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

|**Title**|**Author**|**Date**|Contact<br>**for paper**|
|---|---|---|---|
|WPS2007 Regional Integration Arrangements:<br>Static Economic theory, Quantitative<br>Findings, and Policy Guidelines|Dean A. DeRosa|November 1998|L. Tabada<br>36896|
|WPS2008 Volatility and Contagion in a<br>Financially Integrated World:<br>Lessons from East Asia's Recent<br>Experience|Pedro Alba<br>Amar Bhattacharya<br>Stijn Claessens<br>Swati Ghosh<br>Leonardo Hernandez|November 1998|D. Fischer<br>38656|
|WPS2009 Poverty and the Economic Transition:<br>How Do Changes in Economies<br>of Scale Affect Poverty Rates for<br>Different Households?|Peter Lanjouw<br>Branko Milanovic<br>Stefano Paternostro|November 1998|P. Sader<br>33902|
|WPS2010 The Real Impact of Financial Shocks:<br>Evidence from the Republic of Korea|liker Domag<br>Giovanni Ferri|November 1998|M. Greaves<br>81876|
|WPS2011 Measuring Poverty Using Qualitative<br>Perceptions of Welfare|Menno Pradhan<br>Martin Ravallion|November 1998|P. Sader<br>33902|
|WPS2012 Export Quotas and Policy Constraints<br>in the Indian Textile and Garment<br>Industries|Sanjay Kathuria<br>Aniali Bhardwaj|November 1998|L. Tabada<br>36896|
|WPS2013 A New Database on Investment and<br>Capital for Agriculture and<br>Manufacturing|Al Crego<br>Donald Larson<br>Rita Butzer<br>Yair Mundlak|November 1998|P. Kokila<br>33716|
# Citizen Participation and Political Trust in Latin America and the Caribbean: A Machine Learning Approach

Natalia Pecorari<sup>1</sup> and Jose Cuesta<sup>1,2</sup> 1World Bank, Social Sustainability and Inclusion Global Practice 2 Georgetown University, School of Foreign Service

> ∗Corresponding author: npecorari@worldbank.org

**Keywords:** Citizen participation; political trust; machine learning; Latin America and the Caribbean

**JEL Codes:** D7, P4
money to someone else.<sup>23</sup> Second, it could have been the case that parents substituted away from expenditure on the type of goods obtained by the girl through the voucher system, thus neutralizing the transfer to the girl. We collected detailed information on the girls’ consumption of (23) personal items such as clothes, bags, soap, books, etc..., excluding any item purchased through a voucher, to test for this. While we found – unsurprisingly given substitutability – a negative effect on consumption of personal items other than those purchased with the voucher in the girl voucher treatment compared to the control group, this effect was statistically insignificant and small relative to the amounts transferred (89 Meticais compared to an average of 469 meticais received in vouchers, on average, see Table A-1).

In Column (4) of Table 3, we present results obtained when controlling for the baseline characteristics for which there was at least one statistically significant difference between experimental arms and confirm that results are virtually unchanged.

Column (2) reports results on the effect of our treatments on school enrollment as reported by parents in the household survey. Starting from a high enrollment rate (95% in the control group), and given the fact that the intervention was announced after the end of the official enrollment period (and, in most cases too, after the start of the school year), it is not surprising to confirm that our interventions had no effect on enrollment decisions. The CCT seems to have had a small impact in increasing enrollment by 2.7 percentage points in the baseline specification, but when controlling for baseline characteristics (Column (5)), the point estimate decreases and a t-test cannot reject the null of no effect (p-value: 0.21). Based on this and further tests showing that the effect on enrollment is not robust (Section IV), we conclude that the effect on enrollment was negligible.

23 Of 101 girls chosen at random among the “girl voucher” survey sample to answer this question.

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2001 and 2005, enabling us to make comparisons over time using the repeated cross-sectional data. The five surveyed cities are located in different regions of the country: Shanghai is in the Yangtze River Delta near the coast; Wuhan is in Hubei Province in central China, Shenyang in Liaoning Province in the northeast, Fuzhou in Fujian in the southeast, and Xian in Shaanxi Province in the northwest.

