rafmacalaba/datause-encoder-data huggingface dataset:
# **Table S14. Estimated total number of adults that are (i) moderately or severely food insecure and (ii) severely food insecure and average incidence of (i) moderate or severe food insecurity and (ii) severe food insecurity among the adult population**

|||_Panel A: T_|_otal_|||
|---|---|---|---|---|---|
||All Countries|Ethiopia|Malawi|Nigeria|Uganda|
|Moderate or|100,014,992|24,130,336|6,232,031|62,767,288|6,885,341|
|Severe|1,577,726|846,790|150,580|1,191,869|285,413|
|Severe|38,032,228|6,787,466|2,167,532|27,992,446|1,084,782|
||1,376,278|600,851|123,643|1,144,950|110,423|
|Observations|8,713|3,051|1,646|1,820|2,196|

|||_Panel B: M_|_ean _|||
|---|---|---|---|---|---|
||All Countries|Ethiopia|Malawi|Nigeria|Uganda|
|Moderate or|0.609|0.469|0.676|0.757|0.330|
|Severe|(0.010)|(0.016)|(0.016)|(0.014)|(0.014)|
|Severe|0.231|0.132|0.235|0.338|0.052|
||(0.008)|(0.012)|(0.013)|(0.014)|(0.005)|
|Observations|8,713|3,051|1,646|1,820|2,196|

_Note_ : Each column in Panel A reports the estimated total number of adults who are moderately or severely food insecure, or just severely food insecure. Each column in Panel B reports the average incidence of (i) moderate or severe food insecurity and (ii) severe food insecurity among the adult population. In both panels, food insecurity is measured using the FIES. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi, Nigeria, and Uganda. Standard errors on the estimates are in parenthesis.

41
# **Cross-border Purchases of Health Services: A Case Study on Austria and Hungary**

by Andreas J. Obermaier<sup>∗</sup>

## **Disclaimer**

This paper is part of a World Bank research project on trade in services supported in part by the United Kingdom’s Department for International Development (DFID). The findings, interpretations, and conclusions expressed herein are those of the author and do not necessarily reflect the views of the World Bank and its affiliated organizations.

> ∗ Andreas J. Obermaier is currently researcher at the Institute for European Integration Research, Austrian Academy of Sciences, Vienna, <u>Andreas.Obermaier@oeaw.ac.at.</u>
The different specifications shown in Table 8 demonstrate that weighting observations from meta-analysis substantially reduces heterogeneity. This implies that the coefficients are relatively similar between different types of specifications and methods used, keeping the rest of paper and evaluation methods characteristics constant. The coefficient of additionality impacts on R&D ranges from 0.166 to 0.252, with reasonable confidence intervals at the 95 percent level. The number of R&D impact evaluations, which predominantly target North American and European countries, are limited and such a small number constrains the robustness of the results. As more evaluations are conducted with more robust techniques, more can be said about the direction of the effects with statistical confidence. At this point, the analysis revealed that based on the surveyed studies there is indication of positive R&D impacts, but this effect should be reconfirmed with more rigorous impact evaluations. The estimation results have a large range, suggesting that the meta-data are highly sensitive to the method used. Clearly, the meta-analysis methods corrected biases into a certain extent, but the data is not as rich as other meta-analysis studies that rely on hundreds of studies.

Table 8

|**Meta-Analysis of Additio**<br>_Using Weighted Least_|**nality of Subs**<br>**Estimate**<br>_Squares correct_|**idies on R**<br>**Lower**<br>**Bound**<br>**(95%)**<br>_ing for Publi_|**&D Investments**<br>**Upper Bound**<br>**(95%)**<br>_cation Bias_|
|---|---|---|---|
|WLS|0.252|0.193|0.312|
|WLS Full Specification|0.195|0.105|0.285|
|Fixed Effect(Region)|0.194|0.101|0.271|
|Random Effects|0.166|0.140|0.192|

Source: Own estimations

# 4. Concluding Remarks

R&D evaluation studies are not characterized by having strong and rigorous methodologies. However, data from surveyed studies are heterogeneous enough to allow for meta-data analysis. Very few studies are devoted to quantitatively estimating the impact of innovation, although there is a growing body of literature on the effects of direct R&D grants. Despite this, there is still a knowledge gap on how input, output and outcomes relate to each other in the presence of grants and subsidies (Veryzer, 1998). Some studies that claim to be evaluations fail to fill the gap of endogeneity between R&D interventions and a firm’s outcome variables, in 29
24 Paper 10/116, International Monetary Fund, May.

