rafmacalaba/datause-encoder-data huggingface dataset:
context of the Chilean capital market, Cifuentes et al. (2002) argue that debt issuer diversification in the country occurred largely due to a favorable macroeconomic environment, which made it more attractive to issue local currency denominated debt. Another important factor was found to be the expansion of the institutional investor base, especially pension funds and insurance firms, that provided stable demand for existing and future local issuers. Moreover, regulatory reforms aimed at deepening local capital markets improved market liquidity and attracted SME participation.

Some literature (Brown et al., 2017; Manganelli, 2010; Cortina et al., 2018) is suggestive of capital market expansion at the extensive margin, as capital markets attract firms that are otherwise unable to raise funds. These may include firms that are younger, smaller, riskier, those with high research and development expenses, lower traditional collateral, and greater financial constraints. For instance, a number of studies show that countries with greater capital market development have a higher share of younger, smaller, and innovative listed firms compared to more bank-based countries (Didier et al., 2020). Cortina et al. (2018) find that with the growth in capital-raising activity in the Arab region, an increasing number of firms have been using equity, bond, and syndicated loan markets to obtain financing.

Some evidence suggests that financial development leads to greater diversity in the real economy. For instance, Manganelli and Popov (2015) studied whether international differences in financial market depth can be mapped to country variations in sectoral diversification. Their results show that more developed credit markets are associated with faster convergence to the optimally diversified benchmark. Analyses with measures of stock market and bond market depth yield similar results. However, there is no systematic evidence of the change in either equity or bond issuer composition across countries as capital market activity expands (Didier et al. 2016).

There is mixed evidence that issuer composition in other capital markets, such as that for bonds, varies across different levels of market development. Tendulkar (2015) finds that emerging markets with relatively large corporate bond markets exhibit higher issuer diversity across the financial and non-financial split, though financial issuers typically continue to dominate issuances. Similarly, Abraham et al. (2021) find that growth in domestic bond financing in East Asia was accompanied by an expansion on the extensive margin, which was driven by smaller firms using capital markets to obtain financing. However, Shimizu (2018) finds that corporate bond issuers in Asian capital markets tend to exhibit low sectoral diversity even in more developed corporate markets, with banking and infrastructure being the most represented sectors.

# **3. Data**

To analyze the relationship between issuer composition and stock market growth, this paper uses a novel dataset assembled by the International Finance Corporation (IFC) on capital markets. This 5
Poverty and Social Policy Division Technical Department Africa Region The World Bank

**HOW USEFUL ARE INTEGRATED HOUSEHOLD SURVEY DATA FOR THE POLICY-ORIENTED ANALYSIS OF POVERTY? LESSONS FROM THE C6TE D'IVOIRE LIVING STANDARDS SURVEY**

Christiaan Grootaert This paper is an output of the research project "Poverty and the Social Dimensions of Structural Adjustment in Cote d'Ivoire, 1985-88 - A Policy-Oriented Analysis" (RPO 675-26). The author would like to thank Lionel Demery and Chris Scott for many helpful discussions on the subject of this paper and for useful comments on an earlier draft.
causal mechanism driving the crop failure results seen with the UNICEF data. We examine household level production shocks and their impact on children’s height for age z-scores, but given the smaller sampling frame (1, 248 households) and the three-year window of household production information, there are only 473 children under age five in that analysis.

# _2.4 Ethnicity_

Neither the UNICEF nor the DSA survey registered the ethnic affiliation of the survey respondents, so we are unable to test potential ethnic differences, although we do not think these are critical for the following two reasons. First, although Tutsi account for 10 to 15 percent of the Rwandan population, in the northern civil-war-affected provinces, they account for less than three percent. Given the civil war was confined to the north in the period under investigation, we do not expect ethnicity to be a factor influencing our results for the civil war shock.

