rafmacalaba/datause-encoder-data huggingface dataset:
WPS3931

# **Did Growth Become Less Pro-Poor in the 1990s?**

## **Humberto Lopez**<sup>*****</sup> **The World Bank**

## **Abstract**

This note analyzes the stability of the empirical relationship between growth and changes in inequality over time. It concludes that while during the 1970s and 1980s the growth process was not accompanied by increases in inequality, during the 1990s a positive and significant correlation appears in the data. For this decade, we estimate that a one percent growth rate would be associated with an increase in the gini coefficient of between .3 to .5 percent. This positive correlation is hidden when one estimates the model without allowing for changes in the relationship over the different decades. The finding is robust to a number of departures from the basic specification including: (i) the use of alternative specifications to isolate decadal shifts; (ii) the use of robust estimation techniques that address the potential influence of outliers; (iii) restricting the sample to a balanced panel for the 1980s and 1990s to control for changes in the composition of the sample related to the unbalanced nature of the panel; and (iv) considering the possibility of fixed effects in the data. The paper also explores the impact of this structural change in the rate of poverty reduction and concludes that it is far from negligible.

Keywords: Growth, income inequality JEL codes: O11, O15.

## World Bank Policy Research Working Paper 3931, June 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* A shorter version of this paper with the title “Growth and Inequality: Are the 1990s Different?” is forthcoming in Economics Letters. I would like to thank Luis Serven and an anonymous referee for comments and Patricia Macchi for excellent research assistance. All the data are available from the author upon request. Address for correspondence: MS I8-801, The World Bank, 1818 H Street, Washington, DC 20433. Email: Hlopez@worldbank.org. Tel: +1 (202) 473-4909. Fax: +1 (202) 522-7528
**Table S1.4: Impact of cyclical fluctuations in male and female unemployment rates on IPV; restricted sample of countries for which two or more years of data are available**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A: Physical Violen|ce||||
|ln(U<sup>f</sup>)|0.177|0.237|-0.534***|-0.519***|
||(0.162)|(0.210)|(0.082)|(0.083)|
|ln(U<sup>m</sup>)|-0.355**|-0.432**|0.508***|0.496***|
||(0.158)|(0.200)|(0.086)|(0.089)|
|Observations|344239|344239|344239|311775|
|P-value F-test|0.102|0.110|0.000|0.000|
|Mean Violence|0.184|0.184|0.184|0.183|
|Mean ln(U<sup>f</sup>)|0.017|0.017|0.017|0.023|
|Mean ln(U<sup>m</sup>)|0.008|0.008|0.008|0.012|
|B: Sexual Violence|||||
|ln(U<sup>f</sup>)|0.036|0.105|-0.172|-0.172|
||(0.159)|(0.191)|(0.108)|(0.110)|
|ln(U<sup>m</sup>)|-0.172|-0.222|0.141|0.140|
||(0.138)|(0.182)|(0.108)|(0.108)|
|Observations|238067|238067|238067|231632|
|P-value F-test|0.490|0.388|0.160|0.165|
|Mean Violence|0.073|0.073|0.073|0.072|
|Mean ln(U<sup>f</sup>)|0.019|0.019|0.019|0.021|
|Mean ln(U<sup>m</sup>)|0.001|0.001|0.001|0.003|
|C: Any Violence|||||
|ln(U<sup>f</sup>)|0.208|0.211|-0.584***|-0.581***|
||(0.203)|(0.237)|(0.150)|(0.154)|
|ln(U<sup>m</sup>)|-0.458**|-0.475*|0.535***|0.526***|
||(0.195)|(0.248)|(0.158)|(0.161)|
|Observations|236542|236542|236542|230155|
|P-value F-test|0.107|0.171|0.002|0.002|
|Mean Violence|0.203|0.203|0.203|0.203|
|Mean ln(U<sup>f</sup>)|0.019|0.019|0.019|0.021|
|Mean ln(U<sup>m</sup>)|0.001|0.001|0.001|0.003|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

# _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. The dependent variables, controls and standard error adjustments are as in Table 1. Relative to Table 1, the sample is now restricted to only those countries for which two or more years of data on women are available.
Initial payments for real fixed annuities are on average 36 percent lower than for fixed nominal annuities in the case of single life annuities with or without the 10-year guaranteed period of payment. For joint life annuities, the difference is even greater at 42 percent. Similarly, initial payments for annuities escalating at 3 percent start 28 and 33 percent respectively below those of fixed nominal annuities.

