rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4591

# **Abstract**

Latin American countries are generally characterized as displaying high income and earnings inequality overall along with high inequality by gender, race, and ethnicity. However, the latter phenomenon is not a major contributor to the former phenomenon. Using household survey data from four Latin American countries (Bolivia, Brazil, Guatemala, and Guyana) for which stratification by race or ethnicity is possible, this paper demonstrates (using Theil index decompositions as well as Gini indices, and 90/10 and 50/10 percentile comparisons) that within-group earnings inequality rather than betweengroup earnings inequality is the main contributor to overall earnings inequality. Simulations in which the relatively disadvantaged gender and/or racial/ethnic group is treated as if it were the relatively advantaged group tend to reduce overall earnings inequality measures only slightly and in some cases have the effect of increasing earnings inequality measures.

This paper—a product of the Social Protection Division, Latin America and Caribbean Region, Human Development Department—is part of a larger effort in the department to identify policy to improve the situation of the most vulnerable. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Wcunningham@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
For the urban sector after August 1968, the all-India Consumer Price Index for Industrial Workers (CPIIW) is used as the deflator. For the earlier period, the Labour Bureau's Consumer Price Index for the Working Class is used, which is an earlier incarnation of the CPIIW albeit with a smaller coverage of urban centers (27 against 50). The rural cost of living index series was constructed in three parts. For the period since September 1964, the rural cost of living index is the all-India Consumer Price Index for Agricultural Laborers (CPIAL) published by the Labour Bureau. For the period September 1956 to August 1964 (for which an all-India CPIAL does not exist), a monthly series of the all-India CPIAL was constructed as a weighted average of the state-level CPIALs, using the same state-level weights as those used in the all-India CPIAL published since September 1964. For the initial period August 1951 to August 1956, forecasts were obtained from a dynamic model of the CPIAL as a function of the CPIIW and the Wholesale Price Index (see Datt, 1997, for details).

Our CPIAL series also dealt with a problem to do with the fact that the Labour Bureau used the same price of firewood in its published series since 1960-61. Firewood is typically a common property resource for agricultural laborers, but it is also a market good, and so the Labour Bureau's practice is questionable. Our CPIAL series corrects this by replacing the firewood sub-series in the CPIAL by one based on mean rural firewood prices (only available from 1970) and a series assuming that firewood prices increased at the same rate as all other items in the Fuel and Light category (prior to 1970). This correction to the CPIAL series is made for the period up to the 51<sup>st</sup> round for July 1994-June 1995 when the Labor Bureau rebased the series using a revised weighting diagram. The final CPIIW and CPIAL indices are averages of monthly indices corresponding to the exact survey period of each of the NSSO's rounds.

Our price indices also take account of an issue that has recently been raised in discussions of how the measurement of price trends influences the estimation of poverty in India (Deaton, 2007). It has been argued that the overall weight of food in the CPIAL is too large such that a rise (fall) in the relative price of food results is an overestimation (underestimation) of the rate of inflation. Potentially, the same problem also arises for the CPIIW. Hence, to deal with this issue we reweight the food and non-food components of the CPIAL (and CPIIW) for any round by the predicted food and the non-food shares for the rural (and urban) poor in the 12
FIGURE A6

# MONEY STOCK AND OPPORTUNITY COST

## _NIGERIA_

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In order to not give undue weight to these surveys, the results on poverty and schooling for datasets that identify fewer than 50 children with a disability are not reported.<sup>6</sup>

**<u>Table 3. Number of youth 6 to 17</u>**<sup>**+**</sup> **<u>defined as having a disability in each survey</u>**

|_Country andyear_|_Type of survey_|_Maximum_<br>_age _|_Number_<br>_of youth_|_Number of youth with_<br>_a disability_|
|---|---|---|---|---|
|Burundi 2000|MICS2|14|5,865|73|
|Cambodia 1999|SES|17|10,881|96|
|Cambodia 2000|DHS|17|23765|214|
|Indonesia 2000|SES|17|64,136|326|
|Jamaica 1998|LSMS|17|6,964|58|
|Jamaica 2000|LSMS|17|1,640|14|
|Mongolia 2000|MICS2|17|7,645|245|
|Mozambique 1996|NHS|17|14,520|156|
|Myanmar 2000|MICS2|14|26,329|41|
|Romania 1995|LSMS|17|13,777|82|
|Sierra Leone 2000|MICS2|17|7,534|28|

_Note_ : Data are unweighted in order to show the actual number of observations underlying the analysis. +Maximum ages are 18 in Mongolia, and 14 in Burundi and Myanmar.

