rafmacalaba/datause-encoder-data huggingface dataset:
Collier, P. and J. Gunning (1999). “Explaining African Economic Performance,” _Journal of Economic Literature_ , vol. XXXVII, pp. 64-111.

Edmonds, E. and N. Pavcnik (2005). “The Effect of Trade Liberalization on Child Labor,” _Journal of International Economics_ Vol. 65, pp. 401-419.

Edmonds, E. and N. Pavcnik (2006). “Trade Liberalization and the Allocation of Labor between Households and Markets in a Poor Country,” _Journal of International Economics_ 69 (2), pp. 257-295.

Goldberg, P. and N. Pavcnik (2004). “Trade, Inequality, and Poverty: What Do We Know? Evidence from Recent Trade Liberalization Episodes in Developing Countries,” Brookings Trade Forum 2004, 223-269.

Goldberg, P. and N. Pavcnik (2006). “Distributional Effects of Trade Liberalization in Developing Countries,” forthcoming, _Journal of Economic Literature_ .

Harrison, A. (Ed.), (2006). _Globalization and Poverty_ , forthcoming University of Chicago Press for the National Bureau of Economic Research, Boston, Massachusetts.

Hertel, T. and A.L. Winters (Eds.), (2006). _Poverty Impacts of the Doha Development Agenda_ , Palgrave-McMillan for the World Bank, Washington DC.

Hoekman, B. and M. Olarreaga (Eds.), (2006). _Global Trade Liberalization and Poor Countries: Poverty Impacts and Policy Implications_ , forthcoming Science Po, Paris.

Newey, W. (1987). “Efficient Estimation of Limited Dependent Variable Models with Endogenous Explanatory Variables,” _Journal of Econometrics_ 36, pp. 231-250.

Nicita, A. (2006). “The Price Effect of Trade Liberalization: Measuring the Impacts on Household welfare,” Policy Research Working Paper Series, the World Bank.

Porto, G. (2005). “Informal Export Barriers and Poverty,” _Journal of international Economics_ , 66, pp. 447-470.

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_6 Railway Reform in the Central and Eastern European (CEE) Economies_ 6. Figure 10 shows the changes already happening in the CEE and CIS countries as compared with France and the former West Germany.<sup>3</sup> ' As Figure 10 shows, rail freight activity in the CEE countries has now fallen to 60 percent, or less (to only 25 percent on the DR), of its level in 1988. To some extent, of course, this loss of rail traffic merely reflects (hopefully) temporary factors such as the drop in total economic activity in these economies and disruptions to rail traffic among the former socialist countries (especially with the former Yugoslavia). At the seme time, though, it is clearly early evidence of the more permanent structural changes describod above. Much of the rail traffic that has been lost will never return, and total rail traffic is not likely to enjoy 1988 levels for many years, if ever. Trends in the CIS are, as yet, not as dramatic as in the CEE countries, but that is primarily because economic restructuruig activity in the CIS began later, has proceeded more slowly, and still has not undergone the same degree of change as in the CEE countries.

Figure 10 <!-- Start of picture text --> Rail Freight Traffic Trends in Europe<br>Index Tonne-Km Index, 1988 = 1 00<br>110<br>0 DB<br>DR<br>7& - O PKP<br>i a < OMAV01<br>A FSU<br>40} \ ~ v BDZ (tons)<br>30 X ROM<br>20<br>1968 1989 1990 1991 1992<br>(9 mos.)<br>1992 FSU is actually based on Russia for first 5 months. 1992 BDZ is esbmate for entire year.<br><!-- End of picture text -->

> 3/ Fortunately, the data are still available to compare the former West Germany wvth the former Egst Germany. Traffic on the Deutsche Bundesbahn (DB) in (former) West Germany has remained stable, while rail traffic on the Deutsche Reichsbahn (DR) in the former East Germany has collapsed.
pension funds.<sup>28</sup> <mark>These findings are consistent with studies that analyze US data (Ippolito, 1992; Sirri and Tufano, 1998; Del Guercio and Tkac, 2002).</mark> Although the standard regulation applied to pension funds might complement the market forces that monitor asset managers on a short-run (monthly) basis, it does not indicate to any degree whether pension funds should invest long term or short term. Instead, pension funds are required to yield returns within established margins. Thereby, pension fund managers are penalized by regulations when they deviate from industry standards, having to cover these losses with their own capital. These regulations might also help explain why the companies that run the PFAs seem to monitor their managers through tracking error models that constrain them to be close to the average pension fund (Roll, 1992; Castañeda and Rudolph, 2009). While this regulatory discipline might generate herding among pension funds, the evidence on its effects is mixed (Raddatz and Schmukler, 2013) and it is not clear that herding should necessarily occur at the short-end of the maturity spectrum.

