rafmacalaba/data-use-mentions-extended huggingface dataset:
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Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at the country-sector level have been estimated using employment (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported.

While some low- and medium-tech manufacturing sectors create many jobs when exports rise, capitalintensive sectors show workforce contractions. Some low- and medium-tech manufacturing sectors, in particular exports of paper products as well as rubber and plastics, create many jobs. Surprisingly, we do not find a positive link with employment in the textiles and apparel sector, but only with labor incomes (Figure 3). Possible explanations include the strong reduction of apparel jobs in China, India and other emerging countries (as discussed further in section 3). Other sectors have negative export-employment elasticities, most notably machinery, food and beverages, fishing and aquaculture and mining which tend to be capital-intensive, explaining the decline in the number of jobs. This finding could also be partially explained by the country sample excluding low-income countries for which exports in sectors like fishing or small-scale and artisanal mining create job opportunities.<sup>15</sup>

> 14 Evidence for Greece during the second half of the 1990s found that high technology and capital-intensive manufacturing sectors contributed most strongly to net employment growth (Voulgaris, Papadogonas, Agiomirgianakis 2005).

> 15 - - Globally, around 40 million people are estimated to work in small-scale mining (https://www.reuters.com/article/us <u>mining asm/more-than-40-million-people-work-in-artisanal-mining-report-idUSKCN1S025C), while almost 60 million people work
# **4. 2005 India Human Development Survey**

The 2005 India Human Development Survey (IHDS) is a nationally representative sample of 41,554 urban and rural households covering a wide-ranging set of topics, including energy use, income, expenditure, education, health, time allocation, and irrigation. The 2005 IHDS covers all of India’s key states and union territories, with the exception of Andaman/Nicobar and Lakshadweep. The sampled households were selected from 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks.

# **4.1** **<u>Household Selection</u>**

Urban and rural households were selected using various sampling designs. The urban sample was drawn from all of a state’s urban areas, listed according to their size, with the number of blocks drawn from each urban area allocated based on probability proportional to size. Once the number of blocks for each urban area was set, the enumeration blocks were randomly selected with the assistance of the Registrar General of India. From these Census Enumeration Blocks of some 150 households, a complete household listing was formulated, and a sample of 15 households was selected per block. For ease of sampling, some smaller states were combined with larger neighboring states. The final urban sample covered more than 13,000 households.

The rural sample selection was based on both the random sampling design used in the 2005 IHDS and the method used in the 1993–94 Human Development Profile of India (HDPI) survey, which covered 33,320 households in 16 states. About half of the sampled households were newly selected for the 2005 IHDS, while the other half had been interviewed initially as part of the 1993– 94 HDPI (Shariff 1999). Both surveys follow a similar sampling design. In states where the 1993– 94 survey had been conducted and contact details for future survey were available, 13,593 households were randomly selected for re-interview in 2005.
reveals net counterfactual mean outcomes for those treated; _TT_ gives the mean gain net of losses amongst participants. Instead of focusing solely on the net gains to the poor (say) we may ask how many losers there are amongst the poor, and how many gainers. We already discussed an example in section 7, namely the use of panel data in studying impacts of an anti-poverty program on poverty dynamics. Some interventions may yield losers even though mean impact is positive and policy makers will understandably want to know about those losers, as well as the gainers. (This can be true at any given poverty line.) Thus one can relax the “anonymity” or “veil of ignorance” assumption of traditional welfare analysis, whereby outcomes are judged solely by changes in the marginal distribution (Carneiro et al., 2001).

Heterogeneity in the impacts of anti-poverty programs can be expected. Eligibility criteria impose differential costs on participants. For example, the foregone labor earnings incurred by participants in workfare or conditional cash transfer schemes (via the loss of earnings from child labor) will vary according to skills and local labor-market conditions. Knowing more about this heterogeneity is relevant to the political economy of anti-poverty policies, and may also point to the need for supplementary policies for better protecting the losers.