Second, although Tutsi account for 15 to 20 percent of the population in the southern crop failure provinces, Austin’s (1996) review of the literature did not find evidence of inequality between the Hutu and Tutsi ethnic groups. Similarly, Verwimp (2005) did not find systematic differences between the two ethnic groups in income per adult equivalent, land size, or livestock holdings in several southern provinces. In 1990, there existed no exclusively Hutu or Tutsi villages in Rwanda, both lived as neighbors and were dispersed across the territory of DSA sample. All households that were in the DSA agricultural survey as of 1990 are in the UNICEF sample. The main concern is households that were in the DSA sample in October 1988 but dropped out before the 1990 survey. Of all households who were interviewed during the first round of the DSA survey (from October 1988 to March 1989), 96.8 percent of them were still in the sample in 1990. In the crop failure region, only 14 out of 256 households (5.4 percent) dropped out, while in the civil war region, 3 out of 239 households (1.3 percent) dropped out. Almost all of the conflict-related migration in the civil war region occurred between 1992 and 1994 after the UNICEF survey was conducted. We did not find any significant differences in observable characteristics between the 17 dropped households and the remaining households in the crop failure and civil war regions. Given the low overall attrition rate and the lack of observable differences, we believe the UNICEF data are representative of the Rwandan population.

9
technique with difference-in-difference estimation to deal with concerns about possible unobservable firm characteristics that share the same time dynamics for both treatment and control firms.

Our estimation results suggest that while the reform was effective in reducing the value-added tax paid by firms, its impact on firm behavior in other ways is puzzling. The policy significantly reduced firms’ total number of employees for both domestic and foreign firms. Second, the impact of the tax reduction on firm productive investment was limited: while there is some evidence that SOEs increased their investment, the impact was not significant for most other types of enterprises. The impact of the policy on firm profits was similar: while most of the benefits accrued to SOEs, other firms were generally unaffected. Finally, the tax reform did not have any significant effect on firm productivity for all types of firms, and it decreased export intensity for most types of firms.

Our results differ in significant ways from Nie, Fang, and Lie (2010), who also explore the impact of the value-added tax reform in China. They find smaller, but still negative effects on employment and positive, significant effects on fixed asset investment. Our results differ in large part from Nie, Fang, and Lie (2010) for two reasons. First, they only had one year of data following the 2004 reform, while we have three years, which allows us to identify the longer term effects. In addition, they do not address the potential endogeneity of the reform targets, while we explicitly address this through nonparametric propensity score matching techniques. Using their same approach, we show that the beneficial effects of the reform on investment was limited to SOEs and was 4
Figure 1: Sample Structure and Randomization Process <!-- Start of picture text --> Panel%A.%Sample%Distribution<br>2420%Students<br>From%5%public%schools%in%El%Salvador%<br>1056%enrolled%students% 1364%nonCenrolled%students%<br>Block%randomization<br>Control%Group Treatment%Group<br>25% 75%<br>(258) (798)<br>Panel%B.%Block%Randomization%Process<br>School%A<br>Level%1'A Level%2'A Level%3'A<br>(1'3%grades) (4'6%grades) (7'9%grades)<br>Randomization Randomization Randomization<br>Control% Treatment% Control% Treatment% Control% Treatment%<br>Group Group Group Group Group Group<br>25% 75% 25% 75% 25% 75%<br><!-- End of picture text --> This figure shows the samples composition and block randomization procedure applied in this design, adapted from Dinarte (2018). From the total of enrolled children in each educational level _{_ 1,2,3 _} ∈_ school _A_ , we randomly assigned 25% to C and 75% to T groups. The same procedure was implemented in the remaining schools.

# **2.4 Data**

## **A. Baseline**

During the registration phase, after the first three months of the school year and before the intervention, we collected two strands of data. First, enrolled students provided personal and family information on registration forms. For instance, we gathered information on enrolled children’s age, gender, household composition, mother’s education level, and adult supervision exposure, among other characteristics. We also collected school records of math, reading, and science grades; behavior reports;<sup>17</sup> and absenteeism data from both enrolled and nonenrolled children. Appendix 1

> 17In El Salvador, teachers complete behavior reports each quarter, which are presented in the following discrete scale: Excellent (E), Very Good (MB), Good (B), and Regular (R). It can be translated in a continuous scale that is comparable to course grades. In this paper, we use a reversed continuous scale to facilitate the interpretation and comparability to self-reported measures of violence and crime.

12
sex, women have, on average, lower levels of education than men, which also reflects the lower educational level among migrant women than migrant men (Figure 17). Most migrant women have lower secondary education (24.9 percent), while most migrant men have tertiary education (25.9 percent).

