rafmacalaba/datause-encoder-data huggingface dataset:
started out higher than the former (green dashed line) and the distance between the two lines was largest around the crisis year, which indicates that poorer households suffered relatively more income loss during the crisis. However, poorer households have caught up with higher-income households from after around 2005, when the two lines started converging. These results are consistent with the findings in existing studies that indicate a decreasing poverty rate and increasing income growth for the bottom 40 percent of the income distribution (see, e.g., World Bank (2016)).

Can the trends differ between urban and rural areas? We further disaggregated the national trends in Figure 2 by urban and rural areas and show their combined results in Figure 1.2.<sup>13</sup> Rural areas exhibit lower income levels but somewhat higher inequality—both overall and for poorer households—than urban areas (Figure 1.2, Panels A and B). Indeed, all the lines representing the Gini coefficient and the 90<sup>th</sup> /50<sup>th</sup> and 50<sup>th</sup> /10<sup>th</sup> ratios for rural areas lie above those of urban areas. But similar to the national trends, inequality in urban and rural areas appears to converge over time (Figure 1.2, Panels C and D). Since the trends are qualitatively similar between urban and rural areas, we subsequently show estimates at the national level. We return to more discussion with the multiple regression analysis that controls for location and other individual characteristics in Section IV.4.

# **IV.2. Short-Term and Medium-Term Mobility**

Figure 3 plots the mobility index M<sup>�</sup> , using both the unconditional version (M<sup>�</sup> ) and the conditional version (M<sup>�</sup> ), for each of the four shorter periods: 1994-98, 1998-2004, 2004-09, and

> 13 The population was considered as “urban” if located in cities and small towns known as "PGTs" and “rural” if located in villages. The definition was based on stratification in RLMS (see more details in <u>http://www.cpc.unc.edu/projects/rlms-hse/project/sampling).</u>

16
# **TABLE OF CONTENTS**

|I.|Introduction||1|
|---|---|---|---|
|II.|Motivations|of Capital Flows|4|
|II1.|Data||6|
|IV.|Methodolog|y and Result|16|
|V.|Conclusion||26|
|Refer|ence||27|
|Anne|x I||29|
|Figur|es<br>|||
||Figure 1.<br>|Bond Flows to Latin America<br>|8<br>|
||Figure 2.|Bond Flows to Asia<br>|8<br>|
||Figure 3.<br>|Equitv Flows to Latin America<br>|9<br>|
||Figure 4.<br>|Equity Flows To Asia<br>|9|
||Figure 5.|Regional Monthly Averages for Credit Ratings:<br>||
|||Latin America and Asia<br>|11|
||Figure 6.|Monthly Average for Secondary Market Prices:<br>||
|||Latin America: 1988.01-1992.09<br>|11<br>|
||Figure 7.<br>|Black Market Premiums: Latin America<br>|12<br>|
||Figure 8.|Black Market Premiums: Asia|12<br>|
||Figure 9.|US Short-Run Interest Rate<br>|13<br>|
||Figure 10.|US Industrial Production Index in Deviation Fro,n Time<sup>Trend</sup>|13|
|Table|s<br>|||
||Table 1.|Portfolio and other Long-Term Flows to all Developing<sup>Countries</sup><br>|2<br>|
||Table 2.<br>|Some Statistics for Net Equity Flows and Gross Bonds<sup>Flows</sup><br>|10<br>|
||Table 3.|Panel Data Correlations: 1988.01-1992.09<br>|14<br>|
||Table 4.|Panel Data Correlations Among Explanatory Variables|15|
||Table 5.|Panel Data Estimates: Equity Flows, 1988.01-1992.09<br>||
|||Latin America (9 countries) - Asia (9 countries)<br>|18|
||Table 6.|Panel Data Estimates: Bond Flows, 1988.01-1992.09<br>||
|||Latin America (9 countries) - Asia (9 countries)|19|
||Table 7.|Panel Date Estimates: Equity Flows, 1988.01-1992.09||
|||Latin America (6 countries) - Asia (7 countries)<br>|20|
||Table 8.|Panel Data Estimates: Bond Flows, 1988.01-1992.09||
|||La-in America (6 countries) - Asia (7 countries)<br>|21<br>|
||Table 9.|Standardized Slope Coefficients|23|
||Table 10.|<br>Elasticities|5|
shipping days, three-month for 30 to 59 shipping days, and four-month for more than 60 shipping days.