In each city, representative samples of local residents and migrants were independently selected in a two-stage procedure. Using previous year data on the local resident population of each neighborhood, a fixed number of neighborhoods were selected in each city using probability-proportionate-to-size (PPS) sampling. As the cities had limited information on the number of migrants living in each neighborhood, neighborhoods were first selected based on local resident populations, and weights are used to correct for differences in the relative sizes of migrant and local resident populations based on population estimates provided by neighborhood office staff. These staff helped to construct an updated list of households in sampled neighborhoods to serve as a sampling frame. Neighborhood office staff assisted with documenting unregistered migrants living in the neighborhood, especially those operating small businesses, so as to include them in the sampling frame. A fixed number of households were then randomly sampled in each neighborhood, with 500 local resident and 500 migrant households sampled in each city. The same procedure (but with different sample sizes) was followed in 2000 and 2005.

In the 2010 survey, a number of questions were asked about implementation of the Labor Contract Law, awareness of the Law’s provisions, initiation of labor disputes, and participation in social insurance programs. Detailed work history and other information were collected for all adult members of each household surveyed. One strength of the CULS is that it surveyed migrants (including those from both rural and urban areas) and local residents in an identical fashion, and it collected enough observations per city to calculate city-level 6
- [2] Angrist, Joshua D. and Adriana Kugler. 2003. “Protective or counter-productive? Labour market institutions and the effect of immigration on EU natives.” _Economic Journal_ 113(488): F302-F331.

- [3] Barrios, Salvador, Luisito Bertinelli and Eric Strobl. 2006, “Climatic change and ruralurban migration: the case of Sub-Saharan Africa.” _Journal of Urban Economics_ 60(3): 357-371.

- [4] Banerjee, Abhijit, Esther Duflo and Nancy Qian. 2012. “On the road: access to transportation infrastructure and economic growth in China.” Working Paper.

- [5] Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon. 2011. “Migration and economic mobility in Tanzania: evidence from a tracking survey.” _Review of Economics and Statistics_ 93(3): 1010-1033.

- [6] Behrman, Jere R. and Mark R. Rosenzweig. 1994. “Caveat emptor: cross-country data on education and the labor force.” _Journal of Development Economics_ 44(1): 147-171.

- [7] Berker, Ali. 2011. “Labor-market consequences of internal migration in Turkey.” _Economic Development and Cultural Change_ 60(1): 197-239.

- [8] Bloom, David E. and Jeffrey D. Sachs. 1998. “Geography, demography, and economic growth in Africa.” _Brookings Papers on Economic Activity_ 1998(2): 207-295.

- [9] Borjas, George J., Richard B. Freeman and Lawrence F. Katz. 1997. “How much do immigration and trade affect labor market outcomes?” _Brookings Papers on Economic Activity_ 28(1): 1-67.

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The industry is dominated by SMEs, which accounted for about 70-75 percent of employment and _60-65_ percent of production during the 1980s (Table 9). In 1990, small plants with 1049 workers accounte for 35.7 percent of industry employment, medium plants (50-199 workers) accounted for 40.3 percent, and the remaining 24 percent came from plants with 200 or more workers.

A total of 44 firms were surveyed, 30 of these in Bogota, seven in Medellin and seven in Bucaramanga. Eight had been founded before 1960 (two in the 1940s), another nine in the 1960s, 10 in the 1970s, 16 in the 1980s, and two in the 1990s. Most began with less than 10 workers (23 of'38 reporting this datum) and nearly half with five or less; mean start-up size was about 12 workers. Average size at present is about 130 workers, with only six finns having less than 20, 20 having less than 50 workers, and eight having more than 200 (Table 5). The typical finn in the sample started quite small and grew over ten-fold to reach medium size. Average sales were about US $3 million. Nine of 30 firms engaged with subcontractors undertake training as part of the arrangement.

Of 41 responses, five firms indicated that they are no longer exporting direcdy. including these, nearly 70 percent of the firms were exporting less than 20 percent of their production. Only tree were exporting more thaa half (another two were at 50 percent), with thX highest coming in at 90 percent. The mean export share was 172 percent and the median less than 15 percent. There was considerable volatilty in the exportlsales ratio.