Heterogeneity of impacts in terms of observables is readily allowed for by adding interaction effects with the treatment dummy variable, as in equation (5.1), though this is still surprisingly far from universal practice. One can also allow for latent heterogeneity, using a random coefficients estimator in which the impact estimate (the coefficient on the treatment _T C_ dummy variable) contains a stochastic component (i.e., μ _i_ ≠ μ _i_ in the error term of equation 4). Applying this type of estimator to the evaluation data for _PROGRESA_ , Djebbari and Smith (2005) find that they can convincingly reject the common effects assumption in past evaluations. When there is such heterogeneity, one will often want to distinguish marginal impacts
Policy Research Working Paper 10100

# **Abstract**

This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that experienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. The estimated impacts on measures of reconciliation, post-conflict justice, trust and participation in community groups are mostly statistically insignificant. The paper also explores how these effects differ across different sub-samples based on ethnic composition, land scarcity and attitudes towards return. The results highlight the possible role of new migration-related societal divisions (i.e. returnees versus stayees) in affecting post-return social cohesion.

This paper is a product of the Social Sustainability and Inclusion Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at carlos.vargas-silva@compas.ox.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent
the 2004-05 NSS round 61, I obtain the expenditure shares _νA_ and _νS_ , along with the baseline welfare (per capita expenditure) level, for each household. Figure 3 plots these shares by per capita consumption percentiles, showing, as might be expected, opposite patterns for food and nontradables (services).<sup>31</sup> In addition to the parameters that have already been discussed, I need income shares for each household to compute Ω _._ Appendix B.3 describes the construction of the income components from NSS household data and Figure 4 shows how the component shares vary along the wealth distribution. Not surprisingly, manual labor earnings as a share of total income decline steadily by percentile, whereas earnings from nonmanual labor and land increase.

After combining all of these elements, I plot each household’s value of _ε_ against its per capita expenditure percentile in 2004-05 to obtain the solid curve in Figure 5. The salient feature of this welfare elasticity profile is that it is everywhere positive. Thus, rural Indian households, both rich and poor, would _benefit_ from higher food prices. But the profile is also increasing in household wealth, paralleling the pattern seen in the proportion of income derived from land (cf., Figure 4). So, while the welfare gain to the poorest is 0 _._ 25 percent for every one percent increase in the agricultural price index, the welfare gain to the richest is around 0 _._ 4 percent.<sup>32</sup> How important are assumptions regarding intersectoral labor mobility? The lower dashed curve in Figure 5, which plots _ε_<sup>_′_</sup> by percentile, provides the answer. In moving to a segmented labor market, roughly a third of the gains to the very poorest households of higher agricultural prices are wiped away, whereas the structure of labor markets hardly affects the welfare gains of the richest households at all. The reason for this can be gleaned from Figure 4
0.086|1.066|0.006|
|Heater|0.003|0.000|0.196|0.009|
|Hot water tank|-0.133|0.009|-0.235|0.020|
|Television|0.402|0.050|1.112|0.115|
|Radio|0.102|0.004|0.411|0.029|
|Cell phone|0.018|0.000|0.113|0.003|
|Bicycle|0.215|0.003|0.933|0.039|
|Motorcycle|0.373|0.004|1.04|0.026|
|Flatiron|0.155|0.008|0.172|0.006|
|Fan|0.597|0.069|1.359|0.093|
|Car|1.092|0.029|0.758|0.007|
|Sewing machine|0.308|0.004|0.555|0.012|
|<br>Tools/Material|-0.951|0.122|-0.588|0.090|
|Small business stocks|0.089|0.001|0.068|0.001|
|Partial inertia contribution(%)|14.78||19.16||

Source: Own computations using June 2010 (EFSA II) surveys. Note: *Filtration products are generally used in relatively poor regions so that it can explain the negative weight.

Using retrospective data on assets from the June survey, Figure 4 presents the asset index distributions before the earthquake, in February and in June. Using this index, we can notice that the inequality of household wealth (as measured by the Gini coefficient) has decreased after the earthquake due to higher losses among
? This paper provides empirical evidence from firm surveys to address the first question. Addressing the second question is beyond the scope of the paper, though we note that if countries were equally successful in achieving an optimal financial structure, we would see little variation in financial structure indicators for a given level of development and find no significant relationships between those indicators and firm growth. A quick glance at Table 2 reveals relatively wide variation in indicators of financial development within countries at similar stages of economic development as reflected in per capita income, and we do find significant relationships between financial 4
0.05|0.00|0.21|0|0|0.25|0|0.43|0|1|0.17|0|0.38|0|1|
|Number of rooms<br>|3.55|3.00|1.70|2|6|1.91|2|1.08|1|3|2.55|2|1.57|1|5|
|in dweling<br>* Household survey data m<br>** This category shows a s<br>*** Walls made of wood an|atched to<br>umbsamb<br>d mud a|utility bi<br>ple of ho<br>nd floor|lling rec<br>useholds<br>made of|ords f<br>that<br>mud/d|or both<br>have ele<br>ung.|water an<br>ctricity o|d electric<br>r water s|ity.<br>hared co|nnecti|ons or|no water|connectio|n.|||

