rafmacalaba/datause-encoder-data huggingface dataset:
clinics.<sup>12</sup> Nevertheless, the last two columns of Table 7 show that migrant children are slightly _less_ likely than non-migrant children to die between the ages of one and four, conditional on surviving to at least age one, so that the positive effects of migration for health outweigh any negatives for this group.

# **6. How does migration affect child health outcomes?**

The Grossman (1972) model of a health production function provides a theoretical framework which can be used to delineate the variety of mechanisms through which migration may be observed to impact on child health outcomes.<sup>13</sup> The health status _Hi_ of child _i_ at a particular point in time can be written as: _H i_ = _h_ ( _M i_ , _Ti_ , _K i_ , _Bi_ ,ε _i_ ) (1) where _Mi_ are the medical and nutritional inputs into the health of child _i_ , such as prenatal and post-natal care, maternal and postnatal nutrition, and disease environment; _Ti_ are the time inputs of the parent; _Ki_ is parental health knowledge; _Bi_ are biological endowments such as genetic factors; and _εi_ are random health shocks. In Grossman’s original model health is both an investment and a consumption good, entering the utility function directly. Parents will then maximize utility by choosing health inputs such that the present value of gross investment in health equals the marginal benefits. As discussed in Section 3, the migration decision of the household may be correlated with both its genetic health status, _B,_ and with any random health shocks such as disease outbreaks, _ε_ , and so instrumental variables are used for estimation.

The most obvious channel through which migration may affect child health is through an increase in household income and wealth. Papers which attempt to identify the causal impact of income on health have found evidence of a positive relationship at both the macro (Pritchett and Summers, 1996) and micro (Strauss and Thomas, 1998) levels. As Wolpin (1997) notes, financial resources are not themselves direct inputs into health production, but rather determinants of the behaviors leading to the choices of _Mi_ and _Ti_ . Higher income will allow

> 12 Discussants at the _Economia_ session in Costa Rica also noted that using baby formula in place of breast-feeding is seen to be advantageous in some Latin American countries in light of poor nutritional status of the mothers. However, breast-feeding is associated with a number of positive health outcomes and is recommended by the World Health Organization (see González-Cosslo et al. 2003).

> 13 See also the detailed discussion of the health production approach in Wolpin (1997).

- 14 -
**Table 2. GDP Share and Share of Total Exports by Region**

||**_GDP_**|**_Share_**|**_TRAD_**|**_E Share_**|
|---|---|---|---|---|
||**_1992_**|**_2005_**|**_1992_**|**_2005_**|
|USC|28.2|27.4|21.7|20.2|
|EU|29.1|27.3|19.0|16.8|
|JPN|15.9|14.8|11.9|9.3|
|KOR|1.3|1.8|2.9|4.0|
|TWN|1.0|1.4|3.0|3.9|
|HKG|0.1|0.1|2.2|2.9|
|CHN|2.2|4.6|4.3|7.9|
|IDN|0.6|0.8|1.2|1.5|
|MYS|0.3|0.5|1.5|2.4|
|PHL|0.2|0.3|0.4|0.5|
|THA|0.5|0.8|1.2|1.9|
|LTN|5.3|5.6|5.8|6.2|
|SSA|0.7|0.8|1.3|1.5|
|SAS|1.4|1.9|1.2|1.5|
|ROW|13.1|11.9|22.3|19.5|
|TOTAL|100|100|100|100|

21
where women travel. Unreliable and infrequent public transport systems impose a disproportionately higher burden on women than on men, affecting their access to economic opportunities and basic services (Borker, 2022). This paper examines the effect of several aspects of the public transport network on women’s labor market outcomes in three metropolitan areas: Amman in Jordan, Beirut in Lebanon and Cairo in the Arab Republic of Egypt. In particular, we investigate how the accessibility of jobs throughout each city, the availability of public transportation close to residential locations, and the safety around public transit stops affect the labor force participation of women and their likelihood of employment.<sup>6</sup> By doing so, we shed light on whether improving public transport systems is a necessary or a sufficient condition for enhancing women’s economic empowerment.

To examine these relationships, we rely on three types of data collected in 2022. First, we have mapped the transit network of each city, including timetables and frequencies to be able to construct the measures of accessibility and availability. Second, we have conducted built environment audits to assess the safety around public transportation stops. Finally, we conducted household surveys in these metropolitan areas, in which 5,912 working-age women and men were interviewed and information on their socio-economic characteristics, mobility choices and labor market outcomes were collected.<sup>7</sup> Most of the literature that assesses the impact of improving the transport system on economic outcomes in developing country contexts, relies on one type of dataset. In contrast, we triangulate three different types of datasets to assess how public transport systems influence women’s economic choices.

