rafmacalaba/datause-encoder-data huggingface dataset:
to quantify the potential trade cost changes. Additionally, we are also able to predict total export changes in services in the border scenario, while without tariff data we can not obtain total export changes based on tariffs for services.

The rest of the paper is organized as follows. Section 2 presents our estimation and identification methods. Section 3 motivates the focus on the integration of the CEFTA countries in the EU (in Subsection 3.1), and describes the data and sources that we employ to perform the empirical analysis (in Subsection 3.2). Section 4 presents our empirical findings. Subsection 4.1 offers an analysis of our partial equilibrium estimates, while Subsection 4.2 translates the partial equilibrium estimates into GE effects and discusses our findings. Section 5 concludes. The Supplementary Appendix includes additional empirical results and robustness estimates.

# **2 Estimating “Deep”/“Hard” Trade Cost Changes**

This section presents our methods to estimate ex-ante comprehensive “deep”/“hard” trade cost changes in response to a hypothetical policy change. For expositional simplicity, clarity, and to facilitate the interpretation of our results in the following sections, we will focus on a specific application, i.e., the integration of CEFTA and EU.<sup>6</sup> Our departing point is the following econometric gravity model, which we adapt to accommodate the specific goals of our study:<sup>7</sup> Here, as defined earlier, _Xij,t_ denotes nominal trade flows from source/exporter _i_ to destination/importer _j_ at time _t_ . An important feature of the dependent variable is that, consistent with all of the underlying theoretical models that deliver the structural gravity equation, _Xij,t_ in-

> 6The CEFTA consists of seven countries: Albania, Bosnia and Herzegovina, Kosovo, Moldova, Montenegro, North Macedonia and Serbia. We offer further details on the history and relationship between CEFTA and the EU in Section 3.1.

> 7As famously demonstrated by Arkolakis, Costinot and Rodriguez-Clare (2012), this empirical equation is representative of a very wide class of theoretical trade models. We refer the reader to Anderson (2011), Costinot and Rodriguez-Clare (2014), Yotov et al. (2016), and Baier, Kerr and Yotov (2018) for reviews of the theoretical foundations of the gravity model.

6
financial resources were not constraints to implementation (only 6%). In contrast, they thought that the firm owner mistakenly did not consider the practices to be profitable in 58% of cases. This is consistent with the findings of Bloom et al. (2013) that the main reasons for practices not being implemented were lack of knowledge about the practices, and firm owners not thinking the practices were worth implementing.

# **5.2 Robustness Checks of the Management Improvement**

We consider the robustness of the improvement in management practices to different weighting schemes, to sample attrition, and to alternative measurement tools.

_Robustness to weights:_ Our measures of management practices are averages of the different practices. The Anexo K overall index is an average of the 35 sub-indices, and ranges with 20 (indicating scores of 1 for every individual practice) to 100 (indicating scores of 5 for every individual practice). With any aggregate index, there is always a question as to the appropriate choice of weights, and of how sensitive the results are to alternative weighting schemes.

Table 3 examines robustness to different choices of how to aggregate the 141 practices. Column 1 shows our aggregate index from Table 2. Columns 2 through 5 then consider four alternative weighting schemes. Column 2 uses the first principal component of the 141 practices; Columns 3 and 4 use lasso regression to identify the sub-set of practices which best predicts baseline log employment and labor productivity respectively, and then post-lasso regression to form the weights. This chooses 19 practices to weight according to their predictive power for employment, and 14 to weight for their predictive power for labor productivity. Finally, column 5 uses the subset of firms for which we also have baseline data from the World Management Survey, and uses lasso to choose weights that best predict the baseline WMS score, which selects only 6 practices.<sup>13</sup> The coefficients cannot be directly compared across columns in terms of magnitudes, but can be considered relative to the control group standard deviation. The estimated treatment effects are 0.8 to 0.9 standard deviations (s.d.) when using our aggregate index, 0.9 to 1.0 s.d. when using principal components, 0.6 s.d. when weighting to predict employment, 0.8 s.d. when weighting to predict labor productivity, and 0.7 to 1.1 s.d. when weighting to predict the WMS score. Thus,

> 13 The smaller number of practices chosen is likely because of the much smaller sample for which the WMS is available.