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# Table 5

# EFFECTS OF A FIVE YEAR SUSTAINED<sup>INCREASE IN REAL GOVERNMENT</sup> EXPENDITURES BY ONE PERCENT OF REAL GNP, ON THE ECONOMY OF THE SHOCK ORIGINATING REGION

|P<br>E<br>R<br>**I**<br>0<br>S|GNP|Uni<br>PGNP ||ted Stat<br>MERM|es<br>RS|CURBL|GNP|G<br>PGNP|ermany<br>ER|<br>RS|CURBL|GNP|PGNP|Japan<br>ER|RS|CURBL|Large<br>GNP|O<br>Indust<br>PGNPI|ther<br>rializ<br> ER|ed<br>Coun<br>RSI|tries<br> CURBL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1988|1.14|0.30|2.11|0.16|-11.46|<sup>0.76</sup>|<sup>0.32</sup>|<sup>1.08</sup>|<sup>0.11</sup>|<sup>-5.43</sup>|1.22|0.59|1.37|0.21|-5.01|1.38|0.77|0.63|0.25|-6.79|
|1989|1.32|1.04|2.00|0.27|-16.62|<sup>0.74</sup>|<sup>0.92</sup>|<sup>0.99</sup>|<sup>0.18</sup>|<sup>-6.70</sup>|<sup>1.19</sup>|<sup>1.66</sup>|<sup>1.22</sup>|<sup>0.33</sup>|<sup>-8.09</sup>|<sup>1.34</sup>|<sup>1.99</sup>|<sup>0.44</sup>|<sup>0.40</sup>|<sup>-13.29</sup>|
|1990|1.26|2.17|1.89|0.41|-21.49|<sup>0.48</sup>|<sup>1.63</sup>|<sup>0.83</sup>|<sup>0.23</sup>|<sup>-7.26</sup>|<sup>0.83</sup>|<sup>2.58</sup>|<sup>0.96</sup>|<sup>0.43</sup>|<sup>-10.41</sup>|<sup>0.78</sup>|<sup>3.19</sup>|<sup>0.17</sup>|<sup>0.47</sup>|<sup>-19.25</sup>|
|1991|1.09|3.63|1.78|0.56|<sup>-27.98</sup>|0.19|2.27|0.63|0.27|-7.72|0.44|3.88|0.64|0.50|-12.80|0.22|4.09|-0.11|0.51|-24.87|
|1992|0.87|5.31|1.64|0.74|-35.46|-0.04|2.76|0.41|0.29|-8.31|0.15|4.58|0.27|0.55|-15.23|-0.11|4.58|-0.37|0.53|-29.87|
|1993|0.62|7.09|1.43|0.91|-44.33|-0.17|3.06|0.17|0.31|-9.10|0|4.89|0.09|0.57|17.29|-0.17|4.69|.59|0.55|-29.82|

