.

At the domestic level, if wages in rural areas are relatively elastic to food prices, then higher food inflation will improve or at least not worsen income inequality in rural areas, while its effect in urban areas is likely to be negative, as with nonfood inflation. These effects can be studied more closely in the case of India, where richer subnational data are available. India estimates income inequality for both urban and rural areas across the various states. Consumer price index (CPI) data are estimated at the state level of rural areas within each state, and proxies can be calculated for urban areas. Using these data, we can separately assess the impact of food and nonfood inflation on both rural and urban income inequality. Nonfood inflation should lead to worsening income inequality in both regions, and food inflation in urban areas should also result in worsening income inequality. But if wages in rural areas react elastically to increases in food prices, then higher food inflation should lead to a decline in income inequality in rural areas.

Stylized Facts

Global Inequality

Figure 6.1 shows how Gini coefficients have changed over time for the countries in the sample, compared to GDP per capita. On average, Gini coefficients have not changed drastically across countries in the sample, except in richer countries, where they have risen slightly since 2000. Figures 6.2 and 6.3 show Gini coefficients related to food and nonfood inflation. In general, during 2000–10, income inequality actually declined in countries with higher inflation, both food and nonfood; and the relationship between inequality and food inflation does not appear significantly different than that between inequality and nonfood inflation, though in these charts other control factors, such as GDP growth, are not yet taken into account.

Figure 6.1Cross-Country GDP per Capita and Gini Change, 2000–10

Source: World Bank, World Development Indicators.

Figure 6.2Cross-Country Food Inflation and Gini Change, 2000–10

Sources: World Bank, World Development Indicators; and IMF staff calculations.

Figure 6.3Cross-Country Nonfood Inflation and Gini Change, 2000–10

Sources: World Bank, World Development Indicators; and IMF staff calculations.

China

China’s rapid economic growth over the past few decades has coincided with a noticeable deterioration in income distribution. The country’s Gini coefficient is estimated to have reached 0.42–0.47 in the late 2000s, from below 0.3 in the early 1980s.2 Since 1992 the urban income disparity has replaced the rural one to become the most important driver of overall income inequality.

Figure 6.4 shows that GDP per capita grew very rapidly across all Chinese provinces in the first half of the 2000s, but most provinces also saw an increase in inequality, with the fastest-growing ones seeing slightly larger increases. Figures 6.5 and 6.6 show that the change in the Theil index is only slightly correlated with food and nonfood inflation. However, these simple correlations do not account for (very rapid) growth in income or other significant macroeconomic and structural factors.

Figure 6.4China: Provincial GDP per Capita and Inequality Change, 2000–05

Sources: Data from the University of Texas Inequality Project; and World Bank, World Development Indicators.

Figure 6.5China: Food Inflation and Theil Index Change, 2000–05

Source: IMF staff calculations.

Figure 6.6China: Nonfood Inflation and Theil Index Change, 2000–05

Source: IMF staff calculations.

India

Figure 6.7 shows the pattern of urban and rural inequality across the largest Indian states. Rural inequality is higher than urban inequality in every state, and while the former tends not to vary much across states, urban inequality tends to be greater in the richer states than poorer ones. Figure 6.8 shows how income per capita growth between 1994 and 2004 related to changes in income inequality. In general, as in China, inequality rose more in the faster-growing states, and with the breakdown between rural and urban data available, it can be seen that this effect was stronger in urban areas.

Figure 6.7India: Inequality and GDP per Capita by State

Source: Government of India.

Figure 6.8India: GDP Per Capita Growth and Gini Change in 16 States, 1994–2004

Sources: Government of India; and IMF staff calculations.

Note: Covers 16 Indian states.

