rafmacalaba/data-use-mentions-extended huggingface dataset:
. The z�� are calibrated to match observed employment levels in each sector‐cell and per capita income levels across countries. A��� are calibrated to match the international trade data.

The calibration proceeds in three main steps. First, we calibrate relative z�� within each country, so that equilibrium employment in each sector and cell matches the employment data. Second, the absolute levels of z�� in each country are scaled to match income per capita at a country level. Finally, we calibrate preference parameters A��� such that the share of country _i_ output being sold in country _j_ is consistent with international trade data.

# **_Simulations_**

The calibrated model is ‘shocked’ with new transport time matrices, and firms and households adjust their decisions to optimally respond to the new connectivity of the region. We look at four different levels of economic response (1) The direct trade value of the transport cost reduction. We take the base value of exports of each cell according to the destination and calculate the reduction in costs in shipping the same amount of goods and services that results from the decreased travel times. No demand or supply response occurs.

(2) Armington assumption; the number of firms/ varieties of products produced in each cell is held constant. Output per firm adjusts, as do equilibrium prices and demands.

(3) Monopolistic competition in manufacturing production, where the number of firms in each cell in sectors 2 and 3 adjusts in response to profit opportunities until equilibrium is restored (4) Monopolistic competition with increasing returns to scale in manufacturing (localization economies) and some intra‐country labor mobility.

In setting (4), under increasing returns, z�� , the calibrated productivity of sector s in cell i is composed of two elements. First, a base productivity of sector‐cell, z̅�� , and second, an additional component which represents potential increasing returns to as a function of the density of firms in
**Multiple jobs.** In labor force surveys, workers are typically classified by their primary sector of employment. A potential concern with this classification is for individuals classified as “agricultural” but who work a substantial fraction of their hours in nonagricultural activities (Haggblade, Hazell, and Reardon 2007), as this would lead to an underestimate of labor productivity in agriculture. Gollin, Lagakos, and Waugh (2014) use Living Standards Measurement Study data for several developing countries to estimate labor productivity using hours worked; Adeyinka, Salau, and Vollrath do the same in Chapter 6 of this book on Nigeria. They find that the overwhelming majority of individuals classified as working in agriculture

do in fact allocate almost all of their time to agriculture. Gollin, Lagakos, and Waugh (2014) also find that a significant portion of individuals in rural households is classified as working in nonagricultural activities.

**Accounting for human capital.** If human capital differs significantly across sectors, using the number of workers unadjusted for differences in human capital can be misleading. For example, if most of the labor in agriculture is unskilled and most of the labor in services is skilled, simple measures of productivity will understate labor productivity in agriculture and overstate labor productivity in services. One way to account for this is to adjust employment numbers for educational attainment, which is what Gollin, Lagakos, and Waugh (2014) do for their sample of countries (in the poorest countries, human capital is on average 1.4 times higher in the nonagriculture sector than in the agriculture sector). However, even after making this adjustment, they still arrive at the conclusion that average labor productivity in agriculture is significantly lower than average labor productivity in other economic sectors.

**Average versus marginal productivity.** The country authors of this book compare gaps in sectoral productivities using measures of average labor productivity, as is done in McMillan and Rodrik (2011) and Gollin, Lagakos, and Waugh (2014). It is well
# **_4. Potential Intraregional Exports in Central America_**

One of the main benefits of the GTM is that it allows projecting the "potential level" of exports based on a specific set of coefficients. In turn, the ratio between the observed and the potential export flows provides a measure of the closeness ─ or _proximity index_ ─ of a specific country or country-pair to its expected exports level ─ _expectations frontier_ . Following Benedictis and Vicarelli (2005), this closeness measure �<sup>�</sup> can be expressed as: Where ���� captures the observed export flows from the exporter country � to the importer country � and ����� are the expected exports flows generated by the gravity equation. To calculate the potential export flows, four different sets of coefficients were used for calculating �����, resulting in four different scenarios or “proximity indices.” The first scenario is based on the coefficients for the CA intra-regional trade model (Figure 6). The second one projects the exports flows in CA from the estimated coefficients obtained for the EU15 intra-regional model (Figure 3). A third scenario is obtained by applying the highest country-pair adjacency coefficient, obtained from the disaggregated GTM for Central America (with results illustrated in Figures 7 and 10),<sup>16</sup> while the fourth scenario applies the adjacency coefficient resulting from the EU15 intra-regional model (Figure 3).