The transit network and built environment audits are used to construct indices for three important facets of the public transport system—accessibility, availability, and safety. Accessibility refers to the spatial accessibility of job opportunities throughout the city, it is measured by estimating the percentage of jobs reachable under 60 minutes using public transport (and walking). Availability refers to the ease with which public transport can be found, measured by the spatial proximity of transit stops to residential locations (factoring in the frequency of service at each stop). In this manner, it measures the availability of public transit close to where people live. Finally, safety measures the level of “security” around the public transport stops. Security includes both sexual harassment and incidence of crimes. This is used

> 6 For the employment probabilities we also include men in the analysis to investigate whether there are heterogeneities by gender in the effect of various aspects of transport. For labor force participation this is not possible due to the high participation rates of men. 7 This means that our analysis only includes people living within the limits of the metropolitan region, and therefore not potential commuters who live outside each region.

3
Data Source (0.12), and Data Infrastructure (0.3), and a relatively high Data Services (0.56) and Data Products (0.61) components. Senegal is in the middle range in terms of the SPI distribution and CV spectrum (SPI = 0.56), with uneven performance of individual components. The components of SPI increase from a low of 0.4 for Data Infrastructure and Data Sources components to 0.67 for Data Products, and 0.81 for Data Services.

That pattern of variance of four SPI components, or pillars, may be explained by weak donor coordination in statistical capacity building and donors’ preferences for certain areas of the NSS. It is also likely that resource-constrained countries cannot effectively allocate resources to cover all dimensions of the SPI. The high variance in SPI components among low-capacity countries hinders their progress because components of SPI complement one another in the production and dissemination of statistical information. For example, a weak capacity to collect survey and census data adversely affects the presence of key indicators in international databases.

Figure 3 reinforces the argument that donor preferences could be driving capacity improvements in low-performing countries. Data Infrastructure and Data Products are the largest contributors to the overall SPI scores for this group; until recently, they were donors’ preferred statistical tool, as they contribute to understanding social rather than economic performance. Low scores on Data Services and Data Sources, which are more developed for economic than social statistics, reinforce this hypothesis.

# **4. Assessing the Demand for Data**

We assume that in the long run, a country’s statistical capacity is determined by the interaction between the supply of and demand for data. If the demand for official statistics exceeds the supply, data users will pressure the government to increase investment in the NSS. If the NSS supplies more data than are consumed, the government will eventually reallocate resources to more urgent priorities. The steady-state equilibrium of the statical capacity is reached when the demand for data corresponds to its supply.

The government’s funding preferences for NSSs are formulated based primarily on whether the NSS mobilizes the power of data to help these actors make better decisions. However, by disseminating high-quality products, the NSS has the potential to reshape state capacity (Taylor 2016). Better data from the NSS lead to improved state services. This virtuous cycle is likely left 10
ratio and bid-ask spreads widen despite the increase in trade volume, which in this case reaches 26%.

After a first glance at the data applying these simple tests, we resort to more standard econometric techniques to have a closer look: we regress each liquidity measure on a number of controls, using dynamic panel models with fixed effects. In addition, to check for potential bias associated with the inclusion of the lagged dependent variable in the panel regressions, we run stock-by-stock AR(1) regressions to compare the results (we report the mean group estimators for these tests).<sup>18</sup> The baseline specification, which we report in Table 1, is based on weekly data. It includes as controls the average price change during the current week, splitting positive and negative changes to measure stock market downturns directly (rather than through the SM crisis dummy) and to study the asymmetric behavior documented in previous work. In addition, a separate crisis dummy (based on EMP crises) is included, which we split into an early and late crisis dummy to account for the fact that financial distress is often accompanied by intense but short-lived portfolio reallocations and fire sales.<sup>19</sup> Furthermore, we include month dummies (to control for seasonality), the log change in the local currency-dollar nominal exchange rate (to control for the fact that most of these crises are associated with sharp exchange rate realignments), the lagged dependent

> 18 Nickell (1981) shows that in the AR(1) case the bias in estimating a dynamic fixed effects model becomes less important as T grows, and estimates this bias to be of O(1/T), a finding confirmed by Hahn and Kuersteiner (2002) and Alvarez and Arellano (2003) for panels with large N and large T. In addition, Judson and Owen (1999) show that the bias introduced by including a lagged dependent variable is larger for the parameter estimate of the AR term than for the parameter estimates of the other regressors. Kiviet (1995) uses asymptotic expansion techniques to approximate the small sample bias of the fixed-effects estimator.

