rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Acute Illness: Care Received**

Table 3 combines information on health care utilization conditional on the child being ill (columns (1)-(3)) with data on the incidence of symptoms (columns (4)-(7)), obtained from a 7-day health calendar over the week preceding the survey. For diarrhea, we first asked respondents to report all days on which the child had diarrhea in the last week. If diarrhea was reported, we asked about days with more than three loose stools, and days with blood in the stool (a danger sign that may indicate dysentery). We also asked whether the respondent was concerned about the child’s health on any day. For care received, we recorded any visits to CSComs or associated reference hospitals (CSRef), any other formal provider visits (private or non-profit clinics and hospitals) and whether ORT was given.

We begin by analyzing curative care, and specifically acute care received for diarrhea. Note that the sample in columns (1)-(3) includes only children with diarrhea symptoms<sup>12</sup> Columns (1) and (2) show no significant effect of the subsidy on the use of formal care, either on its own or as part of the full program. While this may seem surprising in the light of the results of Sautmann et al. [2020], the same paper also provides a potential explanation for this finding. Using the more detailed data from the 2013 survey, Sautmann et al. [2020] find that the increase in health care utilization following the subsidy largely occurs on days in which care is required according to the WHO guidelines. Diarrhea on its own does not fall into this category, unless it has lasted for more than 5 days or is accompanied by other symptoms. It is likely that formal care is not the appropriate response to many of the diarrhea spells in the sample.<sup>13</sup> Indeed, mild diarrhea cases can be treated with ORT, which can be prepared at home. Alternatively, pre-mixed ORS packets can be purchased, and providers may also recommend ORT in addition to or in place of prescription medication. Column (3) of Table 3 shows that 12 Unconditional use of care is uninformative, because illness incidence varies between treatment groups, see below.

> 13The 2014 data reported here constitutes a small sample of spells and does not cover a long enough period to identify spells in which care was required per WHO guidelines in an unbiased manner.

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- Treasury includes the central trading, investment, and money management facilities, as well as all other, relatively smaller businesses supplied by some banks that are not captured in the other three business lines

9. **Since the primary focus is to assess differences between Retail and Corporate, the other two business lines are not analyzed in detail** . As shown later in the section, the size of the Government business line is very small and is not representative of the public sector’s importance as a client (e.g., securities) and of its influence (e.g., via bank ownership or directed lending) in the banking system. In addition, since Treasury is effectively the residual of all other bank businesses, its estimated financial performance cannot be taken as fully representative.

10. **Various public data sources were tapped and assumptions were made in order to estimate a stand-alone notional balance sheet and income statement for each of the business lines** . Although the majority of the data comes from Bacen, other sources<sup>11</sup> were included as necessary in order to facilitate the allocation of different line items in the financial statements. Table 2.1 below summarizes the main allocation results, while a detailed description of the methodology and allocation mechanisms can be found in Appendix I. Given that the allocation assumptions (particularly about costs and equity) are important, interviews with senior management of eight Brazilian banks of different size and ownership structures served as a consistency check on the overall methodology. Finally, a sensitivity analysis was undertaken on those variables with higher uncertainty and potentially higher impact on the results of the relevant market segments; the main results are summarized at the end of this section, while the detailed analysis can be found in Appendix II.

> 11 These included, for example, data from the Brazilian deposit insurance agency ( _Fundo Garantidor de Créditos_ or FGC), bank annual reports, and annual 20-F reports filed with the US _Securities and Exchange Commission_ (SEC) by Brazilian banks that are listed in the New York Stock Exchange.

