rafmacalaba/datause-encoder-data huggingface dataset:
# 2. **The questionnaire**

This section gives an overview of the main focus of the questionnaire. The first subsection describes how the questionnaire was developed, and the second subsection discusses 4 the structure of the questionnaire .

# Development of the questionnaire

The survey instrument was developed in several steps during the last four years. It started with a large number of interviews of private entrepreneurs in different Latin American countries that resulted in a short multiple choice questionnaire. This questionnaire asked whether entrepreneurs feared large and unpredictable swings in lawmaking. This questionnaire was then sent out to a very small number of finns in 28 LDCs. No stratification was done in this survey. Given the small number of responses per country, no strong conclusions could be drawn. Nevertheless, these results coupled with growth and investment data proved promising as the political credibility indicator was significantly related to economic performance of the 28 countries (see Brunetti and Weder 1995). Based on the results of this pretest the survey instrument was refined and expanded. In preparation for the WDR 1997 survey the expanded questionnaire was discussed with a number of country experts at the World Bank and at IFC. After these discussions the questionnaire was revised and finalized and resulted in the survey presented in this paper.

# Structure of the questionnaire

The questionnaire first asks for general characteristics of the firm. These questions aim to capture a brief but detailed picture of the firm that is answering in the questionnaire. Five different dimensions are considered. First the firm is asked to define its size-less than 50 employees, between 50 and 200 employees and more than 200 employees. Second the nature of the firm's business is asked (manufacturing, services and agriculture). Third the location of management is inquired (capital city, large city or small city/countryside). The last two questions of this section ask for the internationalization of the firm in the two dimensions foreign participation (yes-no) and exports (yes-no). Section 4 will provide an overview of the actual distribution of all responding firms according to these five criteria.

The main part of the questionnaire consists of 25 mainly multiple choice questions. These questions are divided into five sections each with its own focus . All the questions aim to identify the degree of (un)certainty created by state action. In preparatory interviews for this questionnaire, firms that were confronted unpredictable state action usually came up with very different examples of policy and regulatory uncertainties. These answers ranged from surprising executive decrees to unpredictable court decisions, from uncertainty on the severity of tax audits to unpredictable custom procedures, and from policy reversals whenever a new minister is appointed to uncertainty whether a bribe would lead to blackmailing by government officials.

4 The questionnaire can be found in the appendix.

2
11 creditworthiness is high enough to attract new financing.

Finally, given that the parameters of the<sup>stochastic process followed by</sup> the variable Xt are determined without any<sup>a priori assumption, it is</sup> interesting to test the capacity of prediction<sup>of the model. The expected</sup> value of X in period t' can be expressed as: (5) E(Xt.) - Xt + ST where T a t'- t and t' > t Using the Taylor formula of the second<sup>order, the expected value of the</sup> market value of the debt in period t' can be<sup>approximated as</sup>

- (6) E(Vt,) - Dt.+ o rexp (-r7) [a'ON(a'O) - a'1N(a'1) + (1/r2r) (exp(-a' 0 2 /2) - exp(-a' 1 2 /2)] + exp(-rv)/(2/Twv) (exp(-a'0<sup>2/2)-exp(-a'</sup> 1<sup>2/2))(CT)</sup>

with a'1 a CDtiexp(r7) - E(Xt,) - _pil/_ (Orr) alo = t-B(Xts) - _pi1l/ (t-7)_ Equation (6) indicates the expected value of<sup>the market value of the</sup> debt and, therefore, the price of debt.

# 2. An EmRirical Test **E** stimates and Simulations for 6 Highly Indebted

## Countries

The approach developed in the previous section,<sup>explicitly designed for</sup> empirical testing, is applied to six highly indebted countries<sup>-Argentina,</sup> Brazil, Chile, Mexico Poland and Venezuela - over the<sup>1986:IV-90:IV period.</sup> The data on the maturity **_(i),_** the interest rate (rt) and the volume of debt (Dt) has been furnished by the Quarterly Review published by<sup>the Debt and</sup> International Finance Division of the World Bank. We<sup>also used the data</sup> published by Salomon Brothers for the price of debt.<sup>Note that we defined the</sup>
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36
correct all survey-based regression analyses for survey weights. Survey respondents in treated communities who attended financial education training are assigned survey weights ti , and ti+ nti nti respondents who did not attend training are assigned survey weights . ti represents the total ti+ nti number of community members in SACCO community _i_ who were trained, and nti is the total number of community members in SACCO community _i_ who were not trained. Hence, the sampling weights are proportional to category population size.

