rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1732 Agricultural Trade and Rural<br>Development in the Middle East<br>and North Africa: Recent Developmen<br>and Prospects|Dean A. DeRosa<br>ts|February 1997|J. Ngaine<br>37959|
|WPS1 733 The Usefulness of Private and Public<br>Information for Foreign Investment<br>Decisions|Yuko Konoshita<br>Ashoka Mody|February 1997|R. Reff<br>34815|
|WPS1734 Are Markets Learning9 gehavior in<br>the Secondary Market for Brady<br>Bonds|LucaParbone<br>Lorenzo Forni|_February 1Q07_|_L Rarbono_<br>32556|
|WPS1735 Competition Policy and the Global<br>Trading System: A Developing-Countr<br>Perspective|Bernard Hoekman<br>y|March 1997|J. Ngaine<br>37949|
|WPS1736 Creating Incentives for Private<br>Infrastructure Companies to Become<br>More Efficient|lan Alexander<br>Colin Mayer|March 1997|R. Schneiderman<br>30191|
|WPS1737 Ownership and Corporate<br>Governance: Evidence from the<br>Czech Republic|Stijn Claessens<br>Simeon Djankov<br>Gernard Pohl|March 1997||
|WPS1738 Some Aspects of Poverty in Sri<br>Lanka: 1985-90|Gaurav Daft<br>Dileni Gunewardena|March 1997|A. Ramirez<br>85734|
|WPS1739 Safe and Sound Banking in<br>Developing Countries: We're Not<br>in Kansas Anymore|Gerard Caprio, Jr.|March 1997|B. Moore<br>38526|
|WPS1740 When is Foreign Aid Policy Credible?<br>Aid Dependence and Conditionality|Jakob Svensson|March 1997|R. Martin<br>39026|
|WPS1741 Privatization, Public Investment, and<br>Capital Income Taxation|Harry Huizinga<br>S0ren Bo Nielsen|March 1997|P. Sintim-Aboagye<br>38526|
|WPS1742 Transport Costs and "Natural"<br>Integration in Mercosur|Azita Amjadi<br>L. Alan Winters|March 1997|J. Ngaine<br>37947|
|WPS1743 How China's Government and State<br>Enterprises Partitioned Property<br>and Control Rights|Lixin Colin Xu|March 1997|P. Sintim-Aboagye<br>37644|
**Figure 1: Descriptive statistics across industries in SAR**

<!-- Start of picture text --> Annual relative growth<br>Wholesale and Retail 6.5%<br>Fabricated Metal Products and Machinery and ... 15.7%<br>Non Metallic Mineral Products and Basic Metals 9.4%<br>Chemicals, Plastics & Rubber 8.7%<br>Textiles and Garments 7.1%<br>Real Estate Renting & Business Activities (70-74) 27.3%<br>Transport (60-64) 14.3%<br>Hotels & Restaurants 3.5%<br>Services of Motor Vehicles 11.7%<br>Construction 22.5%<br>Electronics (31-32) 8.1%<br>Food 3.8%<br>Other Manufacturing 5.1%<br>0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%<br><!-- End of picture text --> _Source: INTERIM ES, Own calculations Industries with fewer than 30 observations in the Region were excluded; growth rate over three years; unweighted_<sup>_6_</sup>

**The share of expanding firms dominated shrinking or stagnant companies.** With the exception of food, the share of firms increasing their size was the largest across all industries, which also implies that more firms were growing than shrinking. Particularly broad was the expansion in real estate and business renting, chemicals, plastics and rubber and fabricated metal products and machinery and equipment. The largest share of companies that were decreasing in size was in textile and garments (see table 1).

> 6 In longitudinal establishment-level data studies, a pervasive finding is that idiosyncratic factors dominate the distribution of growth rates of output, employment, investment, and productivity across establishments. Seemingly similar plants within the same industry exhibit behave quite differently in terms of real activity at cyclical and longer-run frequencies. Even in the fastest-growing industries, a significant fraction of establishments decline substantially. Similarly, a large fraction of establishments in the slowest-growing industries grow dramatically. Hence, the underlying gross microeconomic changes in activity dwarf the net changes that we observe in published aggregates (Haltiwanger, 1997). Weights which rely on the stratification of random samples and that aim at reproducing aggregate dynamics may distort performance analysis at the micro level.

