rafmacalaba/data-use-mentions-extended huggingface dataset:
.01)|(0.01)|(0.01)|(0.01)||
|Observations|9,622|1,901|1,829|2,451|3,441||

Notes: The table reports summary statistics for contacted borrowers and tests of covariate balance. Panel A reports summary statistics on borrower characteristics from the lender’s administrative data and credit reports, panel B reports summary statistics on loan characteristics observed in the lender’s administrative data, and panel C reports summary statistics on borrowers’ credit history based on credit bureau data. Column (1) shows summary statistics for the entire sample with standard deviations in brackets, columns (2) to (5) shows means for each variable across treatment conditions, with standard errors in parentheses. column (6) reports the _p_ -value of equality of means across the four main treatment conditions. Because the sampling proportions were changed after the fourth month of the experiment, all balance tests control for an indicator equal to one for all months after this date. Column (1) may differ from weighted averages of columns (2) to (5) for this same reason.

35
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manufacturing sector which is associated with a higher share of jobs in exports (Figure 8, right panel). In Poland, for instance, shares increased from 19 to over 34 percent; in other words, over a third of the workforce is now supported by Poland’s exports. By contrast, the share of employment in exports stagnated in Cyprus and across many commodity-exporting countries, including in Canada, New Zealand, Chile, Colombia, Norway, and Indonesia. The latter is driven by declines after the commodity boom of the 2000s. Overall, employment growth in exports exceeded employment growth sustained by domestic demand between 1995 and 2018 (Figure 9).

**Figure 9: Export-driven vs. domestic demand-driven employment growth, 1995-2018 (CAGR)**

<!-- Start of picture text --> Export-driven vs. domestic demand-driven employment growth 1995-2018 (CAGR)<br>SAU<br>ISR LUX<br>CYP COLNZLIDN CHLAUS CRIZAF ARG<br>CAN MLT IRL<br>FINUSANORSWEBELGBR NLDKORCHEINDISL TURBRA ESPMEX<br>EST RUSCHNDNKITA FRA AUT<br>DEU<br>SVN SVK PRTHUNJPN GRC<br>CZE HRV POL<br>LTU LVA<br>BGR<br>ROU<br>-2 0 2 4 6<br>Employment growth sustained by exports (%)<br>EAP ECA LAC MENA<br>NA SA SSA y=x<br>R-squared= 0.12<br>4<br>2<br>0<br>-2<br>Employment growth sustained by domestic demand (%)<br><!-- End of picture text --> Source: Winkler, Aguilar-Luna, Kruse and Maliszewska (2023). Data: OECD TiE data, covering 51 countries and 45 sectors for the period 19952018. The country sample covers 38 OECD countries plus 13 non-OECD countries. The red line indicates employment growth in exports equals employment growth sustained by domestic demand.

# 3.2 In which
time tending the crop and less time on things that are good for children's health, and so child health outcomes can worsen even though incomes from coffee production increase.

Finally, Woldemichael _et al.,_ (2017) look at the impact of price inflation of teff, maize and wheat on a child’s health at different periods of development in the months both before and after birth. Using data from different rounds of the Demographic and Health Surveys and separate data on prices at the local level, they find a u-shaped relationship between food price inflation and a child’s height for age (stunting). The impact is more pronounced for teff than for wheat and maize. For wasting (weight for height), there is no significant impact of food price inflation on a child’s development. Overall, a common theme that emerges from this literature is that there are significant risks to children’s nutritional development from rising food prices.

# **3. Data on Prices, Households and Child Nutritional Outcomes in Ethiopia**

In this study we use information on individual (child) outcomes and individual, household and community characteristics from the two most recent rounds of the Living Standards Measurement Survey (LSMS) undertaken in Ethiopia.<sup>7</sup> The data set contains detailed information on health statistics for children under 5 years of age which allows the construction of measures of child undernutrition. The community data come with detailed geo-spatial household information including accessibility to markets. Table 1 summarizes the year, survey period and the number of children under 5 years.