Figure 13 below illustrates the results obtained from the four different scenarios, in this case, comparing Costa Rica with Honduras. In Costa Rica’s case, the observed-to-projected exports are close to the unconstrained _Expectations Frontier_ (i.e. a ratio equivalent to 1), except for when the country’s currently observed exports are compared to what its exports would be expected to be under the assumption of an “EU15-like” behavior of all the independent trade variables (distance, adjacency, and the GDP of both the exporting and the importing country). In
used to identify the effects of any non-discriminatory policies on the side of exporters and importers, following Heid _et al_ . (2015), which to some extent are present in this case.<sup>22</sup>

# **4.2 Robustness Checks**

Several robustness checks are performed, mainly by including several additional control variables as part of the C ∗intlo term. More specifically, using equation (2), each regression model includes additional policy variables such as the ones developed by ECIPE’s Digital Trade Restrictiveness Index (DTRI), the OECD Digital Services Trade Restrictiveness Index (Digital STRI), as well as the WEF Technology Readiness Indicator. The first two variables are estimated on the importers side (country _d_ ), whereas the latter is estimated on the side of the exporter (country _o_ ). Finally, as said above, the study adopts a panel series and re-estimates the baseline regression equation (1) and (2), including by using alternative trade data from the ITPD-E.

# **5. Results**

The results of the baseline regressions following equation (1) are reported in Table 3. Column 1-7 follow the sectoral groupings as presented in Annex Table A2. In order to enhance readability of the main variable of interests, the results from the GRAVo and C ∗intlo vectors are featured in a separate table, which can be found in Annex Table A3.<sup>23</sup> Moreover, Table 3 first reports the coefficient results of CB and DR under MODELo when entered separately in order to avoid potential multicollinearity concerns that may arise by entering them simultaneously, even though countries do not necessarily follow the same model for the two components. In a second step the two components are nonetheless entered together.

The results in Table 3 show a positive and negative coefficient result for the MODEL CB variable, which covers cases in which country-pairs share _any_ model regarding the component of cross-border data transfers. However, none of the coefficient results are
(2006a). While the identification of these crisis episodes follows the 3S classification, they coincide with large banking and currency crises over the 1990s as identified by other studies, including Laeven and Valencia (2008).

# **_2.2 Identification of Phoenix Miracles_**

The literature has identified Phoenix Miracles as crisis episodes where there is a collapse in both output and credit but output recovers relatively quickly without a recovery in credit. For our sample, we start with 9 3S episodes over the 1990s that have been identified as potential Phoenix

> 7 Ecuador and Morocco had 3S collapses over the 1990s but we do not have these in our sample due to the unavailability of corporate balance sheet data in these countries from Bloomberg. These countries are not covered in other firm-level datasets such as Worldscope either.

7
- household livelihood characteristics (agriculture, livestock, non-farm enterprises)

- financial characteristics (receiving transfer income, savings, etc.)

- dwelling characteristics (ownership, improved roof/walls/floor)

- household mobile phone ownership

- geographic characteristics (rural / urban status, district of residence)

The first characteristic, i.e. whether the individual is a member of the original household or a tracking target, is particularly important in cases where the implementation of the tracking methodology is of questionable quality. The described methodology thus far assumes perfect implementation of the survey plan. However, in some cases, the field teams may not search for all eligible tracking cases. In an ideal case, it would be possible to know which cases were attempted and construct a separate model at this step to incorporate those probabilities of selection. For example, perhaps the tracking team decided only to look for those split-off members that they thought would be easy to locate. If the analyst knows which households were considered easy and therefore actually targeted, whether or not they were located, the attrition calculations can be done separately for those targeted and those not targeted, as the most important determining factor of attrition in those cases would be the classification as easy. If the explicit division was not recorded, but the tracking team can inform the analyst roughly which criteria they used in making the determination about whether the split-off members would be easy to find, the designation can be modeled and the division predicted. In the absence of any information, the split-off indicator variable is the best way to capture this element of attrition.