17
Table 1: International Poverty Rates: 2004-05 to 2014-15

||**2004-05**|**2009-10**|**2011-12**|**2014-15**|
|---|---|---|---|---|
|National|||||
|Poverty rate|38.9|31.7|21.6|14.6|
|Standard Error|0.4|0.4|0.4|0.8|
|Urban|||||
|Poverty rate|25.4|19.8|13.4|10.0|
|Standard Error|0.6|0.5|0.4|0.8|
|Rural|||||
|Poverty rate|43.4|36.1|24.8|16.8|
|Standard Error|0.4|0.5|0.5|0.8|

Sources: India National Sample Survey Office (NSSO) Surveys and staff estimates.

Figure 1: Historical Poverty Rates: 2004-05 to 2014-15 <!-- Start of picture text --> 37.7<br>30.9<br>21.2<br>14.6<br>2004.5 2009.5 2014.5<br>Year<br>National Urban<br>Rural Year of Survey<br>Sources: India National Sample Survey (NSSO) Surveys.<br>90<br>80<br>70<br>60<br>50<br>Percent 40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Table 2: Average Annual Change in Poverty Rates

||**2004-05 to **|**2011-12**|**2011-12 to 2014-15**|
|---|---|---|---|
|**National**||-2.4|-2.2|
|**Urban**||-1.7|-1.1|
|**Rural**||-2.7|-2.7|

Changes in percentage points. Source: India National Sample Survey Office (NSSO) Surveys.

22
________________________________________________________________________________________________________________________________

|Country|Year|Variable||||||In|dustry||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|____________|______|__________________|DA<br>_______|DB<br>_______|DC<br>______|DD<br>______|DE<br>______|DF<br>_______|DG<br>______|DH<br>_______|DI<br>______|DJ<br>_______|DK<br>______|DL<br>______|DM<br>_______|DN<br>________|
|Colombia|1981|Participation rate|9.9|13.3|20.5|12.2|17.1|5.3|33.2|18.8|15.9|19.1|30.6|28.3|19.7|17.9|
|||Export intensity 17.4|15.8|18.8|12.2|9.3|39.5|5.7|7.1|15.8|9.0|13.9|7.8|15.0|16.3||
|||Number of firms 739|882|171|74|257|19|262|218|245|382|173|173|132|173||
||1991|Participation rate 13.2|26.1|54.4|18.1|24.4|26.1|38.5|35.6|21.3|27.6|33.5|34.5|22.3|24.8||
|||Export intensity|25.5|20.8|33.7|13.9|9.4|14.9|12.3|8.8|18.8|25.0|18.2|11.2|5.0|23.3|
|||Number of firms 828|923|204|83|287|23|322|270|249|391|200|194|148|226||
|Denmark|1999|Participation rate 60.4|83.1|100.0|72.8|69.4|66.7|92.6|89.9|71.7|71.6|86.1|84.5|87.6|84.8||
|||Export intensity|18.1|35.0|38.1|23.7|11.8|21.2|41.6|31.1|20.1|20.3|41.0|44.0|48.7|37.9|
|||Number of firms 578|242|19|180|399|3|108|198|145|571|567|368|129|381||
||2002|Participation rate|56.5|91.9|100.0|69.6|61.4|100.0|90.8|92.4|61.1|72.4|92.4|87.3|91.8|85.0|
|||Export intensity|20.3|41.8|52.0|16.0|10.0|17.5|46.7|35.7|15.0|20.1|46.5|46.6|54.2|38.6|
|||Number of firm|476|124|6|148|345|4|98|198|126|588|525|324|97|267|
|France|1990|Participation rate NA|66.6|78.7|58.4|61.1|80.0|87.6|78.3|54.8|63.1|79.4|73.1|76.4|75.7||
|||Export intensity NA|22.2|18.5|14.8|8.9|11.7|23.5|14.0|17.5|13.9|22.1|20.7|21.1|16.2||
|||Number of firms NA|3,240|587|765|2,565|50|1,094|1,270|928|4,934|2,338|2,287|842|1,320||
||2004|Participation rate NA|77.5|79.3|54.9|68.6|88.0|90.5|81.8|58.7|67.9|82.7|78.6|81.9|81.2||
|||<br>Export intensity NA|27.2|24.6|18.6|11.8|19.0|34.8|20.5|22.9|18.7|30.3|31.7|28.1|19.5||
|||<br>Number of firms NA|1,646|276|607|2,016|25|1,092|1,482|767|4,498|2,038|1,935|803|939||
|West Germany|1995|Participation rate|33.7|74.4|89.1|48.6|56.4|74.6|89.3|80.0|46.7|64.5|82.6|70.4|76.9|74.7|
|||Export intensity 14.6|20.2|19.9|13.2|12.8|21.1|29.9|18.9|18.9|19.3|32.6|28.1|26.9|18.7||
|||<br>Number of firms 3,717|1,988|294|1,319|3,354|67|1,322|2,424|2,030|5,898|5,383|3,897|1,050|1,939||
||2004|Participation rate 32.0|85.3|91.7|58.0|60.9|76.3|92.0|82.6|58.2|68.8|83.7|72.0|81.7|79.4||
|||Export intensity 17.9|30.1|33.0|22.8|15.9|25.5|41.5|26.4|25.4|23.8|39.1|35.8|32.3|26.6||
|||<br>Number of firms 4,117|1,134|168|892|3,123|59|1,417|2,465|1,549|6,140|5,645|4,347|1,233|1,379||