GNP = Gross National Product PGNP = GNP Price Deflator MERM = MERM Weighted Effective<sup>Exchange Rate</sup> RS = Short-Term Interest Rate CURBL = Current Account Balance The figures for GNP, PGNP, MERM and<sup>ERare percentage deviation</sup> from the baseline. RS is the change in the percentage level. CURBL is in billions of dollars.
||Ov|erall|Co|ntrol||HWWS|||TSSM|||HWWS+TSS|M|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|N|Mean|N|Mean|N|Mean|p-value|N|Mean|p-value|N|Mean|p-value|
|_Assets owned by HH at Baseline (cont)_||||||||||||||
|Car|3617|0.01|919|0.00|900|0.01|0.09|878|0.00|0.73|920|0.01|0.51|
|Elctric/Gas Stove|3617|0.00|919|0.00|900|0.01|0.70|878|0.00|0.95|920|0.00|0.17|
|Other Stove|3617|0.25|919|0.20|900|0.30|**0.01**|878|0.26|0.06|920|0.25|0.13|
|Refridgerator|3617|0.01|919|0.01|900|0.01|0.31|878|0.01|0.79|920|0.02|0.70|
|Mattress|3617|0.72|919|0.66|900|0.75|0.05|878|0.72|0.23|920|0.73|0.14|
|Sewing Machine|3616|0.05|918|0.04|900|0.06|0.14|878|0.05|0.93|920|0.05|0.93|
|Mosquito Net|3617|0.88|919|0.88|900|0.89|0.74|878|0.86|0.43|920|0.87|0.59|
|Mobile Phone|3617|0.48|919|0.45|900|0.51|0.08|878|0.47|0.54|920|0.49|0.27|
|Fixed-line phone|3617|0.00|919|0.00|900|0.00|0.31|878|0.00|0.65|920|0.01|0.18|
|Iron|3616|0.13|919|0.11|899|0.14|0.22|878|0.15|0.10|920|0.12|0.71|
|Bed frame|3617|0.70|919|0.67|900|0.73|0.08|878|0.71|0.29|920|0.69|0.59|
|Jewellery|3616|0.01|919|0.01|899|0.01|0.59|878|0.00|0.16|920|0.01|0.44|
|Land or Field|3616|0.66|919|0.67|900|0.69|0.61|877|0.67|0.94|920|0.63|0.36|
|Agricultural Equipment|3617|0.16|919|0.16|900|0.16|0.90|878|0.15|0.91|920|0.17|0.76|
|Electricity Generator|3617|0.01|919|0.02|900|0.01|0.09|878|0.01|0.06|920|0.01|0.08|
|Solar Panel|3617|0.01|919|0.02|900|0.01|**0.03**|878|0.01|0.47|920|0.01|0.37|
|Sponged Sofa|3617|0.07|919|0.06|900|0.09|0.17|878|0.06|0.87|920|0.06|0.96|
|Sofa (non-sponged)|3617|0.09|919|0.08|900|0.11|0.40|878|0.08|0.89|920|0.09|0.89|
|Fan|3617|0.01|919|0.00|900|0.01|0.39|878|0.00|0.74|920|0.01|0.83|
|Camera|3617|0.00|919|0.00|900|0.00|0.46|878|0.01|0.21|920|0.00|0.70|
|_Number of Animals Owned by HH at Baseline_||||||||||||||
|Total Number of Animals Owned by HH|3619|16.12|919|17.11|900|16.57|0.86|880|15.09|0.42|920|15.68|0.59|
|Cows|3619|2.38|919|3.32|900|2.45|0.52|880|1.66|0.16|920|2.04|0.29|
|Bulls|3619|1.22|919|1.90|900|1.01|0.17|880|0.89|0.10|920|1.09|0.20|
|Donkeys|3619|0.14|919|0.21|900|0.13|0.41|880|0.10|0.20|920|0.12|0.28|
|Goats|3619|3.31|919|3.28|900|3.94|0.43|880|2.77|0.43|920|3.23|0.94|
|Sheep|3619|0.68|919|0.85|900|0.59|0.36|880|0.38|0.07|920|0.87|0.96|
|Pigs|3619|0.30|919|0.30|900|0.26|0.68|880|0.34|0.73|920|0.32|0.85|
|Chickens|3619|7.39|919|6.77|900|7.43|0.40|880|8.22|**0.04**|920|7.18|0.53|
|Ducks|3619|0.46|919|0.27|900|0.53|0.08|880|0.36|0.45|920|0.66|**0.03**|
|Geese|3619|0.00|919|0.00|900|0.01|0.31|880|0.01|0.31|920|0.00|-|
|Rabbits|3619|0.04|919|0.06|900|0.07|0.84|880|0.01|0.15|920|0.01|0.11|
|Guinea Fowls|3619|0.06|919|0.05|900|0.04|0.70|880|0.10|0.44|920|0.06|0.91|
|Other Animals|3619|0.14|919|0.10|900|0.10|0.99|880|0.24|0.41|920|0.11|0.93|

Notes: p-values are based on a simple t-test comparison between control and relevant treatment group with standard errors clustered at the ward level. P-values < 0.05 are in bold 40
# **1 Introduction**

In an influential review of the literature on firms in international trade, Bernard et al. (2007) documented that in 2000 only about 4 percent of the 5.5 million firms operating in the United States were exporters. Among manufacturing and agricultural firms, only about 15 percent were exporters. Information provided by Eaton, Kortum and Kramarcz (2011) suggests that only about 15 percent of French manufacturing firms with more than 20 employees were exporters in 1986. Although firm census data are scarce, especially in developing countries, it is safe to speculate that exporting is rare among firms. That is, while all countries export, the vast majority of firms do not.

