rafmacalaba/data-use-mentions-extended huggingface dataset:
# **INTRODUCTION**

The socialist regime in the former Yugoslavia was characterized by a de jure gender equality in labor relations, which translated into equal access to education opportunities and high levels of employment among men and women. Transition to a market economy was marked by sectoral restructuring, privatization of state-owned enterprises, price adjustments, growth of the informal sector and a rapid increase in unemployment. Despite stable economic growth for almost a decade (since 2000 and up until the global recession in 2009), the labor market performance in Serbia remained weak in comparison to other transition economies. Only a half of working age adults was employed in 2009 and the activity rate of population is much lower than the regional average.

While labor statistics have been collected by the Statistical Office of Serbia on an annual basis for the past decade, there are few studies on the differences in access to jobs and general employment patterns between men and women in Serbia. This paper aims to fill this gap by providing a gender-disaggregated picture of the labor market. To distinguish the impact of the global economic crisis, selected employment indicators as well as wages of men and women are compared with the data for 2008; however an in-depth examination of the impact of the crisis on the labor market in Serbia is beyond the scope of the paper. The analysis relies primarily on the results of the Labor Force Surveys (LFS) conducted in Serbia in April, 2008 and October, 2009. Whenever possible, labor market characteristics in Serbia are compared with those in the EU countries, where similar labor force surveys have been organized.

The findings of this paper show that although the overall labor market situation in Serbia is difficult, women are in a much more disadvantageous position than men:

- The employment rate of working age women is 26% lower than men’s while the inactivity and unemployment rates are significantly higher (See Annex 1 for definitions of labor market indicators).

- Women are markedly underrepresented in the business world and in the political arena. Men comprise
and less risky environments. The USA, conversely, stands out as one using country systems at very low rates, regardless of risk levels. It is the donor that best exemplifies Ellmer’s (2011: 19) assertion that “some bilateral donors have extremely low tolerance to fiduciary risks which makes it very difficult for them to use country systems.” Belgium (in the upper left quadrant) exemplifies yet another very different pattern: of the numerous donors that are highly responsive to risk, it is the only one that tends not to rely much on country PFM systems.

# **6. Summary and Conclusions**

The 2005 Paris Declaration (PD) exhorts donors to deliver aid in ways more consistent with recipient country priorities and institution-building. Among other specific provisions, it urges donors to increase their use of country PFM systems, particularly where systems are more trustworthy so that their use does not entail inordinate reputational, fiduciary or other risks to donors. The 2008 Accra Agenda for Action (AAA) asserts that “even where there are goodquality country systems, donors often do not use them,” and commits donors to use them “to the maximum extent possible” and to ensure that the internal incentives for donor agency staff members are aligned with this goal. The PD and AAA acknowledge however that use of country systems may not be feasible in some cases, and numerical targets for their use were set at more ambitious levels where quality of systems is stronger, as measured by PFM Quality.

Based on a three-dimensional panel dataset constructed from all three rounds of the PD Monitoring Survey, this study provides empirical evidence supporting the proposition that use of country systems is strongly related to their quality. This relationship holds not only for donors collectively, but also for most donors individually. This finding is contradictory to the conclusions of other (less rigorous) analyses that quality of PFM systems has little or no effect 28
equation: where _yimt_ is one of each of the reported attitudes by individual _i_ , living in municipality _m_ , and year _t_ . The variable _Econmt_ is the proxy of economic activity (NTL density) or per capita household income for a restricted sample. Both vary at the municipality level and over time. _X_ is a vector of individual-level controls such as gender, age, years of schooling, rural and employment status.<sup>10</sup> This set of individual-level controls is constrained by data availability and are included to improve the precision of the estimates. The vector _Mmt_ includes two time-varying measures at the municipality level: annual homicide rates (per 1,000 inhabitants) and Central Government unconditional

> 8I used 1992 population counts to normalize the amount of government transfers.

> 9Retrieved at https://www.transparencia.gob.sv/institutions/isdem/documents/otra-informacion-de-interes

> 10Since Latinobar ́ometero does not survey individuals in rural areas, I use a “small town” indicator when performing the analysis with this public opinion survey.