> 19 Early crisis is defined as the first 2 crisis weeks. The EMP crisis dummy is based on data from markets other than the stock exchange. The SM dummy is used as an alternative in our robustness tests.

9
individual country models interact. When the model is actually used, the _within_ country and _between_ country relationships are solved for simultaneously.

The APEC CGE model includes several features that are not ordinarily incorporated into other multi-country CGE trade models. First, when modeling import demands, the Almost Ideal Demand System (AIDS) specification is adopted. This specification allows import expenditure elasticities to be different from one and also allows cross-country substitution elasticities to vary for different pairs of countries. Second, to capture the potential dynamic effects of trade liberalization, the APEC model can include equations for generating positive externalities through both export expansion and the importation of new capital goods. These new features will be described below.

The model data base consists of social accounting matrices (SAMs) for each country, including data on their trade flows."<sup>9</sup> The development of a consistent multi-country data base is itself a major task; for our model, we relied primarily on the GTAP database [Hertel (1995)], supplemented by some additional data on factor endowments, particularly labor and non-tariff barriers in Japan. The SAM starts from multisectoral input-output data, which are expanded to provide information on the circular flow of income from producers to factors to "institutions," which include households, enterprises, government, a capital account, and trade accounts for each partner country, and for the rest of the world. These institutions represent the economic actors whose behavior and interactions are described in the CGE models. The parameter estimates for the sectoral production functions, consumer expenditure functions, import aggregation functions, and export transformation functions were drawn from a variety of econometric and informal sources. The various parameters used in the model represent point estimates for the base year (1992) and the model was benchmarked so that its base equilibrium solution exactly replicates the base data.

> 19 Social Accounting Matrices are described in Pyatt and Round (1985).

_Lewis, Robinson, and Wang_ 28
the GIS road network and the 93 nodes, we used the shortest path routing algorithm to compute number of kilometers, travel time, and total road user cost for each origin-destination pair (or route).

_Table 5. Terrain Classes and Percentage of Land Area Peru_

|**Class**|**Description**|**Peru**|
|---|---|---|
|**1**|Plains|0.11|
|**2**|Mid-altitude plains|0.00|
|**3**|High-altitude plains|0.00|
|**4**|Lowlands|10.92|
|**5**|Rugged lowlands|17.41|
|**6**|Platforms (very low plateaus)|1.48|
|**7**|Low plateaus|0.05|
|**8**|Mid-altitude plateaus|0.00|
|**9**|High plateaus|0.72|
|**10**|Very high plateaus|0.14|
|**11**|Hills|20.83|
|**12**|Low mountains|7.44|
|**13**|Mid-altitude mountains|10.37|
|**14**|High mountains|16.81|
|**15**|Very high mountains|13.70|

_Source:_ Authors’ calculations.

# 6.2. GIS Data Limitations

A number of limitations associated with the input data sets and the methods used in this analysis should be taken into account in the interpretation of results.

_Scale of roads_ . The scale of the source data (that is, the level of detail) for roads is unknown, but there are obvious differences between the countries and in comparison with large-scale data sets such as Open Street Map. The smaller the scale, the less likely the features in the geographic information system (GIS) data will accurately represent the geometry of the roads on the ground. As a result, the total kilometers calculated based on the GIS data will deviate, to some degree, from the officially reported statistics. In figure 11 below, scale makes a big difference in the calculated sinuosity index value.

_Spatial resolution of the elevation model_ . The spatial resolution of the elevation model also presents a challenge, particularly with respect to the average width of roads. Figure 11 below shows a road drawn with a width of 7 meters, approximately one-third of the size of an elevation cell. In this case, extracted z- values will be affected by roadside features, particularly in areas of high relief.

