rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>receiving all given below services in one visit).<br>•<br>Examined the cord<br>•<br>Counselled on danger signs<br>•<br>Assessed temperature<br>•<br>Counselled mother on feeding<br>•<br>Observed feeding<br>•<br>Assessed weight|
||**Adequate essential newborn care at birth (**adequacy indicates – a woman reported of her baby<br>receiving all given below services at birth).<br>•<br>use of a safe delivery kit/ bag or boiled blade<br>•<br>nothing applied to the cord or only chlorhexidine applied<br>•<br>after the cord is cut and tied, cord dried within 5 minutes after birth<br>•<br>bathing delayed 72 hours or more<br>•<br>immediate breastfeeding|
|Health/nutrition<br>outcomes|<br>**Under-five Child nutrition**<br>•<br>Stunting (i.e., more than 2 standard deviations below the median for height-for-age)<br>•<br>Wasting (i.e., more than 2 standard deviations below the median for weight-for-height)<br>•<br>Underweight (i.e., less than 2 standard deviations below the median for weight-for-age)<br>**Source**: DHS|

**Table 3: Demand-side determinants***

|**Variables**|
|---|
|Household access toimproved sanitation facilities|
|Household access to improved drinking water|
|Household access to hand washing|
|Household wealth status in quintile|
|Age of mother at childbirth|
|Age at marriage|
|Education status|
|Employment status|

1 Quality of care indices consisted of equally weighted averages of their component variables.

5
consumption may do a better job in this respect, but will still be an imperfect welfare indicator given that inter-temporal markets do not work perfectly. There are also uncertainties about how best to normalize for heterogeneity in consumption needs, such as stemming from demographic differences between households (Pollak, 1991).<sup>4</sup> And even if we had an ideal measure of average economic welfare for a household, health status is an individual attribute, which will depend directly on personal economic welfare. Individual income is a flawed metric of individual command over commodities given some degree of income pooling within households. And household expenditure may well be equally flawed as an indicator of individual economic welfare given that pooling is almost certainly incomplete.<sup>5</sup> In short, heterogeneity in relevant individual and household circumstances, intertemporal consumption smoothing and inter-personal income sharing may entail that neither measured current income nor consumption are particularly good proxies for economic welfare, as relevant to health. This too will lead one to underestimate the true economic gradient in health using the methods commonly found in the literature.

This paper proposes a method of estimating the economic gradient in health status that is likely to be more robust to non-ignorable errors in _SAH_ data and the measurement errors in data on incomes or expenditures. The essential idea is to use largely independent covariates of both _SAH_ and perceived economic welfare to attempt to purge the raw data of the non-ignorable errors, prior to testing for the economic gradient in health status. To calibrate a broader measure of economic welfare we draw on subjective data, though recognizing that this too contains measurement errors when used as an indicator of objective economic welfare. Our estimation

> 4 For example, the poverty lines used as deflators may not correctly weight differences in household size or demographic composition.

> 5 It is instructive that, in the same setting, Ravallion and Loskhin (2001) find evidence that many “non-income” factors at the individual and household levels impinge on perceived economic welfare in Russia at given current
**<u>Table 2: Share of countries borrowing for public sector reforms (approved between FY00 and 10)</u>**

||Total number of<br>countries counted|Number of<br>countries with no<br>PSG operation|Number of<br>countries with one<br>or more operations|Number of<br>countries with 2<br>or more<br>operations|Number of<br>countries with 4<br>or more<br>operations|
|---|---|---|---|---|---|
|#|128|41|87|61|19|
|_%_|_100_|_32_|_68_|_48_|_15_|

Note: PSGB mapped development policy and investment operations only. Source: Business Warehouse data on PSGB mapped policy and investment loans/grants (active and closed).

Nearly half of all potential client countries (with populations greater than 500,000) had two or more Bank operations. A smaller group (19 countries) had 4 or more. It includes several large and/or federal countries (Argentina, Brazil, Mexico, and Russia), some countries with special conditions (Chad related to the Chad-Cameroon pipeline), some fragile states with significant Bank engagement (Afghanistan, Kosovo, Sierra Leone, Timor Leste), and various others (Ethiopia, Tanzania, and Uganda). Regionally, the spread is relatively even, with some patchiness in Sub-Saharan Africa and relatively strong engagement with MICs in Europe and Central Asia (ECA) and Latin America (LAC). The Middle East and North Africa (MENA) region stands out with relatively few public sector operations (only 7 out of 15 countries/territories with at least one lending operation).<sup>24</sup> This pattern is consistent with the notion that incentives to initiate public sector reforms are significant across
Index<br>100<br>50<br>0 0<br>25 160<br>140<br>20<br>120<br>15<br>100<br>Employment level Real Wage Index<br>10<br>80<br>5 60<br><!-- End of picture text --> Figure 4: Trends in Employment Level and Real Wage

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Panels (a) & (b) present trend in average employment and real wages for small, medium, and large firms. Employment data is reported in levels while real wage data is indexed with 1991=100. Panels (c) & (d) repeats the analysis in Panel (a) & (b) respectively for only small and medium firms.