13
of the PAF interventions. As mentioned earlier, PAF makes use of NGOs to initiate communitybased interventions in the rural villages in Nepal. The decision to select a village over another one for early phase-in cannot all be enforced through lottery alone. Implementation readiness, geography, socio-economic conditions and other factors may contribute towards inability to comply with random selection. Allowing the NGOs to implement their work may have resulted in imperfect compliance, but it may have helped maintain a certain degree of external validity (the extent to which the process and results of the interventions are generalized and replicable elsewhere). For the purpose of evaluation, we consider a village in the treatment status if there is a CO in that village supported by the PAF. We also note that not all 15 households (which were

> 3 Piloting for the impact evaluation was initiated in 2006 in a small sample. The design and implementation of the main impact evaluation benefited from lessons learnt from the pilot in term of sampling procedure, survey questionnaire, and accessibility of sampled villages for reasons related to weather seasonality and remoteness.

> 4 For details on national household surveys, see Nepal Living Standards Surveys (NLSS 1995/96, 2003/04, 2010/11). The reports can be found at www.cbs.gov.np

5
percentage point increase in the regulatory burden. Figure 1 illustrates the point graphically. The finding is robust to various controls, specifications, and estimation methods including OLS (crosssection and repeated cross-section estimation), logit, and Poisson.

# **2. Data and Main Variables**

# 2.1 _Data description_

Our main data source is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). Our baseline regression results are based only on the ES data. For the repeated cross-section regressions, we compliment the ES with other data sources such as World Development Indictors, World Bank.

The ES are nationally representative surveys of the non-agricultural and non-financial private economy. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>2</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

Throughout, we focus on firms in the manufacturing sector and all the countries for which ES data are available. The firms were surveyed between 2006 and 2018. For our main or baseline results, the sample used is a pure cross-section in that each country (and firm) is included only once. The most recent round of ES available for the country is used. The sample consists of 25,388 firms for our main overall corruption measure and 20,142 firms for the petty corruption measures. Both these samples are spread over 131 countries and 29 industries (3-digit ISIC Rev. 3.1). The number of country-industry cells equals 1,095 for the overall corruption sample and 1,091 for the

> 2 Details of the sampling methodology and other related information are available at <u>www.enterprisesurveys.org.</u>

6
The voluminous cross-country empirical literature on economic performance presents reflects no particular consensus on dependent variables (e.g. income growth rates or income levels) or on model specification. For simplicity and transparency, we adopt the recent and influential approach of Rodrik, Subramanian and Trebbi (2004), who analyze the “deep determinants” of per capita income levels. They compare the contributions of “institutions,” geography and trade-acknowledging the complex inter-relationships among them--and conclude that “the quality of institutions trumps everything else” (p. 135). Glaeser et al. (2004) argue that “human capital is a more basic source of growth” than the institutional indicators used by RST and Acemoglu, Johnson and Robinson (2001). This argument fits our emphasis on ability, so we add it (measured by educational attainment) as a fourth “deep determinant” of income levels when testing the effects of morality and its correlation with ability.

We follow RST in measuring economic performance by the log of GDP per capita, adjusted for purchasing power parity. Their data are for 1995, but we update this to 2008, because our independent variables from the WVS are from 1995 and later.

# _B. Main Empirical Results_

As discussed above, the “main” effect of the model predicts per-capita income to be increasing in the correlation between morality and ability. This prediction is overturned only if “secondary” effects are present, and on net work sufficiently strongly against the main effect, which seems unlikely. The theory can therefore be said to predict a positive relationship, with the caveat that if such a relationship were not found it would remain to investigate the magnitudes and directions of the secondary effects before concluding that the theory is invalid.

18
Intermediates: Production Sharing and Trade in Value Added.” _Journal of International Economics_ , 82(2): 224–236.

- **Johnson, Robert C., and Guillermo Noguera.** 2014. “A Portrait of Trade in Value Added over Four Decades.” Unpublished Manuscript, Dartmouth College.

