rafmacalaba/datause-encoder-data huggingface dataset:
and biodiesel in the United States and EU [12]. They find that between 2001 and 2006 these drivers were responsible for a 9% increase in the price of U.S. coarse grains, 10% increase in price of oilseeds in the EU-27 region, and 11% for sugarcane in Brazil. Similar impacts were observed on energy-exporting countries in Latin America and Sub-Saharan Africa. The main drawbacks of a CGE model are the large data requirements and the high degree of complexity.

The food crisis of 2007-08 has spawned a large body of literature examining the causes for the spike in food commodity prices. Interest in the food crisis can be motivated by the impact of an increase in food commodity prices on food-insecure and poor households, which is substantial [67]. De Hoyos and Medvedev use domestic food consumer price data to show that the 5.6% increase in average food commodity price between January 2005 and December 2007 implied a 1.7 percentage point increase in the extreme poverty headcount at the global level, with significant regional variation [16] (see also Ivanic and Martin [39]). Nearly all of the increase in extreme poverty is reported to occur in South Asia and Sub-Saharan Africa. Furthermore, Regmi et al. show that when faced with higher food commodity prices, the poor switch to foods that have lower nutritional value and lack important micronutrients [57].

Although the IMF Global Food Index during the 12 months preceding March 2008 increased 43%, the U.S. food Consumer Price Index increased only 4.5%. The global food price index assigns greater weight to raw grains unlike the U.S. food Consumer Price Index where the basket places greater weight on processed foods. The reason for the smaller increase in food commodity prices is that Americans tend to consume highly processed foods. When U.S. consumers purchase foods from supermarkets, convenience stores, or restaurants, a large fraction goes to cover labor associated with preparing, serving, and marketing the food.<sup>5</sup> Similar patterns are observed in other developed countries. This is not the case in developing countries. The poor spend a larger fraction of their income on food, whereas the typical American spends slightly less than 14% of total expenditures on food.<sup>6</sup> In contrast, Africans spend 43% of their expenditures on food,<sup>7</sup> and those subsisting on less than one dollar per day in Sub-Saharan Africa may dedicate as much as 70% of their expenditures to food.<sup>8</sup> We need a statement that the global food index assigns greater weight to raw grains unlike the U.S. CPI where the basket is processed foods. And we should refer to rich nations in general

> _5USDA, http://www.ers.usda.gov/Data/FarmToConsumer/Data/marketingbilltable1.htm 6U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, 2006. ftp://ftp.bls.gov/pub/special.requests/ce/share/2006/income.txt. Typical American refers to an individual at the median income level._

> _7Federal Reserve Board Staff calculation, IMF, and World Bank._

> _8The International Food Policy Research Institute 2020 Discussion Paper No. 43, “The World’s Most Deprived.”_

6
between firms and poverty. Söderbom, Teal and Wambugu (2005) show that, conditional on _all_ observed and unobserved worker skills that are fixed over time, workers’ earnings in Ghana and Kenya vary positively with firm size. That is, two identical employees who work in identical firms and receive identical wages in year 1, will get different earnings in year 2 if the two firms grow at different rates over this period. There is thus a direct link from firm performance, and changes in performance, to the standards of living of the individuals working in the firms. Substantial variability in performance will be associated with large income inequalities. Further, a lot of heterogeneity implies that it is quite pointless to talk about a “typical”, or “representative”, African manufacturing firm, since the average characteristics provide an accurate picture of rather few firms (with the average as the benchmark, African firms are typically atypical, as it were). The more heterogeneity, the more pertinent are questions relating to the causes and consequences of these differences across firms, and so the more pressing is the need for firm data. In our review of the research findings in the next section, we will therefore pay special attention to this issue.