**Table 1: Summary of Ethiopia LSMS data for children aged between 6 to 59 months**

|**Year**|**Survey period**|**No. of children (6-59 months)**|
|---|---|---|
|2013/14|September 2013-March 2014|2,880|
|2015/16|September 2015-April
<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Malaysia Food Textiles, Wood, Media and Chemical, Basic and Electrical Machinery<br>-0.05<br>beverage apparel and paper and other rubber, fabricated and optical and<br>-0.1 and tobacco leather furniture manuf. plastics and metals equipment transport<br>other<br>-0.15<br>minerals<br>-0.2<br>-0.25<br>∆(TFP, 2000-05, 2010) ∆(TFP Dispersion, 2000-05, 2010)<br>Panel C: Philippines<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Philippines Food Textiles, Wood, paper Media and Chemical, Basic and Electrical and<br>-0.1 beverage and apparel and and furniture other manuf. rubber, fabricated optical<br>tobacco leather plastics and metals equipment<br>-0.2 other<br>minerals<br>∆(TFP, 2000-07, 2008-15) ∆(TFP Dispersion, 2000-07, 2008-15)<br>Source: Country data as detailed in Table 1 and authors’ calculation<br><!-- End of picture text -->

# **5. Conclusion**

This paper documents the evolution of productivity in developing EAP since 1990. It examines labor productivity and total factor productivity. <mark>EAP’s productivity has been gradually catching up with the frontier (the United States), with China having the fastest growth. T</mark> he paper identifies and discusses the sources of the countries’ productivity growth. <mark>Productivity growth is driven by sustained
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on care-required days. More than 70% of the doctor visits that the subsidy generates are recommended by WHO guidelines. By contrast, providing health information to parents through CHW visits has no aggregate effects on demand in our sample, consistent with parents already being able to distinguish need for care. That said, for children up to one year old who receive the subsidy, differences in care seeking when care is required vs. not are less pronounced, and CHW visits appear to lead to an additional reduction in underuse of care. This suggests that the subsidy and information interventions may complement each other in improving care for the age group with the highest mortality risk. In an abridged 2014 follow-up survey, we find a corresponding increase in formal care for diarrhea among children under two years of age in the full program group only, although we are unable to classify whether this care is required by WHO standards (Dean and Sautmann (2023)). In the same follow up we also show that CHW improved prevention, and that the full program leads to significant reductions in diarrhea incidence two years later.

Overall, our results are consistent with cost constituting a primary barrier to appropriate care in our population, along with potentially higher information barriers for the youngest age group, in line with qualitative evidence on the greater difficulty parents have in recognizing illness in young children (Charlet et al. (2017)). The results are encouraging for the planned roll-out of subsidized care in Mali, supported potentially by CHW activities focused on better curative care for infants. Our findings complement a study in Bamako over the same period by the NGO Muso and the Malian Ministry of Health in 2008-2015 (Johnson et al. (2013, 2018)), which found reduced incidence of febrile illness and mortality after the introduction of CHW visits, user fee removal, and health system strengthening measures (although the program effects cannot be easily distinguished from other time trends). Our experiment directly shows changes in curative care seeking in response to the subsidy, pointing
# **VI. Discussion – Agriculture Sector**

The above analysis based on the official firm registry data covers almost all sectors in the entire economy, but one, agriculture. The database includes some 20,000 formal enterprises, which employ about 100,000 people in total. Although large agrobusinesses in the formal sector are included, a large number of small agricultural producers, such as self-employed farmers, are not included.<sup>14</sup> One may wonder if the improved transport corridors would affect their agricultural productivity.

To examine this question, the dynamic panel data method is applied to crop production data at the district level: Where _q_ denotes the total production of a particular crop at location _j_ at time _t_ . Four major crops are examined: cereal, potato oilseed and vegetable. The first two crops are traditional food crops in the country, but potato exports have been increased rapidly. Oilseeds and vegetables are also becoming increasingly important as export crops. These cash crops are expected to more sensitive to transport connectivity, _vk_ . No other time-variant variable is included in the equation. Again, the same approach is used to avoid the potential endogeneity between crop production and transport connectivity. All time-invariant characteristics, such as agroclimatic productivity of land, are removed by first differencing.

Four major crops grown in Kazakhstan are considered: cereal, potato, oilseed and vegetables. District-level panel data are available for these four crops in 199 districts during the period of 2011-19 ( **Table 9** ). The analysis only uses the data for 2012 to 2018.

> 14 In Kazakhstan, about 1 million people are estimated to engage in agriculture. According to WDI, for instance, the total labor force in Kazakhstan is about 9 million, of which about 16 percent are estimated to be working in the agriculture sector.
it is based on importers import from the specific markets in two-year averages of 1991-92, 2001-02, and 2007-08.

/b Industrial countries are based on traditional IMF definition, including EU15, United States, Canada, Japan, Australia, New Zealand, Iceland, Norway, and Switzerland.

- /c Developing countries include all countries except 23 industrial countries in the world.

- /d Sample of developing countries includes Brazil, China, India, Korea Rep., Malaysia, Mexico, South Africa, and Turkey. /e Sample of industrial countries includes Canada, France, Germany, Japan, and United States.