Table A4 presents data on annuity conversion factors and the difference between various types of annuities. The annuity conversion factors (ACF) are based on the quotations shown in Tables A1 to A3. The ACF show the amount of annual income for every 100 dollars or pounds in capital premiums. Thus, the ACF for a fixed nominal single life annuity for a non-smoker male aged 65 amounts to 6.54 percent. For the same annuity contract but with a 10-year guaranteed payment, the ACF suffers a very small decline to 6.42 percent or just 2 percent. Issuing a joint life annuity with the spouse aged 60 and a 67 percent reversion rate causes a larger fall to an ACF of 5.58 percent or a further decline of 13 percent. Buying a fixed real joint life annuity results in an ACF of only 3.26 percent. This is half the level of the fixed nominal single annuity. Buying inflation protection causes a marginal fall of 35 percent.

<u>Table A4: Annuity Conversion Factors</u>

|||ACF|% of A|Difference|
|---|---|---|---|---|
|A.|Single M65 Fixed Nominal|6.54%|||
|B.|Single M65 10YGPP Fixed Nominal|6.42%|98%|2%|
|C.|Joint M65,F60,10YGPP Fixed Nominal|5.58%|85%|13%|
|D.|Joint M65,F60 10YGPP Fixed Real|3.26%|50%|35%|

Source: Calculated on the basis of the UK FSA annuity quotations of Tables A1 to A3 This large decline explains the reluctance of retiring workers to opt for fixed real annuities. However, unless retiring workers suffer from a serious illness, they would be unwise to choose fixed nominal annuities, especially in countries with a long history of high inflation. Table A5 shows the impact of inflation on the real value of annuity payments with the passage of time.

Table A5: Impact of Inflation on Real Value of Annuity Payments

|Rate\Years|10|20|30|
|---|---|---|---|
|0.5%|-5%|-9%|-14%|
|1%|-9%|-18%|-26%|
|3%|-26%|-45%|-59%|
|5%|-39%|-62%|-77%|
|10%|-61%|-85%|-94%|

Even at a low inflation rate of 1 percent, the real value of annuity payments falls by 18 percent after 20 years. With a moderate inflation of 3 percent, annuity payments decline by 45 percent in real terms 20 years after retirement and the falls are much more serious with higher rates of inflation.

62
standard deviation increase in mathematics performance at the end of high schools translates into 12 percent higher annual earnings.<sup>7</sup> Murnane, Willett, Duhaldeborde, and Tyler (2000) provide evidence from the High School and Beyond and the National Longitudinal Survey of the High School Class of 1972. Their estimates suggest some variation with males obtaining a 15 percent increase and females a 10 percent increase per standard deviation of test performance. Lazear (2003), relying on a somewhat younger sample from NELS88, provides a single estimate of 12 percent. These estimates are also very close to those in Mulligan (1999), who finds 11 percent for the normalized AFQT score in the NLSY data.<sup>8</sup> Note that these returns can be thought of as how much earnings would increase with higher quality each and every year throughout the persons’ working career. Thus, the present value of the returns to higher quality is large.

