rafmacalaba/datause-encoder-data huggingface dataset:
the K/Y explains little of the decline in the wage-bill share of non-manual workers, with a small and statistically insignificant coefficient. In contrast in 1990-98 the coefficient on growth in K/Y is large and statistically significant, at 0.0263. This larger coefficient in the 1990s indicates that capital-skill complementarity increased between the 1980s and 1990s. That increase in capitalskill complementarity, coupled with the growing K/Y ratio in the 1990s, may explain some fraction of the increased demand for skills in manufacturing during the 1990s.

**Table 6: Estimated Wage-bill Share Equations - National Manufacturing 1984-98 Estimates for 76 four digit industries at the National Level**

|**Dep. variable:**Change in<br>non-manual wagebill share|**198**|**4-89**|**199**|**0-98**|**Po**|**oled 1984**|**-89 / 1990**|**-98**|
|---|---|---|---|---|---|---|---|---|
|1990s indicator|||||**0.0071**<br>(.0010)|**0.0067**<br>(.0010)|**0.0036**<br>(.0014)|**0.0033**<br>(.0016)|
|d Log capital/value added|0.0022<br>(.0156)|0.0010<br>(.0150)|**0.0263**<br>(.0104)|**0.0279**<br>(.0103)||0.0007<br>(.0147)|0.0022<br>(.0157)|0.0011<br>(.0149)|
|d Log capital/value added<br>x 1990s||||||0.0157<br>(.0179)|0.0240<br>(.0188)|0.0281<br>(.0184)|
|d Log value added|0.0050|0.0065|**0.0297**|**0.0317**|||0.0050|-0.0043|
||(.0116)|(.0123)|(.0100)|(.0114)|||(.0117)|(.0147)|
|d Log value added x 1990s|||||||0.0247<br>(.0153)|0.0254<br>(.0165)|
|d Log imports/value added||-0.0065||-0.0033||||-0.0055|
|||(.0067)||(.0030)||||(.0045)|
|d Log exports/value added||-0.0054<br>(.0088)||-0.0041<br>(.0046)||||-0.0054<br>(.0065)|
|Constant|-0.0043|-0.0029|-0.0007|-0.0001|-0.0041|-.0041|-.0043|-0.0030|
||(.0009)|(.0015)|(.0011)|(.0015)|(.0008)|(.0008)|(.0009)|(.0013)|
|**R**<sup>**2**</sup>|0.00|0.05|0.16|0.17|0.33|0.34|0.36|0.38|
|**Observations**|76|76|76|76|152|152|152|152|

**Note:** Figures are annualized changes for 76 four-digit (ISIC) manufacturing industries for all of India in each period. Heteroskedasticity-consistent standard errors are reported in parentheses. Regressions estimated by weighted least squares with wage-bill shares averaged over the period as weights. Coefficients are significant at the 5% level in bold face. See equation (4) in text.

21
employees’ provident pension fund represented about 50 percent of the government’s outstanding domestic debt in the early 1990s (Salleh Harun, 2002).

Table 1

#

|<br>|<br>Do|(Percent<br>mestic Debt|of GDP)|Domestic/ Total|Government|Debt|
|---|---|---|---|---|---|---|
||1990|1995|2004|1990|1995|2004|
|Argentina|32.2|9.0|51.2|0.480|0.267|0.455|
|Bangladesh|3.5|5.7|9.2|0.074|0.119|0.205|
|Brazil ('94)<sup>b</sup>|40.8|32.5|50.8|0.640|0.694|0.690|
|Chile|56.7|36.2|26.8|0.573|0.778|0.747|
|China|4.2|5.1|20.0|0.273|0.308|0.829|
|Colombia|3.4|12.7|32.3|0.136|0.387|0.516|
|Czech Rep.('93)<sup>b</sup>|12.1|9.8|16.9|0.412|0.356|0.637|
|Egypt|61.4|45.8|72.8|0.517|0.446|0.767|
|Hungary ('91)<sup>b</sup>|69.3|80.4|44.2|0.550|0.600|0.678|
|India|49.8|46.7|61.9|0.680|0.656|0.833|
|Indonesia|2.9|4.5|34.3|0.063|0.119|0.504|
|Malaysia|58.8|35.1|44.2|0.698|0.585|0.843|
|Mexico|22.7|15.6|32.5|0.403|0.262|0.745|
|Morocco|27.0|33.2|50.5|0.323|0.323|0.647|
|Pakistan ('93)<sup>b</sup>|38.5|37.1|40.7|0.486|0.484|0.503|
|Peru ('91)<sup>b</sup>|12.3|2.4|12.4|0.194|0.071|0.264|
|Philippines|28.1|37.7|41.2|0.309|0.484|0.494|
|Poland|27.4|20.1|36.2|0.323|0.402|0.762|
|Russia Fed. ('93)<sup>b</sup>|10.2|14.1|6.9|0.119|0.281|0.292|
|Slovak Rep.('93)<sup>b,c</sup>|18.4|19.7|36.7|0.460|0.494|0.748|
|Thailand|12.7|2.0|26.8|0.467|0.165|0.744|
|Tunisia|14.9|19.1|21.9|0.220|0.269|0.306|
|Turkey|14.6|17.5|52.9|0.334|0.312|0.712|
|Uruguay ('91)<sup>b</sup>|24.6|17.2|29.5|0.506|0.440|0.287|
|Venezuela|12.1|14.0|17.2|0.183|0.193|0.402|
|Average|26.3|22.9|34.8|0.377|0.380|0.584|