Figure 14. Distribution of migrant and non-migrant women by marital status <!-- Start of picture text --> Married Cohabitating<br>Divorced/Separated Widow<br>Single<br>100.0<br>30.1 31.0<br>80.0<br>4.1 7.5<br>60.0 9.9<br>9.6<br>40.0 29.7 17.5<br>20.0<br>34.4<br>26.1<br>0.0<br>Women who migrated Women who did not<br>migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 15. Distribution of migrant and non-migrant men by marital status <!-- Start of picture text --> Married Cohabitating<br>Divorced/Separated Widow<br>Single<br>100.0<br>80.0 36.5 36.9<br>60.0 5.8 5.3<br>18.5<br>40.0 28.3<br>20.0 36.9<br>28.3<br>0.0<br>Men who migrated Men who did not<br>migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 16. Distribution of migrant and nonmigrant women by education levels <!-- Start of picture text --> Non-Migrants<br>8.9 Preschool or<br>16.6 6.7 less<br>23.6<br>Primary<br>22.0<br>Lower<br>Migrants 29.1<br>19.5 secondary<br>Upper<br>22.7 secondary<br>24.9 Tertiary<br>25.9<br><!-- End of picture text --> Figure 17. Distribution of migrant and nonmigrant men by education levels <!-- Start of picture text --> Non-Migrants<br>8.2 Preschool or<br>16.7 6.3<br>less<br>25.9 Primary<br>21.4<br>29.3 Lower<br>Migrants<br>19.8 secondary<br>Upper<br>secondary<br>22.3<br>24.2 Tertiary<br>25.9<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Source: Own estimates based on Population and Housing Census 2020.

**Differences in labor force status are marked across migration status and sex.** Most women who migrated are employed (46.8 percent), while the percentage for those who did not migrate is 40.2 percent. Still, 37.3 percent of migrant women are dedicated to unpaid housework. This percentage is even higher among women who did not migrate (41.2 percent). On the contrary, the share of men devoted to unpaid

15
measure is that it does not require a long-run money demand function, only data on money, output, and prices. The omitted variables tests for the monetary overhang are clearly insignificant in all models. Hence, the results are the same as for the error correction term from the cointegration analysis of the monetary sector.

Next, we test if the unrestricted cointegrating vector for the money market is significant. The test could be significant because of misspecification of the long-run relationship. However, it is also insignificant in all three models.

Then we test an alternative specification of the external sector, _e_ + _wp_ − _p_ − _trend_ , the real exchange rate calculated with the CPI inflation. It is not significant, which shows the superior performance of the error correction term with food prices. However, _e_ + _wp_ − _p_ − _trend_ does become significant if the cereal-price error correction term is excluded (not reported). This is evidence of the importance of the external sector for Ethiopia’s inflation.

Finally, we verify that the exchange rate has no short-run impact on inflation by including the contemporaneous value and six lags of changes in the Birr-Euro exchange rate. Again, the omitted variable test is insignificant. The absence of a shortrun impact of changes in the nominal exchange rate might be because the Birr-Dollar exchange rate only adjusts slowly and importers therefore view the rate of change in the Birr-Euro exchange rate as temporary.

Table 5: Omitted variables tests

|||Model 1: Cereals|Model 2: Food|Model 4: CPI|
|---|---|---|---|---|
|Monetary|[<br>]<br>(<br>)<br>*<br>*<br>_m_<br><br><br>_v_<br>−<br>−<br>+|F(1,100) = 0.14|F(1,99) =0.47|F(1,99) = 1.54|
|overhang|1<br><br><br> _t_<br><br>_p_<br>_y_<br><br>−<br>|[0.70]|[0.49]|[0.22]|
|Unrestricted EC-<br>monetary<br>overhang|1<br>1<br>12<br>1<br>,<br>,<br>_t_<br>_t_<br>_t_<br>_m_<br>_p_<br>_y_<br>_eus_<br>−<br>−<br>−<br>−<br>∆|F(3,98) = 0.54<br>[0.65]|F(3,97) = 1.30<br>[0.28]|F(3,97) = 1.01<br>[0.39]|
|De-trended real<br>exchange rate|[<br>]<br>1<br>_t_<br>_e_<br>_wp_<br>_p_<br>_trend_<br>−<br>+<br>−<br>−|N.A.|F(1,99) = 0.01<br>[0.94]|F(1,99) = 1.55<br>[0.22]|
|Nominal exchange<br>rate|6<br>0<br>_i_<br>_t i_<br>_i_<br>_e_<br>=<br>−<br>= <sup>∆</sup><br>∑|F(7,94) = 0.91<br>[0.50]|F(7,93) = 0.58<br>[0.76]|F(7,93) = 0.65<br>[0.71]|

_Note:_ Probability values in brackets.