- (iii) For landlocked exporter and/or importer countries, we add the number of days needed to transport goods by road from (or to) the closest port to (or from) the capital using the shortest road distance from Google Maps as in Akbar et al. (2018) and Zarate (2021).

# **Product vulnerability measures**

We rely on several data sources to construct proxies for production resilience which we map to our HS4 product categories. First, we use data from US NBER-CES Manufacturing Industry Database on the unskilled labor intensity of production: the ratio of unskilled (blue-collar) employment to capital for each 6-digit 1997 NAICS industry in 2011 (the most recent year available).

Second, we rely on OECD’s harmonized input-output tables for 2011 to compute sectoral imported input contents following Hummels et al. (2001).<sup>3</sup> We compute the reliance of production on imported inputs as the ratio of imports over the sum of output plus imports minus exports by ISIC 2-digit industry and exporting country. Using a measure with country variation (from the input-output table) accounts for different countries’ sources of different inputs.

Third, we use export data from UN COMTRADE at the exporting country-HS4-year level combined with the aforementioned OECD harmonized input-output tables to construct measures of (i) the reliance on China as a supplier of inputs for production of each sector and (ii) the

> 3 OECD input-output tables are available for 63 countries (all 36 OECD countries and a variety of other countries including several developing countries). Since our monthly trade data cover exports for an additional 158 countries with no OECD input-output tables, we assign to each of those countries the input-output table from the country that is most similar in terms of three dimensions: level of development captured by GDP per capita, size captured by population and economic structure captured by share of manufacturing value added, all from World Development Indicators. We assign to countries with no input-output table the table of the country with the lowest aggregate difference across the three dimensions, which we aggregate using inverse-variance weighting.

11
By the end of 1997, HDB had expanded its service mix to include orthopedic surgery and plastic surgery, chemotherapy and physiotherapy and had developed a fully equipped up-to-date intensive care unit. These services, in addition to abdominal and pelvic ultrasound, gastroscopic, and bronchofibroscopic tests, changed the service and fee schedule used as an example in Table 5.1 into a longer and more sophisticated list. By this time, HDB had carried out at least two quality control initiatives and two customer satisfaction surveys which yielded satisfactory results (Minutes 3.X.94; 28.X.96). It had received visits from at least three public hospitals that had come to learn about the ALSM's accomplishments at HDB.

# _Explaining HDB 's decision rights complementarities in service delivery_

All ALSM members concurred on the distribution and evolution of decision rights in this area. Decision rights over the range of services and quality control (medical and other services) were held by the director for the majority of the period. For example, the director would propose expansions in service mix to the committee, but his informational advantage gave him significant influence in convincing the committee of what was feasible and reasonable at the hospital at the time. There is some evidence that during the first two years of the experience the ALSM took more initiative than during the latter period in promoting new services and in ensuring quality control through the administration of surveys, especially in medical areas. As did others, these decision rights were diluted over time and stabilized into a less active role for the ALSM. There is some evidence that near the end, the ALSM had too little intervention in service delivery and quality control in the hospital, as no surveys were carried out and efforts to start a pediatric department came to naught. Of all four areas discussed in this section, complementarities were fewest here.

# **6. Taking stock of HDB's complementarities**

This section sums up the most salient complementarities developed at HDB and compares them with the informal system as well as with the system under Law #544. Neither this section, nor Table 6 are meant to be comprehensive. Instead they illustrate key examples suggestive of the type of analysis that can be carried out using the decision rights approach. Nor do Sections 6 and 7 seek to present HDB as a model or "ideal type". Instead, they underscore that HDB's institutional design is worth understanding because it designed by HDB locally, (not by the MOH) in reaction to market and systemic forces, somewhat like a firm in a market adopts the structure that maximizes its chances of success.

32
In total 334 interviews took place, and 198 interns were accepted by firms into jobs.<sup>4</sup> Some of these were accepted with certainty due to insufficient numbers to also allow a control group, and so were not part of the experimental sample. In total 158 of the 430 individuals in the experimental treatment group received an internship (36.7 percent). The take-up rate did not differ significantly by gender (34.8% for females, 37.4% for males, p-value for test of equality is 0.625).