63
household head is either Hindu or Muslim, which covers between 86% and 92% of surveyed households in each data set.

The Indian Human Development Survey (IHDS) collected data from 41,500 households across 384 districts in 33 states and union territories from 2004 to 2005 (Desai, Vanneman and NCAER, 2005). These data cover three sanitation practices: latrine ownership, handwashing, and observed fecal material.

The third wave of the District Level Household and Facility Survey (DLHS-3) collected data from 720,000 households across 601 districts in 34 states and union territories from 2007 to 2008 (IIPS, 2010). DLHS-3 includes data on whether the household reports owning a latrine but does not report data on other sanitation practices.

The third wave of the National Family Health Survey (NFHS-3) collected data from 109,000 households in 29 states and union territories from 2005 to 2006 (IIPS, 2007). NFHS-3 includes data on whether the household reports typically using a latrine but does not report data on other sanitation practices.

The analysis explores the cross-sectional relationship between religion and sanitation practices, and so it uses three data sets from a similar time period that each provide similar but complementary data on sanitation practices (latrine ownership, latrine use, observed fecal material, and handwashing).<sup>7</sup> The data sets each include information about households, which are used to define a set of household characteristics: caste of household head, occupation of household head, education of household head, household size, age of survey respondent, number of female members of the household, whether the household has piped water, and proxies for household wealth (an indicator variable for whether the household owns their house; indicator variables for the presence of electricity, a cell phone, television, bicycle, car, and motorcycle; and distance to

> respondent about the type of latrine that her family “has,” while the NFHS-3 asks the respondent about the type of latrine that her household “uses.” Note that Geruso and Spears (2018) define “open defecation,” using NFHS data, as when the household reports not using a latrine.

> 7As a robustness check, the analysis was run on later waves of the data sets (IHDS-II in 2011-12, DLHS-4 in 2012-13, and NFHS-4 in 2015-16) and similar results are found.

5
shocks saw improvements in child survival in Colombia.<sup>1</sup> Finally, there are a handful of crosscountry studies of the relationship between income and infant mortality in developing countries that also find mixed results (Pritchett and Summers 1996; Filmer and Pritchett 1999; Jamison et al. 2004; Deaton 2006).

Our paper extends the existing literature in a number of important ways. The sample of countries, fifty-nine, is much larger than that from the country-specific studies. This allows us to estimate the relationship between income and health in a variety of settings across recent decades. In addition, and unlike the cross-country studies discussed above, we use unit data rather than working with bi-decennial country averages of infant mortality. As a result, we focus on higher-frequency changes in infant-mortality than previous research. Furthermore, because the analysis is based on unit data, we can control for the changing composition of women giving birth, and assess how income shocks interact with a variety of characteristics of mothers and children, such as mother’s education and the gender of the child. Finally, we include data on a number of potential omitted variables, including rainfall shocks, conflict, and the quality of institutions. None of these controls affects our results in a substantive way.

The next section, Section 2, describes the data for our sample of countries and provides details on the construction of the variables used in the analysis, in particular our measure of infant mortality. In Section 3, we discuss the basic estimation approach and present results. This section also includes a discussion of changes in the composition of women giving birth; omitted variables; the timing of GDP shocks; and heterogeneity of the effects. Section 4 concludes.