Only three firms had been exporting from the time they were founded and one other from widhin a year of that time. These finns were founded with the intent of satisfying a foreign demand that they already knew about For the 26 firms founded before 1980, the average delay between the time of founding and the time that exporting began was 15.6 years. Even for those established between 1980 and 1984, the average wait was 8.4 years. It thus appears that, with the exception of those firms -founded with foreign capital, the firns were established with only local demand in mind. Finns strting with less thm five workers took significantly longer to enter export markets (an average of 19 years) than did larger ones (9.6 years). And firns with less than 25 workers had much lower direct exportstsales ratios (averaging 3.5 percent) than did the large ones (18.2 percent forthosewith 100 workers and up, 23.1 percentforthose with 50-99 workers and 20.4 percent for those with 25-49 workers." The level oe education of entrepreneurs among this group of exporting firms is very high indeed; 88 percent (36 of 41) have had university training. A high percentage are engineers (13) or N1No firm with less than 30 workers had a ratio of over 10 percent, though one of these had a 50 percent ratio in 1992.

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19

# **VI** AvPatlon **of MEXAGICTS** to Counterfactual Trade Lberafation Scearb

As an initlal application, MEXAGMKTS will be used to shnulate a trade liberalization scenarlo that has been urged upon Mexico<sup>by multilateral and bilateral</sup> lenders. This experklent consists of dropping the<sup>system of guarantee prices for the</sup> field crops malze,sorghum and soybeans and<sup>letting the world market determine the</sup> domestic prices for these commodities. Conceptually,<sup>this experknent envisions the o'nd</sup> of internatlonal trading and storage operatlons<sup>by CONASUPO</sup> to validate the politically determined guarantee prices. Of course, there would<sup>still be International trading In</sup> maize, sorghum and soybeans; but It would be by private<sup>fIrms or even by CONASUPO</sup> at International prices and without subsidy. Since<sup>the experkment Is counterfactual,</sup> shaulation over the entire 1974-85 period Is of Interest<sup>as a test of the alternative</sup> policy under a variety of economic conditions. Operationally, the experuiennt Is kmplemented by skuply substituting the real border<sup>price (RBP) variables for the real</sup> price guarantee (RPG) variables; and Interpreting the<sup>supply adjustment varlables as</sup> profit maxhlizing trade at world prices by private<sup>traders or even by CONASUPO.</sup>

# Experknental Results

The results from the experkinent are most easily<sup>Interpreted by noting that only</sup> three exogenous variables are changed, the prices<sup>of malze, sorghum and soybeans.</sup> Since the changed variables are prices, the resource<sup>a'locatlonkIpactIs determined</sup> by the change hI two relative prices, using<sup>one commodity as numeraire. This</sup> comparison Is given in Table 1. Thus, the maize<sup>price decreases from 1.51 to 1.06</sup> sorghum units; and the soybeans prico drops from<sup>2.67 to 2.23 sorghum units when</sup> averaged over the period 1974-85. That Is, the<sup>guarantee prices over the period</sup>
Table 1. Descriptive Statistics