Sources: See Annex 1 a Includes Central Bank bills and bonds.

b Dates in parentheses refer to starting date, determined by data availability.

c Slovak Rep. data end in 2003.

4
WPS 3874

# **Sugar Prices, Labor Income, and Poverty in Brazil**

Ekaterina Krivonos<sup>*</sup> Marcelo Olarreaga<sup>†</sup> This paper assesses the impact that a potential liberalization of sugar regimes in OECD countries could have on household labor income and poverty in Brazil. The authors first estimate the extent of price transmission from world markets to 11 Brazilian states to capture the fact that some local markets may be relatively more isolated from changes in world prices. They then simultaneously estimate the impact that changes in domestic sugar prices have on regional wages and employment depending on worker characteristics. Finally, they measure the impact on household income of a 10 percent increase in world sugar prices. Results suggest that workers in the sugar sector and in sugar-producing regions have better employment opportunities and experience larger wage increases. More interestingly, households at the top of the income distribution experience larger income gains due to higher wages, whereas households at the bottom of the distribution experience larger income gains due to movements out of unemployment.

## **World Bank Policy Research Working Paper 3874, April 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_ _<u>http://econ.worldbank.org.</u>_ We are grateful to Bruce Gardner, Bernard Hoekman, Don Mitchell, Marcia Moraes, Alessandro Nicita, Guido Porto, Maurice Schiff, Thierry Verdier, Ethan Weisman, and participants at a World Bank seminar for very helpful comments and discussions.

> * University of Maryland, email: <u>ekrivonos @arec.umd.edu.</u>

> † Office of the Chief Economist for Latin America, The World Bank,, Carrera 7, #7-21, Piso 16, Bogota, Colombia, and DEPR, Long, UK. Tel. (571) 326-3600: Fax: (571) 326-3600, email: <u>molarreaga@worldbank.org.</u>
The main reason for not registering stated by each business varies greatly between cities in the same country. For example, in Lao PDR, less than 6% of businesses in Pakse report time and fees as the primary reason compared to around 32% in Vientiane. In Zambia, 35% of businesses in Ndola claim that a lack of information about the registration process is the primary reason for not formalizing, whereas this figure is only 10% in Kitwe. Taken together, these findings suggest that the institutions and constraints influencing the motivations of businesses about formalization tend to be city- or region-specific. This is not altogether surprising given that the authorities usually tasked with ensuring compliance with licensing and registration tend to operate at the municipal or provincial level (Bruhn and McKenzie, 2014).

# **4. Conclusions**

This paper introduces and provides a descriptive analysis of business-level survey data of informal businesses in twenty-four cities across seven countries (India, Iraq, Lao PDR, Mozambique, Somalia, Zambia, and Zimbabwe) collected since 2017. The surveys were conducted through face-to-face interviews of owners and managers of informal businesses following the ACS methodology adapted to study this segment of the population. This methodology is examined in much detail in the companion paper Aga et al. (2022). The ACS is a data collection methodology that efficiently provides representative estimates of the population indicators for informal businesses. Importantly, this survey methodology also enables estimating the overall size of the informal sector. While such analysis is beyond the scope of this paper, we note that studies using the data analyzed in this paper show that in Mozambique, for example, informal businesses outnumber formal ones by a factor of 8:1 (Jolevski and Aga, 2019).