Figure 4 shows the trend in average employment levels and real wages across the three categories of firm sizes.<sup>14</sup> It can be observed from panel (a) that large firms increased their average employment level over the decade. On the other hand, the average employment level for small and medium firms stayed almost constant. Panel (b) of the same figure shows an increase in the real wage with relative to the base year for large firms.

Panels (c) and (d) of fig. 4 show the same plots as in panels (a) and (b) but omit 19
the parallel-trend assumption in difference-in-difference analyses).

## **Signs of a “bad” natural experiment (“bad IVs”)**

With these guidelines in mind, we highlight some danger signs of a “bad” natural experiment, one that does not make a credible case for the exogeneity of its key right-hand-side variation.

It is often difficult to make an _a priori_ case for rhetorical plausibilty when seeking to achieve causal identification using only cross-sectional data (data at just a single point in time for each observation). With cross-sectional data, the researcher cannot control for fixed effects for each unit (e.g., individuals or households). Inclusion of unit fixed effects in a regression helps control for time-invariant differences across units, but requires panel data (more than one observation per unit). Only in unusual cases can one make a case for rhetorical plausibility. An example is Shrestha (2017), who uses cross-sectional Nepalese data to analyze the impact of a change in an educational requirement for a migration opportunity varies plausibly exogenously across cohorts (birth-year groups) and localities.

Another danger sign is simply using one existing variable in a dataset as an instrumental variable (IV) for the key endogenous (right-hand-side) variable of interest, without a clear argument for the rhetorical plausibilty of the instrument. Examples of variables that have been used to instrument for the migration decision include network instruments, such as leave-one-out instruments (instrumenting own migration with that of others)<sup>5</sup> , or village lagged migration rates; being the oldest child in a household; the gender of oldest child; parental education; proximity to transport; and proximity to remittance services. These variables are often found to be correlated with migration (or related outcomes like remittances), and thus are argued to have a strong “first stage” in IV estimation. But each one is quite likely to violate the IV exclusion restriction: the assumption that the instrument affects the second-stage outcome of interest only via the instrumented
content requirements was also prevalent in the oil and gas sectors, a tendency reversed in the more recent licensing rounds. A recent analysis conducted by Stone, Messent and Flaig (2015) maps the use of LCR measures applied between 2008 and April 2014 in several countries around the world. Results show that, in this period, Brazil was second only to Indonesia in the number of LCRs used, with 17 LCRs in force.<sup>11</sup> Trade in services, a key enabler of GVC integration and productivity growth, is hampered by policy and regulatory barriers as well as by a distortive tax structure. The internationalization of production and subsequently trading in the GVC also requires attention to the role of services. Next to the fact that many services are used as inputs into the production of GVC goods, they are also essential in the smooth operation of the GVC to connect different production sides across borders. A clear example is logistics services. The latest OECD-WTO TiVA data for 2011 show that the value added of the service sector (both from domestic and foreign sources) contained in Brazil’s gross exports (49 percent) is above the average of Latin American and the Caribbean (LAC) peers (42.3 percent) but below the OECD average (54 percent). In addition, only one-tenth of the total services content of exports originates from foreign providers. This limited contribution is somehow influenced by substantial policy barriers in the service sector. On average, Brazil has more restrictions to trade in services than the average in the LAC region, according to the World

> 11  Overall, the use of LCR tends to undermine export competitiveness over the long run and lead to suboptimal resource allocation that further affect productivity, since the price hike associated with the change from cheaper foreign suppliers to more expensive domestic suppliers leads to substitution away from these now more expensive, though more efficient, goods and services in the rest of the economy. Estimations presented also by Stone, Messent and Flaig (2015) show
</sup> To test for robustness, we use alternative estimators that incorporate different assumptions about the structure of the stochastic error term (εit) in the model. These techniques produce the same point estimates for model parameters, but their differing estimates of standard errors (and the accompanying t-statistics) may lead to very different inferences about the statistical significance of model variables. Table 1 reports our results; we replicate the point estimates in columns (1)– (3) to aid interpretation of the t-statistics. We include standard linear regression results along with results for robust standard errors (SE), and standard errors adjusted for 1,149 clusters defined by level-1 administrative units (states, provinces, etc.) in the 99 countries in the regression database.