The estimated logistic model is used to obtain a predicted probability of response for each household member in the round 2 survey.<sup>9</sup> These response probabilities are then aggregated to the household level (by calculating the mean). Then, using the household-level predicted response probabilities as the ranking variable, all households are ranked into 10 equal groups (deciles). An attrition adjustment factor ( _ac_ ) is
**Table 1:** Descriptive statistics Encuesta Permanente de Hogares (EPH)

||Urb|an|Rur|al|
|---|---|---|---|---|
||Mean|SD|Mean|SD|
|**Income**|||||
|Monthly per capita income, 2017$ PPP<br>|559.7|973.1|335.8|803.8|
|Monthly per capita labor income, 2017$ PPP|425.9|715.6|260.4|763.4|
|Poor (%)|25.8|43.8|43.9|49.6|
|Extremely poor (%)|3.0|17.0|14.5|35.2|
|**Demographics**|||||
|Household size|5.0|2.3|5.3|2.4|
|Age dependency ratio|0.72|0.71|0.95|0.86|
|Female household head (%)|33.8|47.3|25.6|43.6|
|Age household head|47.2|14.2|47.1|14.5|
|Years of schooling household head|8.5|4.6|5.7|3.6|
|Head and spouse only speak Guaraní (%)|15.9|36.6|52.5|49.9|
|**Employment**|||||
|Any HH member in agriculture (%)|23.8|42.6|80.1|39.9|
|Employed (%)|92.9|25.7|96.1|19.3|
|Share of employment in primary sector (%
<!-- Start of picture text --> FIGURE A 4. PREDICTED POVERTY HEADCOUNT BY INCOME GROWTH COUNTRY TYPOLOGY<br>Panel A. LOGCs Panel B. MOGCs Panel C. HOGCs<br>(Based period: 1980–2014) (Based period: 1980–2014) (Based period: 1980–2014)<br>75 75 75<br>60 60 60<br>45 45 45<br>30 30 30<br>15 15 15<br>0 0 0<br>1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040<br>Years Years Years<br>Poverty he adcount outcomes derived from 50 0 random draws per year in each of the Poverty he adcount outcomes derived from 50 0 random draws per year in each of the Poverty he adcount outcomes derived from 50 0 random draws per year in each of the<br>150 select ed countr ies. The 2015 and 2030 corr espondin g results are: i) median = 50.44 150 selecte d countr ies. The 2015 and 2030 corr espondin g results are: i) median = 12.87 150 select ed countr ies. The 2015 and 2030 co rrespond ing results are: i) median = 4.5<br>and 48.41; ii) mean = 50.32 and 47.86; iii) skewness = -0.24 and -0.04; iv) standard and 9.12; ii) mean = 12.89 and 9.15; iii) skewness = 0.04 and 0.33; iv) standard and 0.85; ii) mean = 4.65 and 1.48; iii) skewness = 0.99 and 1.76; iv) standard deviation<br>deviation = 2.55 and 9.87. Estimated number of persons living in extreme poverty deviation = 0.55 and 1
.02)|
|Share of non-western immigrants|-0.53|-0.71*|-0.02|
||(0.34)|(0.41)|(0.01)|
|Share of water surface|2.47|2.22|-0.79***|
||(8.71)|(8.75)|(0.28)|
|Share of overweight|-1.32<br>(0.91)|||
|Share with obesity|1.60<br>(1.53)|||
|Share with long-term illness||0.68|0.01|
|||(0.78)|(0.02)|
|Mean PM2_._5|2.75**|3.13**|0.28***|
||(1.28)|(1.29)|(0.03)|
|_λ_|0.41*|0.40*|0.10|
||(0.24)|(0.24)|(0.19)|
|_ρ_|0.45**|0.46*|-0.16|
||(0.22)|(0.22)|(0.19)|
|R2|0.50|0.50|0.30|
|AICc|3265.34|3268.53|920.81|
|N|355|355|355|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 23
<sup>2</sup> measuring sector outcomes.