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**Figure 12: The quality of road infrastructure index (from 0 (worst) to 7 (the best)) in select countries of Europe and Central Asia**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: Enabling Trade Index, 2010, The World Economic Forum

**While the quality of transport infrastructure in Russia varies significantly across different modes of transport––and different parts of the country––the** **_road_ infrastructure is estimated to have deteriorated the most.** According to the World Economic Forum’s 2010 Enabling Trade Index (ETI), Russia’s railway infrastructure quality ranks 33rd in the world, relatively good but still significantly behind the Western European levels to which the country aspires. But its ranking on the quality of road infrastructure is 111th, among the world’s worst; this––in a vast country that is heavily resource-intensive and that depends on transport of goods and services for its exports. The quality of port and air infrastructure is also comparatively poor––Russia ranks 82th and 87th, respectively. These differences are also evident in comparing the asset deterioration in recent years, by transport modes. According to Rosstat, the asset deterioration rate reached 48 percent for air transport, roads and maritime port infrastructure in 2008. The deterioration in railways was much lower––24 percent. A recent survey of quality of the federal road network indicates that the majority of roads do not meet the minimum riding quality requirements (Table 4).

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In addition, the parameter estimate yields a long-run elasticity in the range of 0.290.39. This magnitude is similar to the benchmark results obtained in the earlier estimate. Given the interpolated nature of these sources, a too strong interpretation of the associated changes makes little sense. Insofar, the sign and significance of the variables are more important than their magnitude. All in all, employing alternative data on human capital confirms the earlier conclusions about the importance of education on growth.

# **_Population Instead of Labor Force Estimate_**

The regression in column 6 employs population data (15-64 years) instead of the labor force. Alternatively ILO labor force estimates could be used. The time series properties, however, are almost identical, and population statistics refer to a longer time period. In any case, the results are rather disappointing. That is, the significance of the coefficients and the overall fit of the model are poor. In order to ameliorate the estimate, the lag structure of the short-run capital coefficients was modified.

Human capital still enters positively but is only weakly significant. A puzzling finding is that long-run capital accumulation has now a negative impact on growth, which is a counterintuitive and implausible result. Overall, given the absence of fluctuations and considering the civil war, Guatemalan population data seems to be a poor proxy for labor as well.

# **6.3 Additional Explanatory Variables**

When the conditioning set of data in the regressions is modified, it is interesting to observe changes in the explanatory variables, such as schooling. For example, the production elasticities of human or physical capital could be larger than their factor shares because of presumed externalities. The benchmarks of the following analyzes are the results in section 5. When possible, the following paragraphs differentiate for the effect of primary, secondary and tertiary schooling.

# **_Quality-Adjustment Capital_**

Column 2 of Table 10 shows an OLS estimate for the period 1971-2002 when the capital stock is adjusted for quality. For comparative purposes, column 1 presents the same regression but without such an adjustment. Following de Ferranti et al. (2002) the intuition behind this exercise is that embodied technological change could have a positive impact on the returns to education, in particular for post-primary schooling. However, in the case of Guatemala, the overall effect seems to be the opposite. An increase in the long-run elasticity for physical capital and a decrease of the importance of education on growth is found.

To interpret this puzzling finding recall that the index of capital quality actually measures a decay by about 20 percent. In contrast, human capital and hence labor quality, have increased substantially over time. This may point in the direction of a

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knowledge of occupational status has in understanding the structural economic and social shifts experienced by migrants. As of recent, discussions about the occupational placement of migrants have also become of more general interest. In the US, there is plenty of anecdotal evidence linked to both real life examples and media coverage about skilled immigrants employed in low skilled jobs, and about concentration of individuals from a particular country into one category of professions. Research focusing on occupational mobility of immigrants could provide rigorous grounds for such observations. More explicitly, since occupational choices are linked to the education level, the analysis of occupational placement may reveal how migrants’ skills are utilized in their destination countries in comparison to where the nominal degrees suggest those migrants should be. Such a topic could potentially open a door for addressing issues related to global creation and allocation of human capital (Mattoo et al. 2008).