From a policy viewpoint, increasing exports is usually the mandate of publicly funded Export Promotion Agencies (EPAs). Data from global surveys of EPAs undertaken by the World Bank in 2005 and 2010 suggest that in all regions of the world the promotion of overall exports dominates other policy objectives (see Figure 1).<sup>1</sup> Consequently, understanding the channels through which EPA services affect export growth is crucial for EPAs' policy design.

The objective of this paper is to identify the mechanisms through which EPA services affect export growth. Do they help firm entry and survival in exporting activities? Do they help incumbents by raising their export shares in total sales? Or do they operate through all channels? The answers to these questions can shed light on the social desirability of EPA programs. Indeed, the economic justification for export promotion is often based on the existence of asymmetric information and other externalities associated with the collection of information on market conditions and business opportunities in international markets (Hausmann and Rodrik, 2003). Private firms have no incentives to share this information with potential competitors after incurring the costs of discovering how to export profitably. This market failure justifies government intervention, but given the nature of the market failure, this should not affect firms' intensive margin, but rather their extensive margin, i.e., the decision to enter and survive in export markets. That is, the cost of collecting information on market conditions does not depend on quantities exported, and therefore the provision of such information by government agencies should not affect the decision of how much to export, but rather whether to enter or survive in export markets.

> 1 The exceptions are Latin American and Caribbean agencies where on average the most important objective is to promote non-traditional exports, underlying the importance of diversifying the exporter base in this region.

2
employer-employee data that capture a random sample of approximately 6.6 percent of production workers and 10 percent of non-production workers in the firms considered. They employ a majority ownership definition of FDI, though they report that a 10 percent definition would change the results only slightly. The acquisitions they study nearly always involve large changes in ownership share; 70 percent of acquisitions occur in firms whose pre-acquisition foreign share is zero. Their propensity score matching is conducted at the firm level, and matches are restricted to the same industry-year cell. When worker fixed effects are included and the analysis is restricted to incumbent workers, the estimated FDI premium is approximately 4.5 percent.<sup>7</sup> The authors find positive effects for all education, experience, and gender groups, occupations, and wage quantiles. Interestingly, subsequent divestment to domestic owners largely reverses the estimated effects.

In sum, the results from the existing literature are in line with the theoretical predictions of Egger and Kreickemeier (2013). There is evidence of a positive FDI wage premium of between 4.5 and 6 percent in emerging markets (Hungary and Brazil), whereas the results from advanced economies are mixed, ranging from a small positive premium to either no significant effect or even an FDI wage discount.

It has been postulated that as a result of knowledge brought by foreign investors to the host country, the marginal productivity of workers in foreign affiliates should be higher than in domestic firms. If this productivity advantage is 8
# **Self-Help Groups, Savings and Social Capital: Evidence from a Field Experiment in Cambodia**

Radu Ban,<sup>1</sup> Michael J. Gilligan<sup>2</sup> and Matthias Rieger<sup>3</sup>

**Keywords:** social capital, poverty, savings, self-help groups, field experiments, Cambodia **JEL Classification Codes:** O1, I38, J16, Q1,D7, C93

> 1 Bill and Melinda Gates Foundation

> 2 Corresponding Author: Department of Politics, New York University, 19 West 4th Street 2nd Floor, New York, NY 10012. Phone: (212) 998-8519. Email: <u>mg5@nyu.edu.</u>

> 3 International Institute of Social Studies of Erasmus University, Rotterdam
labor supply and preschool. _Journal of Human Resources_ , 47(3):583–612.

- Flabbi, L., Flinn, C., and Salazar-Saez, M. (2021). Simultaneous search in the labor and marriage markets with endogenous schooling decisions. _Unpublished manuscript_ .

- Fort, M., Ichino, A., and Zanella, G. (2019). Cognitive and non-cognitive costs of daycare 0-2 for girls. _Journal of Political Economy_ .

- Fu, C. (2014). Equilibrium tuition, applications, admissions, and enrollment in the college market. _Journal of Political Economy_ , 122(2):225–281.

- Garces, E., Thomas, D., and Currie, J. (2002). Longer-term effects of head start. _American Economic Review_ , 92(4):999–1012.

- Gertler, P., Heckman, J., Pinto, R., Zanolini, A., Vermeersch, C., Walker, S., Chang, S. M., and Grantham-McGregor, S. (2014). Labor market returns to an early childhood stimulation intervention in jamaica. _Science_ , 344(6187):998–1001.