16
this area is due to Zaller’s 1992 monograph “The Nature and Origins of Mass Opinion”. Challenging what at the time was the consensus, Zaller rejected the idea that survey responses are manifestation of fixed attitudes, and that deviations are simply due to measurement errors. He proposed the RAS model of the response to opinion survey, theorizing that opinion statements result from a process in which people _receive_ new information, decide whether to _accept_ it and then _sample_ from their stock of considerations at the moment of answering questions. In Zaller’s original approach, which was influenced by advances of cognitive psychology, the formation of opinions is a

> 9 An interesting variation of these empirical studies is found in Alesina and La Ferrara (2005) who, in addition to _subjective_ expectations of upward and downward mobility, consider also the role of general mobility as _objectively_ present in the society (p.899).

> 10 To the best of our knowledge Engelhardt & Wagener (2014) is the only study that examines the determinants of perceived inequality and concludes that it correlates with government social expenditures. 11 Two often cited studies are Page and Shapiro (1983) and Monroe (1979). See also Slemrod (2006), Blendon et al (1997) and many of the additional studies referred in these papers.

> 12 Blendon et al (1997) document differences between objective (or reported by experts) and perceived views about: (a) the current or past economic performance, in terms of income growth or adequate job creation; and (b) explanations of why the economy is not doing better (the role of trade, technology, or government policies).

6
1)|57.8%|49.4%|52.6%|49.9%|
|Fraction of workforce comprised of management employees|5.4%|12.0%|9.9%|14.7%|
|Fraction of workforce comprised of skilled production employees|51.5%|30.3%|38.9%|30.1%|
|Fraction of workforce comprised of unskilled production employees|23.0%|28.9%|34.3%|31.6%|
|Fraction of workforce comprised of female employees|17.4%|23.9%|28.2%|30.1%|
|_Sectoral & Country Business Environment_|||||
|US 4 digit ISIC sector average R&D intensity|1.7%|1.8%|1.6%|1.4%|

_Source: World Bank Enterprise Surveys and Sharma et al. (2010)._ 28
married workers are more productive-whether because of selection or a true productivity effect-or that firms discriminate in favor of married workers. No good theoretical argument explains why married workers should be paid more. However, the Ghanaian manufacturing sector data show that married workers are more productive than unmarried workers, even though the difference is not revealed in earnings.

I could not analyze whether differentials by ethnicity exist in manufacturing in Ghana because too few Europeans or other ethnic groups were interviewed (less than 0.5 percent of the interviewed workers are non-African).

# 5. **Conclusion**

I used a unique dataset from Ghana that combines data on individual workers with data on workers' employers to estimate plant-level production functions and wage equations. I compared relative productivities and relative wages for various groups of workers and jointly estimated wages and output functions. This approach allows not only for assessing the marginal impact of demographic or other characteristics on wages, but also comparisons of the impact of these variables on productivity.

The analysis found that wage differentials match productivity differentials for certain groups of workers, while for others they do not. Female employees are generally paid less than male employees and the negative wage premium females receive does not match the corresponding negative productivity premium. Females' returns to schooling and training are not different from those of males.

37
durable goods acquired during the last _365 days_ other than those used exclusively for entrepreneurial activity”. Second, the NSSO61, NSSO66 and NSSO68 (type 1 survey) has questions for a 30 day and a 365 day recall period while the question on the NSSO72 only asks about expenditure for the 365 recall period. Third, the NSSO61, NSSO66 and NSSO68 asks about expenditure for “purchase and construction”, while the NSSO72 asks about “acquisition” rather than explicitly about purchase. Fourth, the NSSO61, NSSO66 and NSSO68 ask about purchase for “domestic use” while the NSSO72 asks about goods “other than those used exclusively for entrepreneurial activity”. This includes the total value of raw materials, services and/or labor charges and any other charges. There is no explicit mention of “construction” or “repair and maintenance” in the NSSO72, but there is a value of components column that most likely includes raw materials used in construction. Fifth, in the NSSO68, there is a separate question on whether the good was bought for “hire purchase”. The surveyor is asked to make the distinction between “hire purchase” and a loan. The NSSO72 instruction manual does not mention anything about goods bought on “hire purchase”.

