rafmacalaba/data-use-mentions-extended huggingface dataset:
regulation is the industry (regulatory capture view) while in others, it is the politicians and public officials (tollbooth view).

The opportunities created by regulation mentioned above increase the marginal benefit from corruption. This raises corruption when corrupt public officials equate the marginal benefit and marginal cost of corruption (crime and punishment model of corruption due to Becker 1968, and Becker and Stigler 1974). Corruption may also arise because principals (citizens) can only imperfectly monitor the corrupt behavior of their elected agents (politicians and public officials) (the principal-agent model due to Rose-Ackerman 1978 and Klitgaard 1988). More regulation creates greater opportunities for agents to use their discretionary powers to hide their corruption activities behind the labyrinth of rules and regulations.

Motivated by the above arguments, several studies analyze the possible effects of regulation on corruption. Using macro-level data for a cross-section of 85 countries in 1999, Djankov et al. (2002) look at the relationship between entry regulations and the level of corruption. The corruption measure they use is Transparency International’s Corruption Perception Index (CPI). Entry regulation is measured by the number of procedures required to start a business, the time it takes to clear all the procedures, and the official cost of doing so (Doing Business, World Bank). Consistent with the tollbooth view, the study finds strong evidence of higher corruption associated with heavier regulation of businesses. Using data from three worldwide firm surveys, Kaufmann and Wei (2000) confirm that when bribe-extracting bureaucrats can endogenously choose regulatory burden and delay, the effective (not just nominal) red tape and bribery can be positively correlated across firms. Duvanova (2014) employs firm-level survey data for 25 postcommunist economies between 1999 and 2005 and finds that the mechanisms of regulatory implementation, rather than heavy-handed regulatory policy, are responsible for bribery. Also see 3
Policy Research Working Paper 6008

# **Abstract**

This paper presents a broad overview of labor market indicators for men and women in Serbia with a focus on employment patterns, entrepreneurship and career advancement as well as earnings differentials. The analysis relies primarily on the results of the Labor Force Surveys conducted in Serbia in April 2008 and October 2009. The findings show that although the overall labor market situation in Serbia is difficult, women are in a much more disadvantageous position than men. Women are much less likely to be employed, start a business or advance in the political arena. Furthermore, there is a significant wage gap between men and women in a number of sectors and occupational groups with low educated women being particularly disadvantaged. The results of the Oaxaca-Blinder decomposition demonstrate that the wage gap is indicative of discrimination of women in the labor market as earnings differentials cannot be explained by differences in observed characteristics of male and female employees. Based on the obtained results, the paper outlines four broad areas that require the attention of policy-makers: employment generation; enhancement of education outcomes; improvement of the regulatory environment and support to women’s business and political careers; and promotion of transparent performance setting mechanisms.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at areva@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
potential endogeneity of access to urban infrastructure and profits. The productivity variable takes a value of 1 if the firm’s profits per worker is in the top 10 percent of the sample’s distribution of productivity.<sup>36</sup> Finally, we use an OLS model to estimate the correlation between employment type and the urban environment in one country. Taking advantage of available geospatial data and the 2017 Census for Mozambique, we explore how the urban environment – crowding, neighborhood economic development, and cell phone coverage – correlates with employment types. To do so, we create a dataset where each observation is an urban _bairro_ (neighborhood) in Mozambique, resulting in a sample with 212 _bairros_ in five cities _._ Using data from the 2017 Census, we create variables for the distribution of employment type within a _bairro_ and average ownership of a vector of durable goods in each _bairro_ . We use publicly available geospatial data to generate a vector of six urban variables: two measure neighborhood economic development (night light luminosity and electrification), three measures of crowding (population density, building density, and urban density), and one measure of cell phone coverage (derived from cell tower density). The methodology to generate each urban variable is provided in Annex 2. The regression specification is as follows: (5) gg gg gg gg gg gg bbii bbii bbii ii ffii The dependent variable is the share of total employment that is in employment type YY = αα + θθ UU + ββ XX + γγ II + εε _g’_ in _bairro b_ in city _c_ . To remain consistent with our definitions used to generate the employment type variable with the GMD and IES data, we use Census data on job category and employment to define _g’_ ( _g’=_ low-educated self-employed, high-educated self-employed, unpaid family worker, employer, low-educated employee, and high-educated employee).<sup>37</sup> The spatial distribution of each employment type across the five cities is provide
sensitive employment outcome variables are to the design and implementation of a questionnaire. For example, if an analyst chooses to use a 7-day recall period employment variable for analysis, erroneous conclusions can be drawn with respect to key outcomes for specific types of individuals and households. There is also the possibility that different analysts, using variables constructed in different ways, could easily come to different conclusions on level indictors using the same data. Trends over time are even more difficult to compute and interpret. We show that measurement bias can affect economic analysis relating these employment outcomes to other key variables at the household, sectoral or macro level. By implication, these results also apply to country comparisons when different countries use different instruments.