Results

International Sample

In this sample and analysis, headline inflation does not appear to have a strong relationship with income inequality under most specifications (Annex Table 6.2.1). Once simultaneity is taken into account (under the Arellano-Bond specification in column 4 of the table), higher GDP growth is associated with higher income inequality, while higher GDP per capita is associated with slightly lower income inequality. Higher rates of enrollment in primary education are associated with decreases in income inequality. Secondary school enrollment has a less consistent effect, but when significant is also associated with falling inequality. Breaking inflation down into food and nonfood inflation produces somewhat different results, as shown in Annex Table 6.2.2. The relationship between changes in income inequality and food and nonfood inflation is significant only when simultaneity is taken into account under specification (4), and is not consistent across specifications. Nonfood inflation is associated with rising income inequality, the expected result, only under the Arellano-Bond specification. Food inflation is only significant under this specification and is associated with falling income inequality. Income, as measured by GDP per capita, is not significant under any specification, while average real GDP growth is associated with slightly lower income inequality in the fixed- and random-effects specifications, but not when simultaneity is taken into account. The counterintuitive results about education are no longer present, with secondary school enrollment now intuitively associated with somewhat lower income inequality under the Arellano-Bond specification.3

China

The results from Chinese provinces, once endogeneity is taken into account via the Arellano-Bond specification, show that higher headline inflation is associated with more rapid widening of inequality, as measured by a Theil coefficient (Annex Table 6.2.3). Higher GDP growth is associated with a slower pace of deterioration in income inequality, while richer provinces tend to have bigger increases.

When inflation is divided into food and nonfood inflation, the picture is somewhat different (Annex Table 6.2.4). The coefficient on nonfood inflation has the expected positive sign in three of the four specifications, but is only significant under the Arellano-Bond generalized method of moments specification. Food inflation is associated with less income inequality under each specification, but this is not significant. As in the headline CPI regressions, faster GDP growth is associated with declining inequality, while this effect is mitigated in richer provinces, where inequality tends to rise more rapidly.

India

Indian income inequality data are available not only on a state level, but also broken down between urban and rural areas, allowing for some distinction between food-producing and food-importing regions.

Annex Tables 6.2.5 and 6.2.6 show the relationship between headline CPI and income inequality (as measured with Gini coefficients) across rural and urban areas in Indian states. In rural areas (Annex Table 6.2.5) headline inflation shows little relation to income inequality, with the coefficient very close to zero, except under the Arellano-Bond specification. Higher income per capita is associated with higher inequality under three specifications, however. In urban areas (Annex Table 6.2.6), the results are more intuitive: the coefficient on headline inflation is positive and significant under two specifications, including when accounting for simultaneity, while higher levels of income per capita are also associated with rising income inequality. Literacy and real GDP growth are not generally significant.

When the CPI is divided into food and nonfood inflation, the results are stronger. In rural areas (Annex Table 6.2.7), food inflation is strongly linked to lower income inequality, while nonfood inflation, intuitively, is linked to higher income inequality. Again, states with higher levels of income per capita are associated with rising income inequality, though real GDP growth itself, as well as literacy, are not.

In urban areas, as expected, higher nonfood inflation is strongly tied to higher income inequality (Annex Table 6.2.8). Food inflation, on the other hand, is more ambiguous. The coefficient on food inflation is negative in all specifications, implying that wages are flexible and respond to higher food prices, though the result is significant only in two specifications and not under Arellano-Bond. As with rural inflation, states with higher income appear to have rising income inequality, while GDP growth and literacy have little effect.

Conclusion

The results presented in this chapter are relatively agnostic about whether headline inflation is detrimental to income inequality, but they are able to extend the analysis beyond this broad measure of inflation. Higher nonfood inflation is associated with worsening income inequality in all three samples (international, China, India), supporting the results from previous work suggesting that income inequality is aggravated by higher levels of inflation. This is intuitive, given that an individual household’s income can benefit from higher prices only for the goods or services that it produces, and no individual household is likely to be a producer of a sufficiently wide share of the country’s nonfood consumption basket.

However, this detrimental impact is smaller for food inflation. In the international sample, and once the endogeneity of inflation, inequality, and growth are taken into account, higher food inflation is associated with declining income inequality—and the same is true for the Chinese data. These results suggest that food inflation may not be bad for all lower-income people, or at least that the hit to income taken by some groups may be balanced or even exceeded by increased income accruing to other groups, such as low-income food producers.

