rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In the 2000’s, services sector in transition countries experienced rapid development due to major regulatory changes. Deregulation allowed new firms to enter the market resulting in rapid expansion of services as a share of GDP. The focus of this paper is on analyzing the impact of those changes on productivity of manufacturing firms. This question has recently received considerable attention due to the importance of services in the global economy and due to the ongoing debates on the Doha Agenda (Hoekman et al., 2010). The literature documents a positive effect of services deregulation on productivity of manufacturing firms in the Czech Republic (Arnold et al., 2011) and in Chile (Fernandes and Paunov, 2011). Still, as pointed out by Francois and Hoekman (2010), works that try to establish a causal link from services to increase in productivity are plagued with the endogeneity problem and with the problem of disentangling the effect of services liberalization reform from the effect of other reforms. We look at the episode of services liberalization in Ukraine in 2001-2007, which was isolated from other major deregulatory changes and was driven by political pressure imposed by trading partners as a precondition for the Ukrainian WTO accession.

We exploit rich data on Ukrainian manufacturing firms, which allows us to construct a firm-specific index of the services use intensity and interact it with sub-sector and timevarying indices of services liberalization provided by the European Bank for Reconstruction and Development (EBRD). We adopt the standard two-stage approach in the literature of estimating the effect of a policy change on productivity (Pavcnik, 2002; Javorcik, 2004; Amiti and Konings, 2007; Khandelwal and Topalova, 2011). At the first stage, we estimate the production function using the Olley-Pakes methodology (Olley and Pakes, 1996), controlling for demand shocks as suggested by De Loecker (2011), to extract total factor productivity (TFP) of manufacturing firms. At the second stage, we regress TFP on the firm-specific index of services liberalization, controlling for the firm-specific heterogeneity and market structure of manufacturing industries. As a new contribution, we also implement a onestage procedure of estimating the effect of the services liberalization on productivity, which 2
effects on the labor statistics that they generate: the set questions to identify labor force participation and the choice of respondent. With four alternative survey designs, we compare the three assignments with the ‘best practice’ approach of a detailed questionnaire with self reporting.

Our findings suggest that both types of survey design decisions have statistically significant effects on labor statistics. These effects are largest on the measure of labor force participation, but also exist for weekly hours of work, daily earnings, main activity, and type of work. The effects are distinct for different statistics and remain significant even after controlling for individual, household, and village characteristics. Using the short questionnaire lowers female labor force participation and also affects the distribution of workers across sectors, lowering the share of paid employees among the employed. Response by proxy leads to lower reports of labor force participation and weekly hours for both men and women, and higher reports of female earnings among those employed; it also results in a higher share of unpaid family workers among the employed. Combining a short questionnaire with response by proxy, the least expensive approach to implement, results in lower reported female earnings. The differences between proxy and self reporting seem to come from information imperfections within the household, with the distance in age between respondent and subject playing an important role, while gender and educational differences seem less important.

These results provide clear evidence that survey design does matter for measuring labor outcomes. By extension, it also matters for the relationships between labor and other variables, since the types of biases introduced may affect the point estimates of 31
budgets. All except KTA and KPCA, which are membership organizations, must obtain budgetary approval from their bureaucratic principals.

At the operative stage, hierarchical principals control support agencies through regulations and personnel appointnents. The latter is an important means of control in that the bureaucratic principals appoint the executives of all support agencies except those of the aforementioned associations. Although political networks play an important role appointments, Korea is unusual in that competence is about equal in importance to political conmections. At the post-operative stage, the bureaucratic principals evaluate the performance of support agencies mainly through performance audits. All agencies are regularly audited and receive performance evaluationis from their bureaucratic principals. As Table VI.2 shows, controls are probably the most important sources of good governance for both financial and technical support systems.

**Human Resource Policies.** The delivery of quality service cannot be attributed solely to hierarchical supervision of oversight agencies. Organizational behavior literatare postulates that the performance of organizations is a function of both motivation and ability, which suggests that human resource policies are also important to organization performance. Indeed, Table VI.2 identifies human resource policy as the most important source of good governance for all the public support systems.

Most of the sample support agencies (with the exception of SMIPC and KEPCA) emphasize educational qualifications, experience, and competence in their hiring practices. This enables these support agencies to recruit employees who are more qualified than those of the private sector. SMIPC and KEPCA, however, recruit new employees largely on the basis of personal contacts.

The average salaries at the agencies vary widely, from 8 million wonlyear at KEPCA to 20 million won/year at KTA. The average salary level of all sample support agencies (16.1 million won/year) is 50 percent higher than the industry average of 10.4 million wonlyear, according to a 1992 wage survey undertaken by the Ministry of Labor. Not surprisingly, members of these agencies enjoy both high self-esteem and social prestige. In contrast, employees of KEPCA, where salaries average only about half those of the other agencies and are below the industry average, have lower self-esteem and prestige.