One important distinction referring to the structure of data available is whether the data are cross-section or panel. The panel data structure – data in which the same firms are followed over time - is powerful, essentially because it provides an excellent basis for analysis of various aspects of firm dynamics (e.g. productivity growth or firm survival) and because it puts the researcher in a much stronger position to draw conclusions about _causal_ effects (since time invariant “unobserved heterogeneity” can be controlled for). Most of the panel data available on African firms date back to the first round of RPED surveys in the early 1990s. These surveys resulted in panel data with at most three observations per firm, so these panels are still very short. The Centre for the Study of African Economies (CSAE) at Oxford University has organized follow-up surveys designed to lengthen the time dimension of the panels in Ghana, Kenya and Tanzania. Currently, the longest panel on African manufacturing covers twelve years of data on Ghanaian firms. In recent years, efforts have also been made to collate panel data on the individuals working in the firms in these three countries. This will enable researchers to better understand the nature of the income dynamics of these employees whilst employed, and how their incomes tend to change once the employees leave the firm. While the strength of panel data relative to crosssection data continues to be a matter of (sometimes intense) debate amongst 7
# **Earnings Inequality Within and Across Gender, Racial, and Ethnic Groups**

# **in Four Latin American Countries**

Wendy Cunningham _World Bank_ and Joyce P. Jacobsen _Wesleyan University_

# **JEL Codes:** D3, I3, J15

**Keywords:** earnings inequality, income inequality, gender, race, and ethnicity differences

An earlier version of this paper was prepared as a background paper for the 2004 World Bank LAC flagship report _Inequality in Latin America and the Caribbean: Breaking with History_ ? and was made possible through the financial support of the World Bank. We thank Emilie Bagby, Anna Risi, and Lucas Siga for their research assistance, and François Bourguignon, Francisco Ferreira, Francisco Rodriguez, seminar participants at numerous venues, and two anonymous referees for useful comments on earlier versions of this research.
services – can alleviate the pressure on women due to activities (such as housekeeping, and caring for children and the elderly) that traditionally keep them detached from the labor market. Finally, when we explore the impact across different education groups, we again fail to find any statistically significant effect. Even though the employment effect is positive for the highly educated and negative for those with lower levels of education, the effects are not precisely estimated.

Finally, we examine the impact of immigration on natives’ wages, the focus of many papers in the literature (see World Bank 2018 for a review). Results in Table 17 show that we again fail to find any significant impact of migration on natives’ wages. As a caveat to these findings, we underscore that the EU Labour Force Survey only provides information on the wage decile of workers, not their actual wages. In order to circumvent this problem, we construct a variable that measures the gap between the actual and expected probability of being in the top three income deciles given individual-level characteristics (age, gender, education levels), and that varies by country and year. The impact of migrants’ presence on the wage-gap variable, however, remains not statistically different from zero, a result that holds when looking at the share of top three wage deciles of teleworkable, essential, income-safe and health-safe jobs. These results are robust to alternative versions of the dependent variable, such as the share of employees in the bottom three deciles, or the income decile estimated using ordered probit regressions in the initial stage. Therefore, the adjustment in the labor market after the arrival of migrants seems to happen through the composition of employment but does not affect the relative or absolute wages of the different types of occupations in line with a more competitive and mobile labor market.

# 6. Conclusion

This paper provides evidence that exposure to negative economic shocks and health risks associated with the COVID-19 pandemic is greater among immigrant workers than among nativeborn people in high-income Western European countries. Using data from the European Union Labour Force Survey for 2011-2018, we show that this greater risk exposure stems from systematic differences in occupations of immigrant and native workers. Immigrant workers have significantly lower presence in occupations that can be performed from home (telework), and higher presence 30
nutrient availability. While of great relevance for agricultural production, this focused only on one of several elements of a market-aligned land valuation system.

Subsequent regulation put in place ‘normative monetary valuation’ using data of the 1988 Soviet economic evaluation of land.<sup>16</sup> Valuation of agricultural land was based on rental income from grain crop production whereas zoning and capital investments in infrastructure and public utilities were relevant for urban land. Albeit intended as a temporary measure pending the establishment of a market, this valuation, indexed to which is annually updated based on the index of consumer prices, became entrenched, forming the basis for various taxes and fees, including land tax, rent for state and communal lands, state duties, and simplified taxation systems for farmers. Attempts by government to refine this methodology in 2011, 2016, and 2021 did not correct its fundamental flaws.