- **King, Gary, and Langche Zeng.** 2001. “Logistic Regression in Rare Events Data.” _Political Analysis_ , 9(2): 137–163.

- **Kleinbergen, Frank, and Richard Paap.** 2006. “Generalized Reduced Rank Tests using the Singular Value Decomposition.” _Journal of Econometrics_ , 133(1): 97–126.

- **Koopman, Robert, Zhi Wang, and Shang-Jin Wei.** 2014. “Tracing Value-Added and Double Counting in Gross Exports.” _American Economic Review_ , 104(2): 459–494.

- **Lim ̃ao, Nuno.** 2006. “Preferential Trade Agreements as Stunbling Blockks for Multilateral Trade Liberalization: Evidence from the United States.” _American Economic Review_ , 96(3): 896–914.

- **Los, Bart, Marcel P. Timmer, and Gaaitzen J. de Vries.** 2015. “How Global are Global Value Chains? A New Approach to Measure International Fragmentation.” _Journal of Regional Science_ , 55(1): 66–92.

35
There are two indicators of water infrastructure utilized in our analysis. The first is the number of incidents of water shortages per day in a typical month. Firms are asked how many incidents of insufficient water supply their establishment experienced in a typical month, over the last fiscal year. The mean of the number of water shortages per day in a typical month in the sample is 0.02, which translates to about 11 water shortages per year. The second measure is the average duration of water outages in hours. This measure takes a value of zero for firms that do not experience any water shortages. The mean value for the sample (including those firms that did not experience any outages) is 0.86 hours. For the sample of firms that experienced water outages, the average duration of a water shortage is approximately 10 hours.

Our analysis also includes corresponding measures of electricity infrastructure, as the performance of water and electricity infrastructure may be correlated, and we do not wish to confound one with the other. As with the water indicators, measures of electricity infrastructure performance include the number of power outages per day in a typical month and the average duration of these outages. The mean of the number of power outages per day in a typical month is 0.26, which translates to about 119 power outages per year. The mean value for the duration of power outages for the whole sample is 2.19 hours. For the sample of firms that experienced power outages, the duration of a power outages is approximately 4 hours. These statistics show that fewer firms experience water shortages than electrical outages, but for those that do, water shortages last far longer than electrical outages. Summary statistics are presented in table 1, while descriptions and sources of variables are presented in table A1.

The ES data are combined with weather data obtained from Matsuura and Willmott (2012a,b). The weather data set contains monthly observations of precipitation and average temperature at the 0.5-degree grid cell level. The data
logarithm of primary employment income|<br>0.779**|<br>0.906|
|_||(0.085)|(0.067)|
|Constant|Constant|<br>4.067|<br>0.163|
|||(5.411)|(0.209)|
|Observations||2,000|2,351|

Source: Author’s calculations based on RLMS- HSE-2016.

**Table A-13: Results of behavioral model: actual and predicted status.**

|||formal|predicted<br>informal|Total|
|---|---|---|---|---|
||formal|66.4%|7.7%|74.2%|
|actual|informal|13.4%|12.4%|25.8%|
||Total|79.8%|20.2%|100.0%|

Source: Author’s calculations based on RLMS- HSE-2016.

46
<!-- Start of picture text --> ©<br>v.<br>ra<br>O<br>Cc<br>©<br>a<br>N<br>oO<br>-10 -5 0 5<br>Household level residuals<br><!-- End of picture text --> <!-- Start of picture text --> ce)<br>©<br>Pa<br>a<br>6TM.<br>a<br>N<br>oO<br>-4 -2 0 2 4<br>Household level residuals<br><!-- End of picture text -->
of classes taken by the student. Recall that the latter is theoretically an upper bound for the former (see Section 5.2.) As such, our average predicted target is lower than the average observed number of classes.