> 21First, if an asset was bought and sold during the reference period, it is recorded in the earlier NSSO surveys but not in NSSO72. Second, in the earlier surveys if the item has been purchased but not yet in the household’s possession, the expenditure is recorded by the surveyor. However, in the NSSO72 a durable good that is not in the household’s possession even if full payment has been made is not included. Third, if the durable good is in possession but has not been paid for, it is not included in the earlier NSSO surveys but is included in the NSSO72 (Instructions to Field Staff, Chapter Four).

> 22The NSSO refers to PSUs as First Stage Units (FSUs). The term ’village’
Policy Research Working Paper 7806

# **Abstract**

This paper argues that although the bulk of the literature tends to focus on regulatory uncertainty stemming from formal practices, uncertainty that comes from unpredictable informal practices surrounding regulation is an underexplored additional form of regulatory uncertainty. The paper uses the results of empirical analysis of several unique firm-level data sets to argue that firms in Russian institutional environments adapt to informal practices of business-government interactions, so long as these practices are predictable. The paper draws a distinction between differences in levels of relatively well-ordered (and often centralized) and therefore predictable corruption—a predictable component of the cost of doing business—and variation in experiences with corruption, which often results from decentralized, unconstrained (“administrative”) corruption and the rent-seeking incentives of lower level officials. It argues that a significant obstacle to investment decisions at the regional level is not so much formal or informal rules per se, but lack of predictability of their application. It also examines in-country inconsistency in property rights enforcement as another source of underexplored regulatory uncertainty tied to informal practice. Unlike administrative corruption, inconsistent property rights enforcement is a fundamental, existential threat to businesses. To test this hypothesis, the paper draws on a measure that captures private “raiders’” attacks on firms—hostile, often violent takeovers of firms by outsiders aided and often abetted by law enforcement agencies. The paper argues that the greater is the number of raider attacks for a given region, the greater is the uncertainty and the less likely is investment.

This paper is a product of the Governance Global Practice Group. 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 authors may be contacted at gkisunko@ worldbank.org, ayakovlev@hse.ru, ilevina@hse.ru, imarques@hse.ru.

_The Policy Research
can be classified into three broad areas. The first consists of cross-country surveys describing the main features of guarantee schemes (e.g. Beck and al. (2008), Bennett and al. (2005)). The second consists of individual country studies, including efforts to assess additionality (e.g. Ridding (2007), Cowan and al. (2009)). Finally, a third category focuses on best practices and design issues, drawing on the international experience (e.g. Deelen and Molenaar (2004), Green (2003)).

**The World Bank conducted the first large scale cross-country survey of PCGs in 2008** (Beck, Klapper, and Mendoza, 2008). The objective of this survey was to provide an overview of the key features of guarantee schemes around the world, such as eligibility criteria, coverage ratios, fees, and selected indicators of operational and financial performance. The sample comprised 76 guarantee schemes operating in 46 developed and developing countries (However, Egypt was the only MENA country included).  The survey shows that there are large differences in the organizational features and rules of guarantee schemes around the world. Interestingly, these differences are not systematically related to financial and economic development. One of the many interesting findings of the survey is that few guarantee schemes around the world use risk-based pricing or risk-management mechanisms. The authors call for further empirical research on specific schemes to better understand which features work best in practice. They also stress the importance of doing proper cost-benefit analysis to assess whether guarantee schemes are cost-effective.

> 6 Rocha, Farazi, Khouri, and Pearce (2010).

5
.

The recorded levels of particulate matter in the treatment and control households are consistent with measurements in similar environments (Chen et al., 2016; Dutta et al., 2007) and well above WHO standards. Mean (median) concentrations in treatment and control households are 1,162 (464) and 1,297 (525) μg/m<sup>3</sup> for average 24-hour exposure. Twenty-four-hour maximum concentration levels are an order of magnitude higher. For comparison, the WHO’s current recommended 24-hour safety standard is 25 μg/m<sup>3</sup> (WHO, 2006b).

Here, we make use of pollution readings to descriptively assess the possibility of pollutionhealth gradients. For each of our key outcomes we estimate regressions of an individual’s health marker against the natural log of their household’s particulate concentration. As the shape of these 19