The magnitude and pattern of return migration can significantly bias the results in this paper. Although prior literature acknowledged this problem and some researchers made inferences about the nature of the selectivity, the bias stemming from out-migration is typically left unresolved in studies that rely exclusively on census data. I evaluate to what extent attrition of cohort size due to return migration affects the samples by country. I conclude that estimates are not significantly affected by patterns in return migration.

Furthermore, working with data from several Censuses raises serious methodological concerns because the samples are based on responses of different individuals. To check whether results change significantly when correcting for the potential biases, I rerun the individual-level regressions using pseudo-panel techniques. The patterns are robust to the methodological change.

The next section reviews the literature on the performance of US male migrants and its determinants. Section 3 focuses on the dynamic analysis of the occupational placement over time using census data. It includes four sub-sections: the first introduces the empirical strategy; the second describes the data; the third presents the results; and the fourth is dedicated to robustness checks. Section 4 focuses on conditional convergence, while the impact of country attributes on the occupational performance is evaluated in section 5. Both sections 4 and 5 are divided into two sub-sections, one presenting the empirical framework and the other describing the results. Section 6 summarizes the findings and talks about their policy implications.

# **2. LITERATURE REVIEW**

Since the 1940s, the US has been confronted with increasingly large waves of inward migration, which, by the 1990s, had became equivalent in magnitude to the ones taking place around the beginning of the century, i.e. in the period of the First Great Migration. Borjas (1994) 3
# **Figure 7:** Estimated Magnitude of Distortions, 2005-15

<!-- Start of picture text --> BR A CH L CO L CRI ME X PER 0.3<br>0.4<br>0.2<br>0.2<br>0.0<br>−0.2<br>0.1<br>−0.4<br>2005 2008 2010 2012 2015 2005 2008 2010 2012 2015<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>0.0<br>2005 2008 2010 2012 2015 2005 2008 2010 2012 2015<br>Distortion (IND) relative to KOR<br>Distortion (AGR) relative to KOR<br>Distortion (SRV) relative to KOR Distortion (ICT) relative to KOR<br><!-- End of picture text --> Source: OECD data, author calculations. Note: Values range from 1 to -1. A value larger than 0 implies that the country has relatively more input distortions than the frontier country chosen. A value less than 0 implies that a country has fewer distortions than the frontier country. Unlike in the previous figure, input distortions have been falling in ICT while output distortions have been rising.

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country-pair variation, while the exporter and importer fixed effects absorb any unobserved country heterogeneity such as port infrastructure or the quality of institutions.<sup>11</sup> One may also expect unobserved product heterogeneity to affect maritime transport costs. For instance, there is evidence that in the presence of market power, shipping prices depend on the (demand) characteristics of the products to be shipped (Calvo Pardo and Lazarou 2013; Hummels, Lugovskyy and Skiba 2009). Likewise perishable goods may command a premium for timely delivery. All these effects are absorbed by the full set of commodity fixed effects.

The sample includes two importers (New Zealand and the United States, destination countries) and 65 exporters (origin countries) representing a total of 9,984 observations.<sup>12</sup> Among exporters, 12 are high income countries, 43 are middle income and 10 are low income countries. The sample accounts for 69 percent of US total seaborne imports and 52 percent of New Zealand’s total seaborne imports, respectively. We estimate equation (1) on a crosssection of product-country-pairs for the year 2006, for which transhipment data are available from UNCTAD.

Before proceeding to the results for the transport costs equation, the censored nature of the dependent variable _mtc_ odk needs to be emphasized as it is derived from trade flows and thus is missing for zero trade flow observations. Essentially transport costs do exist but are prohibitively high so that no trade is observed (Limao and Venables, 2001). Censoring affects around 50 percent of the sample. We address this issue by estimating a top-coded tobit model. Censored observations’ missing MTC values are replaced by the highest value of transport costs observed in the dataset (Carson and Sun, 2007).

> 11  One such institution is a country’s stance on exempting liner conference from domestic competition law.  In our particular sample the incidence of this institution is collinear with the country fixed effects and thus cannot be identified separately.

