rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6106

# **Abstract**

This paper examines how the applied multi-sector computable general equilibrium (CGE) literature has moved into quantication of the impacts of greater market access for services. This includes discussion of multi-sector linkages to the service sector, as well both measuring barriers to trade and investment (generally with a mix of firm surveys, price comparisons, and econometrics), and how changes in these barriers, however measured, have been implemented in the CGE literature. Three challenges are highlighted. The first is identification of how trade in services takes place and how market access is therefore affected by policy. The second is to find data sufficiently robust for modeling purposes. The third, linked to the data problem, is to quantify the barriers to be examined. Significant progress has been made in modeling foreign direct investment and linking this to productivity, which turns out to be important. The paper also provides an example of modeling productivity linkages to openness and domestic regulation, with an applied CGE model of Italy. This illustrates cross-sector linkages and the integration of economic data and policy measures to define service sector experiments. Priorities for future research include better modeling of market structure, the linkages between sectors and the complementarities between different modes of supplying services.

This paper is a product of the Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at E.M.Christen@gmail.com; Joseph.Francois@gmail.com; or Bhoekman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure 3: INDONESIA <!-- Start of picture text --> (a) (b)<br>flit } Money& Credit0.6<br>0.6<br>6 _.......,,., 1 0.5- -<br>2i 4 ~ ~ ~ ~ ~ ~ ~~-0.4-~ ~ 0.3-<br>0.2- -.<br>4 0 o otab ,eg, e ,,0le<br>-4<br>- tfirst principal component<br>- - weightedaverageooprincipalcomponentsI F (M2/GNDI)t- - - (prvate creditlGNO t<br>(C)(d (d)<br>r t (slyv )<br>20 .............................................................. ........... 0.3<br>-25 -. - i_ T0.25<br>0.2<br>5 ~~~~~~~~~~~~~~~~~~~0.15<br>0.1<br>0.0<br><!-- End of picture text --> _55_
however, and when included has tended to focus on the ability to speak up in public and membership in groups/attendance of meetings. In a broad survey of the effects of a randomized poverty graduation program implemented in six countries, Banerjee et al. (2015) included questions on whether respondents had spoken with village members about village concerns in the last year (Y/N), as well as the number of times respondents approached a village leader in the last year. And in a study of GoBifo, a randomized community-driven development project in Sierra Leone (reviewed further below), Casey, Glennerster and Miguel (2012) surveyed women on their attendance and ability to speak up in meetings, as well as different perceptions they had about community decision-making (the extent to which they felt decisions at meetings were made democratically — i.e., everyone’s say was heard). Humphreys et al. (2012) also surveyed men and women participating in a community-driven reconstruction program in the Democratic Republic of Congo on their attendance and other aspects of participation in the program, as well as on attitudes on women’s rights and responsibilities in the community (surveying whether women should broadly have the same rights as men, the right for women to complain at the community level if they are mistreated by their husbands, and women’s rights to have the same opportunities to occupy administrative or leadership positions in the community).

In the WEAI, respondents were asked whether they “feel comfortable speaking up in public about any issue that is important to you, your family or your community” (not at all comfortable; yes but with difficulty; yes, comfortably, N/A). Recently, this question was revised after cognitive testing in Uganda revealed that the word “issue” translates to problem or challenge in local languages and thus has a negative connotation (Sproule and Kovarik 2014). The revised question reads, “Do you feel comfortable speaking up in public about **_anything_** that is important to you, your family or your community?” Despite this modification, this indicator has not been useful for WEAI analyses and in many places has been sensitive to collect (Malapit 2015).

# _Lab-in-the-Field Experiments_

Recent empirical work in economics to measure individuals’ ability to act on their goals and preferences has included lab-in-the field experiments, where behavioral games are conducted in a controlled environment within a development setting. Within these types of experiments, factors in household decision-making (such as bargaining power) that are difficult to measure or vary empirically are exogenously varied in order to understand how they affect decisions and outcomes.

In a lab-in-the-field experiment from the Philippines, for example, Ashraf (2009) examines financial choices of married couples, by randomly varying whether choices on savings are private or observable to both spouses (both spouses are given the same sets of choices). Given full ability to act on goals, there should be no difference between the groups. However, she finds significant differences between 48
|**Ownership**|0.007|
|---|---|
||(0.012)|
|**Schooling completed**|0.020***|
||(0.002)|
|**Dummy of activity status: employed**|0.133***|
||(0.012)|
|**Self-employed**|-0.123***|
||(0.008)|
|**Public Sector**|0.244***|
||(0.015)|
|**Informal employment**|-0.808***|
||(0.114)|
|**Number of children**|-0.310***|
||(0.029)|
|**Number**<br>**of**<br>**children**<br>**attending**<br>**schooling**|0.072***|
||(0.005)|
|**Number of members in**the**main**<br>**household**|0.199***|
||(0.019)|
|**Number out of labor force**|-0.219***|
||(0.012)|
|**Crime Rate**|0.114***|
||(0.015)|
|**Security**|-0.021***|
||(0.003)|
|**Environmental damages**|-0.036**|
||(0.016)|
|**Financial account**|0.308***|
||(0.013)|
|**Constant**|5.057***|
||(0.169)|
|<br>**Observations**|40,573|
|**Number ofgroups**|226|
|**Standard errors inparentheses**||
|*****p<0.01, **p<0.05, *p<0.1**||

