## _wp12147

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---

### I. INTRODUCTION AND BACKGROUND
- Rapid growth in developing countries, especially China and India, has led to an important decline in poverty at the national and global level, but income inequality has risen in many emerging markets.
- Rapid growth has pushed up commodity prices globally, raising questions about whether a rise in food prices aggravates poverty.
- Distinguishing between food and nonfood inflation is potentially important:
  - Higher food prices can hurt many poor people (particularly in urban areas) but may benefit food producers, potentially reducing poverty among some rural households.
  - Nonfood inflation is hypothesized to be particularly damaging to the poor.
- Literature highlights:
  - Short-run inflation together with growth can support poor incomes, while long-run inflation increases uncertainty and can depress average and poor incomes (Romer and Romer; Easterly and Fischer).
  - Higher inflation tends to worsen the income shares of the poorest population segments (Blejer and Guerrero; Datt and Ravallion; Ferreira and Litchfield).
- Mechanisms through which inflation may hurt the poor more than the rich:
  - Differential access to credit and inflation hedges (financial services, indexed assets).
  - Inadequate indexation of social benefits.
  - Storage technology differences and perishability of food limit the poor’s ability to advance purchases.
  - Portfolio allocation: middle-income households can shift into durables; the poor disproportionately consume perishable food.
- Institutional and political channels:
  - Central bank independence and political economy factors can mediate links between inequality and inflation.
- Hypotheses for empirical analysis:
  - Nonfood inflation likely exacerbates income inequality in both urban and rural areas.
  - Food inflation likely worsens urban inequality; in rural areas, the effect depends on wage responsiveness to food prices — if rural wages are elastic to food prices, food inflation could reduce rural inequality.
  - Internationally, food inflation may immiserate the poor in net food-importing countries and reduce inequality in net food-exporting countries.

### II. STYLIZED FACTS
- Data sources and construction:
  - International food and nonfood inflation data from public country sources used in Walsh (2010).
  - Per capita GDP and real economic growth from IMF’s WEO database.
  - China: provincial food and nonfood price indices derived from CPI data, with weights estimated by IMF staff.
  - India: state-level food and nonfood inflation estimated from CPI:
    - CPI for agricultural workers (state-by-state) used as rural price index.
    - CPI for industrial workers (municipal basis) averaged for cities in each state, weighted by the urban area’s 2001 population, as a proxy for state urban CPI.
- Inequality data:
  - Global regressions use annual Gini coefficients from the World Bank’s Development Indicators estimated from 1990 to 2010 for 75 advanced, transition, and developing countries.
- Descriptive patterns:
  - On average, GINI coefficients have not changed drastically across countries in the sample, except for richer countries where they have risen slightly in the last 10 years.
  - During 2000–2010, countries with higher inflation (both food and nonfood) actually saw declines in income inequality in simple cross-sectional charts; the relationship between inequality and food inflation does not appear significantly different from nonfood inflation in these unadjusted charts.
- China-specific stylized facts:
  - Gini coefficient estimated to have reached 0.42–0.47 in recent years from below 0.3 in early 1980s.
  - Cheng (2007): urban Gini estimated to have increased to 0.33 in 2004 from 0.17 in 1981.
  - An excerpt notes change "0.36 from 0.25 in this period" relating to some subgroup (source text fragment).