A last data constraint concerns the measurement of household poverty. All LSMS and SES surveys include household per capita consumption expenditures (PCE), the variable typically used in poverty analysis. DHS and MICS2 data, however, do not include those variables. In this study, quintiles based on per capita consumption expenditures are used when available. In other datasets, an index of household consumer assets and housing characteristics (an economic status index) was used to classify households into quintiles (following Filmer and Pritchett 2000). The exception is the SES from Cambodia 1999 in which there was a problem in the collection of expenditures data. An economic status index is therefore used in that survey to classify economic status.<sup>7</sup>

# **3. Prevalence of disability and its association with household economic status**

The first issue these data can be used to explore is the prevalence of disability and the association with household economic status. Prevalence estimates range between 0.13 (Myanmar) and 2.77 (Jamaica 2000) percent of the population as having a disability (Table 4). These numbers are consistent with those compiled by the United Nations statistical database on disability (DISTAT).<sup>8</sup> In that source of over 65 surveys and censuses between 1970 and 1992 in developing countries, the mean prevalence rate for the entire population

> 6 Results for Jamaica 2000, Myanmar 2000, and Sierra Leone 2000 are available from the author on request. 7 Consistent with typical poverty analysis, quintiles are derived on the basis of the distribution of people across the socio-economic status measure. Specifically, quintiles are defined such that 20 percent of youth live in each quintile.

> 8 Available online at <u>http://unstats.un.org/unsd/demographic/sconcerns/disability/disab2.asp.</u>

5
to AGOA. South Africa, Botswana, Kenya, Ethiopia and Tanzania registered consistently increasing gains at various levels of trade. An often cited AGOA success story, Lesotho registered significant increases in export which only lessened slightly towards the end of the 2010s.

Since this is the first paper that uses SCM to estimate the impact of a unilateral trade agreement for each country, results from this study may not be directly comparable to previous studies. Because, average effects of PTAs may be hiding cross-country heterogeneity or variations across time, Figure 3 presents a comparable measure - the average treatment effect of AGOA for all eligible countries included in the study. The significantly large trade creation impacts suggest that the results are in line with previous studies such as Mattoo et al. (2003); Frazer and Van Biesebroeck (2010); Cirera et al. (2016). While these studies mostly fail to reveal the heterogeneity across countries that is predominant, results are similar to earlier studies that find a significantly large positive impact of AGOA.

Figure A1 in the appendix presents the export trajectories of major product categories from countries that have expanded exports other than crude petroleum or natural gas. As shown in the figure, South Africa’s major exports to the US are dominated by advanced manufacturing goods such as transport vehicles while other minerals and textile exports account for a small share. In terms of textile and apparel exports, Kenya registered the biggest gains. Kenya also expanded exports into agricultural produce and other manufacturing non-apparel products. For Tanzania, Ethiopia and Rwanda, the biggest share of export gains has accrued from exports of agricultural commodities. Ethiopia and Tanzania also expanded exports into textile and apparel while Rwanda’s secondary export items include minerals, mainly tantalum ores. East African countries have registered 31
63 business functions: seven general business functions (GBF) relevant to all sectors, and 56 sector-specific business functions (SSBF) across 12 sectors (agriculture, livestock, food processing, apparel, leather goods, automotive, pharmaceutical, other manufacturing, wholesale and retail, financial services, land transport services, and health services). In total, the grid spans 305 technologies.

The grid has three properties. First, it is comprehensive both in terms of the business functions and of the technologies considered in each business function. Second, it is relevant for any establishment and country, regardless of its level of development. Third, the technologies in each business function are ranked according to their sophistication, from the simplest to the most complex which represents the world technology frontier.

We implement the grid in the Firm Adoption of Technology (FAT) survey, administered to over 21,000 establishments that constitute representative samples in 15 countries: South Korea, Poland, Croatia, Chile, the Brazilian state of Ceará, Georgia, Vietnam, the Indian states of Uttar Pradesh, Tamil Nadu, Gujarat and Maharashtra, Ghana, Bangladesh, Kenya, Cambodia, Senegal, Ethiopia, and Burkina Faso. FAT collects three types of information. First, it gathers establishment-level data on sales, inputs, education of the workers and the managers, management practices, etc. Second, it records whether each sector-specific business function is conducted in-house. Third, and most relevant, FAT documents the technologies from the grid used by each establishment in each business function and, of these, which one is the most widely used technology.

Using the information from FAT, we develop two measures of technology sophistication at the business function-establishment level: ‘MOST’ for the most widely used technology, and ‘MAX’ for the most advanced technology available. We use these measures to study three topics: the use of technology at the business function level, the cross-establishment variation in technology sophistication, and the relationship between technology sophistication and productivity across establishments.

At the business function level, our analysis reveals that the most widely used technology (MOST) is usually not the most sophisticated one available (MAX). The gap between MAX and MOST is persistent, indicating that MAX and MOST represent two distinct and relatively independent processes of technology upgrading within establishments.