The results in Table 1 are all highly robust, suggesting that an increase of one percentile in the extent of IFPRI-identified irrigated rice fields is associated with a percentile increase of 0.5 in the

> 9 Most grid cells have only one cultivation calendar; this assignment rule applies to grid cells that straddle two or more RiceAtlas calendars.

> 10 Percentile value regressions are robust to alternative specifications of functional forms, imposing only the assumption that dependent and independent variables have a monotone increasing relationship. Rank regressions also have these characteristics.
ratios. The general survey of the Korean population and the surveys of KHB borrowers clearly show the large degree of fragmentation and inefficiency which could be eliminated by the financial liberalization of housing finance and the effective development of a mortgage market.

# V. COMPOUNDING FINANCIAL REPRESSION WITH RIGID LAND REGULATIONS

38. Korea is one of the three economies with the highest population densities in the world, the other two being Taiwan and Bangladesh. Therefore some would argue that one should not be surprised to find that the ratio of the value of a unit to the income of the household is high. Also, were not the high rates of return in the land market caused by the extremely fast pace of growth of the urban population and the rapid rise of household incomes? The empirical evidence supports the view that rapid land appreciation was caused by more than rapid urbanization and the scarcity of urban land in Korea. This evidence is that land use policies have compounded the effects of financial repression, restricted the supply of housing, helped raise rents and thus accelerated the rise of the relative price of housing compared to household incomes.
**Figure 5: Cluster buffer with upstream and downstream sub-basins**

# **2.3. Environmental and Climate Data**

For forest cover loss, we use the Global Forest Change dataset by Hansen et al. (2013). They quantify the global forest change from a supervised pixel-based image classification of the Landsat satellite images at a spatial resolution of 30 meters,<sup>7</sup> using 2000 forest cover estimates as well as annual indicators from 2001 to 2020 for forest loss and gain. To estimate forest cover changes, we compute standing forests in 2000 and track forest cover loss until 2020. We count the number of forest pixels lost each year within each cluster (converting total counts to percentages), thus creating a panel of annual deforestation at the cluster-level from 2001-2020 (Figure 6). For our identification strategy, it is critical to account for annual deforestation in the watershed upstream from the cluster. We also need annual deforestation downstream for our falsification test. We thus follow the same approach to compute annual deforestation in the watersheds that are located upstream and downstream from each cluster.

> 7 Trees are defined as all vegetation taller than 5 meters in height.

11
The survey reveals a near complete electrification rate in urban areas and 92 and 94 percent electrification rates in rural areas of Alwar and Jaipur respectively. The National Family Health Survey (NFHS) of 2015-16 closely reflects these findings, reporting 99 and 96 percent electrification in urban and rural areas of Jaipur, and 97 percent electrification in rural areas of Alwar. Of 2,027 households surveyed in the sample, 1,637 households had a metered electricity connection; 904 of these households had complete socioeconomic information and matched to the administrative billing data. Table 2 shows that the consumption distribution of the sampled households is able replicate the consumption distribution from the billing cycle-level data set over the selected period.<sup>14</sup> These matching patterns allay concerns related to sample selection in the overall distribution, except from the 95<sup>th</sup> percentile onwards of the consumption distribution - where our sample appears to represent a lower fraction of households than in utility’s customer database.

To capture household-level income, the survey contained modules pertaining to farming, livestock, self-employment, casual labor activities, salaries jobs and remittance earnings for each adult member of the household. Total household income was calculated as the sum of incomes across each of these modules and household members. The survey had a non-response rate of approximately 9% on income questions -- these non-responsive households were also excluded from the sample. To keep the survey tractable and short, data on household consumption expenditure or assets and liabilities information was not collected. As a result, household disposable income cannot be calculated separately from total household income. Finally, household-bills for which the annualized electricity bill amount was greater than 75% of annual reported household income were excluded. This restriction is imposed to account for the fact that households also need to pay for food and other essentials besides electricity throughout the year.

After excluding observations based on the income criteria and after matching household survey data to the administrative data, the final sample contained 7,615 billing-cycle level observations
**Table 1: Summary statistics**

||Average|Std. Deviation|Min|Max|
|---|---|---|---|---|
|Exports (million US$)|610738.8|5408312.0|10.0|3.68E+08|
|Number of product traded|116.2|162.6|1.0|744.0|
|Sectoral Avg export|2162452.0|1.05E+08|158.8|2.49E+10|
|Importer Internet penetration|29.5|27.5|0.0|96.5|
|Exporter Internet penetration|35.4|28.2|0.0|96.5|
|Importer GDP (current million US$)|435183.6|1494994.0|0.0|1.68E+07|
|Exporter GDP (current million US$)|558413.7|1665443.0|0.0|1.68E+07|

Finally we will also explore the effect of the Internet adoption on different types of goods. The Internet reduces the information and communication costs between exporters and importers and this effect is most relevant for goods that are differentiated and/or customized. We employ the classification from Rauch (1999) which allows us to classify the products into homogeneous and differentiated goods. In order to match trade date with the Rauch classification and to construct bilateral trade for each type of good, we use disaggregated data at the 4 digit SITC level from COMTRADE.