### III. METHODOLOGY
- Inequality measurement:
  - International sample: change in inequality measured as the three-year change in GINI coefficients (a rising GINI means an increase in income inequality).
  - China: provincial Theil indices (University of Texas estimates) for 31 provinces, municipalities, and autonomous regions over 1994–2006; dependent variable is three-year moving average change in the Theil index.
  - India: state-level inequality from National Sample Survey Rounds over 1990–2004; dependent variable is the four-year change in the GINI coefficient for urban or rural areas.
- Education controls:
  - International sample: primary and secondary school enrollment rates from World Development Indicators.
  - China: primary enrollment ratio changed from 108¾ percent in 1994 to 106¼ percent in 2006; tertiary enrollment rose significantly but treated as province-specific and time-invariant.
  - India: urban and rural literacy rates available by state.
- Inflation disaggregation:
  - Inflation disaggregated into food and nonfood CPI in addition to headline CPI.
- Econometric specifications:
  - Baseline static: y_{i,t} = α + λ y_{i,t-1} + X_{i,t}' β + ε_{i,t}.
  - Dynamic Arellano-Bond (AB GMM): y_{i,t} = α + λ y_{i,t-1} + X_{i,t}' β + μ_i + ν_{i,t}, using lagged levels and differences as instruments to address endogeneity and μ_i country-specific time-invariant factors.
  - Estimation approaches reported: OLS, fixed effects, random effects, Arellano-Bond; system GMM discussed for efficiency gains.
  - China: province-specific CPI and income per capita used.
  - India: literacy used where informative; educational variables dropped in fixed effects models when time variation is minimal.

### IV. RESULTS

A. International sample — headline and disaggregated inflation
- Headline CPI (Arellano-Bond column 4 highlights):
  - Nominal GDP coefficient: 0.00705** (t-stat -3.12).
  - GDP per capita coefficient: -0.000246* (t-stat (-1.98)).
  - Primary school enrollment associated with decreases in income inequality (primary school enrollment coefficient -0.00453 in AB).
  - Lag of dependent variable significant: 0.433*** (t-stat -8.71).
- Food vs nonfood inflation (Arellano-Bond column 4 highlights):
  - Food inflation coefficient: -0.233* (t-stat (-2.59)) — higher food inflation associated with falling income inequality under AB.
  - Nonfood inflation coefficient: 0.318** (t-stat -2.86) — higher nonfood inflation associated with rising income inequality under AB.
  - Secondary school enrollment coefficient: -0.139* under AB.

B. China — provincial Theil index results
- Headline CPI (Arellano-Bond column 4):
  - CPI inflation coefficient: 0.732*** (t-stat (11.03)) — higher headline inflation associated with more rapid widening of inequality (Theil).
  - Real GDP growth coefficient: -0.660*** (t-stat (−8.03)) — higher GDP growth associated with a slower pace of deterioration in income inequality.
  - GDP per capita coefficient: 0.00330** (t-stat (2.83)) — richer provinces tend to have bigger increases in inequality.
  - Lag of dependent variable: 0.859*** (t-stat (279.67)).
- Food vs nonfood inflation (Arellano-Bond column 4):
  - Nonfood inflation coefficient: 0.995*** (t-stat (8.06)) — nonfood inflation positive and significant under AB.
  - Food inflation coefficient: -0.0978 (not significant) — food inflation associated with less inequality but not significant.
  - GDP per capita coefficient: 0.00307* (t-stat (2.06)).
  - Real GDP growth coefficient: -0.609*** (t-stat (−7.35)).

C. India — state-level, rural vs urban
- Headline CPI:
  - Rural areas (Table 5):
    - CPI inflation shows little relation with income inequality with coefficients close to zero except under AB.
    - GDP per capita associated with higher inequality under three specifications (e.g., GDP per capita 0.0604* in OLS; 0.111* in Fixed Effects; 0.0604* in Random Effects; AB shows -0.0288).
    - Lag of dependent variable: 0.162* in OLS (t-stat (2.04)).
  - Urban areas (Table 6):
    - CPI inflation coefficient positive and significant under Fixed Effects and AB:
      - Fixed Effects CPI inflation: 0.111* (t-stat (2.58)).
      - Arellano-Bond CPI inflation: 0.114** (t-stat (2.69)).
    - GDP per capita strongly associated with rising inequality: 0.0908* (OLS), 0.344*** (Fixed Effects), 0.0908* (Random Effects), 0.345*** (AB).
- Food vs nonfood inflation:
  - Rural areas (Table 7):
    - Food inflation:
      - OLS: -0.0497* (t-stat (-2.31)).
      - AB: -0.0790*** (t-stat (-3.44)).
      - Interpretation: food inflation strongly linked to lower income inequality in rural areas.
    - Nonfood inflation:
      - OLS: 0.247** (t-stat (2.83)); AB: 0.256** (t-stat (2.97)) — nonfood inflation linked to higher income inequality.
    - GDP per capita OLS: 0.0691*; Fixed Effects: 0.122**.
  - Urban areas (Table 8):
    - Food inflation:
      - OLS: -0.131*** (t-stat (-4.33)) — negative and significant in OLS.
      - AB: -0.0636 (not significant under AB).
      - Coefficient negative in all specifications but significance varies.
    - Nonfood inflation:
      - OLS: 0.314*** (t-stat (4.16)); AB: 0.344*** (t-stat (4.31)) — strongly tied to higher urban inequality.
    - GDP per capita:
      - Fixed Effects: 0.250**; AB: 0.253** — states with higher levels of income appear to have rising income inequality.