7
Table 43

|Dependent variable: Fiscal|adjustmen|t when nee|ded based|on debt/rev|enue|
|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|
|Rigid expenditure minus|0.001|-0.045|-0.046|0.039|-0.013|
|structural as % GDP|(0.005)|(0.029)|(0.043)|(0.039)|(0.025)|
|**Economic factors**||||||
|GDP growth (t-1)|4.911|6.502*|4.915|5.473|5.097|
||(3.956)|(3.774)|(3.983)|(4.096)|(3.905)|
|Inflation (t-1)|-0.416*|-0.567**|-0.414*|-0.472*|-0.428*|
||(0.222)|(0.270)|(0.222)|(0.256)|(0.225)|
|U.S. Interest rate|-0.004|0.021|0.001|0.007|-0.003|
||(0.023)|(0.032)|(0.027)|(0.032)|(0.023)|
|**Political factors**||||||
|Gini|-0.004|-0.006|-0.013|-0.006|-0.004|
||(0.004)|(0.005)|(0.012)|(0.005)|(0.004)|
|Rule of law|-0.002|-0.042|0.003|0.071|-0.002|
||(0.036)|(0.042)|(0.033)|(0.052)|(0.035)|
|Margin of Majority|-0.013|0.043|-0.042|-0.009|-0.161|
||(0.173)|(0.165)|(0.156)|(0.166)|(0.178)|
|**Interactions**||||||
|EMEs||-0.794*||||
|||(0.440)||||
|EMEs * Deviation||0.073<br>(0.045)||||
|Gini * Deviation|||0.001<br>(0.001)|||
|Rule of law * Deviation||||-0.010<br>(0.010)||
|Majority * Deviation|||||0.022<br>(0.036)|
|lambda|-1.435|-2.013*|-1.467|-1.647|-1.489|
||(1.077)|(1.115)|(1.108)|(1.162)|(1.068)|
|Constant|1.758|3.036**|2.134|1.734*|1.923|
||(1.116)|(1.492)|(1.422)|(0.942)|(1.240)|
|Observations|709|709|709|709|709|
|R-squared|0.063|0.070|0.063|0.067|0.064|
|CountryFE|No|No|No|No|No|
|Standard errors in parenth|eses|||||
|*** p<0.01, ** p<0.05, * p|<0.1|||||

- 72 -
The finding that the process of direct democracy increases villagers‘ satisfaction is consistent with Olken (2010). However, our finding that selection and prioritization procedures affect allocation outcomes differs from Olken (2010), who finds no effect of procedure on the choice of general projects, but does observe some differences between procedures in project location.<sup>20</sup> Although this difference might be driven by the differences in sample size between the two studies,<sup>21</sup> there are also reasons to believe that the differences in results are driven by differences in the local context. Two differences in context can be particularly important in explaining the differences between the results of the two studies.

First, Indonesian villages generally consist of several hamlets separated by as much as two kilometers, whereas Afghan villages generally consist of a single hamlet. As a result, project location is highly salient in the selection process in Indonesian villages, but less important in Afghan villages. In Afghanistan, project type is thus more likely to be the main source of disagreement between villagers in the allocation process. This is confirmed by monitoring data which indicates that only 19 percent of villagers consider location to be the most important consideration in project choice, and by the absence of a correlation between this share and the geographical size of the village.<sup>22</sup> Second, Indonesian villages receive funding for general projects periodically, which allows for ̳logrolling‘ and inter-temporal trade between different interest groups within a village, so that each part of the village gets its most preferred projects in turn. Repeated interactions can sustain such intertemporal trade under both procedures, which make the analysis of a single instance of selection quite misleading. However, a women‘s project included in the sample studied by Olken (2010) was considered a one-shot event, which is comparable with the situation in Afghanistan where no commitments for follow-up grants were made to villagers. In both cases, the analysis reveals that, absent opportunity for inter-temporal trade in project choice, direct democracy results in allocation outcomes more aligned with the preferences of the general public.

> 20 Specifically, Olken (2010) finds some evidence that projects selected by referenda are more likely to be located in poorer hamlets.

> 21 49 villages in 3 provinces in Olken (2010) as compared with 250 villages in 10 districts in our study.