|||Mean|Std. Dev.|Min|Max|Obse|rvations|
|---|---|---|---|---|---|---|---|
|Growth (WB)|overall|2.0|10.7|-44.1|89.4|N =|320|
||between<br>||4.2<br>|-4.1<br>|18.4<br>|n =<br>|25<br>|
||within||9.9|-42.3|73.0|T-bar|= 12.8|
|Growth (IMF)|overall|2.8|7.4|-23.4|29.6|N =|290|
||between||2.6|-1.1|9.5|n =|25|
||within||7.0|-22.7|25.6|T-bar|= 11.6|
|War Dummy|overall<br>|0.1|0.3<br>|0.0<br>|1.0<br>|N =<br>|325<br>|
||between||0.1|0.0|0.5|n =|25|
||within||0.2|-0.4|1.0|T =|13|
|Ln(inflation) (WB)|overall|2.7|1.8|-3.0|8.5|N =|252|
||between||0.9|1.3|4.9|n =|22|
||within||1.6|-2.1|8.2|T-bar|= 11.4545|
|Ln(inflation) (IMF)|overall|3.0|2.1|-2.3|9.7|N =|305|
||between||1.0|0.4|5.0|n =|25|
||within||1.8|-1.1|9.1|T-bar|= 12.2|
|Fiscal Balance (in percent of GDP) (WB)|overall<br>|-3.5|4.1<br>|-30.6<br>|3.0<br>|N =<br>|251<br>|
||between||2.5|-9.8|0.2|n =|24|
||within||3.2|-26.5|3.2|T-bar|= 10.4583|
|Fiscal Balance (in percent of GDP) (IMF)|overall|-4.7|6.5|-56.8|4.9|N =|316|
||between||3.2|-12.3|0.0|n =|25|
||within||5.6|-49.2|9.5|T-bar|= 12.64|
|Fiscal Balance (in percent of GDP) (EBRD)|overall|-4.5|5.1|-31.2|5.0|N =|305|
||between||2.6|-10.9|0.2|n =|25|
||within||4.4|-27.8|5.4|T-bar|= 12.2|
|Liberalization Index (de Melo et al 1996)|overall<br>|0.6|0.2<br>|0.3<br>|0.8<br>|N =<br>|286<br>|
||between||0.2|0.3|0.8|n =|22|
||within||0.0|0.6|0.6|T =|13|
|EDRD Index of small-scale privatization|overall|3.5|0.9|1.0|4.3|N =|325|
||between<br>||0.6<br>|2.0<br>|4.2<br>|n =<br>|25<br>|
||within||0.6|1.1|4.6|T =|13|
|EDRD Index of price liberalization|overall|3.8|0.6|1.0|4.3|N =|325|
||between||0.4|2.8|4.3|n =|25|
||within||0.5|1.4|5.0|T =|13|
|Index of Reform Progress|overall<br>|23.7|6.2<br>|10.0<br>|34.7<br>|N =<br>|325<br>|
||between||4.9|14.9|32.1|n =|25|
||within||4.0|10.7|30.3|T =|13|
|Private investment (% of GDP)|overall|16.6|6.6|0.0|49.9|N =|225|
||between<br>||4.9|7.9|28.8|n =<br>|23<br>|
||within||4.3|0.3|37.7|T-bar|= 9.78261|
|Public investment (% of GDP)|overall|4.4|2.3|0.0|15.5|N =|245|
||between||1.7|1.5|8.6|n =|24|
||within||1.6|-4.2|12.7|T-bar|= 10.2083|
|Total government expenditure (% of GDP) (WB)|overall|36.1|11.9|5.4|68.4|N =|262|
||between||10.6|15.2|56.8|<br>n =|<br>25|
||within||6.1|17.9|73.6|T-bar|= 10.48|
|Total government expenditure (% of GDP) (IMF)|overall|37.2|11.2|10.9|84.9|N =|316|
||between||9.2|12.7|52.8|n =|25|
||within||7.4|20.2|89.3|T-bar|= 12.64|
|Government effectiveness|overall<br>between|-0.2|0.6<br>0.6|-1.5<br>-1.3|1.0<br>0.8|N =<br>n =|248<br>25|

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<!-- Start of picture text --> Figure 2a: Job Finding Rates from inactivity (Trends and Cycle): Mexico an Brazil<br>Mexico Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>O-S O-I O-S O-I<br>O-F O-F<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>O-S (HP) O-I (HP) O-S (HP) O-I (HP)<br>O-F (HP) O-F (HP)<br>.08 .04<br>.03<br>.06<br>.02<br>.04<br>.01<br>0<br>.02<br>.2 .2<br>.1 .1<br>0 0<br>-.1<br>-.1<br>-.2<br>-.2<br><!-- End of picture text --> Notes: The graph shows the transition rates from inactivity (O=Out of the Labor Force) into the three employment sectors (S=Informal Self-employed, I=Informal Salaried, , F=Formal Sector). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

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curate information allows expeditious updating of _ex ante_ probabilities as new information emerges. All these features of IT act to compress realized variation in the conditional forecast of the probability of non-compliance. This motivates our analysis of reduced ambiguity in the theoretical model.

# **3 Descriptive evidence**

In this section, we use data provided by the Serbian customs agency to support two arguments that are central to the paper: (1) ambiguity is a pervasive phenomenon in the oversight of import shipments, and (2) agencies with oversight responsibilities respond to ambiguity when allocating scarce inspection resources. We lack a direct measure of ambiguity, so our arguments are necessarily indirect. In our view, a primary source of ambiguity in the oversight of import shipments is the infrequency with which inspecting agencies observe many shipment types. We use the frequency with which shipment types are observed as an inverse proxy for ambiguity. We show that shipment types that are infrequently observed are numerous and that infrequently observed shipment types are inspected at higher rates. The negative relationship between inspection rates and shipment frequency remains, even after controlling for available indicators of risk.