**Figure 9: Goodness of Fit: College Outcomes.**

<!-- Start of picture text --> 100<br>On−time graduate Late graduate<br>90 Dropout 2nd year Dropout 1st year<br>Dropout after 2nd year<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Ability quintile<br>Percent<br><!-- End of picture text --> _Source_ : SPADIES for observed data; fitted values for predicted data. _Notes_ : Whole lines show observed values; dashed lines show values.

**Figure 10: Goodness of Fit: Tiers of Cumulative Classes Completed, by Year.**

<!-- Start of picture text --> 100 Tier 1 Tier 2&3 Tier 4<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Year<br>Percent<br><!-- End of picture text --> _Source_ : SPADIES for observed data; fitted values for predicted data.

_Notes_ : For each year, the figure shows the percent of students by tier of cumulative classes completed. Whole and dashed lines show observed and predicted percentages, respectively.

**Table 5: Goodness of Fit: Dropout Year.**

|Year|Observed|Predicted|
|---|---|---|
|1st|27.41
|Of which due to state dummies<br>|0.055|0.018|0.017|0.022|
|---|---|---|---|---|
|Conditional R<sup>2</sup>|0.127|0.061|0.025|0.028|
|Skewness of state effect|N/A|N/A|N/A|N/A|
|Kurtosis of state effect|N/A|N/A|N/A|N/A|
|Estimated variance of state effect|N/A|N/A|N/A|N/A|
|Estimated household residual|N/A|N/A|N/A|N/A|
|Ratio of estimated state effect variance to total<br>|N/A|N/A|N/A|N/A|

variance

# **Municipality - Simple Random Sample within Selected AGEBs**

|Number of (non-state) variables selected<br>|41|22|19|10|
|---|---|---|---|---|
|Marginal R<sup>2</sup>|0.132|0.071|0.038|0.032|
|Of which due to state dummies<br>|0.055|0.018|0.017|0.022|
|Conditional R<sup>2</sup>|0.293|0.170|0.132|0.077|
|Skewness of municipal effect|-0.102|0.222|4.047|1.011|
|Kurtosis of municipal effect|4.840|6.168|36.395|5.679|
|Estimated variance of municipal effect|0
the inclusion of ODS and Montreal Protocol provisions in RTAs lead to significantly negative response on target deviations for both Annex A gases (CFCs and halons) and from most Annex B gases (CTC and TCA). Negative point estimates here correspond to _increases_ in overcompliance, i.e. the Montreal Protocol targets become even more slack with the inclusion of the provisions. For CFCs, CTC, and TCA, the provision magnitude is such that in

> `12` UNEP CFC data does not differentiate CFC gases in ODS consumption data. Different CFC gases have different global warming potential. Our calculation here is based on the average GWP across all CFCs.

> `13` As above, individual halon gas consumption quantities are not separately reported in the UNEP data. This number is based on average ODP and GWP for halons and aggregated halon consumption data.

17
**Figure 3: Distribution of tax collection and transfer allocation by decile, in Mongolia (2016)**

<!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Income Taxes VAT Excise Tax SSC Pension Child SA Mortgage Electricity Heating Education In‐ Health In‐Kind<br>Allowance Subsidies Subsidies Subsidies Kind<br><!-- End of picture text --> <u>Source: Authors elaboration using HSES 2016</u> <u>Notes: Each bar represents the share of taxes paid (or transfers received), out of total specific tax collection or transfer</u> expenditure, by each pre-tax-transfer income decile.

22 MONGOLIA Distributional impact of taxes and transfers / November 2018
that state capacity is built over time as societies become more complex and demand public goods that only a state can provide, such as defense against external aggressors (Tilly, 1992; Besley & Persson, 2009). The driving force of state capacity in this research is citizen willingness to pay taxes to a state they regard as legitimate (Besley, 2020; Fergusson et al., 2020; Weigel, 2020). Consistent with this research, the survey data show improvements over time in citizens’ reports of actual payment as well as willingness to pay taxes so that local governments can develop their capacity. That is, the survey data suggests that state legitimacy to collect taxes has improved over time, as reflected in greater reports of compliance with taxes. The pattern of citizen responses also exhibit improvements in trust that local governments will deliver services and listen to citizen demands. Importantly, there is no difference in these survey measures of increasing trust in and legitimacy of the local state across the project and non-project areas. The data thus suggest that improvements in state capacity in Tanzania stem from endogenous changes in trust and legitimacy in the country rather than as a result of financial incentives offered by external partners.