By aggregating across all functions of an establishment, we derive establishment-level measures of technology sophistication that inherit the comprehensiveness of the grid. We observe significant variation in technology sophistication across establishments, both across and within countries. The dispersion in technology sophistication across establishments varies considerably across countries, increasing with per-capita income. Technology sophistication is positively associated with establishment size, the human capital of its workers, 2
This outcome could not be derived straightforwardly from the estimation of the migration choice. In fact, the estimations showed that potential migrants were found to be poorer, in particular landless, heads, but also better educated, hence less poor, non-heads. The explicit quantitative measurements allowed by micro-simulation were needed to highlight the poverty reducing role of changes in the sectoral composition of employment.

In the trade scenarios, a moderately additional poverty reduction is observed. This is due to the income increases trade reforms induce in the non-agriculture sectors, but also because the fewer households that still move out of agriculture under the trade scenarios are actually poorer.

_<u>Table 16: Poverty impact of trade, sectoral movers</u>_

||2001 levels <br>Agri hous|<sup>BaU 2001-</sup><br>15 changes<br>eholds who|Doha % of<br>BaU<br>change<br>have become|Full % of<br>BaU<br>change<br>non-agri|
|---|---|---|---|---|
|P0|56.6|-22.4|105.1|108.2|
|P1|26.0|-14.0|102.0|105.4|
|P2|15.2|-9.4|101.7|105.1|
|Population %||3.1|98.0|92.5|

Source: Authors’ calculations.

One final category needs to be examined: the non-agricultural stayers. Representing 80 percent of the population, this is a large group; however, given the negligible migration out of the non-agricultural sector observed in the data, this group is explicitly excluded from the migration choice. For these households, the full liberalization brings about an additional reduction in the headcount of 0.4 percentage points, and through its favorable impact on non-agricultural unskilled wages the Doha scenario, too, makes a small but noticeable difference.

_-_ _<u>Table 17: Poverty impact of trade, non agri stayers</u>_

||2001 levels <sup>BaU 2001-</sup><br>15 changes<br>Non-agri househol|Doha % of<br>BaU<br>change<br>ds before and|Full % of<br>BaU<br>change<br>after|
|---|---|---|---|
|P0|18.6<br>-3.8|104.0|110.7|
|P1|7.1<br>-1.8|103.3|109.8|
|P2|3.7<br>-1.0|103.2|109.5|
|Population %|82.4|||

Source: Authors’ calculations.

46
the MPDF survey in 1999, exporters appear to have increased their direct dealings with customers in high income West European markets.

**Table 4.7 Revenues in Exporting Manufacturing Enterprises (%)**

|**Market**|**Share in Revenues**|
|---|---|
|Province|16.5|
|Other provinces|13.5|
|Asia|29.6|
|Europe|35.5|
|Northern America|3.1|
|Other|1.8|
|**_Total_**|**_100.0_**|

Source: World Bank Survey, 2001

# **The Managers**

29. The profile of the manager found in this survey is very much in line with earlier studies of private companies. The manager of a private enterprise is on average 45 years old. The youngest entrepreneur in the sample was 21 years old, while the oldest was 71. Three quarters are men and one-quarter women. The proportion of women entrepreneurs is higher in rural than in urban areas. Owners of enterprises are well educated: more than two-thirds of owners graduated college.<sup>23</sup> The average level of education is also relatively higher in the North and in urban areas. It is not clear, however, that this education advantage translates into any advantage in business.<sup>24</sup>

**Table 4.8 Gender of Firm Managers (%)**

||**Urban**|**Rural**|**Total**|
|---|---|---|---|
|Male|81.0|68.1|76.4|
|Female|19.0|31.9|23.6|
|**_Total_**|**_100.0_**|**_100.0_**|**_100.0_**|

Source: World Bank Survey, 2001

**Table 4.9 Education of Managers by Firm Location (%)**

||**North **|**South **|**Urban **|**Rural **|**Total**|
|---|---|---|---|---|---|
|Graduated College|77.5|58.1|78.7|46.5|67.2|
|<br>Graduated High School|21.4|26.5|19.0|33.3|24.1|
|<br>Graduated Elementary School|1.1|12.1|1.9|16.0|7.0|
|<br>Did Not Yet Graduated Elementary|0.0|3.3|0.4|4.2|1.7|
|<br>School||||||
|**_Total_**|**_100.0 _**|**_100.0_**|**_100.0 _**|**_100.0 _**|**_100.0_**|

Source: World Bank Survey, 2001 30. The majority of owners (83%) did not own a business before. On average, 30 percent of the owners worked in the informal sector before, a higher 39 percent in rural areas and 23 percent in urban areas. About 40 percent used to work in a state enterprise, but less than ten percent had previously been in government. The proportion of owners that worked for a state enterprise before is particularly high in the North (52%).