For the purpose of benchmarking Lesotho, we compare its available indicators of infrastructure performance with all the world geographical comparator groups--Sub-Saharan Africa, OECD, South Asia, East Asia & Pacific, Middle East & North Africa, Latin America & the Caribbean, Europe and Central Asia, and the world—and four groups of countries clustered by the level of income per capita— low income, middle income, upper middle income, and OECD countries. Therefore, both region and income level are used as comparator criteria. The main benchmarking group on which much of the discussion is focused is low-income country group to which Lesotho belongs. Because of the strong association between infrastructure and income level, this is economically the most meaningful group comparator. This is supplemented by relevant comparisons with other income groups and major world regions to provide a wider geographical comparison of Lesotho’s infrastructure performance. Finally, where comparative rural/urban indicators are available, we benchmark Lesotho’s infrastructure performance for both rural and urban areas.

As with any comparative indicators summarizing performance in a vast number of different countries, interpretation must be made carefully and with caution. Some of the variations between the indicators may reflect other factors that may make single indicator comparison less revealing, or even misleading. The database itself has its limitations detailed in the cited World Bank analysis by Estache and Goicoechea.<sup>3</sup> In addition, where appropriate, we explain or qualify how specific circumstances in

> 1 The percentage of available country observations from the total number of countries (207) varies by indicators from close to 90% for measures of access to roads, 85-95% for ICT access and pricing measures, 75-85% for access to water, and 60% for access to electricity.  The quality indicators generally show lower degrees of completeness than those of access.

> 2 The numbers of indicators in each sector are as follows: 7 in energy, 4 in water & sanitation
# **Table A.1: Description of SPI Dimensions**

|Dimension|Brief Description|
|---|---|
|Dimension 1.1: Data<br>use by national<br>legislature|Not included because of lack of established methodology. In principle it may be<br>possible to utilize websites of national legislatures but this will require further work and<br>assessment.|
|Dimension 1.2: Data<br>use by national<br>executive branch|Not included because of lack of established methodology. There are some usable data<br>sources (as used by (PARIS21 2019)) but gaps in data across countries have prevented<br>full adoption.|
|Dimension 1.3: Data<br>use by civil society|Not included because of lack of established methodology. There are some usable data<br>sources with good coverage, for example from social media but more data is required to<br>help assess and allow for likely biases between and within countries.|
|Dimension 1.4: Data<br>use by academia|Not included because of lack of established methodology. We have not been able to<br>find usable data sources with global coverage on which a new methodology could be<br>developed.|
||Five measures usefulness or reliability of country produced measures for international<br>organizations have been included. First, on comparability of poverty estimates for the<br>World Bank reporting on international poverty usingPovcalnet.Second on usable|
|Dimension 1.5: Data<br>use by international<br>organizations|surveys for statistics on child mortality for theUN Inter-agency Group for Child<br>Mortality Estimation.Third on accuracy of debt reporting as classified by the World<br>Bank (Source: World Bank WDI metadata). Fourth, on availability of safely managed<br>drinking water data for use by WHO/
Section 2 presents the data and descriptive statistics. Section 3 compares subjective and objective measures of welfare, at the individual and country level. Section 4 explores the measurement of subjective measures, their consistency and the issue of measurement error.

# **2. The data: Measures of welfare in the Life and Transition survey**

This paper uses data from two rounds of the Life in Transition Survey<sup>9</sup> . Following the first LiTS (2006), the second LiTS was conducted in 2010 simultaneously in 29 ECA countries, and in five Western European ‘comparator’ countries (Germany, Italy, UK, France, Sweden). In both surveys, the questionnaire was administered to a nationally representative sample of at least 1,000 respondents in each country<sup>10</sup> , using face-to-face interviews. Taken together, the two surveys provide a wealth of information on prevailing living standards, opinions and attitudes in 2006 and 2010. Most of the analysis in this paper is based on the second round of the survey (2010), although some comparisons are also drawn with the first round.