There were three main reasons why some individuals assigned to the internship treatment group did not receive an internship. First, by design there were more individuals assigned to treatment than there were internship jobs available, in order that firms could be offered several candidates to interview and find a good fit. Second, some of the youth assigned to the internship program dropped out either because they found a job without the internship, because they couldn’t be reached, or because of other circumstances. Third, some of the firms that had been selected to receive matching grants for interns dropped out of the program. This resulted because several months had passed since they had applied, and either they had recruited directly from the market during this time, or had decided as a result of the changing economic situation that they could no longer afford to offer this position. When this happened SMEPS attempted to replace the firm with another firm requesting graduates from the same specialization in the same city, but in some cases this was not possible, and as a result some of individuals assigned to treatment would be in specializations where there was no longer any demand for them.

# **4.3 Data Collection**

We use three main sources of data. The first consists of baseline information available from the applications. The second consists of administrative data from the program, which provides key information on initial take-up of the internship, and internship completion rates. The third and main source of data comes from a follow-up survey. We had originally planned surveys at 6 months and 12 months after the end of the internship. However, with the worsening situation in Yemen, we quickly launched a follow-up survey in March 2015. This timing took place 3-6 months after the end of the internships, and right before civil conflict broke out. The survey was conducted as a phone survey given that safety issues, gas shortages, and time constraints prohibited an inperson survey. The survey was conducted by Apex consulting, and had a response rate of 78.7%

> 4 Table 2 shows data on 197 individuals, which is what our data indicate. Administrative records show 198 in total were placed.

9
-10-

**TABLE 1.4**

**Thi** Size of Our Sample in **Terms** of Value Added (US Census Concept) by Manufacturing Branch. (1975), (national currencies and percentages)

||Braz|il||Nex|ico.||US|A||
|---|---|---|---|---|---|---|---|---|---|
|<br>|Universe<br>(mill.<br>cru|Sample<br>zeiros)|%|Universe<br>(mill.|Sample<br>pesos)|**_%_**|Universe<br>(mill|Sample<br>. USS)|%|
|Food Products|34,681|6,564|18.9|<br>28,964|5.808|20.1|<br>39,985|2,522|<br>6.3|
|Beverages|5,494|2,022|36.8|<br>1 2 <sup>,</sup>9 9 4<sup>a</sup>|6,875a|52.9|8.110|2,130|26.3|
|Tobacco|3,212|3,212|100.0|<br>1,817a|<br>1,8178|100.0|<br>3,722|3,722|100.0|
|Textiles & Wearing<br>Apparel|<br>**b**<br>27,247|10,247|37.6|<br>19,678|8,365|42.5|<br>26,793|6,217|23.2|
|Footwear & Leather<br>Products|<br>4<sup>,</sup>79 8<sup>b</sup>|<br>4,746|98.9|<br>3,330|3,330|100.0|<br>3,187|2,942|92.3|
|Wood and Paper<br>Products|34,086|4,648|13.6|<br>17,805|6,279|35.3|<br>59,231|7,626|12.9|
|Chemicals|48,552|23,163|47.7|<br>4<sup>0,</sup>3 34 <sup>'C</sup>|<sup>15,125'</sup>|<sup>c 37.5</sup>|<br>55,476|17,184|31.0|
|Rubber & Plastic<br>Goods|12,028|3,159|26.3|<br>8,276|2,954|35.7|<br>13,599|3,463|**25.5**|
|Stone, Clay&<br>Glass Products|19,161|7,516|39.2|<br>11,930|4,317|36.2|<br>14,849|2,049|13.8|
|Metal Products|38,782|18,080|46.6|<br>31,280|13,535|43.3|<br>64,570|15,783|24.4|
|Electrical<br>Machinery|17,655|3,637|20.6|<br>13,430|2,566|19.1|<br>34,845|1,543|<br>4.4|
|Machinery & Trans-<br>port Equipment|<br>51,192|14,411|28.1|<br>26,743|14,542|54.4|<br>96,381|21,466|22.3|
|Other|10,005|----|----|3,241|----|----|<br>21,738|**----**|**----**|
|Total<br>|306,893|101,404|33.0|219,820|85,511|38.9|442,485|86,645|19.6|

- a) indirect taxes and subsidies are deducted (see table<sup>2.3).</sup>

- b) the footwear industry (3,188 million cruzeiros) was reallocated<sup>from wearing apparel</sup> to footwear and leather.

- c) Includes 7,148.0 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the Resumen General but taken from Sistema de Cuentas Nacionales de Mexico, 1981.