The introduction of this cross-city, cross-country business-level database of informal businesses is one of the contributions of this paper. The data combines surveys that cover a wide range of topics through interviews that are administered to a representative sample of informal businesses rendered through the enumeration process (for more details, see the companion paper Aga et al., 2022). Two databases are prepared and made publicly available through the website of the World Bank's Enterprise Analysis unit: one combining raw data where question-wording and response categories have been carefully compared and mapped across surveys and standardized, if possible; and another, the indicators database transforming raw data into a set of indicators that aggregate the results in an easily analyzable format.

29
Table 4 contains summary statistics on the worker-level panel dataset used for analysis. There are 335,300 unique individuals in the administrative data that registered for any of the firm-requested courses, including registrants supplied by requesting firms, unemployment beneficiaries, and others. 60 percent of the sample is male, and 40 percent completed the training course for which they registered. Although only eight percent of the sample was denied a seat for exogenous reasons (which we term “administrative constraints”), 60 percent of all students were in a class that had at least one registrant not complete the course due to these reasons. Nine percent of the students were registered through MDIC by requesting firms, and while the average employment rate was 60 percent, only four percent were employed in requesting firms. The average employment rate across person-months was 57 percent, and the mean real monthly earnings (excluding months unemployed) was approximately 1,250 (in 2012 Reais).

Since courses are typically not exclusive to a particular type of registrant, the firmrequested courses are comprised of a mix of trainees who registered through different channels. Appendix Tables 1, 2, and 3 contain summary statistics on the three focal subsamples of registrants in the firm-requested courses: those who were pre-registered by requesting firms, unemployment beneficiaries, and those who were registered through neither channel.<sup>7</sup> As we expected, and these tables show, these groups of registrants have very different labor market situations: the firm-supplied registrants have higher employment rates (75%, compared to 60% for UB registrants and 53% for others), were far more likely to be employed in requesting firms (23.7% vs. _<_ 2.5% in both other groups), and earned higher wages (R$1,492/month vs. R$1,135/R$1,129). In the analyses below, we estimate effects both for all registrants in firm-requested courses as well for as these three focal subsamples.

> 7 Unemployment beneficiaries are required to register for a _Pronatec_ training course as a condition to receive unemployment benefits in certain cases (for those seeking unemployment benefits for the second time within 10 years).

10
# **3.  The Data and Its Description**

This paper uses NSS data to study the relationship between mothers’ employment, presence of young children and household structure. The NSS surveys are the largest and most frequently used data sets on employment for India. We use six “thick” rounds of the Employment and Unemployment schedules of the NSS: 1983, 1993–94, 2004–05, 2007–08, 2009–10 and 2011–12. While the NSS include an extensive set of questions on employment and individual characteristics, they do not have questions on child care or norms of motherhood and caregiving.

Our analytic sample comprises wives of household heads aged 25 to 55 years in urban areas.<sup>6</sup> The age range excludes women who may be in college or may have exited the labor market due to early retirement. It is worth noting here that marriage in India is early and near universal for women, with mean age at effective marriage in urban areas standing at 22.7 according to the Census.<sup>7</sup> Other analysis shows that marriage by itself is an important predictor of employment, in that it depresses women’s labor force participation in several countries, and certainly in India (Das and Desai, 2003; Das, 2006).

We have 25,000 to 29,000 observations for each year in our sample, and the pooled sample has nearly 162,000 observations. The dependent variable is women’s employment, which is estimated based on their activity in the last 365 days (i.e. whether a woman worked for the major part of the year preceding the survey, also called usual principal status). The main variable of interest is the presence of at least one young child under age 6 in the household, because by age 6 most children start going to school and require less intensive care. Other explanatory variables are woman’s education, age, a squared term for age, region of residence and household characteristics such as Scheduled Caste (SC), Scheduled Tribe (ST) and Other Backward Caste (OBC) status, religion and household size.

In line with fertility decline in India, we observe that the share of women with children under age 6 fell almost by half from 42.9 percent in 1983 to 23.2 percent in 2011. This has been accompanied by a plunge in average household size, which dropped from a high of 5.8 in 1983 to 4.6 in 2011. The mean age at first birth for women aged 25 years and older in urban areas is

> 6 To capture the effects of children on mother’s employment we focus only on the spouse of household head as a unit of analysis and thus exclude other women who may be residing in the same household. However, other women in the household enter the analysis as explanatory variables in terms of possible childcare support to the wife of the household head, and our protagonist.