26
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**<u>Annex 3.8f: TF Disbursements by Managing Unit of the Trustee Fund (US$ million)</u>**

|**VPU of Main TF**|**FY02**|**FY03**|**FY04**|<br>**FY05**|<br>**FY06**|<br>**FY07**|<br>**FY08**|<br>**Total**|
|---|---|---|---|---|---|---|---|---|
|CFPVP|-|-|0|0|0|735|236|**972**|
|SDNVP|10|19|26|31|51|63|206|**406**|
|HDNVP|1|1|28|25|7|33|13|**108**|
|AFRVP|12|19|14|16|7|16|15|**100**|
|IFC|-|-|1|7|18|26|43|**95**|
|EAPVP|-|3|1|11|12|22|8|**58**|
|FPDVP|2|3|3|5|10|10|14|**47**|
|SARVP|-|-|-|-|-|10|1|**11**|
|LCRVP|-|-|-|-|-|-|9|**9**|
|ECAVP|-|-|-|-|9|0|0|**9**|
|WBIVP|0|0|0|0|2|2|3|**8**|
|DECVP|0|1|1|1|2|1|2|**7**|
|CSRND|-|-|0|0|0|0|1|**2**|
|OPCVP|0|1|0|0|0|0|-|**1**|
|PRMVP|-|-|-|-|-|0|0|**1**|
|MNAVP|0|0|0|0|0|(0)|-|**1**|
|MIGEX-VPU|0|-|0|0|0|-|-|**0**|
|LEGVP|-|-|-|-|-|-|-|**-**|
|**Total**|**25**|**48**|**75**|**98**|**118**|**920**|**552**|**1,835**|

**<u>Annex 3.8g: TF Disbursements by Managing Unit of the Grants (US$ million)</u>**

|<br><br>**VPU of Grant**|**FY02**|**FY03**|**FY04**|**FY05**|<br>**FY06**|<br>**FY07**|<br>**FY08**|<br>**Total**|
|---|---|---|---|---|---|---|---|---|
|CFPVP|-|-|-|-|-|735|236|**971**|
|SDNVP|10|19|26|31|51|63|206|**406**|
|AFRVP|12|19|14|17|8|16|17|**103**|
|IFC|-|-|1|7|18|26|43|**95**|
|HDNVP|1|1|28|23|5|21|7|**85**|
|EAPVP|-|3|1|12|12|22|9|**59**|
|FPDVP|2|3|3|5|10|10|14|**47**|
|SARVP|-|0|-|0|0|19|2|**21**|
|ECAVP|-|-|0|0|10|1|1|**12**|
|LCRVP|-|-|0|0|0|0|10|**11**|
|WBIVP|0|0|0|0|2|2|3|**8**|
|DECVP|0|1|1|1|2|1|2|**7**|
|MNAVP|0|0|0|1|1|1|1|**4**|
|CSRND|-|-|0|0|0|0|1|**2**|
|OPCVP|0|1|0|0|0|0|-|**1**|
|PRMVP|-|-|-|-|0|0|0|**1**|
|MIGEX-VPU|0|-|0|0|0|-|-|**0**|
|LEGVP|-|-|-|-|-|-|-|**-**|
|**Grand Total**|**25**|**48**|**75**|**98**|**118**|**920**|**552**|**1,835**|

48
GROSSMAN, Gene M. & HELPMAN, Elhanan. ”Protection for Sale”. American Economic Review, v. 84, n. 4, pp. 833-50, 1994.

GROSSMAN, Gene M. & HELPMAN, Elhanan. ”The Politics of Free-Trade Agreements”. American Economic Review, v. 85, n. 4, 1995a.

GROSSMAN, Gene M. & HELPMAN, Elhanan. ”Trade Wars and Trade Talks”. Journal of Political Economy, v. 103, n. 4, pp. 675-708, 1995b.

GROSSMAN, Gene M. & HELPMAN, Elhanan. Special Interest Politics. Cambridge, Mass.: MIT Press, 2001 KAUFMAN, Robert R. & Barbara STALLINGS. "The Political Economy of Latin American Populism", Chapter 2 in Dornbusch, Rudiger & Edwards, Sebastian (Eds.). The Macroeconomics of Populism in Latin America. Chicago: University of Chicago Press. 1991.

KRUEGER, Anne O. "The Political Economy of the Rent-Seeking Society ", American Economic Review, v. 64, n. 3, pp.291-303, 1974.

LOURY, G. "Intergenerational Transfers and the Distribution of Earnings", Econometrica, v. 49, pp.843-67, 1981.

OLSON, Mancur. The Logic of Collective Action. Cambridge, Mass.: Harvard University Press, 1965.

PERSSON, Torsten & TABELLINI, Guido. ”Is Inequality Harmful for Growth? Theory and evidence”. American Economic Review, v. 84, n. 3, pp. 600-21, 1994.