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research question. The fallback option could always be to accept each person’s response as to whether they are an owner. An alternative option is to reconcile the responses so that one owner or set of owners is identified for each asset, based on reconciling the responses to arrive at one answer. The Gender Asset Gap Project, particularly for Ecuador, used information on the marital property laws, the respondents’ marital status, and when the asset was acquired to report the owner or owners when there was a discrepancy among respondents.

# **4.5 Quantification, Valuation, and Quality Assessment**

Beyond knowing whether individuals own particular types of assets, we may want to know whether they own more than one and whether their assets are valuable. While simply identifying the owners of assets allows us to calculate measures of the incidence of ownership, often we want more information about their assets such as quantity, value, and/or quality.

Identifying the quantity owned of most assets is relatively straightforward. Survey designers use two approaches. The first approach uses the asset as the unit of analysis and identifies the owners of each asset. It is then possible to calculate the quantity owned by each individual.<sup>26</sup> A second approach, which can be employed when the individual is the unit of analysis, is to ask each respondent how many of each asset they own, whether individually or jointly with someone else.

When the asset is the unit of analysis in the survey, then it is relatively easy to ask additional questions about the quality or value of each item. Identifying the value of each asset allows for the aggregation across assets and the calculation of the gender wealth gaps.

There are, however, a number of challenges to obtaining good measures of asset values. For consistent answers, one should specify the measure of value being asked. Asking the original purchase price and the year of acquisition facilitates calculation of the depreciated value, assuming a typical amount of wear. A common approach is to ask the price that would be received if the asset were sold, assuming that there are markets for such goods and the respondent is aware of the market price. When sales markets are limited, rental markets for land or housing may provide insights into value. A final measure is cost to replace the asset.

When markets are thin or nonexistent for a particular asset, it may be hard to have respondents provide a value. For example, rural houses that are constructed by the

> 26 At least one exception in reliable self-reported asset quantification is farmer-reported land area measurement (Carletto et al., 2015; 2016).

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# **A.B Characterization of Treatment Variables**

Figure A.5. Geographical Distribution of Taliban, Allies, and Contested Districts - Surveyed Sample <!-- Start of picture text --> District Control in 2017 District Control in 2018<br>Out of the Sample Out of the Sample<br>Taliban Presence Taliban Presence<br>Government Control Government Control<br>Contested Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Control in 2019<br>Out of the Sample<br>Taliban Presence<br>Government Control<br>Contested<br>52<br><!-- End of picture text --> <!-- Start of picture text --> District Control in 2020<br>Out of the Sample<br>Taliban Presence<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Control July 2021<br>Out of the Sample<br>Taliban Presence<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Control in Aug. 2021<br>Out of the Sample<br>Taliban Presence<br><!-- End of picture text --> _Notes:_ Source: FDD’s Long War Journal
ers.<sup>7</sup> Across 32 tests, we fail to reject the null of no correlation in just two tests at the 10% level. First, in Column 4, sub-Districts with high mask manufacturing exposure purchase significantly more inputs when we include Province fixed effects, but we do not residualize mask manufacturing intensity when constructing mask manufacturing exposure. Second, in Column 5, we reject the null of balance in the omnibus F-test when we do not include Province fixed effects, but we do residualize mask manufacturing intensity when constructing mask manufacturing exposure. We find no evidence of imbalance for specifications for which we report results in Section 3.3; these specifications are analogous to Column 3 (no controls and no residualization of mask manufacturing intensity when constructing mask manufacturing exposure) and Column 6 (controls for, and residualization of, mask manufacturing intensity when constructing mask manufacturing exposure on Province fixed effects, log textile manufacturers, and log population density).<sup>8</sup>

# **3.3 Results**

## **3.3.1 Impacts of mask manufacturing exposure**

We now present our results on the impact of mask manufacturing exposure on mask prices, purchases of masks from manufacturers, and COVID-19 infections. Table 2 reports estimates of these impacts from Equation 2; we focus our discussion on estimates from Columns 1, 3, and 5 from our most parsimonious specification without controls, as estimates are quantitatively and qualitatively similar when we include

> 7We choose log population density and log number of textile manufacturers as these controls are the most strongly correlated with mask manufacturing intensity in Columns 1 and 2. These two characteristics plausibly affect mask manufacturing intensity—as textile manufacturers produce masks, sub-Districts with more textile manufacturers will likely produce more masks, and lower population density sub-Districts are more likely to specialize in manufacturing relative to intermediation or importing.

> 8We report results from specifications analogous to Columns 4 and 5 in Appendix F.3, and find our estimates are qualitatively similar under these alternative approaches to introducing controls.