Next, we test how mutual and pension funds react when they underperform, following a large literature based on the US that finds mixed evidence (Brown et al., 1996; Busse, 2001; Goriaev et al., 2005; Chen and Pennacchi, 2009). The idea is that even when mutual and pension funds might choose a low volatility portfolio on average to try to minimize withdrawals, it could still be the case that when their returns are low, they move to more risky allocations.

> 28 In the case of pension funds, for regulatory reasons PFAs send monthly reports of their real returns to future pensioners and must base their publicity on real returns. Thus, the reduction in real returns resulting from inflation can potentially affect their ability to capture new affiliates or generate outflows. Although there is not much evidence that the number of affiliates changes with returns, the ranking of PFAs by returns (typically used in advertisements) seems to be positively correlated with the number of affiliates across PFAs (Cerda, 2005).

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of countries in the GTAP data set for which the foreign affiliate sales data are missing. Fukui and Lakatos use an econometric model to estimate the missing values and thus produce estimates for all regions and sectors in the GTAP data set. For the share of sales in the sector by the host country, we use the GTAP data set for total sales in the sector; we subtract the total of foreign affiliate sales from total sales to obtain the host country share of sales.

In the case of insurance services in African regions, we used the Axco database as our primary data source. Documentation is in appendix D. In the case of telecommunications services in our six African regions, we used communications commission data and other publicly available sources, taking South Africa as our proxy for SADC. Details are in appendix E.

# **4.9 Share of Expatriate Labor Employed by Multinational Service Providers**

The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use domestic labor less intensively than their domestic competitors, if multinationals use mostly domestic labor, their expansion is likely to increase the demand for domestic labor in these sectors.<sup>21</sup> For this version of the paper, we used representative estimates from earlier studies of the share of expatriate labor or specialized technology not available to domestic firms. In earlier studies we have found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or specialized technology.

# **4.10 Key Elasticities**

Our key elasticities are shown in table 7. Dixit-Stiglitz elasticities in services are based on estimates from Broda and Weinstein (2006). Dixit-Stiglitz elasticities in goods are the estimates of the elasticity of substitution for imports from different regions in the GTAP data set. Armington elasticities of substitution of domestic for foreign are from the GTAP data set. Armington elasticities of substitution of imports from different regions in the four CRTS sectors listed in table 7 are based on the estimates of Reidel (1988). Supply elasticities are based on the estimates of Schiff, Wang and Olarreaga (2002) and Schiff and Wang (2006); the methodology

> 21 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement.

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Plant specific parameters are proportional to those used in Campbell (1998) for the U.S. There are two reasons to do so. First, long series of plant level data are not available for a large sample of countries. Second, economies in our model are equal in all respects except specific parameters related to policies and development. We use the U.S. as our undistorted long-run developed benchmark. The parameters used by Campbell (1998) do a good job generating a distribution of firm’s productivities for the U.S. economy, but they fail to capture the larger dispersion of individual productivities observed in developing economies (as reported by Bartelsman, Haltiwanger, and Scarpetta, 2004). Hence, in order to find meaningful values for the productivity threshold (i.e., values of θ with strictly positive measure) we must use a substantially larger variance. We choose values for σθ and σ twice as large as those used by Campbell (1998).

Regarding the policy-related and developmental barriers, we calibrate their corresponding parameters, π and _s_ , to match data from the World Bank Doing Business database.<sup>10</sup> We choose to use data for 2007 (in part to ensure that GDP figures are not preliminary). Two specific indexes are of interest for the purpose of our paper: the cost of starting a business and the percentage of the initial investment that is preserved (or recovered) when a firm exits the market. The mapping between these indexes and the model’s parameters, π and _s_ , is not exact, but we believe it is reasonably appropriate. The mapping is quite close when we interpret the model in a limited perspective, according to which π and _s_ simply represent entry and exit costs, respectively. Admittedly, the mapping becomes weaker when we interpret the model and its parameters more broadly, that is, when we associate π with policy barriers and _s_ with developmental constraints. In reality, both indicators from _Doing Business_ have elements of policy and development imbedded in them. However, the design of the _Doing Business_ indicators makes the entry costs mostly driven by policy barriers, that is, dictated by _de jure_ regulations, while the recovery rate is determined to a large extent by the confluence of _de facto_ governance and human capital factors. We acknowledge that the indicators from _Doing Business_ are neither complete nor exclusive proxies of the model parameters. However, for the purpose of

> 10 On entry costs, this is the same source used by the Fraser Institute to construct their index of regulatory freedom.