The Serbian customs agency already operates under risk management protocols that exploit sophisticated IT. Since our thesis is that risk management involving IT reduces the salience of ambiguity in the inspection decision, the agency’s response to ambiguity is likely to be muted relative to what one would observe in a pre-reform environment. For our purposes, it would be preferable to use data from a pre-reform environment, but unfortunately agencies that have not adopted modern IT usually lack the high quality data necessary for our exercise. We use the Serbian customs data because it is high quality and because it is available to us.

The Serbian customs agency provided us with comprehensive data for the years 2006-16. The raw data are defined at the level of an import transaction (hereafter, a shipment). Each shipment record contains information about the shipment’s value, weight, origin country, associated duties and taxes paid, the importing firm’s identity, 10-digit Harmonized System 10
# **_The main causes for unexpected clearance delays are inadequate documentation, congestion at the border, and trading in goods that require lengthy inspections_**

Regarding the main causes of unexpected delays in the clearance process, a large majority of freight forwarders indicate that inadequate documentation (29%), or congestion at the border (25%), or trading in goods that require lengthy inspections (21%) are the first main cause, as seen in Figure 9. Only a tenth of the freight forwarders indicate the performance of the customs agency as the first main cause of delays and close to another tenth indicate the performance of other agencies operating at the border being the first main cause of delays.<sup>14</sup> These proportions increase slightly when we combine the responses of freight forwarders to what were the first main cause, the second main cause, and the third main cause of clearance delays, as seen in the right set of bars in Figure 9. These patterns are quite similar across freight forwarders of any size.

**Figure 9.  One of Three Main Causes of Unexpected Clearance Delays**

<!-- Start of picture text --> 35%<br>29%<br>30%<br>25% 25%<br>25% 22%<br>21%<br>20%<br>16%<br>15% 13%<br>11%<br>10%<br>10% 9%<br>5% 3% 4% 2% 3% 1% 2% 1% 3%<br>0%<br>First main cause of delays One of 3 main causes of delays<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

Further evidence on inspections as a cause of clearance delays is shown in Figure 10 that shows 63% of freight forwarders indicating that intensive physical inspections frequently lead to

> 14 Many freight forwarders indicate that they do not trade in goods that require them to interact with the non-customs agency.

19
Similarly, sales revenue<sup>8</sup> of the average female entrepreneur in a given country in ECA is significantly (63.1%) smaller than that of the average firm owned by a man (using robust regression methods);, once we control for the (two digit) industry in which the firm operates, the difference falls dramatically to 37.2% (see second panel in Table 2a). Hence, on average sales revenue in a female-owned firm in a given industry and country in ECA is less than two-thirds that of a male-owned firm in that industry.

These findings suggest that women’s firms are smaller in terms of sales revenues and profits than men’s firms. Once we control for concentration of female entrepreneurs in different industries, the difference in their size and profits falls, but remains significant. We return below to the question “What is it about the industries in which women are located in that drives down these differentials?” In the third panel of Table 2a we next explore gender gaps in efficiency, using both a measure of financial efficiency and total factor productivity (TFP) We ask whether the profit gap we found above could be brought about by a different scale of operation, hence we estimate the differences in men’s and women’s profits controlling for sales, which can be seen as a measure of financial efficiency.<sup>9</sup> We find that by this measure, there are no statistically significant differences between male and female entrepreneurs in ECA. Hence the originally detected lower profit in female-owned firms is accounted for entirely by the lower scale of operation.

Are female entrepreneurs less productive in terms of the revenue that they generate from given inputs than males? The TFP findings are obtained from estimating a Cobb-Douglas production function with pooled firm-level data from all 26 countries:<sup>10</sup> where _lnY_ is the log of sales revenues,<sup>11</sup> _i_ and _j_ index firm and industries (at the two-digit NACE level), respectively. The inputs include: _K_ , capital stock (at replacement value); _L,_

> 8 In the analysis, sales revenue is logged but profits are not logged in order to preserve negative observations for profits.

> 9 The financial literature often uses the profit rate (profit/sales) as a measure of efficiency.  We also ran this regression with the profit rate on the left hand side and a female dummy on the right hand side and find that there is no significant gender difference in the profit rate.  We prefer our specification it allows us to see that differences in profits by gender are brought about by the scale of operation.

> 10 Equation (1) can also be interpreted as a first order approximation for more complicated revenue (production) functions.

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