34
relatively conventional prescriptions sufficient? Answers to this question require a great deal more work on political economy than is often encountered in development research studies.

Agriculture deserves special emphasis in growth-related development studies. Higher agricultural productivity has been a pre-condition for industrial transformation in most successful developing countries. Many of the world’s poorest depend on agriculture and so agricultural growth is often more poverty-reducing than growth in other sectors.<sup>9</sup> Private firms, especially small, family farms, seem to be remarkably efficient in adopting and using available production technologies, but they have no incentive to develop new technologies. How might agricultural productivity be raised, especially in Africa where there has been remarkably little progress on this front? How much might improvements in technology, in land tenure, in irrigation, in marketing, and in access to finance contribute? By how much might improvements in rural infrastructure promote higher farm productivity and a more diversified rural economy? How can formal and informal mechanisms for effective management of natural resources “commons” – shared fodder, forested areas, and water catchments – be strengthened to increase agricultural productivity, and thereby also increase household surpluses available for other investments in human as well as built capital?

While agricultural development remains important to poverty reduction, in some economies—notably those with high inequality in command over agricultural land—the service sector emerges as no less important to the poor.<sup>10</sup> The importance of industrial growth tends to be highly contingent on prior inequalities, notably in access to human and financial capital— linked in turn to delivery mechanisms for education, health and financial services. This is an instance of a more general point that both the aggregate level and distribution of initial endowments influence both the growth path and how the benefits of that growth are distributed. In time, and with the right policies, the distribution of endowments can also be improved in a virtuous cycle of more inclusive development.

> 9 See World Bank, _<u>World Development Report 2008: Agriculture for Development</u>_ <u>. World Bank, Washington, DC,</u> 2007.

> 10   See the discussion of the relative importance of agriculture versus services in the economic history of poverty reduction in Brazil, China and India in Martin Ravallion, “A Comparative Perspective on Poverty Reduction in <u>Brazil, China and India.” Policy Research Working Paper 5080, World Bank, Washington, DC, 2009.</u>

17
SPLIT DECISIONS Table 2: **Checking discontinuity for 23,448 households in sampling universe**

|Outcome|_μs↑_0|_μs↓_0_−μs↑_0|s.e.|_p_-val.|band-<br>width|
|---|---|---|---|---|---|
|_Migration behavior afte_|_r applicati_|_on_||||
|Applicant deployed?|0_._0175|0_._678|0_._0284|_<_0_._001|2_._113|
|_Traits of applicant at the_|_time of ap_|_plication_||||
|Age|30_._21|_−_0_._270|0_._387|0_._486|4_._428|
|Female|0_._210|0_._000834|0_._0548|0_._988|2_._073|
|College grad.|0_._326|0_._0397|0_._0632|0_._529|2_._149|
|Months experience|70_._56|_−_2_._536|3_._456|0_._463|8_._112|
|Employed|0_._291|0_._0103|0_._0609|0_._865|2_._211|
|Married|0_._447|_−_0_._0491|0_._0666|0_._460|2_._203|
|Test batch 1|0_._0244|_−_0_._0131|0_._0102|0_._199|1_._610|
|Test batch 2|0_._155|_−_0_._0317|0_._0483|0_._511|2_._151|
|Test batch 3|0_._279|0_._0647|0_._0602|0_._282|2_._381|
|Test batch 4|0_._368|0_._00516|0_._0647|0_._936|2_._413|
|Test batch 5|0_._174|_−_0_._0247|0_._0504|0_._625|2_._165|

Data for households in survey sample. _N_ fail( _s<_ 0) = 12 _,_ 577, _N_ pass( _s_ ⩾0) = 10 _,_ 871. _μs↑_ 0 is the mean of the local regression using data for test-failers only, evaluated at _s_ = 0. Optimal bandwidth selected by the method of Imbens and Kalyanaraman (2012). Triangular kernel.