The discrepancy between normative and market-based valuation leads to economic distortions, violates the principle of tax fairness, and may encourage inefficient land use. As local revenue is not linked to increases in land values, local governments’ ability to benefit from and their incentive to undertake actions such as planning and infrastructure or public service provision to promote economic development, is limited. Ukraine’s local governments also hold a large amount of land as state or communal property and transparent valuation would ensure that lease revenues reflect the true economic value of the land, maximizing public income and discouraging undervaluation or corrupt practices.

Land valuation is also key for valuing land pledged as collateral to compute reserve adequacy ratios for banks and pivotal for the effective functioning of land and credit markets. Not having a way to identify the market value of land also makes it impossible to tax capital gains from land appreciation, an issue that is particularly important in Ukraine where both agricultural and non-agricultural land is likely to appreciate after the war, creating a potential revenue source that could provide an important and incentive-compatible source of funding for reconstruction. Land valuation would also help identify areas where public and private investments will yield the highest payoff, facilitate strategic planning to rebuild infrastructure, housing, and economic activity centers, and make provisions for enhanced resilience via spatial analysis.

Recognizing the need for more efficient valuation methods, legislation introduced centralized registration in the State Register of Property Rights (SRPR) of information on the price (value) of land plots, property rights, and the amount of payment for land use during real estate transactions. While initial data quality was sub-standard, with prices missing for more than 50% of transactions, significant improvements have been

> 16 Resolution No. 213 on the "Methodology for Monetary Valuation of Agricultural Land and Settlements" by the Cabinet of Ministers of Ukraine (March 23, 1995).

11
|Table Al.1:<br>TabJle<br>A2.1:<br>Table A2.2:|Earnings Function (1nW2) with Parental<br>Education Variables<br>**1...............**<br>23<br>Comparison Between VV Estlmates and This Paper......<br>127<br>All Employees Earnings Function with<br>Different Dependent Variable ..................<br>a.. 130|
|---|---|
|Table A3.1|: Institutional<br>Costs for Secondary Level VTE<br>Institutions**.........**<br>**.**<br>133|
|Table A3.2:<br>Table A3.3:|Institutional Costs: INSET<br>**.........................**<br>136<br>Institutional<br>Costs of Teacher Training Institutes**..**138|
|Figure 1:<br>Figure 2:<br>Figure 3:<br>Figure 4:|Structure of the Education System in C8te d'Ivoire .... 15<br>Earnings Profiles: Effect of Previous Job Experience .<br>54<br>Earnings Profiles: Effect of Apprenticeships**........**<br>55<br>Earnings Profiles: Effect of Secondary VTE**...........**<br>56|
|Figure 5:|Earnings Profiles: Effect of Post-Secondary<br>VTE<br>and Teacher Training<br>**................................**<br>57|

This paper was prepared by Christiaan Grootaert while he was an Economist working with the Policy Division in the Education and Training Department. Mr. Crootaert is currently Senior Economist in the Social Dimensions of Adjustment, Unit of the Central Africa Department at the World Bank.
## confirms that human capital is positively associated with child health.

_Table 3 Anthropometric_ indicators _of child nutritional status at 1 and 5 years old, by maternal membership and education._

||**No**<br>**No**<br>(N|**educatio**<br>**member**<br>694)|**n/ prim**<br>**M**<br>(N|**ary educ**<br>**ember**<br>177)|t-test<br>**ation**|**Hig**<br>**No member**<br>(N 852)|(N<br>**her edu**<br>**M**|227)<br>**cation**<br>**ember**|t-test|
|---|---|---|---|---|---|---|---|---|---|
||_Mean _|_Std. Dev._|_Mean _|_Std. Dev._|pvalue|_Mean Std. Dev. _|_Mean _|_Std. Dev._|pvalue|
|At 1 year old||||||||||
|HAZ|-1.3|(0.048)|-1.2|(0.107)|(0.386)|-0.37<br>(0.042)|-0.49|(0.081)|(0.172)|
|Stunted|0.29|(0.017)|0.24|(0.032)|(0.090)|0.09<br>(0.010)|0.08|(0.018)|(0.924)|
|At 5 years old||||||||||
|HAZ|-2|(0.038)|-2|(0.076)|(0.426)|-1.05<br>(0.035)|-1.15|(0.069)|(0.192)|
|Stunted|0.50|(0.019)|0.47|(0.038)|(0.547)|0.17<br>(0.013)|0.19|(0.026)|(0.457)|

Note: Maternal membership is measured at wave 1. Average values; standard deviation in parentheses. T- test for differences in mean.