These estimates are obtained fairly early in the work career (mid-20s to early 30s), and analyses of the impact of cognitive skills across the entire work life are more limited. Altonji and Pierret (2001) find that the impact of achievement on earnings grows with experience, because the employer has a chance to observe the performance of workers. The pattern of how returns change with age from their analysis is shown in Figure 2.1, where the power of school attainment differences to predict differences in earnings is replaced by cognitive skills as workers are in the labor force longer. The evidence is consistent with employers relying on readily available information on school attainment when they do not have other information and switching to observations of skills and performance as that information becomes available through job performance.<sup>9</sup> On the other hand, Hanushek and Zhang (2006) do not find that this pattern holds for a wider set of countries (although it continues to hold for the United 7 Because the units of measurement differ across tests, it is convenient to convert test scores into measures of the distribution of achievement across the population. A one-half standard deviation change would move somebody from the middle of the distribution (the 50<sup>th</sup> percentile) to the 69<sup>th</sup> percentile; a one standard deviation change would move this person to the 84<sup>th</sup> percentile. Because tests tend to follow a bell-shaped distribution, the percentile movements are largest at the center of the distribution.

8 By way of comparison, we noted that estimates of the value of an additional year of school attainment are typically about 10 percent. Of course, any investment decisions must recognize that quality and quantity are generally produced together and that costs of changing each must be taken into account.

9 Note that Altonji and Pierret (2001) observe a limited age range, so that these changing returns may well be thought of as leveling off after some amount of labor market experience.

8
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43
distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. Unorganized establishments do not pay taxes and are generally outside the purview of the state, thus approximating common definitions of the informal sector (e.g., Kanbur 2011).

For services, there is no simple legal distinction as in manufacturing. Service establishments, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. There are various existing methodologies to comparably differentiate small-scale, autonomous establishments from larger employers which constitute the organized sector, as generally defined. We assign establishments with less than five workers and/or listed as an ―own-account enterprise‖ (OAE) to the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Using this demarcation, the organized sector makes up approximately 25% of employment in both manufacturing and services.

The organized manufacturing sector is surveyed by the Central Statistical Organisation every year through the Annual Survey of Industries (ASI), while unorganized manufacturing and services establishments are separately surveyed by the National Sample Survey Organisation (NSSO) at approximately five-year intervals. The survey years we employ are the most recent data by sector for which the young establishment identifiers are recorded. Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. Districts are administrative subdivisions of Indian states or territories. We use the provided sample weights to construct population-level estimates of total establishments and employment by district and three-digit NIC industry. Employment is formally defined as ―persons engaged‖ and includes working owners, family and casual labor, and salaried employees.

Currently there are approximately 630 districts spread across 35 states/union territories. In order to overcome empirical issues presented by small districts or those with an insufficient sample size, we exclude any districts with a population less than one million (based on 2001 census) or with fewer than 50 establishments sampled. For our main specifications we also exclude states with persistent conflict and political turmoil (Jammu-Kashmir and Nagaland) that make data quality questionable. After these adjustments, the resulting sample retains districts in 20 major states that include more than 94% of Indian employment in both manufacturing and services.

9
to the poverty gap is more significant than the baseline scenario, the overall income inequality level remains unchanged (Figure 10, panel d).

**Figure 10. Simulated performance and poverty and inequality impacts of the heating allowance scheme under policy scenario 1**

**a. Simulated Coverage (% of households in each decile) b. Simulated Benefit incidence (% benefits allocated to each decile)**

<!-- Start of picture text --> 83.4 4.2 0.2 1<br>2<br>52.3<br>3<br>40.4<br>54.8 4<br>4.1 0.2 0.4<br>5<br> 1 2 3 4 5 6 7 8 9 10 6<br><!-- End of picture text -->

- **c. Simulated generosity (benefits as % of MYPP)**

**d. Simulated Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 12.9<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.54<br>3.4 Marginal Contributions to<br>0.58<br>Poverty<br>0.3 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by comparing the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**The adjustment in the Guaranteed Minimum Income (GMI) significantly impacts the overall transfer coverage, doubling financial assistance.** This shift in transfer coverage signifies a strategic effort to reach a broader spectrum of the population and extend the protective umbrella of social support (Figure 11, panels A and b).