# **3.3. Empirical Specification**

We pool both endline household survey rounds for the regression analyses. Based on the random assignment, we measure the impact of the financial education program using the following intention-to-treat (ITT) OLS regression: Y= α+ β1. Treatment1 + β2. Treatment2 + δ. Strata+ θ. SurveyRound+ ε (1) where _Y_ is the outcome of interest, β1 and β2 are the treatment coefficients of interest for autonomous selection (T1) and fixed selection (T2) groups, respectively, and δ captures province stratification controls since we stratified the randomization at the province level. All regressions with survey data further include survey round dummies and analytical weights, as described in section 3.2. Standard errors in all specifications are clustered at the SACCO level.

We also analyze treatment-on-treated (TOT) estimates where we instrument take-up of financial education with treatment assignment in the first stage, and then use the predicted values to estimate second stage outcome regressions of _Y_ on program take-up in T1 and T2.

# **4. Summary Statistics and Program Take-up**

Table 1 presents the summary statistics and tests of randomization from both administrative and survey data. Administrative data on savings deposits is available for two periods prior to the intervention, August 2015 and March 2016. Since we did not conduct a baseline household survey, we present summary statistics and tests of randomization for fixed respondent attributes.

11
# **1. Introduction**

The purpose of this paper is to examine Mexican employment dynamics at the micro level. An important and growing body of literature suggests employment expansions and contractions at the establishment level ("job flows") hide a considerable amount of employeelevel turnover ("worker flows").<sup>1</sup> Prior to the IDB project “Market Institutions, Labor Market Dynamics, Growth and Productivity: An Analysis of Latin America and the Caribbean,” of which this paper is a part, nearly all<sup>2</sup> of the literature on worker flows or job flows had focused on developed countries. Understanding these gross worker and job flows, however, is especially critical in developing countries that seem to consistently experience larger shocks or are experiencing substantial reform.

For our analysis, we use established techniques on a novel data set. We match workers and firms over time using Mexican social security records. This matching capability, which is rare, allows us to calculate within-establishment employee turnover as well as labor reallocations across establishments that differ across time period, firm size, geographic location, and worker characteristics (gender and age).

Our analysis produces many new results. First, although Mexico differs from the United States in several important ways,<sup>3</sup> Mexico's basic patterns are similar to the patterns in the U.S. and in other developed countries. Perhaps most importantly, we show that aggregate statistics hide a great deal of labor market churning. Constant creation and destruction implies that job destruction is a consistent characteristic of Mexican labor markets (even in economic expansions).
To determine the share of policy selective aid disbursed to recipients, we run recipient-year aid allocation regressions for each donor, using the lagged CPIA index as the key explanatory variable. In these regressions we control for GDP per capita and total population, both lagged. All covariates are expressed in logs.<sup>1</sup> If a donor has a positive and statistically significant (of at least 10%) CPIA coefficient in these regressions for at least four of the most recent 5 years, a donor is classified as being policy-selective in that year. The premise is that only donors known to disburse aid policy-selectively are likely to influence recipient policies. In order to assure that these donor-specific recipient-year aid allocation regressions are not influenced by outlier observations, we also estimate these regressions by removing observations that exhibit a DFBETA that is larger than 2 _/_<sup>_√_</sup> _<u>n</u>_ hereby following Belsley, Kuh, and Welsch (1980). The share of policy selective aid, our variable of interest, is then calculated as the sum of aid allocated by policy selective donors divided by the sum of aid allocated by all donors. Because policy-selectivity is measured based on two samples, the full sample and a sample where we remove outliers, we obtain two such measures for our analysis.