> 22 The size of the village was measured as the mean distance from the households surveyed in the follow-up survey to the center of the village, where the center of the village was determined as the average of the coordinates of all the surveyed households in a village.

16
allow for more accurate monitoring of SME lending in line with the existing official definition. These data may also be useful to banks themselves for client segmentation and development of SME scoring models. In the short run, collecting data using loan size criteria as a proxy may serve as a reasonable proxy of SME volumes for regulators.

# **2.2 Values and numbers of outstanding loans**

In the _Financial Access 2010_ sample, 50 out of 142 regulators were able to provide data on the total value of outstanding loans to SMEs. Out of these, 30 are middle-income and 14 are highincome countries (Table 3). Among low-income countries, only Afghanistan, Bangladesh, Liberia, Pakistan, Tajikistan and Uzbekistan were able to provide these data points. The ratio of SME lending to GDP ranges from less than 1 percent in Tajikistan to more than 50 percent in the Netherlands and Portugal. The median ratio of SME credit to GDP is 6.4 percent, and in 75 percent of the economies in the sample, it is below 15 percent. The value of outstanding loans to SMEs as a percentage of total loans also shows a high degree of variation reflecting the structure of the local financial market. Overall, high-income countries tend to have higher ratios of SME finance volume to GDP and total loans, suggesting a more developed SME finance market compared with developing countries.

It is less common for regulators to collect data on total numbers of outstanding loans to SMEs than on total values of outstanding loans to SMEs. Only 26 countries provided data on the number of outstanding loans to SMEs, 9 of which are in Latin America. All the 26 countries that provided data on the total number of outstanding loans to SMEs also provided data on the total value of outstanding loans to SMEs.

Only 16 countries provided data on the number of SMEs with outstanding loans, out of which four are high-income countries, eight middle-income countries, and four low-income countries. All of these countries also provided data on values of outstanding loans to SMEs, and 11 of them provided data on total number of outstanding loans to SMEs.

9
5 <!-- Start of picture text --> Trade Weighted Real Exchange Rate<br>(based as produce price Irda)<br>a<br>7.5-<br>Si-<br>vow<br><!-- End of picture text --> Figure 1 <!-- Start of picture text --> Primary Fiscal Balance and Oil Prices.<br>35 _<br>20<br>15<br>-0 -<br>Bo0 e1 a a 54 86 0 57 to 86 90<br>-. ku IO<br><!-- End of picture text --> Figure 2 <!-- Start of picture text --> Consolidated Domestic Pubric Debt.<br>(aid ov'o9 debf a X of GW)<br>.0. WE 1<br>1982 1908 i0" 1iSI 184 1987 1Si 1989 1090<br>Ye=<br>Fi- gur 3 I<br><!-- End of picture text --> Figure 3
Interviews were transcribed verbatim and translated from Kiswahili to English. The transcripts were analyzed in Dedoose version 8.3.17. Coding was done iteratively, with codes initially developed based on the interview guide and revised throughout the coding process as concepts emerged.

# **Ethics**

Approval for this study was obtained from the Institutional Review Boards at the University of California, Berkeley and National Institute for Medical Research in Tanzania.

# **Results**

At enrollment (wave 1), the mean age of participants was 26 years and more than half (58.9%) had completed at least primary education (Table 1). Most were never married/single (75.8%), had children (70.6%), and living alone or with family (77.9%).

Among the subset of control participants selected for phone surveys (wave 2), 346 (59%) were successfully reached. Half of all participants were lost to follow up (49.4%) at 24 months (wave 3). Participants reached for wave 3 were slightly older in age, and more had children and were previously partnered (divorced/separated) than the overall sample at enrollment.