33
|Mixed race (mulato) household head|0.041|0.050|0.262|*<br>0.068|
|---|---|---|---|---|
|Otherethnicityhouseholdhead|0.093|0.350|----||
|**Education of Household Head**|||||
|1 - 4 years|0.255 *|0.047|0.156|*<br>0.028|
|5 - 7 years|0.452 *|0.046|0.276|*<br>0.029|
|8 - 10 years|0.572 *|0.047|0.397|*<br>0.037|
|11 - 13 years|0.765 *|0.051|0.520|*<br>0.050|
|14 - +years|1.135 *|0.056|0.913|*<br>0.101|
|**Education of Spouse**|||||
|1 - 4 years|0.020|0.043|0.036|<br>0.030|
|5 - 7 years|0.124 *|0.044|0.086|*<br>0.029|
|8 - 10 years|0.156 *|0.046|0.144|*<br>0.040|
|11 - 13 years|0.267 *|0.045|0.216|*<br>0.052|
|14-+ years|0.472 *|0.048|0.512|*<br>0.075|
|**House Ownership**|||||
|Rent|-0.130 *|0.018|0.042|<br>0.060|
|Own and paying|-0.004|0.036|0.269|*<br>0.127|
|Other|-0.146 *|0.020|-0.057|*<br>0.027|
|**Position in Occupation**|||||
|Employer|0.313 *|0.027|0.297|*<br>0.052|
|Self-employed|-0.051 *|0.017|-0.062|*<br>0.031|
|Employee no pay|0.057|0.048|-0.154|*<br>0.054|
|Farm labourer|-0.131 *|0.030|-0.169|*<br>0.026|
|Owner farmer|0.238 *|0.055|0.217|*<br>0.039|
|Self-employed farm labourer|-0.232 *|0.057|-0.197|*<br>0.029|
|Not working|-0.046 **|0.027|-0.302|*<br>0.041|

_Source:_ Own calculations based on ECV.

_Note:_ Number of observations: 7950 (urban) and 5481 (rural). R<sup>2</sup> =0.59 (urban) and 0.52 (rural), * indicates that the coefficient is significant at the 5 percent level, and ** at the 10 percent level. The base categories are: Pichincha area (Quito belongs to that area), male household head, married with spouse, indigenous household head, no education of household head, no education of spouse, own house and employee.

According to the _F_ tests, the null hypothesis _H o_ :  _l_ 1, _U_   _l_ 1, _R_ ,  ,  _lM_ , _U_   _lM_ , _R_ , where<sup></sup> _lm_ is the coefficient for the category _m_ of variable _l_ and _M_ is the number of categories of this variable—i.e., the hypothesis that the effects of all categories of variable _l_ are the same in urban and rural areas—is rejected for almost all variables (Table 3). In other words, all the explanatory variables but education of the spouse have different effects in urban than in rural sectors.

**Table 3. Test of Equality of Coefficients Between Urban and Rural Regressions**

|**Variable**|**Number of**<br>**restrictions**|**_T_ value**<br>**/****_F_ value**|**P value**|**Test (5%**<br>**level)**|
|---|---|---|---|---|
|Constant|1|-2.61|0.01|Rejected|
|**Province**|20|5.38|0.00|**Rejected**|
|**Demographics**|18|5.87|0.00|**Rejected**|
|**Education variables**|10|2.04|0.03|**Rejected**|
|**Education of Household Head**|5|2.95|0.01|**Rejected**|
|**Education of Spouse**|5|0.63|0.68|**Not rejected**|

7
# **Labor Institutions and Their Impact on Shadow Economies in Europe**<sup>1</sup>

**_Kamila Fialová, Ondřej Schneider*_**

JEL classification: _J08, O17, O52_ Keywords: _labor market institutions, shadow economy, shadow employment, European Union_

> 1 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 authors are grateful to IZA's program area "Labor markets in emerging and transition economies" for providing data (http://www.iza.org/en/webcontent/research/ra5). The authors wish to thank Truman Packard, Johannes Koettl and Claudio Montenegro for their comments and help with supplying data. The usual disclaimer applies.

* Kamila Fialová, Institute of Economic Studies, Charles University in Prague (kfialova@email.cz); Ondřej Schneider, Institute of Economic Studies, Charles University in Prague (schneider.ondrej@gmail.com).