Inadequate questionnaires mean that analysis of labor force participation and employment outcomes is not a simple prospect in low-income countries. In Uganda, older surveys used questionnaires which clearly led to an underestimation of LFP at the household and aggregate level. Many countries still use these formats today. This complicates even the simplest aggregate trend analyses, such as whether the share of the labor force in agriculture is going up or down. Even data from the most recent Ugandan surveys, which include formats specifically designed to measure LFPR more accurately, have to be used carefully owing to multiple economic activities. Although the 7-day recall period is an international standard, and has worked just fine for OECD countries employment statistics, using it in a low-income setting will underestimate, and possibly distort, analyses of individual outcomes such as underemployment and household livelihoods.

Some have argued that the concepts of labor force participation and employment embedded within the SNA are not suited to a low-income setting. For example, it has been suggested that economic activities which produce output included in the SNA (such as growing food for home use) should not be considered employment as no income is earned.<sup>20</sup> The problem with this argument is that it goes against decades of revisions of the SNA, all designed to include more economic activities, not less
**Figure 2** **_:_ Types of hazards threatening households in urban and rural areas (% of respondents) – (20212023)**

<!-- Start of picture text --> 65.9 64.6 66.4 68.5<br>6157.9<br>54.4 52.5 52.6 51.9 50.3<br>45.7 44.9 45.8<br>35.731.3 33.8 37.332.9 37.1 36.6<br>27.3 27<br>17.2 14.111.7 18.6 19.6 15.121.4 12.3 9.6 14.6<br>3.6 5.2 4.7<br>Heat waves Hurricanes Extreme rainfall Drought Severe storms Floods Earthquake Wildfires Tsunami Landslides Storm surges Other<br>Urban 2021 Urban 2022 Urban 2023<br>57 58.6 64.4 66.559.8 61.7 61 57.6 63 69 68.2 71.1<br>52.9 53.4<br>47.5<br>42.6 41.1<br>36.7 36 35.2<br>25.5 29.4 29.7 25.2 27.1 22.625.5 29.5<br>18.4 15.8 13.6 11.3 13.1<br>7.1 3.7 8.2<br>Heat waves Hurricanes Extreme Drought Severe Floods Earthquake Wildfires Tsunami Landslides Storm surges Other<br>rainfall storms<br>Rural 2021 Rural2022 Rural 2023<br><!-- End of picture text --> Note: Results are from entire sample of wave 2, wave 3 and wave 4 households.

Most natural hazards threaten in greater proportion those in the bottom
. The approach to collecting and constructing harmonized firm-level data in this project differs from projects like the ICA project that use the same survey instrument in a number of countries. A discussion of the advantages and disadvantages of the alternative approaches as well as the relationship on key findings from the ICA dataset vs. the type of firm-level data used here is provided in Haltiwanger and Schweiger (2004).  Recent papers that have used the ICA data to study firm performance include Bastos and Nasir (2004), Dollar et. al. (2003), HallwardDriemeier  et al. (2003).

2
people well below the energy poverty line, this means that, even if their incomes increase, their use of energy does not because they are at the bare minimum amount necessary to sustain daily life. This also means higher shares of energy expenditure because, below a certain threshold, energy use does not change but people have less and less income; higher shares of energy expenditure, in turn, mean lower levels of household welfare because people have less to spend on other goods and services, including food and non-food items. This approach is data-intensive, requiring the analysis of household surveys that cover details of energy consumptions. But its attractiveness is that the definition of energy poverty is based on how people actually consume energy, based on local resource conditions, energy prices, and policies.

# **A Demand-based Approach for Defining Energy Poverty**

The assumption underlying the measure of energy poverty is that household consumption of energy and other non-energy goods and services is related to overall well-being. The relative shares of energy and other expenditures reflect the underlying price and availability of energy of different types and their impacts on overall welfare. However, higher shares of energy expenditure mean lower levels of household welfare because a lower percentage of spending is available for other goods and services, such as food and non-food items. At the same time, a higher monetary expenditure on energy does not necessarily imply a higher quantity of energy use as this depends on energy price, efficiency, type of use, and other factors. It may make a household worse off by lowering the observed level of its welfare. This suggests that the role of energy use in household welfare should be examined from the demand for energy services and not from expenditure on energy alone.