**Table 1. Baseline demographic characteristics of study participants in the RESPECT II trial**

||**Wave 1 (2018)**<br>**(n=2,206)**|**Wave 2 (2020)**<br>**(n=346)**|**Wave 3 (2021)**<br>**(n=1,117)**|**p-value**<br>**(Wave**<br>**1,3)**|**p-value**<br>**(Wave**<br>**1,2)**|
|---|---|---|---|---|---|
|**Age**|26 (6.7)|28 (6.6)|28 (7.1)|<0.01|<0.01|
|**Education**||||0.57|0.15|
|None or some primary|226 (10.2%)|28 (8.1%)|122 (10.9%)|||
|Primary complete|1,300 (58.9%)|203 (59.7%)|662 (59.3%)|||
|Some secondary|282 (12.8%)|40 (11.6%)|141 (12.6%)|||
|Secondary complete|398 (18.0%)|75 (21.7%)|192 (17.2%)|||
|**Marital status**||||<0.01|<0.01|

5
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|---|---|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|BO: Investingin Children and Youth|Gaston Mariano Blanco|
|Results and Accoutability (REACT)DPL|David Ian Walker|
|Results and Accoutability (REACT)DPL|David Ian Walker|
|Results and Accoutability (REACT)DPL|David Ian Walker|
|Results and Accoutability (REACT)DPL|David Ian Walker|
|Results and Accoutability (REACT)DPL|David Ian Walker|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
|Egypt National Railways RestructuringProject|Arturo Ardila Gomez|
# **What Explains Prevalence of Informal Employment**

# **in European Countries: The Role of Labor Institutions, Governance, Immigrants, and Growth**<sup></sup>

## **_Mihails Hazans_**<sup>**_*_**</sup>

_University of Latvia and IZA_ JEL classification: J08, J21, J51, K31, Keywords: _labor market institutions, informal employment, immigrants, ethnic minorities_

>   This paper—a product of the Human Development Economics Unit, Europe and Central Asia Region—is part of an effort to understand the underlying factors that determine the size of informal employment in the shadow economy, providing background technical analysis for a forthcoming World Bank regional study ―In from the Shadow: Integrating Europe's Informal Labor‖. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.  The paper has greatly benefited from discussions with and suggestions by Truman Packard.  I thank Alexander Tarvid for excellent research assistance. The usual disclaimer applies.

> * mihazan@lanet.lv
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<!-- Start of picture text --> a<br>P , [| OccurrenceSelectedEndemic UnprotectedSpeciesRegions of<br>x a Protected Area<br>a.Wi, & :<br>“43 e<br>os ,<br>e<br>4<br><!-- End of picture text -->
overall gross enrollment rate in primary, secondary and tertiary education ( _Education gap_ ). The data source for the variable is the United Nations.

Before proceeding to the empirical results, we would like to mention that some of the controls discussed above show somewhat high correlation with our main variable, _GII_ . However, these correlations are not too large to pose any significant estimation problems. The only exception is the level of education ( _Education_ ) which has a correlation coefficient of -0.807 with _GII_ . While this may not be much of a problem given that our focus is on the interaction term (GII*Income) rather than on the level term (GII), nevertheless, we check all the results discussed below completely excluding the _Education_ variable form the analysis. Our main results are slightly stronger compared to the ones discussed below when we exclude the _Education_ variable from the regressions.

# **3.  Estimation**

As mentioned above, our main result concerns how the relationship between growth and gender inequality differs between high vs. low income countries. That is, the estimation of the interaction term between gender inequality and income level. Before proceeding to the results for this interaction term, we briefly discuss how growth correlates with gender inequality and income level without the interaction term. This helps to juxtapose our results against existing ones in the literature on gender inequality and growth and also to see how the results change when we add the interaction term to the specification.

The regression results provided in Table 3 show how gender inequality and income correlate with growth with and without the various controls discussed above. Briefly, regressing growth on the gender inequality index without any other controls, we find no significant 12
6 of our knowledge, this is the first paper to assess the impact of structural reforms on firms’ productivity in developing countries. The paper is close to Tressel (2008), which investigates the effects of financial and trade sector reforms on real output growth at the industrial level in 91 countries, including developed countries. However, Tressel (2008) focuses on financial and trade liberalization and does not examine the effects of these reforms on firm-level productivity. The few studies examining the impact of reforms on firms’ productivity in developing countries mainly focus on a specific reform in China, Colombia, India, and Indonesia.<sup>8</sup> We take advantage of the MONA and the WBES data sets and cover a broad range of sectors (financial, fiscal, real, and trade sector reforms) in a large sample of developing countries.

The rest of the paper is structured as follows. Section II describes the data sets. Summary statistics and the empirical strategy are discussed in Section III. Section IV reports and discusses the estimation results. Section V examines the sensitivity of the findings. Finally, Section VI presents concluding remarks.