### V. CONCLUSIONS (selected substantive findings and implications)
- Cross-sample pattern:
  - Higher nonfood inflation is associated with worsening income inequality in all three samples (international, India, China).
  - Food inflation is less uniformly detrimental; in several specifications higher food inflation is associated with declining income inequality (international sample under AB; China; India rural areas).
- Interpretation of mechanisms:
  - Nonfood inflation widens income inequality plausibly because households can benefit from higher prices only for goods or services they produce; few households produce a broad share of nonfood consumption.
  - Food inflation effects differ by context:
    - In rural India, food inflation is strongly associated with lower inequality, suggesting benefits to low-income food producers may offset adverse effects on consumers.
    - In urban India, the effect of food inflation on inequality is ambiguous; negative coefficients suggest some wage flexibility, but significance varies by specification.
    - In China and internationally (under AB), higher food inflation linked to declining inequality — possibly reflecting gains to low-income food producers where staple crops are important and economies are relatively self-sufficient in staples.
- Caveats and conditionalities:
  - Results depend on endogeneity treatment; significance and sign for food and nonfood inflation often change across OLS, fixed/random effects, and Arellano-Bond specifications.
  - India and China are relatively self-sufficient in staple foods (rice and pulses), limiting generalizability to countries that import a large share of staple crops.
  - Within-country variation matters: food-importing states/provinces may exhibit different dynamics; household-level data needed to study heterogeneity across producers and consumers.
  - High growth and labor mobility (especially in China) may dampen rural inequality responses to food inflation by enabling shifts from rural to urban employment; slower-growing economies could see stronger immiserating effects on rural consumers from food inflation.

*Source: Excerpt from IMF working paper content as provided.*

### References .............................................................................................................

### _wp12147 - References .............................................................................................................