_Source: World Bank estimates based on EHPM (2019). The number of observations is driven by missing variables in the variable indicating the highest grade obtained. For all other variables, we replace missing values with zeros to avoid a selective deletion of these individuals from the model estimation. See Annex 6 for the details._

# 5.1. Vulnerability to Poverty: Recent Estimate and Drivers

**Our estimates show that vulnerability to poverty in El Salvador in 2019 was slightly higher than poverty, with 24 percent of households being vulnerable; vulnerability is mainly driven by riskinduced vulnerability** . The overall vulnerability rate in El Salvador in 2019 was 23.7 percent and, therefore, slightly higher than the poverty rate.<sup>15</sup> The vulnerability rate was higher in rural areas with 41.2 percent and only 13.5 percent in urban areas (see Figure 1). In addition, risk-induced vulnerability played a more significant role in overall vulnerability in El Salvador, mainly driven by urban areas (see Figure 2). The ratio of risk-induced to poverty-induced vulnerability was 1.7 in total, 1.4 in rural areas, and 2.7 in urban areas. These results suggest that vulnerability in the country is mainly driven by climate and environmental shocks or crime rather than by low access to assets and low human capital.

> 15 This is the poverty rate estimated by the model, and not the one observed in the data.

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to which the changes in public good provisioning are added. With respect to the indirect gains in endowments (and so income), separate waiting lists for skilled and unskilled labor are introduced, these being a function of a health status variable, itself a Cobb Douglas function of NHS and private health consumption. The equations are calibrated such that the effective supplies (waiting lists) of skilled and unskilled labor are increasing (decreasing) in the health composites, at a decreasing rate. As best and rather conservative estimates of the indirect health effects, we use elasticity values of 0.06 and 0.09 for skilled and unskilled labor respectively, so that a doubling of their health status (following from a rise in NHS and/or private health care provisioning) will lead to a rise in the effective endowments of skilled and unskilled labor of 6% and 9% respectively.<sup>xxi</sup> Finally, we adjust the model specification to account for the fact that doctors and nurses are highly-skilled and specific to the health sector, and therefore immobile in the short-run. Doctors and nurses account for approximately 85% of skilled labor employed in health care and earn a fixed wage, whereas the remaining 15% of skilled labor in the health sector remains mobile and thus earns the market-clearing wage.

Table 3 and Figure 5 display the changes in household and overall welfare resulting from experiment 1, for each of the remittance and wage scenarios, and experiment 2.

INSERT Table 3 and Figure 5 Here _Experiment 1: Importing Doctors and Nurses at the Current Wage_ In the absence of remittances abroad the specified rise in the NHS budget (of 12.8%), which is targeted towards the immigration of foreign health care-specific skilled workers, yields a rise in real levels of NHS provisioning of approximately the same proportion. The demands for and the domestic production of pharmaceutical products
The framework in this paper builds on an observation in Chor (2010) that his model can be used not just to predict trade intensity, i.e., the level of bilateral trade, but also to predict the probability that two countries engage in trade at all (trade propensity, or in terms of the concepts developed above, tradability). There are strong reasons to believe that the same factors influencing the intensive margin of trade also influence the extensive margin. To take this proposition to the data, Chor (2010) contains estimates from a Probit model with a binary dependent variable—non-zero trade observations are coded as unity, and zero trade observations are coded as zero—to supplement the OLS results for the intensive margin gravity model. The econometric results are again strong for the case of goods trade: the Heckscher-Ohlin variables have the expected signs and are statistically significant, and the same is true of three of the six institutional variables. The preliminary evidence therefore strongly suggests that the Chor (2010) model, as extended by van der Marel (2011) to deal with services, is an effective and theoretically-grounded way of explaining the likelihood that countries engage in bilateral trade, in addition to explaining the amount of that trade.

In this paper, we apply the binary dependent variable approach of Chor (2010) to the area of services trade. We estimate the model using a binary dependent variable rather than a continuous one, such as the value of exports, because the binary choice model has a natural interpretation in terms of tradability: estimated values equate to the likelihood (estimated probability) of observing non-zero trade for a particular exporter-importer-sector triad. Although this approach necessarily entails some loss of information, it does not appear to be great in this case: estimated probabilities from the binary choice model correlate very strongly with fitted values from an OLS regression using export value as the dependent variable (rho = 0.898). The interpretational benefit of using a binary dependent variable model—which is logically associated with the concept of tradability, as discussed above—more than compensates for this small difference in results.