Comparing members and non members within the two samples of lowly educated and higher educated mothers, being a member at wave 1 is negatively correlated with the likelihood of being stunted for those 1 year old children with lowly educated mothers. There are no differences in nutritional indicators measured at age 5 for children whose mothers were members or were not when they were 1 year old.<sup>21</sup> This suggests that, if there is any casual relation between membership and child health, there might be a positive effect only in the short run and among children of lowly educated mothers.<sup>22</sup>

# **6. The Empirical Framework**

The present empirical analysis focuses on one dimension of child nutritional status, HAZ, which is a measure of long-term nutrition. The _contemporaneous specification_ relates child HAZ score to contemporaneous inputs and maternal social capital (Todd and Wolpin, 2007).<sup>23</sup>

## _Contemporaneous specification: homogeneous effect specification_

First, let’s start with the simplest specification and assume that maternal social capital has, if any, a

> 21 Notably, if the same comparison is repeated using maternal membership measured at wave 2, being a member is associated with a lower likelihood of being stunted at 5 years old and a higher value of HAZ for the sample of children with higher educated mothers (results not reported).

> 22 This is explored later on in Section 7.2.

> 23 As discussed by Todd and Wolpin (2003), the assumptions behind the contemporaneous specification are that “only contemporaneous inputs matter to the production of current achievement” or these “inputs are unchanging over time, so that current input measures capture the entire history of inputs” (Todd and Wolpin, 2003, pp. 16). I assume independence between the contemporaneous inputs and the potential unobserved endowment conditional to the control variables (in particular birth weight) (Todd and Wolpin, 2003).

15
**TABLE 2. DIFFERENCES IN RESPONSES BETWEEN CRISIS AND NON-CRISIS COUNTRIES (CONT.)**

||**Question(Yes/no)**|**Crisis**|**Non-crisis**|**p-value**|
|---|---|---|---|---|
|**Asset**<br>**classification**<br>**mechanisms**|Do you have an asset classification system under which banks<br>have to report the quality of their loans and advances using a<br>common regulatory scale? (% Yes)||||
||<br>2007|<br>57|88|0.001|
||2011|65|89|0.004|
||Does accrued, though unpaid, interest/principal enter the bank's<br>income statement while the loan is classified as non-performing?<br>(% Yes)||||
||<br>2007|26|12|0.092|
||2011|50|24|0.021|
||Are banks allowed to upgrade the classification of a loan or<br>advance immediately after it has been restructured? (% Yes)<br>2011|<br>53|30|0.062|
||Do you require banks to write off non-performing loans after a<br>specific time period? (% Yes)<br>2011|0|47|0.000|
||Are there minimum levels of specific provisions for loans and<br>advances that are set by the regulator? (% Yes)<br>2011|30|79|0.000|
||Is there a regulatory requirement for general provisions on loans<br>and advances? (% Yes)<br>2011|25|70|0.000|
|**Supervisory**<br>**powers in cases of**<br>**bank losses**|Can the supervisory agency require commitment/action from<br>controlling shareholder(s) to support the bank with new equity<br>(e.g. capital restoration plan)? (% Yes)||||
||2011|65|83|0.059|
||Can the supervisory agency order the bank's directors or<br>management to constitute provisions to cover actual or potential<br>losses? (% Yes)||||
||2007|90|96|0.313|
||2011|90|98|0.049|
||Can the supervisory agency suspend the directors' decision to<br>distribute: Bonuses? (% Yes)<br>2007|32|68|0.002|
||Can the supervisory agency suspend the directors' decision to<br>distribute: Management fees? (% Yes)<br>||||
||2007|37|69|0.007|
|**Incentives and**<br>**information for**|Is there an explicit deposit insurance protection system? (% Yes)<br>2007|100|48|0.000|
|**markets to**|2011|100|72|0.006|
|**monitor banks**|Does the deposit insurance agency have the power to insure<br>liabilities beyond any explicit deposit insurance scheme? (% Yes)<br>2007|11|2|0.065|
||Must banks disclose to public: (% Yes)<br>Risk management procedures? 2007|71|40|0.008|
||Regulatory capital and capital adequacy ratio? 2011|95|74|0.034|
||<br>Transactions with related parties? 2011|<br>90|66|0.025|
||<br>Scope of consolidation 2011|<br>90|73|0.087|
||Which bank activities are rated: (% Yes)<br>Bonds issuance? 2007|95|47|0.000|
||Commercialpaper issuance? 2007|79|32|0.000|
||Are financial institutions required to produce consolidated<br>accounts covering all bank and any non-bank financial<br>subsidiaries (including affiliates of common holding companies)?<br>(% Yes)||||
||<br>2007|100|83|0.044|
||2011|100|96|0.399|