Assessing the role of energy use in household income and non-energy consumption involves some tricky issues, such as determining the direction of causality between energy use and income or 8
, ultimately undermining reform progress and the country’s development.

Finally, and related to all of the above, reformers must confront politics. This holds true for governments and donors alike. Reform success has hinged on the ability of policy makers to harness politics in their favor. Many of the failures of the good governance agenda, which has been promoted by international development agencies since the 1990s, can be traced back to an attempt to replicate ‘best practices’, which have tended to ignore, bracket, or underplay the social and political context in which institutions are located, rather than develop ‘best fit’, cognizant of the country’s context and of the power struggles at play in governance reforms (Grindle 2004, World Bank 2017<sup>48</sup> ). The uneven impact of donor support for governance reforms results to a large extent from the inadequate consideration of local politics and power relations. In recent years, recognition of the limits of donor support has pushed development practice further toward confronting power and politics, as evidenced by the analyses offered in the 2017 WDR that saw the gap between policy adoption and implementation (and its resolution) in relation to “power asymmetries” and “political settlements”. Such an approach calls for unpacking policy makers’

> 47 Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents (World Development Indicators, World Bank).

> 48 See also Andrews, M., Pritchett, L. & Woolcock, M. 2012. “Escaping capability traps through problem-driven iterative adaptation (PDIA)”. _CGD Working Paper_ No. 299. Washington, DC, Center for Global Development. Grindle, M.S. 2004. “Good enough governance: poverty reduction and reform in developing countries”. _Governance: An International Journal of Policy, Administration, and Institutions_ , 17(4): 525–548.

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18

## _Quality_

Even if the pre-autonomized hospital and the autonomized hospital are motivated equally by patient benefit and the quality of care, autonomization could still impact on quality of care. One possible mechanism is that autonomization gives hospitals greater decisionmaking in areas that matter to the quality of care. They can hire new staff, acquire new equipment, and so on. In this sense, autonomization might be expected to lead to an improvement in the quality of care. But there is a second mechanism that will work to reduce the quality of care, namely that once autonomized hospitals have an incentive to focus on profit – this may be tempered, as mentioned above, by any motivation the hospital has to increase the degree of sophistication of its care, assuming that this is positively correlated with the quality of care. On balance, therefore, the effect of autonomization on the quality of care is ambiguous.

# **IV. METHODS**

As already mentioned, we have two datasets – a hospital dataset, and a household dataset. The former is a panel, albeit unbalanced, while the latter is a series of cross-sections. We use somewhat different methods in the two cases.

## _Hospital-level analysis_

In econometric terms, our analysis of the impacts of autonomization on hospitals is based on a generalized differences-in-differences analysis. We have multiple years. In each year some ―units‖ have been ―treated‖, and others not. Let _yht_ be the outcome of interest in hospital _h_ at time _t_ , _Xht_ be a vector of covariates, and _AUTONht_ be a dummy variable taking
The second follow-up survey in addition included a labor market history module as well as a specific module on households’ nonagricultural self-employment activities, which together with the module on economic activities in the last 12 months is used to define the outcome variables.

Finally, we draw on the first follow-up survey to obtain a measure of how the program impacted attitudes towards the future early during program implementation. The sample size for the attitudes questions is lower (1519 households equally divided between productive investment grant, training and control households) as they were part of an additional questionnaire administered only to primary caregivers of

> 7 The analysis in this paper does not include the 964 households that only received the basic CCT.

> 8 The expenditure modules were taken from the 2001 Nicaragua Living Standards Measurement Study (LSMS) survey and include detailed information on various expenditure categories. For example, food expenditures include questions about 63 food items from actual expenditures, home production, and food consumed outside the home. The income module was designed specifically for this study with the objective to capture all the different economic activities household members are engaged in.

9
tasks that are more female-intensive in a gender-segmented labor market. Second, increased foreign competition can temporarily displace male workers, reduce household incomes, and induce females to supply labor outside the household – an added worker effect.

These factors highlight that both the nature of the trade shocks and domestic labor market institutions matter in determining the impact of trade liberalization on gender inequality. This paper sheds light on the relationship between _exports_ and gender-segmented labor markets by studying the effects of increased foreign demand shocks on local labor markets.