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share of bank time deposits in total deposits may be even lower than shown in figure 2.1.<sup>11</sup> Moreover, structural constraints to lending still in place in several of these countries (discussed below) mean that banks tend to be highly risk averse.

**Figure 2.1 Demand deposits as a share of total bank deposits**

<!-- Start of picture text --> As of end-2006 /1<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Chile Malaysia DRC Tanzania South Africa Zambia Ghana Uganda Kenya Mozambique Sudan Cape Verde Rwanda Malawi Ethiopia Cameroon Nigeria Burkina Faso Cote d'Ivoire Senegal Benin Madagascar Namibia Niger Lesotho Chad<br><!-- End of picture text --> Source: International Monetary Fund International Financial Statistics. Note: Data for 2006 with the exception of the Democratic Republic of the Congo, Nigeria, and Rwanda (2005).

The typical financial liabilities of institutional investors, which are largely medium- to long-term, would better match the longer terms of infrastructure projects. Pension funds and insurance companies would thus seem to have significant potential as sources of medium- to long-term financing. But institutional investors in Africa remain largely underdeveloped, impeded by factors that can include a continued predominance of state-controlled pension funds/systems in a number of countries and a lack or small number of private pension funds, underdeveloped capital markets and a narrow range of alternative financial investment instruments, investment practices that consequently often favor illiquid real estate holdings, short-term bank deposits and government securities, and inappropriate or nonexistent regulations governing investment of their assets (see chapter 4 on institutional investors). Moreover, these institutional investors lack the ability to undertake the credit-risk evaluation necessary to involve themselves in infrastructure projects. The nature of the risks to which infrastructure projects are exposed would necessitate the development of some mechanism(s) for sharing and/or reducing risks associated with investments in infrastructure projects. In Chile, a public-private risk-sharing arrangement that evolved during the late 1990s centered on the issuance of local currency-denominated bonds for infrastructure financing of government road construction projects. A private monoline insurance company,<sup>12</sup> and the Inter-American Development Bank as coguarantor, provided a financial guarantee on future timely payment of interest on the project financing. This long-term financing instrument eliminated the need for potential investors in the bond to undertake specialized credit risk evaluation. By mitigating

> 11 Banks in the Democratic Republic of the Congo moved many of their assets abroad during the civil war earlier in this decade (Beck and Honohan 2007).

> 12 A monoline insurance company provides guarantees to issuers, which enhance the credit of the issuer.

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# **INTRODUCTION**

While it is commonly believed that labor market returns to education are highest for the primary level of education and lower for subsequent levels, in Colclough, Kingdon and Patrinos (2010), we found evidence to suggest that the pattern is changing. The causes of such changes, and their implications for both education and labor market policy, are significant. The possible reasons put forward for this include: (1) technological change favoring higher order skills; (2) increasing coverage at lower levels of schooling; and (3) the quality of schooling. Of course, it could be a combination of all three factors. The issue is revisited here.

The pattern of economic rates of return to investments in education can help us to understand the benefits of schooling. The pattern of the returns to education are important for students and their families making important and costly schooling decisions. They are also important signals for policy makers. Since the 1980s, the returns to schooling overall have increased (Psacharopoulos and Patrinos 2018). More recently, the returns to higher education have increased the most (Montenegro and Patrinos 2014).

A review of current patterns of the returns to schooling by level and region is presented, along with a discussion of the possible causes of changes and the subsequent policy implications. Among other aspects, the education investment patterns over time are assessed. The analysis is based on estimates of the returns to schooling from more than 140 countries and more than 1,000 estimates from 1980 to 2014. The data come from countries all over the world, representing more than 90 percent of the world’s population, and are disaggregated by gender.

The fact that the more educated have improved their position despite an increase in their numbers must mean that the demand for more educated workers has increased more than supply over time, causing an increase in the overall returns to schooling.

# **LINK BETWEEN EDUCATION AND EARNINGS**

The origins of scientific interest in the contribution of schooling to development is attributed to Adam Smith, although classical philosophers in the East and the West promulgated education as 2
of state resources from pro-poor programs to the litigant class, they merely strengthen the ability of middle class users to negotiate with their providers, when the services involve goods protected by constitutional rights. Cross-country and cross-policy area differences in the number of cases counsel against aggregating across these cells, but on average these cells include only 23% underprivileged beneficiaries, while the cells where we expected a more progressive effect include, on average, 70% underprivileged beneficiaries. Even if we drop the cells with very small numbers (say, less than one thousand), the cases with more regressive expected effects average 27% underprivileged, while the cells where we expected more pro-poor outcomes average a full 67% underprivileged.