> 12  The number of origin countries is in the first instance constrained by the total number of countries in the Services Trade Restrictions Database (103), of which 22 are landlocked and thus drop out.  In addition, for 13 EU member states, for which policy information is in principle available, trade flows cannot be tracked back to the port of entry through which they entered EU territory (typically Rotterdam), so they drop out as well.  We end up with 65 exporters; the country sample is detailed in Annex 2.

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14 Data on lobbying in tourism by foreign principals residing in the Caribbean destinations for the period 1990-99 were compiled from FARA reports for those years. In each year there were about 1,300 entries containing data on the calendar year of the activity, the country of the principal, the name of the principal, and the amount of money the agent and principal transacted. On the basis of the description of the representation activity by the US agent in the FARA report, each entry was assigned a primary 3-digit SIC industry. Over 25 percent of the FARA entries were in the tourism sector (SIC 961). Those entries from the Caribbean destinations are used in this study.<sup>11</sup> They are aggregated up to the country level for each year.

In the FARA reports, dollar figures for the three years 1992-94 are completely missing. However, foreign principals who lobbied are quite completely identified. In order to obtain a complete sample with no discontinuities, we imputed lobbying expenditures for each identified principal for the missing years, based on how much they spend on lobbying in other years. The imputed value, in 1992 for example, for a specific principal is simply the predicted lobbying spending from a trend equation for that principal for 1992. These imputed values are then aggregated up to the country level.

We restrict the sample to countries that participated in foreign lobbying at least once during this period, since we wish to exploit within-country variation in lobbying. If in any year one of these countries did not participate in US lobbying, lobbying spending for that country was set to $1, so that ln(Lobbying) equaled zero. GDP per capita is taken from the CTO. The real exchange rate is measured as the nominal exchange rate (island’s currency per dollar) times the ratio of the US CPI (1995 base) to the island’s price level (1995 base). The real exchange rate, taken from World Development Indicators (2003) and CTO, is standardized to a value 1 in the 1995.

Complete data on the lobbying and tourism variables were available for one or more years during the ten-year period for these 13 countries: Aruba, Barbados, Netherlands Antilles (Bonaire and Curaçao), Belize, Bermuda, Cayman Is., Dominica, Dominican Rep., Grenada, Jamaica, Montserrat, St. Lucia, and Trinidad and Tobago. Our sample is thus a balanced panel consisting of 130 observations.

> 11 Only data on lobbying devoted to tourism (SIC 961) are used in this study. Data on lobbying devoted to advertising (SIC 731) and to media and media relations (SIC 874) are excluded from the study. Thus, the analysis is focused exclusively on lobbying for tourism.
# **Annex Table 1. 1. Correlates of investment growth**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|VARIABLES|**EMDE**|**EMDE:**<br>**incl pol.**<br>**risk**<br>**events**|**GMM**|**5-year**<br>**ahead**<br>**forecasts**|**AE**|**Private**<br>**Investment**|
|Lagged Real GDP growth<br>(percent)|0.429**|0.415**|0.441***|1.717|0.829***|0.381**|
||[0.163]|[0.164]|[0.168]|[1.219]|[0.157]|[0.186]|
|Change in FDI inflows|||||||
|<br>(percentage points of GDP)|0.605**<br>[0.269]|0.602**<br>[0.271]|0.468**<br>[0.232]|0.179<br>[0.158]|0.145***<br>[0.049]|0.997***<br>[0.342]|
|Political stability|0.473***|0.405***|0.297**|0.602***|0.017|0.509***|
||[0.138]|[0.140]|[0.145]|[0.172]|[0.116]|[0.181]|
|Lagged credit to GDP ratio|||||||
|<br>(percent of GDP)|-0.095|-0.126*|-0.217**|-0.092|-0.029|0.018|
||[0.072]|[0.074]|[0.098]|[0.096]|[0.053]|[0.079]|
|Lagged credit to GDP ratio|||||||
|,<br>squared|-0.001**|-0.001*|0.001|-0.002***|-10e<sup>-5</sup>|-0.002***|
||[0.000]|[0.001]|[0.001]|[0.000]|[0.000]|[0.000]|
|Terms of trade growth (percent)|0.131***|0.132***|0.133***|0.277***|0.026|-0.093|
||[0.037]|[0.035]|[0.032]|[0.069]|[0.119]|[0.061]|
|Large reform spurt|4.503**|4.266*|2.862*|3.607|-0.149|6.831*|
||[2.223]|[2.232]|[1.727]|[3.232]|[1.028]|[3.744]|
|Large deterioration in political<br>stability||<br>-3.854**<br>[1.526]|||||
|Sudden stop dummy|-4.094***<br>[1.544]|-4.059**<br>[1.544]|-5.381***<br>[1.220]|-7.495**<br>[2.664]|-4.543***<br>[0.901]|-7.151***<br>[1.703]|
|Constant|-19.315**<br>[9.450]|-13.811<br>[9.585]|-7.974<br>[8.822]|-33.95***<br>[9.765]|3.162<br>[9.781]|-22.974*<br>[11.804]|
|Observations|1,098|1,092|1,098|327|411|809|
|R-squared|0.126|0.136||0.270|0.272|0.128|
|Numberofcountries|73|73|73|20|26|59|