- Gonz ́alez, L. (2013). The effect of a universal child benefit on conceptions, abortions, and early maternal labor supply. _American Economic Journal: Economic Policy_ , 5(3):160–88.

- Grantham-McGregor, S. M., Powell, C. A., Walker, S. P., and Himes, J. H. (1991). Nutritional supplementation, psychosocial stimulation, and mental development of stunted children: the jamaican study. _The Lancet_ , 338(8758):1–5.

- Grimshaw, D. and Rubery, J. (2015). The motherhood pay gap: a review of the issues, theory and international evidence. _ILO Conditions of Work and Employment Series No. 57_ .

- Havnes, T. and Mogstad, M. (2011a). Money for nothing? universal child care and maternal employment. _Journal of Public Economics_ , 95(11-12):1455–1465.

- Havnes, T. and Mogstad, M. (2011b). No child left behind: Subsidized child care and children’s long-run outcomes. _American Economic Journal: Economic Policy_ , 3(2):97– 129.

- Heckman, J. (1974a). Shadow prices, market wages, and labor supply. _Econometrica_ , pages 679–694.

- Heckman, J., Moon, S. H., Pinto, R., Savelyev, P., and Yavitz, A. (2010a). Analyzing social experiments as implemented: A reexamination of the evidence from the highscope perry preschool program. _Quantitative Economics_ , 1(1):1–46.

- Heckman, J. J. (1974b). Effects of child-care programs on women’s work effort. In _Marriage, Family, Human Capital, and Fertility_ , pages 136–169. NBER.

- Heckman, J. J., Krueger, A. B., et al. (2005). Inequality in america: What role for human capital policies? _MIT Press Books_ , 1.

- Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., and Yavitz, A. (2010b). The rate of return to the highscope perry preschool program. _Journal of Public Economics_ , 94(1-2):114–128.

- Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., and Yavitz, A. (2010c). The rate of return to the highscope perry preschool program. _Journal of Public Economics_ , 94(12):114–128.

- Heckman, J. J. and Mosso, S. (2014). The economics of human development and social

42
|**Total Revenue & Grants**|27.0%|||
|---|---|---|---|
|Revenue|22.5%||18.5%|
|Tax Revenue|20.7%||18.5%|
|Direct taxes of which|1.7%||0.3%|
|Taxes on income, profits and capital gains, of which:|1.2%|Partial|0.3%|
|Corporate Income Tax|0.9%|No|0.0%|
|Personal Income Tax|0.3%|Yes|0.3%|
|Taxes on payroll and workforce|0.4%|No|0.0%|
|Taxes on property|0.0%|Yes|0.0%|
|Total Contributions to Social Insurance|1.4%|Yes|1.4%|
|Indirect Taxes of which:|19.0%||18.2%|
|General Tax on goods and services (VAT)|5.9%|Yes|5.9%|
|Excise|5.2%|No|0.0%|
|Taxes on international trade and import duties|7.1%|Yes|7.1%|
|Taxes on other goods, permissions|0.8%|No||
|**Primary Government Expenditure**|27.4%||11.6%|
|Social Expenditure|14.1%|Partial|11.4%|
|Social Assistance of which:|4.5%|Yes|2.8%|
|Family and children|0.0%|n/a|n/a|
|Social protection n.e.c.|4.5%|Yes|2.7%|
|Social contributions (employee benefits)|1.8%|Partial|1.4%|
|Education of which:|5.0%|Partial|4.0%|
|Basic|2.2%|Yes|2.2%|
|Secondary|1.7%|Yes|1.7%|
|Health of which:|3.9%|Partial|3.3%|
|Primary healthcare facilities|0.8%|Yes|0.8%|
|Hospital healthcare facilities|2.5%|Yes|2.5%|
|Non-Social Expenditure, of which:|13.3%||0.2%|
|Subsidies*|0.2%|Yes|Less than<br>0.2%**|

Source: Tax revenue from PCBS. Government Financial Statistics for 2017 prepared by the Ministry of Finance. For further disaggregation, data was supplemented with reports from the Ministry of Education (for education expenditure disaggregation) and National Health Accounts (for health disaggregation).

* Disaggregation for fuel and water subsidies is not available in the Government Financial Statistics and amounts in other technical documents included both explicit and implicit subsidies therefore it is not included here, but instead used in the macro-validation section.

** We cannot estimate the proportion of the total subsidy amount that we cover given that we do not know the share of total subsidies allocated to types of subsidies such as non-fuel and electricity subsidies, water.

11