RODRIGUEZ, Francisco. ”Inequality, Redistribution and Rent-Seeking”. University of Maryland Working Paper, 1999.

SEN, Amartya K.. ”Poor, Relatively Speaking”. Oxford Economic Papers, v. 35, pp. 153-69, 1983.

STURZENEGGER, Federico A. ”Description of a Populist Experience: Argentina, 1973-1976”, Chapter 4 in Dornbusch, Rudiger & Edwards, Sebastian (Eds.). The Macroeconomics of Populism in Latin America. Chicago: University of Chicago Press. 1991.

31
operations and 0 otherwise ( _Courts Obstacle_ ). Information on the capital stock used by firms is missing for most firms in the sample. Thus, we follow Amin and Soh (2021) and Islam et al. (2019) and use a proxy measure, which is a dummy variable equal to 1 if the firm purchased fixed assets during the last year and 0 otherwise ( _Firm Purchased Fixed Assets_ ).

The country-level controls, all taken from WDI unless stated otherwise, include the following: the log of GDP per capita, PPP adjusted and in constant 2011 International Dollars ( _GDP per capita_ ); a measure of the overall regulatory burden proxied by the Fraser Institute’s “Freedom from Regulation” sub-index; _Rule of Law_ and _Control of Corruption_ indicators from the WGI; the ratio of merchandise exports plus imports to GDP; the gross enrollment rate in primary education ( _Primary Education_ ); market size proxied by the log of the total population of the country ( _Population_ ); the annual rate of inflation based on the GDP deflator ( _Inflation rate_ ); and the annual growth rate of real GDP per capita ( _Growth Rate of GDP per capita_ ).

# **4. Empirical Results for Labor Productivity**

This section discusses the main regression results for the level of labor productivity (log values). Results for the growth rate of labor productivity are provided in the next section.

# 4.1 _Overall Relationship_

Table 1 presents the regression results for the overall or linear relationship between ethnic fractionalization and the level of labor productivity in the full sample. All specifications include controls for industry and year fixed effects. The remaining controls discussed above are added sequentially.

20
regression. To check whether this high correlation are blurring the individual impact of each variable, we run the same gravity regression without controlling for tariffs (column _b_ in Table 3), without variables for exporters’ trade facilitation (column _c_ in Table 3) and without variables for importers’ trade facilitation (column _d_ in Table 3). It can be seen that main estimates are fairly similar along the four formulations.

Thus, figures following column (a) are useful benchmarks against which to compare the impact on trade of changes in trade facilitation indicators. Port Efficiency of both the importer and the exporter is positively associated with trade. That is, an improvement in the indicator toward best practice is associated with an increase in trade flows. Unlike results from WMO (2004) where global trade flows get a bigger boost when the exporters’ Port Efficiency improves, our results imply a higher impact of an improvement in Port Efficiency of importers. The differences could be due to the change in the estimation approach as well as in the period covered.<sup>12</sup> So, for countries and regions that are well below the global best practice, such as Bolivia and Slovak Republic (see Table 1) there is great potential for improvement in terms of Port Efficiency.

This is the case also for Mexico whose Port Efficiency indicators as measured here are 13% below the average of the sample (level of .582 versus the average of 0.673). The Regulatory Environment, captured here by a single variable (control of corruption from the KKZ data set), turned out to be only statistically significant for the exporter (an elasticity of 1.191). This result is in line with WMO (2004) who find a bigger impact of exporters’ Regulatory Environment, statistically significant at the 1% level, whereas that of the importers’ had lower magnitude and was only significant at the 10% level. As mentioned above, we are letting the data tell whether we need to use measures of Customs Environment for importers and for exporters or, as in WMO (2004), only for importers.

When using this variable for both exporters and importers, it was found that only the Customs Environment of the exporter country has a significantly positive effect on exports with an elasticity of 0.676, whereas Customs Environment of importer turned out to be statistically

> 12 We use two more years of data (2002 and 2003) and also estimate the gravity equations using the average 20002003 of trade and GDPs for the whole period. Besides the period covered and the poisson-equation approach (versus the OLS approach of WMO) there are two other differences with WMO: i) for Regulatory Environment we are using only data from KKZ for Control of Corruption, since data from WYC on Transparency of Government Policies were not available for all the countries in our sample, and ii) we are using Custom Environment measures for both importers and exporters and not only for importers.

12