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# **3 Data and Empirical Findings**

The data used to examine Banco Azteca’s entry combine three sources of information. First, the data for households come from the Mexican Family Life Survey (MxFLS), waves 2002 and 2005. At the time of the 2002 wave, Banco Azteca had not opened its branches, but by 2005, its presence varied across municipalities. MxFLS is representative at the national level and importantly, it is the only survey providing detailed information about Mexicans’ credit and savings habits during these years. I focus the analysis on households that were surveyed in both waves and in which the household head is between 18 and 65 years old. The final sample from the baseline survey consists of 5,639 households in 136 Mexican municipalities. Second, municipalities from the household data were merged with a panel dataset from the National Banking and Securities Commission (CNBV), which contains the location and year of opening of all bank branches across Mexican municipalities. With this dataset it is possible to identify the location decisions of Banco Azteca across municipalities over the period examined. Finally, aggregate variables at the municipality level such as population size and percapita income were added to examine the characteristics of municipalities attracting Banco Azteca’s branches. These variables were obtained from the 2000 Mexican Census and the Human . Development Index of Mexican Municipalities<sup>5</sup> To first explore which factors determine the location decisions of Banco Azteca, I classify municipalities in four groups according to the presence of Banco Azteca: i) municipalities that had an Azteca branch from 2002 to 2005 (63 of them); ii) municipalities where Azteca entered in 2002 but by 2005 the branches were no longer there (2 of them); iii) municipalities where Azteca did not enter at the beginning but by 2005 the bank was already there (4 of them); iv) and finally, municipalities that never had an Azteca branch in these years (67 of them). In table 1, I examine the distribution of population size and percapita income for each group. The table also includes the fraction of municipalities that had traditional bank branches by presence of Azteca.

There are three patterns in the location of Azteca to highlight from the table. First, population size, per capita income and presence of traditional bank branches are strongly correlated with the presence of Azteca’s branches. Municipalities with an Azteca branch are more populated, have higher percapita income and are also more likely to have traditional bank branches than the average municipality in Mexico. On the contrary, municipalities where Azteca never entered have smaller

> 5The Human Development Index imputes the percapita income for each municipality in Mexico using information from the National Income and Expenditure Survey (ENIGH) and the Census (see López Calva and de la Torre García (2003) and López Calva and de la Torre García (2005)). The index can be downloaded from: http://www.undp.org.mx/desarrollohumano/disco/index.html.

7
and we are interested in profits (gross revenue net of production costs). We go back below to how we impute costs to individuals who do not file taxes. For those filing income taxes we take the following steps:

- As mentioned before, taxpayers often mislabel wages and service income in their tax declaration: if we take the withholding reports as the ground truth, we can often observe taxpayers declaring the correct amount of income but labeling wage income as honorary, for example (which does not have impact on tax liabilities and therefore can be seen as low stakes). In order to deal with that, we adopt the following rule: to assure that our measure of income is as encompassing as possible, we define the total income from wage and services as the maximum of the self-declared income in the yearly filing and that in third-party reports. We then use the values from the source with largest total amount as the correct amounts for both wages and service income.

- We then proceed to apply the allowable deductions for all self-declared revenue (wages, service, commercial, interest, rent and other income) to obtain income net of expenses.

- Finally, we consider the possibility that total revenue informed by third-parties is larger than the total revenue claimed by taxpayers. Once again, since taxpayers often mislabel sources of income in their filings, we do not attempt to make comparisons by specific income sources: we simply compute the difference between total self-reported income (already incorporating the previous adjustment for and and the total revenue amount. When the wages honorary) third-party

- third-party amount is larger, we impute that difference as additional revenue to the taxpayers and apply deductions as discussed below to obtain net income.

For individuals who do not file an income tax declaration but are reported as having revenue from commercial activities, we need to impute deductions in order to obtain net income. We do so by using deductions from individuals who file taxes. For each year, we compute the median profit margin rate by department (19 regions) and 2-digit industry. When information at that level is missing, we simply impute the median margin at the 1-digit industry.

We then estimate the net income from commercial and service provision activities for individuals not filing taxes by multiplying revenue by the imputed profit margin rate. We use the same imputed profit margin rate to recover the additional unreported net income, using third-party reports.