**Table 7: Percentage of underprivileged in each class of litigation**

|**Class of cases**|**Percent**<br>**underprivileged**|**N underprivileged**|
|---|---|---|
|Brazil Health Provision|0.36|1,722,185|
|BrazilObligation|0.32|12,765|
|Nigeria Ed Obligation|0.1|500|
|India Health Obligation|0.13|219,216|
|South Africa Health Provision|0.76|206985|
|South African Education<br>Provision|1|73125|
|South African overall|0.8|280110|
|Indonesia Health & Education<br>Abstract Review|0.36|359709|
|Indonesia Health concrete<br>review|0.35|168|
|Nigeria Education Regulation|0.25|18053|
|Nigeria Health Provision|1|700|
|Nigeria Health Regulation|1|600|
|India Health Provision|1|9,481,667|
|India Health Regulation|0.47|259,156|

The discussion above and in the Appendix suggests that nearly all cases have some indirect effects. The difference between Provision cases in Brazil versus those in India, Indonesia and South Africa, is that the indirect effects are tied to, and the product of, individual cases that 35
# **The economic outcomes of the boom**

Figure 4. Evolution of global fossil fuel and metals and minerals indices.

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Fossil fuels Metals and minerals<br>Price index (2010=100)<br>1990 1991 1992 1993 1995 1996 1997 1998 2000 2001 2002 2003 2005 2006 2007 2008 2010 2011 2012 2013 2015 2016 2017 2018 2020 2021<br><!-- End of picture text --> _Notes: Monthly indices based on nominal US dollars, 2010=100. Fossil fuels include oil, natural gas and coal. Metals and minerals index includes aluminum, copper, lead, nickel, tin, and zinc. Source: authors’ elaboration using World Bank staff estimates._ As illustrated in Figure 4, the boom saw prices for hydrocarbons, minerals and metals hit record levels and these high prices were sustained for over a decade. Even the financial crises of 2007-08 saw only a temporary dip in the cycle, with a rapid recovery of oil prices in 2009-10. To estimate the impacts of the boom compared to the counterfactual this study takes three time periods for analysis – the ’pre-boom’ period of 1998-2003, the boom of 2004-2014, and the post-boom 2015 to 2018. We then compare resource-rich countries to non-resource-rich counterparts.

We trace four major macroeconomic outcomes of the commodity price boom. We conclude from these stylized facts that in aggregate the commodity boom was a huge opportunity that did not deliver on its promise: a dog that didn’t bark in the nighttime.<sup>7</sup> First, outcome 1, we find that Sub-Saharan Africa became a more resource-dependent region during the boom, and compared to the period prior to 2004. This rising resource dependency can be traced in terms of the number of countries – a majority of countries

> 7 The title of the paper is drawn from Sherlock Holmes’ The Adventure of Silver Blaze, Arthur Conan Doyle, 1892 and the curious incident of the dog in the nighttime.

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Table 9: Effects of LEAP on self-reported group memberships

||(1)<br>SHG|(2)<br>Producer|(3)<br>Rice<br>Seed|(4)<br>Funeral|(5)<br>Youth|(6)<br>Irrigation|(7)<br>Women|(8)<br>Average<br>effect|
|---|---|---|---|---|---|---|---|---|
|_Full_<br>_sample_|||||||||
|LEAP|0.27**|0.03|0.26**|0.12|0.04*|0.03**|0.12|0.36**|
||(0.07)|(0.02)|(0.07)|(0.09)|(0.02)|(0.02)|(0.09)|(0.09)|
|Constant|0.28**|0.03**|0.21**|0.19**|0.03**|0.02**|0.19**||
||(0.04)|(0.01)|(0.04)|(0.06)|(0.01)|(0.01)|(0.06)||
|N|548|548|548|548|548|548|548||
|_ID Poor 1_|_and 2_||||||||
|LEAP|0.30**|0.03|0.30**|0.13|0.03|0.02|0.13|0.36**|
||(0.09)|(0.02)|(0.08)|(0.09)|(0.03)|(0.02)|(0.09)|(0.11)|
|Constant|0.28**|0.04**|0.21**|0.20**|0.03**|0.02*|0.20**||
||(0.06)|(0.01)|(0.05)|(0.06)|(0.02)|(0.01)|(0.06)||
|N|367|367|367|367|367|367|367||
|_Non-poor_|||||||||
|LEAP|0.21**|0.02|0.18*|0.11|0.06**|0.06**|0.11|0.36**|
||(0.09)|(0.02)|(0.09)|(0.10)|(0.03)|(0.03)|(0.10)|(0.13)|
|Constant|0.26**|0.01|0.20**|0.19**|0.02|0.02|0.19**||
||(0.06)|(0.01)|(0.06)|(0.07)|(0.02)|(0.02)|(0.07)||
|N|181|181|181|181|181|181|181||

Note: Columns 1-7 report OLS estimates with village-clustered standard errors. Column 8 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-7

* = significant at the five percent level, **=significant at the one-percent level

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