Note: Results of a panel regression with country fixed effects for 73 EMDEs during 1998‐2015. Column (1) denotes the baseline regression. All coefficient estimates (except that for the squared credit‐to‐GDP ratio) are expected to be positive; the coefficient estimate for the squared credit‐to‐GDP ratio is expected to be negative. Column 2 controls for episodes of large deterioration in political stability, as defined by two standard deviation below the historical mean. GMM stands for generalized methods of moments. Column 4 replaces five‐year ahead forecasts for lagged growth. AE stands for advanced economies. For the GMM regression in Column (3), the Wald chi square statistic is 84.25. Column (6) replaces dependent variable with private investment growth. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 22
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markets, the Bank supports the transfer of their ownership to the p.ivate sector.

# II. REFORM EXPERIENCE

# A. Documentation of Reform

28. On the basis of the _PE_ sector-wide approach, the Bank had, by June 1989, approved 147 projects in support of PE reform, of which 24 operations were approved during the last year alone (July 88-June 89). (See Table 1). Most of these reforms were associated with structural adjustment loans (67 projects), followed by technical assistance loans (34 projects), and sectoral adjustment loans (33 projects). In addition, 13 loans focus exclusively on PE reform (PELs) .20/ 29.

29. Bank support for PE reform has been concentrated in Africa, which alone accounted for 54 % of the total number of PE lending operations, followed by the Latin American and Caribbean region (LAC), the Europe, Middle East and North African region (EME.NA) and, finally, the Asian region. The widespread and apparent incidence of market and bureaucratic failures in African countries in comparison with others, and the excessive size of the PE sector in this region largely account for the concentrated effort.

Table l: Number of PE Reform Projects a/

|Tec|hnical<br>Stru|ctural<br>PELs b/|<br>Sec|toral<br>Total||
|---|---|---|---|---|---|
|Region|Assistance|Adjustment||Adjustment||
|..<br>..............................|.................................|||||
|Africa|21|36|8|15|80|
|LAC|10|16|2|4|32|
|EMENA|3|8|2|11|24|
|Asia|0|7|i|3|11|
|Total|34|67|13|33|147|

Notes: a. Approved as of June 1989, b. Includes PE rationalization/rehabilitation loans, PE institutional development projects, and PE sector adjustment loans.

Source: Statistical Annex.

20/ Public enterprise loans (PELs) include PE rationalization/rehabilitation loans (PERLs), PE institutional development projects, and PE sector adjustment loans.