This interpretation can be illustrated by examining a particular project component which targeted processes of oversight by and accountability to citizens. One-fifth of the performance score of LGAs under the project is allocated to indicators of consultation with citizens and disclosure of budget information for accountability (World Bank, 2012). In fact, this accountability and oversight component is the one where almost all project LGAs, 17 out of the 18, achieved the highest possible score by the time of their performance assessment in 2016-17 (World Bank, 2018b). Yet, we find no difference between project and non-project LGAs in the 2016 survey (as we might expect if project communities had already improved dramatically and non-project communities had not), nor in changes between 2016 and 2018, in household responses to questions about knowledge of public budgets and consultation by the local governments.
Russian firms are very heterogeneous. Of the 978 manufacturing firms surveyed in the five federal districts in Russia, 947 of them have more than 10 non-missing responses that allows for computing a more precise management score (Bloom et al., 2019a).<sup>15</sup> The average management score for manufacturing firms in the surveyed federal districts in 2017 was 0.432 – that is, on an average the surveyed firms adopt 43% of overall structured management practices (Figure 1). The spread is large, however. Looking at the tails of the distribution in figure 1, it is visually clear that the left tail is much thicker than the right – a large share of the surveyed firms are poorly managed, only 3.5% of them adopt more than 75 percent of the structured management practices while a similar share turns away from adopting any (a management score of 0). Nearly 60 percent of the surveyed firms adopted less than 50 percent of overall structured management practices. Recall data from 2012<sup>16</sup> indicates that the average management score was slightly lower at 0.415, suggesting a very small increase of 0.017 in the period 2012-2017.<sup>17</sup>

**_Figure 1: There is large heterogeneity in adoption of structured management practices_**

<!-- Start of picture text --> Distribution of management score<br>0 .25 .5 .75 1<br>2017 2012<br>2012 average:0.415 >0.75:2.7% >0.50:34.9% N:878<br>2017 average:0.432 >0.75:3.5% >0.50:40.2% N:947<br>12<br>10<br>8<br>6<br>Percent<br>4<br>2<br>0<br><
# **1. Introduction**

Housing, measured as the welfare value of the flow of services households derive from their dwelling, is one of the most relevant components of households’ welfare aggregate, which is used as a basis for distributional analysis (Deaton and Zaidi 2002). Nevertheless, due to data limitations and the difficulty of estimating rental values for non-market tenants and owners, the housing component is often neglected in the construction of welfare aggregates. In fact, although we can reasonably assume that the flow of services enjoyed by living in a dwelling is correctly approximated by the monthly rental payment to landlords among tenants, no such measure is readily available for home-owners. Furthermore, not all tenants pay a market price for the dwellings they live in, as many enjoy subsidized rental arrangements (see, for instance, Lara Ibarra et al 2017), or live for free in a dwelling provided by their employer or by a family member. Therefore, in order to include in a measure of the welfare aggregate the value of the flow of services households derive from the dwellings they live in, one would have first to estimate rental values for home-owners and adjust those for non-market tenants.

As argued by Frick et al. (2010), excluding estimated rental prices from the welfare aggregate used in distributional studies may undermine the validity of comparisons of any distributional outcome, both in timeseries within a particular country and in cross-sectional analysis across countries. The need for including the housing component for the purpose of measuring welfare has been stressed by different communities of practitioners. For instance, the US Interagency Technical Working Group on Developing a Supplemental Poverty Measure Census (2010) acknowledges the need to adjust poverty thresholds by housing status. The European Commission financed the project “Accurate Income Measurement for the Assessment of Public Policies” (Sutherland et al., 2009) with the objective, among others, of understanding the distributional impact of the inclusion of rents in income aggregates. Törmälehto and Sauli (2010 and 2013) analyze the implications of