> 1 Some of the country-specific studies rely on vital statistics data to construct infant mortality. In many developing countries, these data suffer from serious under-reporting—the degree of which may itself be correlated with aggregate economic conditions.

4
4

# II. The Destination and Composition of African Exports

Empirical analyses show major short, or even medium-term changes, in the origin or destinations of a country's trade do not frequently occur (see UNCTAD, 1992 and other years). Finance, commercial ties, transport and other logistical problems such as North-South liner conference routes are preventive factors, as are distance to alternative markets, or cultural elements like language differences (Safadi, Primo-Braga and Yeats, 1994). Established shipping routes tie some developing countries to a relatively few OECD markets, and efforts to trade with others may involve costly transshipment through way-ports. Therefore, one should focus on the effects of trade barriers in markets that are presently the major destinations of African exports since they will, in all likelihood, be of major importance within the near future.

Table 1 shows the value and share of individual African countries' exports to all developed and developing countries, as well as to selected regional destinations, while Figure 1 shows trade weighted totals for all sub-Saharan Africa. The latter also provides similar statistics for all developing countries as a group in order to show the extent that Africa's direction of trade differs from that of other developing countries. This is an important consideration since empirical research shows that countries which are overly dependent on a relatively few markets for imports and exports may experience adverse trade effects.<sup>4</sup> Some caution is warranted regarding these comparisons, however, since it is generally accepted that some intra-African trade is unrecorded.

Table I and Figure 1 show sub-Saharan Africa's reliance on developed countries is considerably greater than average for developing countries with their dependance on OECD Europe being especially important. About 4Numerous "structure-performance" studies of industral countres' domestic markets show a common pattern. When aggressive inter-firm competition is absent consumers pay higher (monopoly) prices, while other firm performance measures are poorer than in more competitive markets (See Bain 1951, Bell and Murphy 1969, or Mann 1966 for Ulustrative examples). Economist frequently employ measures like the share of the three largest firms in a market as an (inverse) proxy for the level of aggressive competition. Subsequent empirical studies show these conclusions also hold for international markets. That is, a country is likely to receive a lower price for exports, and pay a higher price for imports, if aggressive competition is absent from its foreign trade sector (See Avramovitc 1978, Edward 1972, Helleiner 1978, or Yeats 1981).
objective indicator should load onto a latent variable measuring the overall good governance (absence of corruption) of a given country. The results are especially strong in the OECD sub-set with the objective indicator having a bigger, absolute loading (0.61 versus 0.30) onto the only significant latent variable.

_Table 9 Factor Analysis of the Objective and Subjective Indicators of Corruption_

||**Facto**<br>**(**|**r Loading**<br>**for 1**<sup>**st**</sup>**Lat**|**for each m**<br>**ent Variab**|**easure**<br>**le)**|**Eigen**<br>**value**|**Other**<br>**Significant**<br>**Latent**<br>**Variables**|
|---|---|---|---|---|---|---|
|**Variable**|CPI|CC|GE|Tickets|||
|**Pooled**<br>**(n=131)**|0.99|0.99|0.94|-0.30|2.95|No|
|**OECD**<br>**(n=19)**|0.99|0.98|0.95|-0.61|3.25|No|

_Source: Author’s calculations using scores from early 2000s._

# **_IV. Conclusion_**

Can we measure corruption effectively? In order to test any hypothesis regarding the determinants of corruption and thereby monitor progress on how to reduce it, it is essential to ensure that an effective measure of the phenomenon can be identified and utilized. A good candidate indicator for this agenda must be substantively focused on the overall (vs. sectoral) level of corruption and be reliably measured over time. Using an extensive literature review, correlations, and factor analysis, this paper has shown that while both objective and subjective indicators of corruption meet these criteria, the most appropriate indicators are the composite subjective indicators: the CPI and the CC. This set of findings shows that it is possible to use robust subjective indicators of rent-extraction to measure underlying levels of corruption – an outcome that will be invaluable for measuring and monitoring progress against the Sustainable Development Goals.