Figure 1 depicts the share of policy-selective aid in the global aid budget for the years between 1985 and 2014. The two lines in bold show that share, with the solid line showing the full sample results and the dashed line showing the outlier adjusted results. Figure 1 shows a substantial increase between 1990 and 2001 from zero to 60%, and then declines to just above 20% in 2014. Since 2011, the decline has been fairly steep. However, note that the decline has been not as pronounced when removing outlier-recipient observations from our donor aid allocation regressions using our mechanism explained earlier. The large discrepancy between the two lines, particularly from 2012 onward, is mainly driven by the US. In the full sample analysis, the US is classified as policy-selective only in the years 2010 and 2011, whereas in the analysis where we remove outliers the US is classified as being policy-selective from 2008 onward. Consistent outliers are for example Chile, Iraq and Zimbabwe, which all bias the CPIA estimate downward. Iraq and Zimbabwe because of too much aid and Chile because of too little aid given their CPIA. Note that from 2003 onward, Chile has been identified as an outlier for about 14 donors in average in our sample of 72 donors. We believe that these examples illustrate that our outlier adjusted policy-selectivity measure is probably the better measure for policy-selective aid as it seems hardly plausible to us to see recipient countries such as Chile and Iraq as serious participants in

> 1GDP and population data are obtained from the Penn World Tables (pwt90).

6
Brenton and von Uexkul (2009) find that, in a simple before-after comparison, sectors that receive aid support perform better, but a difference-in-differences regression of country-sector exports on aid flows controlling for heterogeneity through matching does not show significant effects (in particular once outliers are eliminated), suggesting that crude comparisons that fail to control for aid endogeneity pick up reverse causation.

Ferro, Portugal-Perez, and Wilson (2011) exploit the differential intensities of service use across manufacturing sectors (based on input-output tables from the U.S. and Argentina) to evaluate the impact of aid for trade flows directed at five services sectors — transport, communications, energy, banking/financial services, and business services — on the exports of downstream manufacturing sectors in 106 aid-recipient countries over the period 1990–2008. Their identification strategy aims at circumventing reverse causality problems common in the AfT literature; and their results show that aid flows directed at the energy and banking sectors have a significant positive impact on downstream manufacturing exports.

# 3.  AFT:  IMPACT THROUGH INFRASTRUCTURE AND TRADE COSTS

We begin with studies exploring the determinants of trade costs, and then consider the effects through improvements in hard infrastructure, such as roads and ports, and soft infrastructure, such as customs. The key constraints to estimating the effects of such trade-facilitating interventions are the endogeneity of program placement and the absence of well-defined treatment and control groups. Thus, the pre-treatment unobservable characteristics that determine infrastructure placement and affect outcomes are likely to differ between treatment and comparison groups (where groups are, in this case, most likely to be locations). Therefore, most of the studies that we review in this section do not involve rigorous impact evaluation.

6
9, are very similar to the results obtained using their elasticities estimated at a higher level of desegregation.

# **6 Conclusions**

In this paper we estimate the elasticity of substitution of exports to the United States using detailed trade data over the 1990-2003 period. We use a two-stage least squares framework to correctly identify the elasticity parameter of interest. Our elasticity estimates are in line with those of other recent studies.

We use those estimates to assess the extent to which Latin American and Chinese goods compete in the U.S. market by providing forecasts of how alternative policy scenarios may affect exports to the United States. We consider the following scenarios: (i) currency revaluation in China; (ii) elimination of US tariffs on Latin American exports under a free trade agreement among all countries of the Americas; and (iii) the elimination of quotas on apparel and textile exports under the Multi-Fiber Agreement.

We find that a 20-percent appreciation of the renminbi reduces Chinese exports to the United States by a fifth, although since other regions increase sales to that market (0.5 percent for Latin America), US imports decline by only 1.7 percent. With respect to productivity, we find that faster TFP growth in China explains about half of the gap in export growth between that country and Latin America. An FTAA would increase Latin America's exports to the United States by around three percent. The removal of MFA quotas would lead to sharp increase in Chinese sales to the United States (40 percent), but Latin America would see its share of the US market decline by around 2 percent (2.5 percentage points). China's gains would come mainly at the expense of other regions of the world.

18
takes the value 0). A concise summary of the definitions per outcome variable is presented in Table A1 in the Appendix.

To measure intervention exposure, at the end of the endline survey we asked households what radio stations they listened to most during the intervention period, whether they heard the radio clip (after playing a short audio sequence as part of the automated survey), and whether they received a flyer about solar lighting products during the intervention period. We also documented participation in the call-in contest using call-in data records.