# **2.1. “Objective” measures of welfare: Consumption and assets**

Using recalled household expenditure data in the LiTS, we construct a consumption aggregate by summing all expenditure categories, expressed in per capita annual equivalents, and converted to US dollars according to market exchange rates (see Appendix 1 for more detail on the measurement of consumption in the LiTS and the construction of our consumption aggregate).

We also construct, overall and separately for each country, an asset index as an alternative “objective” measure of household welfare. The asset index is based on household ownership of the following items: a car, a secondary residence, a bank account, a debit card, a credit card, a mobile phone, a computer, access to internet at home, and household access to water, electricity, a fixed telephone line, central heating,
contribution to economic growth ranging from less than I percent in Mexico to as high as 23 percent in Ghana (Psacharopoulos 1984).

In the late 1980s, as dissatisfaction with the neoclassical growth framework, endogenous growth models began to emphasize the accumulation of human capital, which is not subject to diminishing returns. In these models, growth is explained by endogenously driven technical change, which may be brought about by a variety of factors: learning by doing (Romer 1986), spillover effects of human capital formation (Lucas 1988), production externalities of public expenditure (Barro 1990), and quality improvements through the invention of new products (Grossman and Helpman 1991).

While these theories have incorporated human capital or education as an important input to growth, empirical evidence is still far from unanimous and conclusive. Numerous studies, some using cross-country data, have investigated the relationship between formal education and economic growth in developing countries. While many studies found that additional years of education per person in the labor force increase real output or growth rates (World Bank 1991, 1993 for example), a few studies found that human capital accumulation had a significant negative or an insignificant impact on economic or productivity growth.

Using cross-country data from 91 countries, Pritchett (1996) found a large and significant negative impact of human capital accumulation on productivity growth. Pritchett offered three possible explanations: schooling creates no human capital, the marginal returns to education are falling rapidly where demand for educated labor is stagnant, and perverse institutional environments have misdirected educated labor to activities that reduce economic growth. Islam (1995) finds consistently negative and significant coefficients on the natural log of years of schooling in panel estimates of the level of income. This finding replicated with various panel and GMM estimators in Hoeffler (1997). Benhabib and Spiegel (1994) and Spiegel (1994) find a negative effect for increased years of schooling using a standard growth accounting framework. Spiegel (1994) shows that the negative effect is robust to the inclusion of a wide variety
# clinics.

We attribute the effects on Ebola reporting to improved perceptions in the quality of care and greater trust and confidence in the government health system. The medium-run effects on satisfaction and utilization are also present in the subset of clinics used in our Ebola analysis. Moreover, we find that individuals in both treatments express greater confidence in western medicine in our endline survey relative to traditional and spiritual healers, the principal alternatives to government clinics in rural Sierra Leone. We combine our measures of utilization, satisfaction with public health workers, and the relative efficacy of western medicine, into a perceived quality of care index. Instrumenting that index with our treatment, we find that a one standard deviation change in the perceived quality of care increases total reported cases by 0.43 per section-week.

We do not find support for two alternative explanations. First, the interventions do not appear to have increased actual Ebola transmission. We find that all types of cases, including patients with both confirmed and negative test results, increase in sections with treated clinics. As a consequence, the ratio of confirmed to total cases does not change with treatment. To rule out contagion within clinics, we investigate whether confirmed cases test positive due to exposure that may have occurred upon reporting (e.g., through contact in a waiting room). We can rule out such nosocomial transmission (i.e., exposure originating in a clinical setting) in 99 percent of cases, based on the timing symptom onset and reporting. Second, we also do not find that sections with treated clinics have more resources for top-down surveillance: sections with treated clinics do not host more facilities specializing in Ebola care, and there are no differences in laboratory testing or case workers. If anything, we find that a higher proportion of confirmed cases undergo contact tracing (i.e., the process of identifying recent contacts to flag at-risk individuals) in sections with control clinics.

Our results highlight two important points. First, accountability interventions can improve health systems