### I. INTRODUCTION AND BACKGROUND
- Rapid growth in developing countries, especially China and India, has led to an important decline in poverty at the national and global level, but income inequality has risen in many emerging markets.
- Rapid growth has pushed up commodity prices globally, raising questions about whether a rise in food prices aggravates poverty.
- Distinguishing between food and nonfood inflation is proposed as potentially important:
  - Higher food prices can hurt many poor people (particularly in urban areas) but may benefit food producers, potentially reducing poverty among some rural households.
  - Nonfood inflation is hypothesized to be particularly damaging to the poor.
- Literature summary on inflation and inequality:
  - Romer and Romer (1999): short-run inflation with growth can support poor incomes; long-run inflation increases uncertainty and can depress average and poor incomes.
  - Easterly and Fischer (2000): across many household surveys, the poor more likely to cite inflation as a problem; inflation tends to worsen their assessment of wellbeing more than the rich.
  - Blejer and Guerrero (1990), Datt and Ravallion (1998), Ferreira and Litchfield (2001): higher inflation leads to lower share of income held by the poorest population segments.
- Mechanisms through which inflation may hurt the poor more than the rich:
  - Differential access to credit and inflation hedges (financial services, indexed assets).
  - Inadequate indexation of social benefits (less significant in developing countries).
  - Storage technology differences and perishability of food limit the poor’s ability to advance purchases.
  - Portfolio allocation: middle-income households can shift into durables; the poor disproportionately consume perishable food.
- Alternative channels and institutional factors:
  - Dolmas et al. (1997): central bank independence links income inequality and inflation; democracies with higher inequality may see inflation-financed redistribution when central banks lack independence.
  - Albanesi (2001): in unequal societies, the poor’s vulnerability to inflation reduces their negotiating power, potentially leading to higher inflation.
- Food-price-specific literature:
  - Deaton (1989): higher food prices can benefit many groups; middle-class food producers may benefit most.
  - Ravallion (2000): food prices do not show an independent long-run effect on (real) wages once agricultural output and overall inflation are considered.
  - Christaensen and Demery (2006), Rashid (2002), Warr (2005): wages may not fully adjust to higher food prices; poor suffer more due to higher food shares in consumption.
  - Wodon and Zaman (2008): urban areas more affected than rural by higher food prices; food-importing countries more affected than food-exporting countries; second-round effects (producer gains trickling down) can be important.
- Migration and relative prices:
  - Higher rural wages from higher food prices can alter rural-urban migration, potentially reducing urban inequality if poorer urban workers return to rural areas; migration effects can operate in either direction depending on which households move.
- Overall hypotheses for empirical analysis:
  - Nonfood inflation likely exacerbates income inequality in both urban and rural areas.
  - Food inflation likely worsens urban inequality; its effect in rural areas depends on wage responsiveness to food prices — if rural wages are elastic to food prices, food inflation could reduce rural inequality.
  - At the international level, food inflation may immiserate the poor in net food-importing countries and reduce inequality in net food-exporting countries; aggregate effects depend on relative magnitudes and wage responsiveness.

### II. STYLIZED FACTS
A. Inflation and Macroeconomic Data
- International food and nonfood inflation data compiled from public country sources used in Walsh (2010).
- Per capita GDP and real economic growth from the IMF’s WEO database.
- Macroeconomic data for China and India from CEIC.
- China: provincial food and nonfood price indices derived from CPI data, with weights estimated by IMF staff.
- India: state-level food and nonfood inflation estimated from CPI data:
  - CPI for agricultural workers (state-by-state) used as rural price index.
  - CPI for industrial workers (municipal basis) averaged for cities in each state, weighted by the urban area’s 2001 population, as a proxy for state urban CPI.

B. Inequality Data
- Global regression uses annual Gini coefficients from the World Bank’s Development Indicators estimated from 1990 to 2010 for 75 advanced, transition, and developing countries.
- Descriptive patterns:
  - On average, GINI coefficients have not changed drastically across countries in the sample, except for richer countries where they have risen slightly in the last 10 years.
  - During 2000–2010, countries with higher inflation (both food and nonfood) actually saw declines in income inequality in simple cross-sectional charts; the relationship between inequality and food inflation does not appear significantly different from nonfood inflation in these unadjusted charts.
  - Figures referenced: Figure 1 (Change of Gini Coefficient vs GDP Per Capita, 2000-2010), Figure 2 (Food Inflation and Change of Gini, 2000-2010), Figure 3 (Non-food Inflation and Change of Gini, 2000-2010).

C. China (stylized facts summary)
- Rapid economic growth in China coincided with a noticeable deterioration in income distribution:
  - Gini coefficient estimated to have reached 0.42–0.47 in recent years from below 0.3 in early 1980s.
  - Cheng (2007): since 1992 urban income disparity replaced rural disparity as the most important driver of overall inequality; urban Gini estimated to have increased to 0.33 in 2004 from 0.17 in 1981.