# **_D. Study Participants_**

The baseline survey yielded 5,887 short surveys and 1,198 long surveys, for a total of 7,085 (above the 1,000 households required by the power calculations). Baseline descriptive statistics for the study population are displayed in Table 1 (Column 2).

More than three-quarters of households did not have access to grid electricity, with dry cell batteries (73.2 percent) and candles (36.9 percent) being the most common energy sources in our study population. Awareness and understanding of solar energy is quite high. For instance, the majority of households that are aware of pico-PV technology (81 percent) identify ‘sunny’ as the condition most conducive for solar lighting products (second panel). Despite these high levels of awareness, less than 15 percent of households report to have ever owned a solar lamp and approximately 3 percent report to have ever owned a superior-quality solar lamp (top panel of the table). The average age of the study population is 45, and survey respondent’s gender is approximately equally represented (53 percent men, 47 percent women; third panel of the table).

Of the households eligible for the baseline mobile phone survey, one-third completed the entire survey. Considering that in our study non-response is higher for mobile phone surveys than face-to-face surveys (statistically meaningful non-response rates are observed only for short-survey households), this yielded a disproportional drop in baseline information of about 45 percent (from 7,085 observations to 3,148).<sup>26</sup> However, despite the different response rates for both data collection methods, analysis of the follow-up data suggests that attrition rates are evenly distributed across experimental groups. Table A2 displays results from a regression of a dummy variable that indicates sample in- and outflows on the treatment indicators for the main outcome variable for the entire sample and separately for households participating in the face-to-face as well as mobile phone surveys at baseline. Attrition rates are orthogonal to treatment status, which we take as indication

> 26 Mobile phone response rates for our main outcome variables were slightly higher as we placed them at the beginning of the questionnaire anticipating increasing non-response rates with survey duration.

13
Table 6. Explaining relative concentration between high-skill and low-skill and change in relative concentration

||(1)<br>Rel conc|(2)<br>Rel conc|(3)<br>Rel conc|(4)<br>Rel conc|(5)<br>�RC|(6)<br>�RC|
|---|---|---|---|---|---|---|
|Lagged diasp|-0.502<br>(5.87)***|-0.294<br>(3.54)***|-0.514<br>(9.67)***|-0.347<br>(5.73)***|-0.008<br>(16.05)***|-0.008<br>(15.45)***|
|Col. links|-4.635<br>(4.68)***|-7.085<br>(6.41)***|-4.619<br>(10.69)***|-7.008<br>(14.75)***|-0.040<br>(9.93)***|-0.043<br>(10.45)***|
|Language|0.338|0.373|0.321|0.369|-0.004|-0.005|
||(0.84)|(0.78)|(1.09)|(1.02)|(1.58)|(1.75)*|
|Log(dist)|0.266|0.628|0.269|0.615|0.006|0.006|
||(1.24)|(3.73)***|(1.69)*|(3.91)***|(3.78)***|(4.26)***|
|Schengen|-0.193|-0.076|-0.180|-0.068|0.002|0.001|
||(0.50)|(0.16)|(0.36)|(0.11)|(0.49)|(0.26)|
|Pop. at dest||0.956<br>(7.13)***||0.988<br>(9.33)***||0.003<br>(3.50)***|
|Immig pol||-0.014||-0.013||0.000|
|||(1.31)||(0.84)||(1.51)|
|Soc exp||-1.509||-1.573||0.002|
|||(4.38)***||(4.44)***||(0.52)|
|Wage at dest||0.217<br>(7.69)***||0.217<br>(8.57)***||0.001<br>(4.68)***|
|Constant|5.607|-18.397|-3.240|-10.824|-0.037|-0.111|
||(0.29)|(4.70)***|(1.19)|(2.77)***|(1.60)|(3.33)***|
|Dest dum|Yes|No|Yes|No|Yes|No|
|Orig dum|Yes|Yes|Yes|Yes|Yes|Yes|
|Method|OLS|OLS|Heckman|Heckman|Heckman|Heckman|
|Mills|||-0.405|-0.680|-0.873|-1.684|
||||(1.07)|(1.94)**|(2.44)**|(6.12)***|
|Observations|3920|3367|5730|4966|5730|4966|
|R-squared|0.29|0.17|||||

Robust t statistics in parentheses * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% 30