In this paper, we make two main contributions to the literature: one theoretical and another empirical. First, we present a general equilibrium model that combines international trade and gender-segmented labor markets. Production is divided between a female-intense sector and a maleintense sector and households optimize their combined consumption and leisure by supplying different quantities of female and male labor, given wages. In this framework, a foreign demand shock can either increase or decrease the equilibrium female-to-male employment ratio. The response depends crucially on (a) the sectoral composition of the demand shock; and (b) the relative market shares of each industry in the destination market.

We then present empirical evidence that is consistent with this theoretical mechanism. We construct a new dataset that combines labor market data and trade data from Tunisia, a developing country with a high degree of gender segmentation in the labor market and where foreign demand shocks were concentrated in male-intensive industries. We then use plausibly exogenous variation in export exposure of different regions in Tunisia to show that the direction of the effect of exports on labor markets is the same as the one predicted by the theoretical model.

Tunisia is a good case study because it has a high level of gender segmentation in labor markets but females are an important part of the labor force, participating at higher rates than other Middle East and Northern Africa (MENA) countries. During the sample period, the female-to-male employment ratio decreases in response to a foreign demand shock. This effect was concentrated par-

> 1For a review of gender imbalances
Journal of Economics_ 127: 1663-705.

- [18] christensen.g.s and e.miguel (2016) “Transparency, Reproducibility, and the Credibility of Economics Research,” _Journal of Economic Literature_ , forthcoming.

- [19] clemens.m, (2015) The Meaning of Failed Replications: A Review and Proposal, _CGD WP._

- [20] cowley.e and s.smith (2014) “Motivation and Mission in the Public Sector: Evidence from the World Values Survey,” _Theory and Decision_ 76: 241-63.

- [21] dal bo.e, f.finan and m.rossi (2013) “Strengthening State Capabilities: The Role of Financial Incentives in the Call to Public Service,” _Quarterly Journal of Economics_ 128: 1169-218.

30
s Far East and the rest of Russia. Furthermore, they argue that exposure to foreign competition, both in the product market and the labor market, explains the variation in adoption of good management practices across firms and broad regions.

To quantify the importance of management on the performance of Russian firms and identify potential avenues for boosting firm productivity, during July-October 2018 we conducted a survey on managerial practices in Russia using a slightly modified version of the US Census Management and Organizational <u>Practices Survey (MOPS). The survey was deliberately designed to replicate the US Census MOPS to</u> maximize comparability with the US as well as other countries where this survey has been conducted (e.g. Mexico and Punjab province in Pakistan, UK, Canada, Japan, China and Croatia). In consultations with the Russian authorities, the survey collects data on 978 small-medium sized manufacturing firms ranging from

> 1 Despite problems in identifying the actual role of the state sector in the economy, even by moderate assessment state-owned enterprises and state corporations present much more than 20% of Russia`s GDP (by some expert assessment up to 50% considering subsidiaries, affiliates, minority shares in different assets, etc.) and almost half of all internal procurements (Danilin, 2015).

> 2 The other 9 countries include - Belarus, Bulgaria, Kazakhstan, Lithuania, Poland, Romania, Serbia, Ukraine and Uzbekistan. The survey was modeled in a way that is similar to MOPS.

> 3 Operating revenue is the income or sales generated from a firm’s day-to-day business activities, that is, it is the revenue posted from selling the firm’s products and services.

> 4 Profit margin is computed as the firm’s net profit per unit of sale.

> 5 Return on assets is defined as the ratio of net income (profit after taxes) over total assets of the firm.

> 6 As in Bloom’s earlier works, the differences in the quality of management practices can be
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|||A|ll household|s||Poor|Househo|ld head|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|households|Female|Male|
|Median|0.5|0.9|0.8|1.5|0.4|1.3|1.0|0.7|

_Source_ : World Bank staff calculations based on household survey data. _Note_ : n.a. = information on connection costs not available.

a. The connection payment is the first payment in an installment plan (same first payment irrespective of the number of months over which payments are spread). A one‐off payment is 4.8 times higher.

b There is no connection fee but customers buy the materials needed and present them to the utility. There is supposed to be no exchange of money between the customer and the utility.

c. The connection payment is the first payment in an 18‐month installment plan. A one‐off payment is twice as high.

Table 11: Initial connection payment as multiples of monthly cash household expenditures

|||A|ll household|s||Poor|Househo|ld head|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|households|Female|Male|
|Botswana<sup>a</sup>|1.1|3.1|1.9|7.5|0.6|4.1|2.2|1.7|
|Burkina Faso|1.1|0.9|1.