*Source: _wp12147 - References .............................................................................................................*

### 0.36 from 0.25 in this period. Meanwhile, the income or expenditure of the richest 10 percent of

### III. METHODOLOGY

### Data and measurement
- Change in inequality is measured as the three-year change in GINI coefficients for each country in the international sample; a rising GINI coefficient means an increase in income inequality.
- For China, provincial Theil indices (University of Texas estimates) are used to measure inequality; data cover 31 provinces, municipalities, and autonomous regions over the period 1994–2006.
- For India, inequality data come from National Sample Survey Rounds and cover the period 1990–2004; the dependent variable for Indian states is the four-year change in the GINI coefficient for either urban or rural areas.
- Education:
  - International sample: primary and secondary school enrollment rates from World Development Indicators.
  - China: primary enrollment ratio changed from 108¾ percent in 1994 to 106¼ percent in 2006; tertiary enrollment rose significantly but is treated as province-specific and time-invariant in the model.
  - India: urban and rural literacy rates are available by state and used in analysis.
- Inflation is disaggregated into food and nonfood CPI in addition to headline CPI.

### Econometric specification
- Baseline equation (static): y_{i,t} = α + λ y_{i,t-1} + X_{i,t}' β + ε_{i,t}  (equation (1))
  - y denotes change of Gini coefficients; X includes inflation (headline, food, nonfood), growth in GDP, average GDP per capita, primary and secondary enrollment ratios.
- Dynamic specification (Arellano-Bond AB GMM): y_{i,t} = α + λ y_{i,t-1} + X_{i,t}' β + μ_i + ν_{i,t}  (equation (2))
  - Uses lagged levels and lagged differences as instruments; addresses endogeneity and country-specific time-invariant factors μ_i.
- Estimation approaches reported: OLS, fixed effects, random effects, Arellano-Bond (AB) GMM; also system GMM considerations (Blundell and Bond, 1998) discussed for efficiency gains.
- China model: dependent variable is three-year moving average change in the Theil index for each province; province-specific CPI and income per capita used.
- India model: dependent variable is four-year change in GINI by state and area (urban/rural); literacy used where informative; educational variables dropped in fixed effects models when time variation is minimal.

### IV. RESULTS

### A. International sample — headline and disaggregated inflation
- Headline CPI (Table 1) — main patterns:
  - Headline inflation does not show a strong relationship with income inequality under most specifications.
  - Under Arellano-Bond (column 4):
    - Nominal GDP coefficient: 0.00705** (t-stat -3.12 reported in parentheses but table shows significance marking **).
    - GDP per capita coefficient: -0.000246* (t-stat (-1.98) with significance * p<0.05).
    - Primary school enrollment associated with decreases in income inequality (primary school enrollment coefficient -0.00453 in AB but primary school enrollment significant in other specs).
    - Lag of dependent variable significant: 0.433*** (t-stat -8.71 listed).
- Food vs nonfood inflation (Table 2) — main patterns:
  - Significant relationships appear mainly under Arellano-Bond specification (column 4):
    - Food inflation coefficient: -0.233* (t-stat (-2.59)) — higher food inflation associated with falling income inequality under AB.
    - Nonfood inflation coefficient: 0.318** (t-stat -2.86) — higher nonfood inflation associated with rising income inequality under AB.
  - Real GDP growth shows mixed results: associated with slightly lower income inequality in fixed- and random-effects specifications but not under AB.
  - Secondary school enrollment becomes associated with somewhat lower income inequality under the Arellano-Bond specification (secondary school enrollment coefficient -0.139* in column 4).

### B. China — provincial Theil index results
- Headline CPI (Table 3):
  - Under Arellano-Bond (column 4):
    - CPI inflation coefficient: 0.732*** (t-stat (11.03)) — higher headline inflation associated with more rapid widening of inequality (Theil coefficient).
    - Real GDP growth coefficient: -0.660*** (t-stat (−8.03)) — higher GDP growth associated with a slower pace of deterioration in income inequality.
    - GDP per capita coefficient: 0.00330** (t-stat (2.83)) — richer provinces tend to have bigger increases in inequality.
    - Lag of dependent variable: 0.859*** (t-stat (279.67)).
- Food vs nonfood inflation (Table 4):
  - Under Arellano-Bond (column 4):
    - Nonfood inflation coefficient: 0.995*** (t-stat (8.06)) — nonfood inflation positive and significant (expected sign) only under AB.
    - Food inflation coefficient: -0.0978 (not significant) — food inflation associated with less inequality under each specification but not significant.
    - GDP per capita coefficient: 0.00307* (t-stat (2.06)).
    - Real GDP growth coefficient: -0.609*** (t-stat (−7.35)).