The Indian data allow some finer conclusions to be drawn. By differentiating between urban and rural inequality, they provide further support to the view that nonfood inflation widens income inequality in both urban and rural areas. Food inflation has different effects, however. The effect on urban inequality is ambiguous, but in rural areas it is strongly associated with lower inequality. This is somewhat counterintuitive. Because few urban dwellers are likely to be food producers, it seems reasonable to expect that higher food prices have a negative effect on households that are most exposed to food prices; that is, the poor. But here the effect of rising food prices on urban inequality does not appear to be particularly strong. And in rural areas, the effect appears to be strongly positive (in the sense of lower inequality).

These results should also be taken with a number of caveats. China and India are relatively closed economies for staple foods. Countries that import a large share of their staple crops, such as wheat or corn, may have very different dynamics of food prices and income inequality than countries where some important staple crops (rice in both countries, and also pulses in India) are not as susceptible to global commodity shocks. Unlike corn or wheat, the global market for rice and pulses is small and relatively unimportant; beyond that, even differing provinces in China or states in India have limited substitutability of crops. Thus, self-sufficiency means that China and India are as likely to have a higher share of households producing staple crops as countries that either export or are reasonably self-sufficient in staples. Even within China and India, the effect of food inflation is likely to be greatest in food-importing states and provinces, though this would have to be studied using household data across states or provinces.

India and particularly China, as high-growth economies, may have a different relationship between food production and rural wages. With relatively good opportunities for labor in urban areas and, especially in China, rising agricultural productivity, the ease with which unskilled workers are able to shift from the rural to the urban labor force may be greater than in other countries. This process mutes increases in income inequality in rural areas and provides more of a safety valve for rural workers than would exist in countries with slower growth. With more limited employment opportunities in urban areas, food inflation could be significantly more immiserating for rural consumers in slower-growing economies.

Annex 6.1. Data Sources

International data on food and nonfood inflation are compiled from public country sources and were used in Walsh (2010). GDP per capita, as well as real economic growth, are from the IMF’s World Economic Outlook database. Macroeconomic data for China and India come from CEIC. For China, provincial-level food and nonfood price indices were derived from consumer price index (CPI) data, with weights estimated by IMF staff. For India, state-level food and nonfood inflation were also estimated based on CPI data. CPI for agricultural workers is calculated on a state-by-state basis; this is used as the rural price index. CPI for industrial workers is calculated on a municipal basis. For each state, CPI for industrial worker indices for each city in the state were averaged and weighed by the urban area’s 2001 population to arrive at a proxy for urban CPI indices for each state.

Inequality data come from a variety of sources. For the global regression, the data for inequality are measured by annual Gini coefficients from the World Bank’s World Development Indicators database estimated from 1990 to 2010 for 75 advanced, transition, and developing economies. Because there is no official publication of Gini coefficients for Chinese provinces, this chapter uses provincial Theil indices estimated by the University of Texas to measure inequality. The data cover 31 provinces, municipalities, and autonomous regions over 1994–2006. Indian inequality data come from various government sources and are based on the government’s National Sample Survey Rounds covering 1990–2004, when India began to open to reforms. However, there are not yet income inequality data for the high-growth period of the mid-2000s.

The following sources were used for other data. For the international sample, education level is presented as the levels of primary and secondary school enrollment rates from the World Bank’s World Development Indicators database. For China, the picture is more complicated. According to data published by the Ministry of Education, the primary enrollment ratio changed by a small margin from 108¾ percent in 1994 to 106¼ percent in 2006, meaning there is little variation over time that can be used in estimation. However, although provincial-level data are not published in a comprehensive way, it is believed that western provinces tend to have lower enrollment than the more developed provinces in the middle and coastal provinces. For tertiary education, there is some difference: during the period under study, enrollment in higher education rose significantly. However, the dramatic mobility of educated workers makes it difficult to gauge the relationship of well-educated workers with inequality. Therefore in this model, education level is treated as a province-specific and time-invariant factor. In India, educational attainment differs more across states than it does in China, and both urban and rural literacy rates are available by state. These data are used in the analysis.