20
in the net in-migration rate decreases the employment probability of non-migrants in the destination region by 7.8 percentage points. Our results further show that this negative impact is significantly stronger in Ugandan regions less conducive to capital mobility (i.e., showing below-median road density): a 10 percentage points increase in the net in-migration rate in these areas decreases the probability of being employed of non-migrants by more than 10 percentage points.

Our findings suggest that the development of road infrastructure which ranks high on the World Bank’s agenda<sup>17</sup> could considerably mitigate the negative spill-over effects of weather shocks on local labor markets in countries dependent on rain-fed agriculture (the bulk of countries in Sub-Saharan Africa). Note that road infrastructure development may also help circumvent the expected negative impact of internal migration induced by other types of shocks in source regions, such as conflict outbreaks that have plagued Sub-Saharan African countries over the last decades. Estimating such impacts, as well as defining the conditions of an efficient road infrastructure policy (prioritizing construction or maintenance; focusing on international, national or rural road networks... etc) constitute important avenues for future research.

# **References**

- [1] Alesina, Alberto, Johann Harnoss and Hillel Rapoport. 2013. “Birthplace diversity and

economic growth.” Working Paper.

> 17See World Bank (2009). See also Hulten, Bennathan and Srinivasan (2006) and Freund and Rocha (2011) for evidence on the positive impact of road infrastructure on development.

34
the nature and industry of employment and non-farm enterprise is very important. To explore the gender dierences in the industry of salaried employment and nonfarm enterprise, we categorized these economic activities into 17 industries based on the International Standard for Industrial Classication (ISIC) and analyzed the gender dierences.

In both countries, there are signicant gender gaps in access to salaried employment in RNFE. Only 7 and 17 percent of female-headed households had salaried o-farm employment in Ethiopia and Uganda, respectively. These gures are lower than maleheaded households by 2 and 5.7 percentage points, respectively (Table 1).<sup>12</sup><sup>_,_13</sup> The analysis of employment by industry (as per the ISIC) reveals that there are notable dierences, especially in Uganda. Female-headed households have lesser access to employment in construction, transportation and communications, shery, public admin, and `other social services' sectors of Uganda than their male-headed counterparts. In Ethiopia, a smaller proportion of female-head households had salaried employment in agriculture and `other social services' sectors. However, a higher percentage of femaleheaded households are employed in health and social work in both countries, and hotel and restaurant services in Uganda.

Similarly, a smaller proportion of female-headed households in Ethiopia (19.8%) and Uganda (33.5 %) owned a non-farm enterprise, which is lower compared to male-headed households respectively by 6 and 8 percentage points (Table 1). Based on the ISIC, a higher proportion of male-headed households own enterprises that operate in mining and quarrying, construction, `other social services', and electricity, gas and water industries

> 12In Table 1, we present the proportion of households that have access to employment in each industry. Therefore, the sum of the proportion in each industry should be roughly equal to employment in all industries.

> 13For Ethiopia, we have additional information on employment as casual laborer and under the Productive Safety Net Project (PSNP). Only 18 percent of female-headed households had access employment as casual laborer. This is lower than that of male-headed households by 8 percentage points. However, there is no gender-based dierence in employment under the PSNP. About 9 percent of both female- and male-headed households are employed under the PSNP.

11