> 7 http://www.censusindia.gov.in/vital_statistics/srs_report/9chap%202%20-%202011.pdf

9
The following notation clarifies the calculation. Let =B xL(CMA(P __ ,3),k) oper or. sion is fitted for is t to calculate the fi on that are predicted o be in s expe iencing IPC IPC assessments are contingent on sufficient evidence for high-confidence declarations, with no phases declared when information availability does not meet confidence criteria. The graph therefore highlights the likely access issues that may have <!-- Start of picture text --> 350<br>300<br>& 250<br>= 200<br>=<br>z<br>8 150<br>E<br>z 100<br>50<br>:<br>2019 2020 2021 2022 2023<br>Date<br>AA @IRG<br><!-- End of picture text --> This fin metric enables the monitoring of how alerts and alarms collectively correspond to the
**Table 12:** **<mark>Proportion of underweight women who fall into the bottom 20 and 40 percent of predicted values for all women</mark>**

||<br>**predicted values for all**<br>Model 2|<br>**women**<br>Mo|<br>del 3|
|---|---|---|---|
||Bottom 20%<br>Bottom 40%|Bottom 20%|Bottom 40%|
|Benin|0.271<br>0.482|0.369|0.620|
|Burkina Faso|0.351<br>0.597|0.368|0.610|
|Burundi|0.287<br>0.562|0.318|0.579|
|Cameroon|0.494<br>0.746|0.481|0.749|
|Congo, Rep.|0.287<br>0.537|0.358|0.631|
|Côte d'Ivoire|0.254<br>0.459|0.346|0.569|
|Congo, Dem. Rep.|0.389<br>0.654|0.416|0.669|
|Ethiopia|0.283<br>0.515|0.302|0.511|
|Gabon|0.228<br>0.513|0.433|0.755|
|Gambia|0.293<br>0.528|0.389|0.618|
|Ghana|0.374<br>0.624|0.447|0.673|
|Guinea|0.314<br>0.553|0.326|0.575|
|Kenya|0.363<br>0.629|0.389|0.654|
|Lesotho|0.408<br>0.601|0.484|0.639|
|Liberia|0.301<br>0.530|0.350|0.612|
|Malawi|0.302<br>0.514|0.378|0.572|
|Mali|0.274<br>0.490|0.314|0.541|
|Mozambique|0.303<br>0.565|0.365|0.591|
|Namibia|0.350<br>0.589|0.397|0.667|
|Niger|0.320<br>0.582|0.379|0.630|
|Nigeria|0.335<br>0.577|0.418|0.682|
|Rwanda|0.327<br>0.584|0.402|0.608|
|Senegal|0.307<br>0.537|0.376|0.629|
|Sierra Leone|0.280<br>0.510|0.333|0.577|
|Swaziland|0.354<br>0.588|0.451|0.759|
|Tanzania|0.346<br>0.587|0.360|0.601|
|Togo|0.399<br>0.652|0.388|0.645|
|Uganda|0.406<br>0.636|0.380|0.629|
|Zambia|0.325<br>0.549|0.331|0.573|
|Zimbabwe|0.343<br>0.585|0.432|0.635|
|Mean|0.322<br>0.562|0.369|0.611|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of underweight women who fall into the bottom 20<sup>th</sup> and 40<sup>th</sup> percentiles of predicted values from the regressions with log BMI as the dependent variable. For example, 27.1 percent of underweight women in Benin have predicted BMI values that fall into the bottom 20 percent of all predicted values for women.

49
institutions endorse rent-seeking and production as competing activities, leading investments to be allocated into unproductive activities, hence, resulting in poor growth performance. This channel of causation has not only been empirically supported by the authors, but also by others using different measures of institutional quality and natural resources endowment (Boschini et al., 2013).

While institutional quality may either operate indirectly or interactively with resource wealth on economic performance, the role of local institutions has relatively been neglected in the resource curse debate (Lawer et al., 2017). Notwithstanding, local institutions have borne increasing responsibilities since the wave of decentralization reforms that have taken place on the African continent in the last decades. In resource-rich communities, as in others, decentralization is intended to strengthen local authorities as they complement national authorities in ensuring that these communities benefit from resource revenues through improved welfare. As advanced by Mitton (2016), the distinction between national and sub-national entities is important to consider, as they may not operate the same way. Even though national policies may promote accountability and transparency of resource revenues, Lawer et al. (2017), in a qualitative analysis, denotes that decentralization and by extension local government institutions do not necessarily improve living standards in mining communities, but rather gives more rooms for rent-seeking behaviours when local institutions are weak in nature.