Annex 6.2. Regression Results
Annex Table 6.2.1International Sample: Headline Inflation
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
CPI Inflation0.00698−0.0455−0.01360.0433
−0.32(−1.77)(−0.59)−1.73
Nominal GDP−0.001590.00291−0.0007380.00705**
(−1.74)−1.23(−0.59)−3.12
Real GDP Growth−0.000164−0.0174−0.01020.0133
(−0.01)(−0.89)(−0.58)−0.82
GDP per Capita0.000150*0.00002640.000126−0.000246*
−2.09−0.2−1.4(−1.98)
Primary School Enrollment−0.0244*−0.0753*−0.0341*−0.00453
−2−2.24−2.25−0.12
Secondary School Enrollment0.00545−0.116***0.000887−0.0273
−0.65(−3.78)−0.08(−0.87)
Lag of Dependent Variable0.433***
−8.71
Constant−3.206*1.121−3.741*
(−2.58)−0.31(−2.56)
R20.0290.05
Adjusted R20.016−0.162
N446446446242
Source: IMF staff calculations.Note: t-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: t-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Annex Table 6.2.2International Sample: Food and Nonfood CPI
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
Food Inflation−0.03380.2080.0377−0.233*
(−0.27)−1.26−0.3(−2.59)
Nonfood Inflation−0.0549−0.325*−0.1710.318**
(−0.45)(−2.08)(−1.34)−2.86
Nominal GDP−0.003870.00625−0.002320.00346
(−1.30)−0.62(−0.57)−0.71
Real GDP Growth−0.0924−0.153*−0.145*0.0372
(−1.58)(−2.17)(−2.42)−1.06
GDP per Capita0.0000428−0.00004980.00009310.0000068
−0.33(−0.21)−0.63−0.06
Primary School Enrollment−0.0944*−0.116−0.0991−0.101
(−1.99)(−0.67)(−1.57)(−1.22)
Secondary School Enrollment0.00534−0.1290.000702−0.139*
−0.17(−1.20)−0.02(−2.17)
Lag of Dependent Variable0.342***