# **II.   DATA SETS**

The data are compiled from four different sources. Reform indexes are computed from the IMF Monitoring of Fund Arrangements (MONA) database, firms’ characteristics are culled from the World Bank Enterprises Surveys (WBES), and the other macroeconomic variables are collected from the World Bank’s World Development Indicators (WDI) and World Governance Indicators (WGI).

# **A.   The IMF Monitoring of Fund Arrangements (MONA) database**

As argued in the literature, reforms are more difficult to measure than typical macroeconomic policies, limiting the scope for quantitative analysis of the micro effects of economic reforms. They typically concern policies geared towards raising productivity by improving the technical efficiency of markets and institutional structures and by reducing or removing impediments to the efficient allocation of resources. Thus, reforms have typically been associated with

> 8 See for instance Arnold et al. (2015); Javorcik and Li (2013); Bas and Causa (2013); Topalova and Khandelwal (2011); Eslava et al (2004).
Table A9: Survey Instruments and Construction of Sensitive Outcomes

|Outcome|Source(s)|Outcome construction|Item – Original version|Item – Spanish version|
|---|---|---|---|---|
|High stress|Knowledge<br>Incorpora-<br>tion<br>Survey<br>(KI)|Dummy indicator that takes the value of 1 if<br>the individual responded ”Very stressed,” 0<br>otherwise|How stressed did you feel during the<br>past week?<br>Response options: Very stressed, Mod-<br>erately stressed, Mildly stressed, Not<br>stressed at all|¿Qu ́e tan estresado se ha sentido esta<br> ́ultima semana?<br>Opciones de respuesta:<br>Muy estresado,<br>Estresado, Poco estresado, Nada estresado|
|High stress|Baseline/<br>Follow<br>up<br>Surveys–<br>(DASS-21)|Each of the 7 items in the category of stress<br>in the DASS-21 are measured on a scale of<br>0–3 points (_Never, Rarely, Almost Always or Al-_<br>_ways_). We estimate the total stress score fol-<br>lowing Lovibond and Lovibond (1996). The<br>outcome is a dummy indicator that takes the<br>value of 1 if the individual has “High Stress,”<br>that is, if the score was greater than 18.|During the past week, how often did<br>you experienced the following?<br>1. I found it hard to wind down<br>2. I tended to over-react to situations<br>3. I felt that I was using a lot of nervous<br>energy<br>4. I found myself getting agitated<br>5. I found it diffcult to relax<br>6. I was intolerant of anything kept me<br>from getting on with what was doing<br>7. I felt that I was rather touchy|Durante la semana pasada, ¿que tan fre-<br>cuente experimento lo siguiente?<br>1. Me costo mucho relajarse<br>2. Reaccione de forma exagerada en cier-<br>tas situaciones<br>3. Senti que estaba muy nervioso<br>4. Note que estaba muy agitado/a<br>5. Me costo mucho calmarme<br>6. Fui intolerante con lo que me distraia o<br>desconcentraba<br>7. Senti que estaba muy irritable|
||||Response options: Never, Rarely, Al-<br>most Always, Always|Opciones de respuesta: Nunca, Algunas<br>Veces, Casi Siempre, Siempre|
|Physical<br>punish-<br>ment is effective|KI,<br>Baseline<br>and<br>Follow<br>Up Surveys|Dummy indicator that takes the value of 1 for<br>all response options except ”No, it’s never ef-<br>fective”|Do you think physical punishment<br>is an effective method to discipline<br>children?<br>Response options: No, it’s never effec-<br>tive; It’s rarely effective; It’s sometimes<br>effective; It’s effective most of the time;<br>It’s always effective|¿Piensa que el castigo f ́ısico es un m ́etodo<br>de correcci ́on efectivo?<br>Opciones de respuesta: No, nunca es efec-<br>tivo; Casi nunca es efectivo; En ocasiones<br>es efectivo; Casi siempre es efectivo; Si,<br>siempre es efectivo|

_Notes:_ This table describes the measures of sensitive outcomes used in the consistency analysis. It also presents the questions –and their respective response options– used to construct the measures, survey sources, and a description of how the sensitive outcome was constructed.

46