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# **Table 8. Summary Statistics for the Variables**

|**Variable**|**Obs**|**Mean**|**Std.**<br>**Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|bank_corup|8075|1.77|1.05|1|4|
|t_245_new|9932|5.26|0.88|1.67|6|
|avelf|7968|0.35|0.29|0|0.89|
|lat|9059|27.88|17.70|0.23|59.28|
|gvt_yn|9645|0.12|0.33|0|1|
|frk_yn|9673|0.19|0.39|0|1|
|exp_yn|9463|0.36|0.48|0|1|
|manuf|5463|0.24|0.43|0|1|
|service|5463|0.35|0.48|0|1|
|lsale|9034|9.87|8.00|-2.12|25.33|
|lncomp|9538|0.83|0.34|0|2.20|
|inflat_9599|9607|27.27|43.60|0.76|252.66|
|growth_9599|10032|3.56|4.04|-5.46|33.26|
|lpriv9599|9807|3.06|1.61|-7.74|5.31|
|suppower_pc|6658|-0.01|0.96|-3.05|1.14|
|pmindex_pc|6658|-0.10|0.87|-1.83|1.46|
|gdppn9599|9932|8.41|0.93|6.21|10.37|
|apmindex|8825|7.85|2.10|1|12|

_Source: see appendix._ The results in Table 9 below indicate that greater economic transparency reduces corruption in bank lending and should increase access to bank finance. These results are robust to the inclusion of several firm and country specific variables, though surprisingly, the coefficients on the supervisory indicators are not significant. In every specification, more transparency clearly reduces bank corruption- the results are robust across specifications. IV specification strengthens the results. Interestingly, exporting firms’ access to credit is not hindered by bank corruption (they presumably have more potential sources of credit-such as trading partners). Restrictive supervisory practices are associated with _more_ corruption in all specifications and the monitoring index, pmindex pc, with less. The private monitoring index is not significant in the IV specifications.

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compares the accuracy of the different design choices to the realized distribution, and Section 5 concludes.

# **2. Setting and Survey Instrument**

# **2.1. Sample and Setting**

We carry out our methodological experiments using a sample of 272 boat owners from seven villages in the southern tip of Tamil Nadu, India. These boat owners were first surveyed in 2004 as part of an ongoing panel study by Giné and Klonner (2007). We chose this setting for the experiment for three main reasons. First, as we will explain further, these boat owners offer an excellent setting for comparing elicitation designs of subjective distributions, because the distributions can be compared to individual-specific distributions of realizations, rather than just point estimates. Secondly, the boatowners average 5.5 years of education, and so are broadly representative of the types of individuals with at most primary education for whom visual aids are most needed. A final reason was expediency – we could piggyback on the existing survey infrastructure and other data collected to carry out this experiment for low cost.

The seven villages which the boat owners come from each have a population of about 1,500, and most villages have neither a harbor nor a jetty, a fact that restricts operations to beach-landing boats only. All year-round operating vessels have a crew of one to four men and are operated by local households. All of these households belong to the exclusively catholic fishing community of the village, which used to belong to a particular fishermen's caste within the Hindu caste system before collectively converting about 400 years ago.

We ask expectations about the value of future catches, and so it is useful to understand the basic context in which fishing takes place and the main sources of uncertainty in the value of catches. Boat owners fish using either a traditional kattumaram, or a more modern fiber boat. The kattumaram is a raft-like vessel made of two Alphesia logs tied together with two crossbeams at the two ends. The boat owner 5
**Table 1: Available evidence on the impact of outreach interventions**

||**Access**|**Graduation**|
|---|---|---|
|**Number of studies by type of interventions**|||
|Not specified (Any outreach programme)|1|0|
|Information|8|0|
|Information & support|18|3|
|Information, support & tutoring|3|1|
|**Total number of studies**|**28**|**4**|
|**Studies' characteristics**|||
|RCT design (in % of total studies)|82%|50%|
|Diversity of national contexts (nb of country)|6|2|
|National‐scale interventions (in % of total studies)|25%|25%|
|Single‐institution interventions(in % of total studies)|11%|0%|
|**Source**: Tables B.1, B.2, B.3, B.4, B.5 in Appendix.|||

# _4.1 Impact on access to higher education_

Only one study provides a quasi‐experimental evaluation of outreach programs in general, not limited to one specific intervention. Domina (2009) uses longitudinal data to compare the efficiency of outreach programs and found an increase in enrollment (+5.5 p.p.) in any higher education institution, but this was not statistically significant (Table B.1 in Appendix). Since no information was available on the type of services offered, it is possible that different program designs have very different impacts on college enrollment.

The evaluations of specific outreach interventions indeed suggest a great variety of effects on enrollment, depending on the characteristics of interventions. As shown by figure 2, interventions providing disadvantaged students with _additional information_ only on higher education seem to have very little impact on access patterns, while interventions which complemented information with _assistance or individualized guidance_ on college or financial aid applications seem to be more efficient. Among the 18 studies included, the range of the estimated effects is wide, but most found a statistically positive effect on the enrollment rates of disadvantaged students and more than half found an increase in enrollment rates by at least 10%.

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