−3.67
Constant11.52*25.6512.55*
−2.36−1.32−1.97
R20.0860.132
Adjusted R20.016−0.15
N99999959
Source: IMF staff calculations.Note: t-statistics in parentheses. OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: t-statistics in parentheses. OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Annex Table 6.2.3China: Headline Inflation
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Theil CoefficientΔ Theil CoefficientΔ Theil CoefficientΔ Theil Coefficient
CPI Inflation−0.5180.1650.09300.732***
(−0.76)(0.36)(0.21)(11.03)
Real GDP Growth0.391−0.184−0.114−0.660***
(0.79)(−0.47)(−0.30)(−8.03)
GDP per Capita−0.008880.01620.01390.00330**
(−0.75)(1.90)(1.65)(2.83)
Constant5.7286.4386.0146.677***
(0.87)(1.37)(1.02)(5.98)
Lag of Dependent Variable0.859***
−279.67
R20.0050.015
Adjusted R2−0.006−0.122
N271271271270
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.** p < 0.01; *** p < 0.001.
Annex Table 6.2.4China: Food and Nonfood CPI
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Theil CoefficientΔ Theil CoefficientΔ Theil CoefficientΔ Theil Coefficient
Nonfood Inflation−1.6412.0201.6640.995***
(−0.82)(1.61)(1.31)(8.06)
Food Inflation0.458−0.877−0.764−0.0978
(0.47)(−1.45)(−1.24)(−1.81)
GDP per Capita−0.009460.0179*0.01460.00307*
(−0.79)(2.09)(1.70)(2.06)
Real GDP Growth0.379−0.153−0.0695−0.609***
(0.76)(−0.39)(−0.18)(−7.35)
Lag of Dependent0.860***
Variable
(152.74)
Constant5.0977.0296.4666.348***
(0.77)(1.49)(1.13)(5.33)
R20.0070.026
Adjusted R2−0.008−0.115
N271271271270
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; *** p < 0.001.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; *** p < 0.001.
Annex Table 6.2.5India: Headline CPI, Rural Areas
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
CPI Inflation0.002000.02150.00200−0.0341
(0.11)(0.98)(0.11)(−1.41)
Real GDP Growth−0.0231−0.0104−0.02310.00936
(−1.00)(−0.39)(−1.00)(0.39)
GDP per Capita0.0604*0.111*0.0604*−0.0288
(2.21)(2.60)(2.21)(−0.48)
Literacy Rate−0.00000212−0.00000212
(−0.00)(−0.00)
Lag of Dependent Variable0.162*
(2.04)
Constant−1.276−2.953*−1.276
(−1.27)(−2.11)(−1.27)
R20.0450.053
Adjusted R20.024−0.033
N194194194132
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05.
Annex Table 6.2.6India: Headline CPI, Urban Areas
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
CPI Inflation−0.009950.111*−0.009950.114**
(−0.34)(2.58)(−0.34)(2.69)
Real GDP Growth−0.0133−0.0132−0.0133−0.0308
(−0.39)(−0.35)(−0.39)(−0.85)
GDP per Capita0.0908*0.344***0.0908*0.345***
(2.38)(4.75)(2.38)(4.22)
Literacy Rate−0.0305−0.0305
(−0.90)(−0.90)
Lag of Dependent Variable−0.124
(−1.41)
Constant3.550−7.336**3.550
(1.28)(−2.99)(1.28)
R20.0650.176
Adjusted R20.0400.078
N152152152127
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Annex Table 6.2.7India: Food and Nonfood CPI, Rural Areas
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
Food Inflation−0.0497*−0.0366−0.0497*−0.0790***
(−2.31)(−1.55)(−2.31)(−3.44)
Nonfood Inflation0.247**0.273**0.247**0.256**
(2.83)(2.96)(2.83)(2.97)
Real GDP Growth−0.0180−0.00676−0.01800.0154
(−0.80)(−0.26)(−0.80)(0.64)
GDP per Capita0.0691*0.122**0.0691*−0.00686
(2.56)(2.89)(2.56)(−0.11)
Literacy Rate−0.00493

(-0.40)
−0.00493

(-0.40)
Lag of Dependent Variable0.145

(1.83)
Constant−2.115*−4.112**−2.115*
(−2.06)(−2.87)(−2.06)
R20.0840.094
Adjusted R20.0600.006
N194194194132
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Annex Table 6.2.8India: Food and Nonfood CPI, Urban Areas
(1)(2)(3)(4)
OLSFixed EffectsRandom EffectsArellano-Bond
Δ Gini CoefficientΔ Gini CoefficientΔ Gini CoefficientΔ Gini Coefficient
Food Inflation−0.131***−0.0484−0.131***−0.0636
(−4.33)(−1.14)(−4.33)(−1.61)
Nonfood Inflation0.314***0.303***0.314***0.344***
(4.16)(3.63)(4.16)(4.31)
Real GDP Growth0.01890.01440.0189−0.00232
(0.57)(0.38)(0.57)(−0.07)
GDP per Capita0.04220.250**0.04220.253**
(1.13)(3.15)(1.13)(3.08)
Literacy Rate0.00111

(0.03)
0.00111

(0.03)
Lag of Dependent Variable−0.174*

(-2.06)
Constant−0.646−6.572**−0.646
(−0.23)(−2.72)(−0.23)
R20.1840.217
Adjusted R20.1560.118
N152152152127
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
Source: IMF staff calculations.Note: f-statistics in parentheses. CPI = consumer price index; OLS = ordinary least squares.* p < 0.05; ** p < 0.01; *** p < 0.001.
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1They are unlikely to be the very poorest among rural households, however, because the lowest-income rural households, including the elderly, disadvantaged groups, and extremely small households, are unlikely to have the assets to leave home.
2Estimates of the Gini coefficient by international institutions range from 0.42 to 0.47 in 2005–07. Cheng (2007) estimates the Gini coefficient at 0.29 in 1981.
3This in itself may be surprising, because access to primary education might be expected to be a more important driver of reducing income inequality than secondary or tertiary education.

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