Concretely, we estimate the following model by fixed effects Logit: 10
Table 2. Effects of Guerrilla Territorial Control on Night Light Luminosity, Wealth, and Human Capital

||Night Light Arcsine|Wealth Index|Years of Education|
|---|---|---|---|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.186***|-0.121***|-0.279**|
||(0.0247)|(0.0355)|(0.109)|
|Observations|3,652|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|3.536|-0.0160|6.573|

_Notes:_ The table presents the results of estimating equation 1 for the main outcomes. Column 1 shows the effect of whether a census tract was under guerrilla control on the arcsine of night light luminosity from NOAA. Column 2 uses the standardized score of household wealth as dependent variable in the same estimation. Column 3 shows as dependent variable years of education of the population older than 18 years. The unit of observation in all columns is the census tract. Information from Columns 2 and 3 was obtained from the Population Census of 2007. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with an indicator of whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Figure 4 presents effects on night light luminosity for all years of data from 1992 to 2013 to test whether effects endure over the years. The results suggest that not only did negative effects persist since 1992, but also the magnitudes barely changed over the years. Overall, these results confirm our quantitative findings that guerrilla control produced a lasting negative effect on long-term development outcomes. Section VI explores mechanisms to explain this persistence.

# **V.B Robustness checks**

We used three approaches to test the robustness of our results: (i) the estimation of Conley standard errors and the use of alternative RD and Ordinary Least Squares (OLS) specifications, (ii) a placebo test that uses difference in altitude to define artificial boundaries, and (iii) a restriction in population sorting across boundaries.

# **V.B.1 Alternative specifications**

First, to account for spatial correlation in our data, we estimate Conley standard errors following Conley (1999). As we show in Table D.3, the statistical significance of the estimated effects remains the same. Next, to see whether the results originate in specific regression discontinuity specifica23
Policy Research Working Paper 5556

# **Abstract**

Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the effects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration flows by education levels for the period 1990–2000, the results show the following: First, immigration had zero to small positive long-run effect on the average wages of natives, ranging from zero in Italy to +1.7 percent in Australia. Second, emigration had a mild to significant negative long-run effect ranging from zero for the US to −0.8 percent in the UK. Third, over the period 1990–2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labormarket downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cozden@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
losses in significance as gains. In contrast, using cross-validation instead of Ancova is much more likely to result in a gain of statistical significance than a loss: there are 31 (4%) of the treatment estimates which lose significance at the 5 percent level, and 59 (7.8%) which would gain significance by using cross-validation instead of Ancova.

# **5.2 How many and which covariates should researchers include in the control set?**

The theoretical treatments of PDS Lasso take the vector of potential covariates _x_ , and its dimension _p_ , as given, and provide a method for choosing which subset of controls from this larger set should be included. However, in practice, researchers typically have considerable choice over which variables they consider as potential covariates. In order to reduce further the p-hacking concerns of Simmons et al. (2011), this set should ideally be pre-specified. But then researchers need guidance on how many and which covariates to choose. One approach is the “kitchen sink” approach, of choosing a very large number of baseline covariates, potentially with _p_ even larger than _n_ . This approach seems particularly important in observational studies, where the assumption of exogeneity of treatment conditional on observables may be deemed more plausible if a very large set of observables is considered. Applied researchers then sometimes see this as showing robustness with no downside. For example, in introducing the Lasso for inference, StataCorp gives an example with 104 covariates and writes “we do not worry about overfitting the model, because the control variables that we specify are potential control variables. Lasso will select the relevant ones”.<sup>14</sup> This kitchen sink approach describes a lot of the papers we replicate, given a mean (median) of 405 (182) inputted controls.

However, as equation 9 shows, the penalty parameter _λ_ does increase with _p_ , and so by starting with a very large set of potential controls, it is possible that none get selected, whereas some might get selected if PDS Lasso was run on a smaller subset. Belloni et al. (2014a, p. 40) note that distinguishing true predictive power from spurious associations becomes more difficult as more variables get considered, and so advise selection over a collection of variables that is “not overly extensive” and “where some persuasive economic intuition exists” for their inclusion. However, at the other extreme, if the number of controls being considered is not very large at all ( _p_ small relative to _n_ ), then there may be no advantage at all of running PDS Lasso. Belloni et al. (2014b) note that post-double-selection is first-order equivalent to a regression that just includes the full set of controls when _p_ is small relative to _n_ .

We illustrate this via simulations which vary the sample size, and number of inputted controls. Figure

> 6 shows the mean and median number of controls selected by PDS Lasso when we include 5, 40, and 1,600

> 14 `https://www.stata.com/features/overview/lasso-inferential-methods` Accessed February 21, 2023.

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