- Source: Figures derived from the production censuses, i.e. Censo Industrial for<sup>Brazil,</sup> Resumen General for Mexico and Annual Survey of Manufactures for the USA; see for the universe table 2.1 (Brazil), table 2.3 (Mexico) and table 2.5 (USA); see for the sample figures tables 3.11 and 3.12
Figure 2 presents in greater detail comparative results regarding intangible spending for two countries, Brazil and the US (with the US data here based on the original CHS 2005 figures). Not surprisingly, as a share of GDP, US firms spend considerably more than Brazilian ones in nearly all types of intangible assets. Overall, at about 13 percent of GDP, US intangible expenditures are nearly thrice those in Brazil. Moreover, since the US estimates correspond to an earlier period (1998-2000), the current gap between the US and Brazil is likely to be even larger.

**Figure 2: Annual average spending by category of intangibles (% of GDP)**

<!-- Start of picture text --> 3.13%<br>2.53%<br>2.17%<br>1.65% 1.60%<br>1.29% 1.25%<br>0.90% 0.81% 0.86%<br>0.62%<br>0.33% 0.31%<br>0.11%<br><!-- End of picture text --> Looking at major categories of intangibles, the US lead over Brazil is largest in two areas: scientific R&D, and economic competencies. The CHS methodology suggests that as a share of GDP, the US spends about 10 times as much as Brazil on organizational capital, three times as much on brand equity, and about 4 times as much on scientific R&D. The cost of employee time spent in training in Brazilian manufacturing is 0.1 percent of GDP; even if one quadruples this to account for missing data for services (in proportion to the ratio of value added in manufacturing and services), it would be less than half that spent in the US (1 percent).

As a result, the composition of intangible expenditures differs markedly across Brazil and more advanced economies like the US and the UK. Brand equity, organizational capital and firm specific human capital comprise more than half of all intangible expenditures in the US and the UK, in contrast to about 35 18
implemented as one-time shocks and do not take into account potential growth effects through changes in capital accumulation rates or variations in productivity.

Our main simulation envisions the full removal of import tariffs and export taxes/subsidies on agriculture and food products around the globe. The liberalization schedule includes 17 out of 57 commodities in GTAP, and the initial level of protection by exporter is shown in Table 3.<sup>14</sup> We also consider an alternative scenario where all border distortions are removed and use it as a second reference point in our analysis; in other words, we are interested not only in the pattern of changes in the global economy following the removal of agricultural distortions, but also how this pattern compares with the adjustments likely to take place if all trade were liberalized.

**Table 3 Developing income countries face higher tariffs than high income countries**

|Importer|Low and middle|income countries|High incom|e countries|
|---|---|---|---|---|
|Exporter|Tariffs faced<br>(%)|Exports<br>(% oftotal)|Tariffs faced<br>(%)|Exports<br> (% oftotal)|
|World total|13.0|31.5|10.6|68.5|
|High income countries|12.9|23.7|7.8|76.3|
|United States|10.0|48.3|24.1|51.7|
|EU 15|14.7|15.0|2.9|85.0|
|Low and middle income countries|13.0|44.3|16.6|55.7|
|East Asia and Pacific|16.5|41.4|23.7|58.6|
|China|16.7|25.3|27.6|74.7|
|Indonesia|15.6|57.6|11.3|42.4|
|Europe & Central Asia|9.2|51.7|9.3|48.3|
|Poland|12.3|35.0|3.6|65.0|
|Russia|13.3|59.0|21.4|41.0|
|Latin America and the Caribbean|14.1|40.9|16.5|59.1|
|Brazil|18.2|47.9|24.3|52.1|
|Mexico|16.2|7.4|5.4|92.6|
|Middle East & North Africa|10.4|55.1|12.2|44.9|
|Egypt|9.4|55.3|12.8|44.7|
|Morocco|12.8|17.7|7.8|82.3|
|South Asia|12.0|57.2|15.4|42.8|
|India|12.3|55.5|15.5|44.5|
|Pakistan|9.5|72.8|27.4|27.2|
|Sub-Saharan Africa|9.7|39.1|9.8|60.9|
|South Africa|13.5|39.4|8.8|60.6|
|Nigeria|10.9|17.6|1.6|82.4|

_Note:_ “Tariffs faced” column shows the import-weighted average tariff imposed by the column country/region on exports from the row country/region. “Exports” column shows the exports of the row country/region to the column country/region as a share of the former’s total exports.

_Source_ : Authors’ calculations with GTAP7.0 database Due to the removal of barriers to trade in agriculture and food products, global consumption rises by 0.29 percent, two-thirds of the improvement expected under a full trade liberalization scenario. Low and middle income countries gain more than the

> 14 Trade in other beverages and tobacco is excluded from the liberalization list. See Annex Table XX for a full list of commodities where trade is liberalized.