Sources: Computations based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

was negative but not significant (Annex table 3). Market share changes are negative correlated with demand increases across countries and periods.

6
the export flows of virtually all exporting firms, and provides detailed information on the product exported, the destination market, and the value and quantity exported. Export values in these data are free-on-board, thus excluding any duties or shipping charges.

**Management practices survey** : We further use data from _Inqu ́erito `as Pr ́aticas de Gest ̃ao (IPG)_ [Management Practices Survey] for 2016. IPG is a non-periodical survey conducted by INE, which collects information on the perceptions of top executives about the management practices of their firms. The 2016 survey was the first and only of its kind collected in Portugal. It seeks to evaluate the importance of management practices for firm productivity, as well as other key indicators that make it possible to evaluate differences in productivity between Portuguese firms. IPG employed a stratified sample of firms operating in Portugal covering the whole non-financial private sector in 2016, excluding micro firms (with fewer than five employees). The sample is representative by sector (20 sectors corresponding of aggregations of the 2-digit level of the CAE), firm size and age, as well as belonging (or not) to a conglomerate. The IPG survey includes questions seeking to evaluate management practices in three main areas: (1) Strategy, monitoring and information; (2) Human Resources; and (3) Management and social responsibility systems. We selected 18 variables that are closely related to those adopted in Bloom and Reenen (2007). Following their approach, our measure of management quality was constructed by z-scoring (normalizing to mean 0, standard deviation 1) the 18 individual questions in IPG, taking averages, and then z-scoring the average. This process yields a management practice score with mean 0 and standard deviation 1.

**Actual and forecasted GDP growth** : We further use yearly information on actual and recently forecasted GDP growth from the World Economic Outlook (WEO) of the International Monetary Fund (IMF). WEO is usually published twice a year (in
, mimeo, The World Bank.

Filmer, Deon, Elizabeth King, and Lant Pritchett, 1998. "Gender Disparity in South Asia: Comparisons Between and Within Countries"' World Bank Policy Research Working Paper No. 1867. The World Bank.

Filmer, Deon and Lant Pritchett, 1998. "Estimating wealth effects without income of expenditure data -- or tears: Educational enrollment in India," World Bank Policy Research Working Paper No. 1994. DECRG, The World Bank. Washington, DC.

Filmer, Deon, and Lant Pritchett, 1999a "The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries". _Population and Development Review 25(1)._ Filmer, Deon and Lant Pritchett, 1 999b, "Determinants of Education Enrollment in India: Child, Household, Village and State Effects," _Journal of Educational Planning and Administration._ 40
- 16 -

should be attributed to the remaining differences with respect to the country coverage (New Zealand), data periods used, the air cargo rates, the purchasing power adjustment to CNP data, etc.

28. In addition, Balassa (1986b) also undertook an exercise specifically focused on the ratio of manufactured imports to GNP. This differs from Saxonhouse (1985), who focused on a mixed range of products, manufactured and primary, and Bergsten and Cline (1985) who focused on total imports (including primary and manufactured goods and nonfactor services). Balassa (1986b)'s regression results showed that the Japan dummy's coefficient was negative and significant in every case examined except for two, indicating that Japan was an "outlier" on the downside among the industrial countries. The two exceptions were the regression for total imports when physical distance (instead of transport costs) was used and the regression for total imports from developing countries.

29. Among the possible issues arising from the results of Balassa (1986b), the most important is the measure of transport costs. Since good ready-made data for transport costs for the purpose at hand did not exist, Balassa (1986b) derived transport cost data from available sources, but the transport costs thus derived for manufactures were markedly lower than those implied by the data from an authoritative source, Lipsey and Weiss (1974). 1/ Balassa (1988) used alternative estimates of the transport costs, or generally higher transport costs for manufactures. Yet, the results continued to show that Japan dummy's coefficient was negative and significant in every case except for a few cases. The exceptions were I/ Cited by Noland (1987).
8%|
|Senegal|Poverty Monitoring Survey|2010|331|7%|0.8%|
|Sierra Leone|Labor Force Survey|2014|220|27%|3.1%|
|Tanzania|Labor Force Survey|2014|396|12%|1.4%|
|Uganda|Living Standards Measurement<br>Survey|2013|170|15%|2.6%|
|Zambia|Labor Force Survey|2014|486|10%|2.4%|

Source: The surveys were downloaded from the World Bank Microdata Library.