**The GMI increase, in conjunction with the heating allowance, results in a remarkable generosity level, particularly for households in the lower income deciles.** For those in the first decile, this combination accounts for more than 40% of their total income, providing a substantial income boost (Figure 11, panel c). Similarly, the second and third deciles experience increased support, albeit slightly lesser, with close to  4% of their income originating from these transfers. In contrast, the effect is comparatively minor when considering the entire population, constituting only 0.6% of their total income.

**However, despite these transfer coverage and generosity changes, their impact on poverty reduction is relatively moderate** . The poverty rate decreases by 0.85 percentage points, and the poverty gap

29
<!-- Start of picture text --> Plc,Research, and External Affk're<br>Trade Policy<br>WPS 481<br><!-- End of picture text -->

- This paper- a product of the Trade Policy Division, Country Economics Department was prepared for the World Bank research project, "The Effects ofTrade Regimes on Industrial Competition and Efficiency" (RPO 674-46). Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Sugui, room NIO-031, extension 37951 (44 pages, including tables).

How did industrial structure and performance change after Chile's dramatic trade liberalization?

A comparison of the 1967 and 1979 censuses shows little improvement in productivity overall - but these figures don't separate the effects of trade liberalization from the effects of recession, high interest rates, and real appreciation.

To isolate the effects of trade liberalization, Tybout, de Melo, and Corbo compared industries in which protection was significantly reduced with industries in which it was not. Several findings emerged.

First, in industries for which protection was lifted, the smallest plants tended to expand output more. Cross-plant estimates of retlims to scale dropped significantly. These findings are consistent with the view that exposure to foreign competition forces suboptimally small producers toward minimally efficient scale.

Second, production levels became higher and more uniform across plants in those industries undergoing dramatic reductions in protection.

Taken together, these results support the received wisdom that increased exposure to trade improves competition within an industry.

The PRE Working Paper Series disseminates the findings of work undcr way in the Bank's Policy, Research, and Extemal AffairsComplex. An objective oftheseries is to get these findings outquickly. even if presentations are less than fullypolished. The findings, interpretations, and conch:-ions in thcse papers do not necessarily represent official Bank policy.

Produced bv the PRE Dissemination Center
Another significant variable affecting these requirements is the price of oil, and the uncertainty caused by the Middle East crisis in the last half of 1990 has increased the likelihood that oil prices will be higher than previously projected. The longer-run impact of higher oil prices on externa! financing requirements in the coming years depends on the scope for increased energy efficiency in Africa and the impact on OECD growth, import demand, and inflation, as well as on oil prices themselves. Assuming that Sub-Saharan countries continue to accelerate GDP growth to achieve the target proposed in Sub-Saharan Africa: From Crisis to Sustainable Growth by 2000, and that higher oil prices dampen consumer demand and stimulate increased energy efficiency within Africa, each dollar increase in oil prices will raise financing requirements by about $250 million a year for the oil-importing African countries. About $150 million of this increased cost would fail on the IDA-only countries. If higher oil prices cause Sub-Saharan Africa's non-oil terms of trade to deteriorate (because of weakened demand for Afric3n commodities and rising costs of manufactured imports), the additional financing requirements would be higher. 14/ The additional cost to the oil-importers would be less than the larger revenues of the five oil-exporting countries. At current export levels (roughly 2 million barrels a day), each dollar increase in oil prices would raise export revenues by $750 million a year (mainly for the five largest oilexporting countries). Because the external financing requirements of the oil exporters are a relatively small amount of total Sub-Saharan requirements, however, the reduction in their financing requirements as a result of their higher export revenues would not offset the larger requirements of the other countries caused by higher oil prices.

j4/ This analysis is based on price and income elasticities calculated for 1973-88 for all Sub-Saharan oil-importing countries in aggregate. On the basis of a simple regression, the price elasticity is equal to about -0.24 and the income elasticity is equal to about 0.74.

44