### C. India — state-level, rural vs urban
- Headline CPI (Tables 5 and 6):
  - Rural areas (Table 5):
    - CPI inflation shows little relation with income inequality with coefficients close to zero except under AB.
    - GDP per capita associated with higher inequality under three specifications (e.g., GDP per capita 0.0604* in OLS; 0.111* in Fixed Effects; 0.0604* in Random Effects; AB shows -0.0288).
    - Lag of dependent variable: 0.162* in OLS (t-stat (2.04)).
  - Urban areas (Table 6):
    - CPI inflation coefficient positive and significant under Fixed Effects and AB:
      - Fixed Effects CPI inflation: 0.111* (t-stat (2.58)).
      - Arellano-Bond CPI inflation: 0.114** (t-stat (2.69)).
    - GDP per capita strongly associated with rising inequality: 0.0908* (OLS), 0.344*** (Fixed Effects), 0.0908* (Random Effects), 0.345*** (AB).
- Food vs nonfood inflation (Tables 7 and 8):
  - Rural areas (Table 7):
    - Food inflation:
      - OLS: -0.0497* (t-stat (-2.31)).
      - AB: -0.0790*** (t-stat (-3.44)).
      - Interpretation: food inflation strongly linked to lower income inequality in rural areas.
    - Nonfood inflation:
      - OLS: 0.247** (t-stat (2.83)); AB: 0.256** (t-stat (2.97)) — nonfood inflation linked to higher income inequality.
    - GDP per capita OLS: 0.0691*; Fixed Effects: 0.122**.
  - Urban areas (Table 8):
    - Food inflation:
      - OLS: -0.131*** (t-stat (-4.33)) — negative and significant in OLS.
      - AB: -0.0636 (not significant under AB).
      - Coefficient negative in all specifications but significance varies.
    - Nonfood inflation:
      - OLS: 0.314*** (t-stat (4.16)); AB: 0.344*** (t-stat (4.31)) — strongly tied to higher urban inequality.
    - GDP per capita:
      - Fixed Effects: 0.250**; AB: 0.253** — states with higher levels of income appear to have rising income inequality.

### V. CONCLUSIONS (selected substantive findings and implications)
- Overall pattern:
  - Higher nonfood inflation is associated with worsening income inequality in all three samples (international, India, China).
  - Food inflation is less uniformly detrimental; in several specifications higher food inflation is associated with declining income inequality (international sample under AB; China; India rural areas).
- Interpretation:
  - Nonfood inflation widens income inequality plausibly because households can benefit from higher prices only for goods or services they produce; few households produce a broad share of nonfood consumption.
  - Food inflation effects differ by context:
    - In rural India, food inflation is strongly associated with lower inequality, suggesting benefits to low-income food producers may offset adverse effects on consumers.
    - In urban India, the effect of food inflation on inequality is ambiguous; negative coefficients suggest some wage flexibility, but significance varies by specification.
    - In China and internationally (under AB), higher food inflation linked to declining inequality — possibly reflecting gains to low-income food producers where staple crops are important and economies are relatively self-sufficient in staples.
- Caveats and conditionalities:
  - Results depend on endogeneity treatment; significance and sign for food and nonfood inflation often change across OLS, fixed/random effects, and Arellano-Bond specifications.
  - India and China are relatively self-sufficient in staple foods (rice and pulses), limiting generalizability to countries that import a large share of staple crops.
  - Within-country variation matters: food-importing states/provinces may exhibit different dynamics; household-level data would be needed to study heterogeneity across producers and consumers.
  - High growth and labor mobility (especially in China) may dampen rural inequality responses to food inflation by enabling shifts from rural to urban employment; slower-growing economies could see stronger immiserating effects on rural consumers from food inflation.