33
# **B.2 Robustness**

In the first robustness check, we estimate the average treatment effect in levels as percentage changes from the controlled mean using a negative binomial. Specifically, we estimate the following model: where _yij,t_ is the count number of patent applications. _Zi_ is the number of pre-determined, time-invariant firm characteristics that might affect firms’ patenting capability. Following Blundell et al. (1999), we use the average pre-sample patent applications to control for firms’ fixed effects in the negative binomial model. We use the firm’s patent age and patent stock to approximate firm _i_ ’s patent capability. Table A.7 displays the estimated coefficients and the implied average treatment effect, _exp_ ( _β_ + _ψ_ ) _−_ 1.

Table A.7: Estimated Average Treatment Effect using Negative Binomial

||(1)<br>Total|(2)<br>**Patent Ap**<br>International|(3)<br>**plication**<br>Triadic|(4)<br>High-Tech|
|---|---|---|---|---|
|Treatment_×_Post|-0.365***|-1.249***|-3.389***|-0.571***|
||(0.057)|(0.299)|(0.360)|(0.108)|
|Treatment|0.125***|0.266**|0.561***|0.400***|
||(0.040)|(0.135)|(0.051)|(0.108)|
|Implied ATE|-0.213***|-0.630***|-0.941***|-0.157*
φ is 83%. Either at 60% or 83%, the value is much larger than those commonly used in the theoretical literature. Our empirical estimate of φ has been cited and used in recent theoretical literature on macro-prudential policy, for example, by Bianchi and Mendoza (2017).

In the second part of this paper, we run cross-section regressions to find associations between firm characteristics, bank characteristics and loan characteristics with collateral requirements. We focus on two questions: (1) whether an existing loan requires collateral (i.e. the extensive margin), and (2) when the loan does require collateral, what is the collateral value relative to the loan value (i.e. the intensive margin)?

On the extensive margin, we find that foreign firms are less likely to pledge collateral when they borrow. This result is not new. Berger and Udel (1995) with U.S. data and Jimenez et al (2006) with Spanish data have similarly found that loans to low-risk borrowers are less likely to be collateralized.

What is new and more interesting is that small firms are found to be significantly less likely to pledge collateral compared to medium and large firms. Loans from non-bank financial institutions are also more associated with no collateral. These two results are related, because small firms are significantly more

> 3 Among 43,879 firms that provided valid answers to whether collateral was required, 22,263 firms have approval of loans within three years of the interview. These 22,263 firms form the sample for us to analyze the likelihood of collateral requirement.

3
of these outcomes. However, we present them in the Appendix.

We structured the questionnaire to capture impact of the CCT after accounting for his/her partner’s exposure to CCTs during adolescence. By design, this estimation would only include married/partnered individuals. Unfortunately, we have very limited variation in the sample for women: partners of only 25 women were exposed to CCTs in adolescence: less than 10 percent of the sample of partnered women with non-missing information about partner’s exposure to CCTs.

We did not discuss explicitly in the PAP whether we are using only FTFDQ data as our primary specification, or pool FTFDQ and ACASI. We chose to pool outcomes yielded by the two methods, given lack of significant differences across methods, and in order to increase sample size.

19
# **Box 3: Trends in Public Education Spending in Fragile and Conflict-Affected Countries**

Fragility, conflict, and violence can have a profoundly negative impact on a country’s education system and its ability to provide a quality education to all children. A lack of funding often exacerbates the difficulties faced by the governments of these countries (Commins 2017; UNESCO 2011).

On the face of it, countries on the World Bank’s Fragile and Conflict-Affected Situations (FCS) list seem to devote similar shares of GDP to public education spending as other low-income and lower-middle-income countries, and to spend the money in similar ways. The figure below compares the public education spending aggregates for these three categories of countries. The data show that patterns and trends in public education spending for FCS countries are similar to those for other countries at a similar stage of development.

<!-- Start of picture text --> Public Education Spending in FCS Countries is Similar to Funding in Other Low-income and<br>Lower-middle-income Countries<br>a. Public education spending b. Public education spending by education level,<br>(% GDP) 2014 – 17 (%)<br>5<br>24% 20% 17%<br>4<br>3 27% 29% 42%<br>2<br>50% 51%<br>1 42%<br>0<br>2006–09 2010–13 2014–17 LIC FCS LMIC<br>LIC FCS LMIC Primary Secondary Tertiary<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases.

_Note:_ World Bank income group classifications in 2017 are used to group countries and are as follows: LIC = low-income country, and LMIC = lower-middle-income country. FCS = country classified as a fragile and conflict-affected situation.

The lack of data on financing in fragile and conflict-affected countries and the need to go beyond national aggregates