> 23 This represents an increase from the finding of one half in the MPDF study.  This may be accounted for by higher education levels among service firm managers.

> 24 As in MPDF’s 1999 study, educated managers actually did somewhat worse than the rest of the sample.

15
multiple types of fertilizer, we take the average price across all fertilizer types.<sup>6</sup> We winsorized the fertilizer price per kilogram at both the 5<sup>th</sup> and 95<sup>th</sup> percentiles within each country. In addition to quantitative data on fertilizer prices, we collected self-reported data on the change in fertilizer prices compared to the previous agricultural season and, for those reporting higher prices than last season, the strategies they used to cope with high fertilizer prices.

# _2.3. Estimation_

Our analysis is mostly descriptive, focusing on the estimation of national-level means by survey wave. To examine the correlates of fertilizer use and shortfall, we estimate linear probability models using OLS. When examining trends over time, we fit a linear trend for each country by regressing the dependent variable on a discrete variable taking the value of 1 for the first round we collected data on the outcome in that country, a value of 2 for the second data point, and so on. Throughout our analysis, we weight our estimates using the HFPS survey weights that are designed to correct for selection bias in the HFPS sample compared to a nationally representative sample (Ambel, McGee, and Tsegay 2021; Zezza et al. 2023).

# **3. Results**

# _3.1. Agricultural Engagement_

To provide context, we first show the share of households engaged in crop farming over time (Figure 1). For five of our study countries – Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda – we observe this before the COVID-19 pandemic and then at various points during and after it, from 2020 to 2024. In the pre-COVID period, between 56 percent (Ethiopia) and 80 percent (Malawi) of households engaged in crop farming. The first data points during the COVID-19 pandemic suggest there is a significant increase in the household-level incidence of crop farming, as pandemic lockdowns limited other income generating activities, of 5.2 points in Burkina Faso, 13.5 points in Ethiopia, 9.3 points in Malawi, and 8.4 points in Uganda (Figure 1).

The initial increase at the onset of the pandemic leveled off during the course of 2020 but remained above pre-COVID levels until 2023 in Ethiopia, Malawi, and Nigeria. In Malawi and

> 6 We do so to maximize longitudinal coverage as fertilizer prices as reported in the price module and as reported in the agriculture module were collected across distinct survey rounds. To lend confidence to this approach, we compared the most common fertilizer types, compound and nitrogen-based fertilizers, using the type-specific price data. The median difference in prices across the two types was approximately zero.

6
**Data Appendix**

This Data Appendix discusses the reliability of sources of data other than the LSMS surveys which could potentially be used to evaluate the impact of the 1988-92 crises in Peru on education and employment outcomes. I also present details concerning the construction of variables used in the analysis.

Data reliability: I made several attempts to supplement the LSMS with other data, with only limited success. Demographic and Health Surveys (DHS) have been conducted in Peru in 1986, 1992, 1996, and 2000. Unfortunately, the 1986 survey did not collect information on the schooling and employment status of clhildren. The DHS therefore have the disadvantage of not having data for the period before thie crisis.

I use national accounts data for a general description of national economic trends in the 1980's and 1990's. Up to 1995, regional branches of INEI also computed estimates of GDP for each one of Peru's 25 departments. These data would have been useful for a difference-indifference analysis of the impact of the crisis. But combining these data with the survey data raises serious problems of measurement error. The department-level pattern of crisis and recovery which comes out of the nationial accounts data is strikingly varied-indeed, so varied as to seem less-than-credible given anecdotal evidence about the depth of the crisis throughout the country.'<sup>8</sup> There are also problems merging these data with the household-level data from the surveys. Only 15 departments were covered in both the 1985/86 and 1991 surveys, and the sample sizes vary wildly across departments-ranging from more than 2,000 individuals between ages 6 to 17 in Lima to less tlhan 50 in some of the smaller departments. A further source of measurement error is introduced by the fact that the department-level GDP figures purport to cover both urban and rural areas, whereas the analysis conducted in this paper is based on the urban samples of Lima, the coast, and tihe sierra only. For these reasons, I do not provide difference-in-difference estimates of the impact of the crisis.

The Ministry of Education keeps information on total public recurrent and capital expenditures on education during the period, and I use these data to inspect whether changes in school outcomes could be attribuLted to clhanges in spending patterns, rather than to changes in the economic environment. These data are considered to be reasonably accurate and comparable

> **18** This large regional variation in the estimates occurs even though INEI "imputed" part of the departmentlevel flows using data for the entire country: For example, in some economic sectors, national changes are attributed to different departments according to the fraction of the stock of total employment or total industry output located in each department. In general, one would expect that this procedure would tend to make estimated department-level growth rates _more_ similar to each other.

18