*Source: Excerpt from IMF working paper content as provided.*

### REFERENCES

### _wp12147 - REFERENCES

### Cited works (alphabetical list as provided)
- Albanesi, S. (2001). “Inflation and Inequality.” Luxembourg Income Study Working Paper No. 323.  
- Berg, A. and  J.D. Ostry (2011). “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” International Monetary Fund Staff Discussion Note 11/08.  
- Blejer, M. and I. Guerrero (1990). “The impact of macroeconomic policies on income distribution: An empirical study of the Philippines.” Review of Economics and Statistics, vol. 72(3), 414-423.  
- Braumann, B. (2000). “Real Effects of High Inflation.” IMF Working Paper 2000/85.  
- Cardoso, E., R. Paes de Barros and A. Urani. (1995) “Inflation and Unemployment as Determinants of Inequality in Brazil: The 1980s.” Reform, Recovery and Growth: Latin America and the Middle East, 151-176. University of Chicago Press.  
- Cheng, Yonghong (2007) “Evolution of Gini Coefficient Since the Start of the Reform and Its Urban-Rural Decomposition”, Social Sciences in China 2007/04.  
- Christiaensen, L., and L. Demery (2006) “Down to Earth: Agriculture and Poverty Reduction in Africa,” Directions in Development. Washington DC: World Bank.  
- Datt, G. and M. Ravaillion (1998). “Farm Productivity and Rural Poverty in India,” Journal of Development Studies, vol. 34, 62-85.  
- Dolmas, J. et al. (1997) “Inequality, Inflation, and Central Bank Independence.” Federal Reserve Bank of Dallas, Research Working Paper 97-05.  
- Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis.” The Economic Journal, vol. 99(395), 1-37.  
- Easterly, W. and S. Fischer (2000). “Inflation and the Poor,” NBER Working Paper 2335.  
- Ferreira, F.H.G., P.G. Leite, and J.A. Litchfield (2007). “The Rise and Fall of Brazilian Inequality: 1981-2004.” Macroeconomic Dynamics, June 2007, 1-32.  
- Ferreira, F.H.G., and J.A. Litchfield (2000). “Desigualdade, pobreza e bem-estar social no Brasil: 1981/95.” Desigualdade e Pobreza no Brasil. Rio de Janeiro: IPEA.  
- Ivanic, M. and W. Martin (2008). “Implications of Higher Global Food Prices for Poverty in Low-Income Countries.” World Bank Policy Research Working Paper 4594.  
- Neri, M. 1995 (1995) “Sobre a mesuração dos salários reais em alta inflação.” Pesquisa e Planejamento Econômico 25(3), 497-525.  
- Rashid, S. (2002) “Dynamics of Agricultural Wage and Rice Prices in Bangladesh: A Reexamination.” International Food Policy Research Institute Discussion Paper No. 44.  
- Ravallion, M. (2000) “Prices, wages and poverty in rural India: what lessons do the time series data hold for policy?” Food Policy, vol. 25, 351-364.  
- Romer, C.D. and D.H. Romer (1998) “Monetary Policy and the Well-Being of the Poor.” NBER Working Paper 6793.  
- Walsh, J.P. (2010). “Reconsidering the Role of Food Prices in Inflation.” IMF Working Paper 11/71.  
- Warr, P. (2005) “Food Policy and Poverty in Indonesia: A General Equilibrium Analysis.” Australian Journal of Agricultural and Reserouce Economics, vol. 49(4): 429-51.  
- Woden Q.C., et al., (2008) “Potential Impact of Higher Food Prices on Poverty: Summary Estimates for a Dozen West African Countries,” mimeo, World Bank, Washington DC.  
- Wodon, Q., and H. Zaman (2008). “Rising Food Prices in Sub-Saharan Africa: Poverty Impact and Policy Responses.” World Bank Policy Research Paper 4738.

*Source: _wp12147 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12147.pdf_