27
shape the pace and sources of growth in the medium and long term. Historical evidence from European and Asian growth (Japan and also the so-called East Asian Tigers) in 1950-1975 shows that the sources of growth can vary enormously: while total factor productivity (TFP) dominated the growth in Western Europe and Japan, increased factor inputs seem to be the predominant factor in East Asia.

Four factors usually explain diverging patterns of growth across nations:<sup>8</sup> (i) differences in initial income levels: Western countries had a much higher per capita GDP relative to the United States in 1950 than did the East Asian countries in 1960; (ii) demographic trends: East Asia experienced a rapid rise in the labor force, which was not the case in Western Europe where the demographic transition was completed prior to the postwar golden age; (iii) differences in skill: the quality of the labor force (measured by average years of education) was much higher in Western Europe than in East Asia; and (iv) differences in market development: Western Europe had already established a well-functioning market system in 1950 whereas Asian countries relied on large integrated enterprises, with the state playing a major role in economic policy.

# **3.12 Lessons for Latvia**

From this historical experience, it appears that Latvia (like many transition economies) resembles Western Europe at the outset of its golden age in all the four policy areas identified above: • Latvia’s income level relative to the United States (at current international prices) was about 25 percent in 2002, below the range of Western European countries in 1950 (from 32 in Italy to 59 percent in the United Kingdom). This initial income level means that the scope for growth from increased capital is likely to be as important as it was for East Asian countries.

• Latvia’s labor force is projected to decrease over the next decade. This demographic trend implies that labor may not be available to make a significant contribution to growth as it did in East Asia.

• The average number of years of education in Latvia is relatively high, close to the levels prevailing at the outset of Western Europe’s growth spurt (12 years).<sup>9</sup> This means that the labor force may be better able to assimilate and use new technologies and make productivity gains than was the case for East Asian labor at the outset of the convergence process.

• Finally, Latvia has already achieved significant progress in establishing many of the types of market institutions that were essential to the economic development of

> 8  See Doyle et al. (2001).

> 9  Source: 2001 World development Indicators CD-ROM, World Bank. According to Doyle et al. (2001), average years of schooling in Western Europe in 1950 varied between 4.9 in Italy to 9.4 in the United Kingdom. It should be noted that the quality and nature of schooling in transition countries during the socialist period, even for highly educated workers, has been subject to debate.

12
_Box 1. Living with floods – frequent exposure from a household perspective_ Asha lives in Msisiri with her husband and kids. She runs a food catering business from her house, where she has lived for 15 years. Every year during the rainy season, her house is flooded. The frequent floods are disrupting her day-to-day life.

The water tends to stay in the house for months. To avoid damage to their possessions, she and her husband put all their furniture on concrete blocks. During the rainy season, Asha cannot maintain her catering business and the family must rely on the income of her husband, who is a driver.

Asha sends her children away to stay with relatives out of town when it gets really bad. Several members of her community do the same. “The kids cannot swim, and the water is dirty. It’s dangerous for them,” she explains.

The cost implications make it difficult to plan and to invest in their house and business **_._** “It’s discouraging,” she says. “Maybe you improve something and next year it’s damaged and you have to re-do it.” When they first moved there, it was not as bad, but since then, more and more families have moved in, and drainage systems have become blocked and blocked the drainage. During the rainy season, there is no garbage collection in the area, so waste floats everywhere.

Some of the more well-off families have moved from the area. If Asha and her family could afford it, they would also move.

_Interview in Dar es Salaam on March 17, 2018_

# _Affected by the 2018 flood_

Estimates suggest that the 2018 flood affected far more people than available information reports. According to the analysis, it is estimated that between 500,000 and 950,000 people (110,000 and 230,000 households) had water in the house at the time of the flood. The lower bound estimate is calculated using the information from the households that responded to the follow-up interview applying the flood risk weights. The underlying assumption for the lower bound is that households that were not reached in the follow-up were not affected by the flood. On the contrary, the upper bound scenario assumes that the households that were not reached in the follow-up interview were affected in the same way as the respondents.<sup>19</sup> When asset losses are included, estimates increase to between 550,000 and 1,040,000 people

> 19 As robustness checks, predictions of the likelihood of being affected were also derived from regression analysis, using the information on variables associated with being affected in 2018. Estimated coefficients were then applied to households that were not reached. Results were very similar to the ones obtained with the less sophisticated approach described above.

14