# **3. Results**

- (a) Bilateral Exports, Intensive and Extensive Margins

We examine the effects of the Internet adoption on bilateral export flows and the results are presented in Table 2. Column (1) contains the results of the OLS regression with year, importer and exporter fixed effects and while it is not the preferred specification, it affirms
**Table S15:** Association of market access on outcome variables using OLS, panel data

||N|TL|Urb|an|Crop|land|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._004<sup>_∗_</sup>|_−_0_._0003|0_._001|0_._02|_−_0_._02|_−_0_._02<sup>_∗_</sup>|_−_0_._08<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._002|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._02)|(0_._03)|(0_._01)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 L|it|0_._11<sup>_∗∗∗_</sup>||0_._06<sup>_∗∗∗_<
a set of variables related to the initial conditions of the birth cohorts in the workforce (Ict)— namely, the log GDP per capita, the population size, and the infant mortality rate in the decade of birth. The initial conditions during childhood may have long-term effects on adult productivity and affect the relationship between mobility and economic development.

The misallocation of talent that results from low educational mobility within a given birth cohort may impact productivity only when this birth cohort enters the workforce. To take this into account, we follow Neidhofer and others (2023) to estimate cohort participation profiles for each country-year. We estimate the aggregate level of mobility in the following way: where _mbc_ is the mobility of cohort _b_ (out of a total of _B_ cohorts) in country _c_ , and _nbct_ is the number of employed people in cohort _b_ in country _c_ in year _t_ . The aggregate level of mobility in a country at a given time is thus the weighted mobility of the different cohorts, where the weights are the share of each cohort in the country’s labor force at that point in time. We apply this same weighting procedure to the cohort-specific variables in equation (7).

# **5. The data**

This paper combines data from two main sources: the Life in Transition Survey (LiTS) and the Global Database on Intergenerational Mobility (GDIM).

LiTS is used as the primary data source for deriving intergenerational educational mobility in the countries of Europe and Central Asia. LiTS has been implemented by the European Bank of Reconstruction and Development (EBRD) and the World Bank. The survey covers the transition economies of Europe and Central Asia<sup>8</sup> and several comparator countries in Western Europe, the Middle East, and North Africa (EBRD 2023). The first round of the LiTS was carried out in 2006, the second in 2010, the third in 2016, and the fourth in 2022–23. This study uses the second, third, and fourth
**Table 1. The main characteristics of data sources - Tax Administrative Data vs. EU-SILC.**

||**Tax Administratve data**|**EU – SILC**|
|---|---|---|
|**Partcipaton**|Compulsory<br>(includes<br>all<br>employees who paid tax based<br>on their declared labor income)|Voluntary|
|**Coverage**|Cover the full labor income<br>distributon|Underreportng at the top is<br>usually a problem in the survey<br>data|
|**Tax evasion**|Data is impacted by tax evasion,<br>as the informal economy is not<br>covered|Partcipants report their total<br>income earned in the formal and<br>informal economy|
|**Data quality**|The employers report income to<br>the tax ofce|Income is self-reported; recall<br>errors and rounding may be a<br>problem|
|**Income defniton**<sup>**4**</sup>|Gross salary, 24 monthly fles|Gross labor income, annual|
|**Data size**|6.9 – 7.4 million taxpayers<br>monthly|16,630 individuals; 5,758 with<br>positve labor income|
|**Source**|Ministry of Public Finance|Harmonized data from Eurostat<br>based on data collected by the<br>Natonal Statstcs ofce|

_Source: Own elaboration_

**To compare the coverage of both data sources, we compare the aggregate value of labor income in national accounts and administrative tax data, and EU-SILC.** One option to check for potential biases

> 4 While in the EU-SILC, the income was initially collected in local currencies in a data set shared with researchers, it is recorded
would still be possible that exporting results in further productivity improvements.

> 1 Söderbom and Teal (2003) find that exports—although not manufacturing exports in particular—were associated with income growth in nine countries in Sub-Saharan Africa.

> 2 Several investment climate assessments, which calculate productivity data for the firms in this study have found evidence consistent with the idea that exporters are more efficient.  See, for example, World Bank (2004a) for Tanzania.  These studies are available on the World Bank’s website (http://www.worldbank.org/ privatesector/ic/ic_ica.htm ).  A similar relationship has been observed in many developing and developed countries.  The large literature on this topic is summarized in Tybout (2003).

2