To date, and to the best of our knowledge, no empirical and quantitative studies have explored the local effects of natural resources through the lens of decentralization, sub-national governments taxing rights, and their legal attribution in revenue mobilization. Therefore, as part of our second contributions, this paper assesses the mediating role of local governance and decentralization in the relationship between natural resources endowment and local socioeconomic outcomes.

# **3. Data Description and Sources**

Our empirical analysis relies on geo-referenced Afrobarometer surveys data for the period of 2005 to 2014. The Afrobarometer constitutes a large pool of public opinion surveys and a reliable source of information on various political and socio-economic characteristics of residents and local communities in Africa. The surveys capture the availability and residents’ appraisal of their perceived quality of public goods and services in local communities -- these latter identified by the enumeration areas (primary sampling unit). The geospatial coordinates of the enumeration areas facilitate the merging the Afrobarometer surveys data with the records of the Raw Materials Database (RMD) of SNL Metals and Mining by S&P. Each enumeration area and their corresponding respondents in the surveys are matched with its closest mine based on a cut-off distance value (Picard, 2010).

6
UN-Habitat. 2002. “Global Urban Indicators Database: Version 2.” Nairobi: Global Urban Observatory, UN-Habitat.

_________. 2007. “State of the World’s Cities 2006/7.” Nairobi: UN-Habitat.

- _________. 2009. “State of the World’s Cities 2008/9.” Nairobi: UN-Habitat.

- _________. 2010. “Malawi Urban Housing Sector Profile.” Nairobi: UN-Habitat.

- _________. 2011a. “Affordable Land and Housing in Africa.” Nairobi: UN-Habitat.

- _________. 2011b. “Ghana Urban Housing Sector Profile.” Nairobi: UN-Habitat.

- _________. 2011c. “Uganda Urban Housing Sector Profile.” Nairobi: UN-Habitat.

- _________. 2012a. “Senegal Urban Housing Sector Profile.” Nairobi: UN-Habitat.

- _________. 2012b. “Zambia Urban Housing Sector Profile.” Nairobi: UN-Habitat.

_________. 2013. “State of the World’s Cities 2012/13.” New York: Routledge.

Walley, Simon. 2013. “Housing Finance,” in _Financial Sector Development in Africa_ , eds. Thorsten Beck and Samuel Munzele Maimbo, 111-150. Washington, D.C.: World Bank.

World Bank. 1993. “Housing: Enabling Markets to Work.” A World Bank policy paper. Washington, D.C.: World Bank.

- _________. 2013. _Urbanization Beyond Municipal Boundaries: Nurturing Metropolitan Economies and Connecting Per-Urban Areas in India_ . Washington, D.C.: World Bank.

27
Also in line with RZ, we use as proxies for countries’ financial development the following variable: first, the ratio of total capitalization to GDP, with total capitalization defined as the sum of domestic credit to private sector (as provided by the World Development Indicators) and stock market capitalization (as provided by the S&P, formerly IFC, Emerging Markets Database).<sup>7</sup> Although we mostly use total capitalization to GDP, we also study separately the two subcomponents, private credit to GDP and stock market capitalization to GDP. Furthermore, as RZ did, we use the quality of the country’s accounting standards as provided by CIFAR (Centre for International Financial Analysis and Research) as a measure of financial sector development. The financial development measures apply to the beginning of our sample (1980) and the accounting standards measure to 1983, the earliest period for which the index on accounting standards is available (we also used 1990 data and found similar results).

As our measure of the degree of competition in the banking system, we estimate the _H_ - measure using the PR methodology, just as CL did. In CL the _H_ -measure was estimated over the period 1994—2001, a period for which more individual bank data are available from Bankscope. For our purposes, the 1994—2001 period is less attractive because it does not overlap much with the period for which we measure the growth rate of value-added, 1980 to 1990, or the period for which benchmark external financing needs are calculated, 1980 to 1989. Bankscope does not, however, cover a large enough sample of countries before 1987. We also need to estimate the _H_ -statistics over several years (we choose a period of ten years) to have

> 7 In RZ, total domestic credit to GDP is used as a measure of financial development; the authors refer to the sum of total domestic credit to GDP and stock market capitalization to GDP as “total capitalization”. Here, we focus on domestic credit to the private sector and use “total capitalization” to denote the sum of private credit to GDP and stock market capitalization to GDP. We prefer to use private credit as the measure of banking sector development because it covers credit extended to the private sector and does _not_ include credit extended to governments, government agencies, and public enterprises, as is typical of many other analyses (e.g., Beck et al. 2000). However, including credit to the public sector in the measure of total capitalization does not change our results.

17