12
## _page 8_

services. Services were in tum disaggregated into transportation & communications, wholesale & retail trade, and other services. Tradables are probably most appropriately identified with agriculture and manufacturing, nontradables with the other non-oil categories. All the above variables were in per capita, constant local currency terms. As explanatory variables, we used data on the terms of trade, a debt crisis dummy, and the world real interest rate. All variables were at an annual frequency. A full description of the data used and its sources is in Appendix 1.

# IV. Econometric framework and issues.

## A. Estimation

Throughout, we use a panel ier than a time-series methodology since, if the underlying assumptions are satisfied, this allows for more precise estimates and renders omitted variable bias a less serious problem (see next sub-section). We have no strong priors, and dteory provides little guidance on the speed at which our variables adjust to terms-of-trade shocks. Therefore we take what we think is a flexible approach to the issue of dynamics. As a first pass, we try to pick out long-ran relationships by selecting years when sufficient time has passed under a given regime for the countries to be in long-run equilibrium. Specifically, we note that 1973 was the end of a long period of stable oil prices; in 1981, after eight years of rising prices, agents geneally accepted the higher prices as prmanent, and had had some time to adjust to them; in 1989, after an eight-year period of falling prices, agents likewise accepted and had had some time to adjust to the lower prices; and, after 1989, oil prices rosr. sharply around the time of the Gulf War. Thus in 1973, 1981, and 1989 the countries in our sample could arguably have achieved long-run equilibrium. We therefore use these three benchmark years to carty out fixed-effects estimation of the following panel regression equation: where y denotes the natural log of each of the variables we study (except for the trade balance, which is left in levels), TOT denotes the log of the terms of trade, the i subscript refers to countries, and t refers to
# **Table A6. Survey Methods’ Differential Effects on Employment and Work Status by Prevalence of Informal Employment**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|Informal employment in the EA|-0.016|-0.042**|
||(0.023)|(0.020)|
|Female|-0.297***|-0.292***|
||(0.031)|(0.030)|
|LOA|0.002|-0.002|
||(0.030)|(0.027)|
|ESR|-0.024|-0.026|
||(0.033)|(0.028)|
|LOA x Female|0.063|0.082**|
||(0.043)|(0.041)|
|ESR x Female|<br>0.043|<br>0.027|
||(0.046)|(0.043)|
|Informal employment in the EA x Female|0.024|0.058*|
||(0.037)|(0.034)|
|LOA x Informal employment in the EA|-0.008|0.029|
||(0.031)|(0.028)|
|ESR x Informal employment in the EA|0.013|0.029|
||(0.034)|(0.029)|
|LOA x Informal employment in the EA x Female|0.007|-0.031|
||(0.048)|(0.046)|
|ESR x Informal employment in the EA x Female|-0.002|-0.025|
||(0.051)|(0.047)|
|_Treatment Effects_|||
|Effects of LOA on men in EAs with low informal employment|0.002|-0.002|
|Effects of LOA on men in EAs with high informal employment|-0.006|0.027|
|Effects of LOA on women in EAs with low informal employment|0.065*|0.080**|
|Effects of LOA on women in EAs with high informal employment|0.064|0.078*|
|<br>Effects of ESR on men in EAs with low informal employment|-0.024|-0.026|
|Effects of ESR on men in EAs with high informal employment|-0.010|0.002|
|<br>Effects of ESR on women in EAs with low informal employment|0.019|0.001|
|Effects of ESR on women in EAs with high informal employment|0.030|0.004|
|Observations|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by the prevalence of informal employment. This prevalence measure is estimated as the share of individuals living in the same EA as the respondent and employed in the informal sector (see the descriptive statistics and definitions of this indicator in Table A4). We present estimated coefficients from a model that includes a triple interaction between the treatment, sex, and the exposure measure. We control for a vector of variables ( ) that includes sex, the prevalence measure, double interactions, and the list of control variables selected using a double-LASSO procedure for each outcome. iihss Under _Treatment Effects_ we report the total effects of the LOA and ESR on subsample relative to the group of individuals XX with similar exposure in the control group. For example, we present the total effect of LOA on women in EAs with high exposure to informal employment relative to other women with similar exposure in the control group (see Panel A in Figure 2 for a graphic representation of these total effects). The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 43