rafmacalaba/datause-encoder-data huggingface dataset:
Olken, B. A. 2006. Corruption and the costs of redistribution: Micro evidence from Indonesia. Journal of Public Economics, 90, 853-870.

Reinikka, R. & Svensson, J. 2004. Local Capture: Evidence from a Central Government Transfer Program in Uganda. The Quarterly Journal of Economics, 119, 679-705.

Reinikka, R. & Svensson, J. 2006. "Using Micro-Surveys to Measure and Explain Corruption." World Development 34(2): 359-370.

Reinikka, R. & Svensson, J. 2011. "The power of information in public services: Evidence from education in Uganda." Journal of Public Economics 95(7-8): 956-966.

Rose-Ackerman, S. 2004. Governance and Corruption. In: LOMBORG, B. (ed.) Global crises, global solutions. Cambridge; New York and Melbourne: Cambridge University Press.

Rubio, G. M. 2011. Measuring governance and service delivery in safety net programs, World Bank Social Protection Working Paper Series, Washing ton DC.

Serra, D., 2009. "Combining Top-down and Bottom-up Accountability: Evidence from a Bribery Experiment", Centre for the Study of African Economies, University of Oxford.

Van Stolk, C. And E. Tesliuc, 2010. Toolking on Tackling Error, Fraud and Corruption in Social Protection Programs. World Bank Social Protection Working Paper Series, Washing ton DC.

Woo, J.-Y. 2010. The Impact of Corruption on a Country's FDI Attractiveness: A Panel Data Analysis, 1984-2004. Journal of International and Area Studies, 17, 71-91.

World Bank, 2009. Implementation Completion And Results Report, for Northern Uganda Social Action Fund.

World Bank, 2015, Uganda Systematic Country Diagnostic, Boosting Inclusive Growth and Accelerating Poverty Reduction. World Bank, Washington DC.

32
Foreign direct investment (FDI) worldwide has grown markedly since the 1970s, reaching $1.3 trillion in 2018 based on data from governments’ balance of payments statistics (BoP) which is the standard source of recording financial flows. Asia has been one of the main destinations for FDI, receiving almost 43 percent of the global total in 2018, up from 17 percent ($132 billion) in 2000. Despite global FDI steadily decreasing since it peaked in 2015 at $1.8 trillion – due to lower rates of FDI return, a slowdown in the expansion of international production, and the uncertainty in the global trade and investment policy environment – inflows to Asia remained relatively stable as shown in figure 2. This illustrates the region’s increasingly important role in international production networks as well as strengthening intraregional trade and investment linkages. Data since the COVID-19 pandemic are not available yet, but initial signs indicate that inward FDI to Asia remains comparatively resilient compared to global trends, though mostly due to flows to India and China (UNCTAD 2020). East Asia consistently attracts just over 50 percent of the inflows largely due to investments in China followed by Southeast Asia (around 25 percent).

**Figure 2: Global inward FDI, global and Asia, 2001-2018**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>$trillion<br>1.0<br>0.5<br>0.0<br> Inward FDI to Asia Inward FDI to non-Asia<br><!-- End of picture text -->

**Note:** Asia here includes Australia and New Zealand

**Sources:** ADB calculations using data from Association of Southeast Asian Nations Secretariat. ASEANstats Database. https://www.aseanstats.org/ (accessed July 2019); and United Nations Conference on Trade and Development. World Investment Report 2018 Statistical Annex Tables (accessed June, 2020) http://unctad.org/en/Pages/DIAE/World%20Investment%20Report/Annex-Tables.aspx

In fact, intraregional investment linkages have continued to strengthen since the Global Financial Crisis – in 2007 the intraregional share of FDI inflows to Asia was around 30 percent. In 2018, over
<mark>US$ 711 millions (0.018*amount of trade in 2005 )—</mark> a much bigger ‘trade bang for the aid buck’.

_Table 2: Narrow vs. Broad Definition (Bilateral 5-Year Fixed Effects and Year Fixed Effects Included)_

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>A4TF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|**_Narrow_**<br>|**_Aid Defini_**<br>|**_tion_**<br>||||||
|**ln_itfa**|**0.005*****|**0.004****|**-0.005**|**0.007****|**0.003**|**0.004****|**0.001**|
|**ln_etfa**|**0.004*****|**0.005*****|**0.011**|**0.011*****|**0.002**|**0.005*****|**0.004**|
|L.ln_itfa|||||||0.005|
|L2.ln_itfa|||||||0.004|
|L.ln_etfa|||||||0.006**|
|L2.ln_etfa|||||||0.001|
|**_Broad A_**|**_id Definiti_**|**_on_**||||||
|**ln_itfa**<br>**ln_etfa**|**0.011*****<br>**-0.001**|**0.011*****<br>**-0.001**|**-0.023**<br>**0.021**|**0.017*****<br>**-0.001**|**0.013*****<br>**-0.005***|**0.011*****<br>**-0.001**|**0.010***<br>**0.002**|
|L.ln_itfa|||||||0.059***|
|L2.ln_itfa|||||||0.004|
|L.ln_etfa|||||||0.003|
|L2.ln_etfa|||||||-0.001|
|Robust stan<br>* significan<br>See appendi|dard errors in<br>t at 10%; ** s<br>x tables for a|brackets; incl<br>ignificant at 5<br>ll coefficients|udes Bilatera<br>%; *** signi|l 5 year fixed<br>ficant at 1%|effects, year e|ffects||

In Table 3 we consider **_hard_** vs. **_soft_** aid flows. **_Soft_** aid (such as education and training, administration and management, and aid for trade policy and regulatory adherence) is positively and significantly associated with more imports, but more often then not is not related to exports. A4TF directed toward **_hard_** infrastructure (such as ports, but also industry-sectoral targeted aid programs) is mixed in terms of relationship to trade, and often appears to not statistically related to trade flows.

A key aid category that is in both **_soft_** and **_broad_** relates to the financial sector. Thus, reconciling these various results points to additional research on the role that trade finance plays in trade.

14
# **Appendix A.1. Government Support Policies in El Salvador**

This Appendix compiles the list of support that the Government of El Salvador provided to the private sector during the COVID-19 crisis as of January 15, 2021. To create this list, we used the Map of SME-Support Measures in Response to COVID-19, and supplemented through sources as provided below. Colleagues at the World Bank’s office in El Salvador kindly verified and confirmed the support measures, for which we are grateful.

## **Salvadoran Company Recovery Trust (FIREMPRESA)**

- Formed as of October 15, 2020, and administered by administered by the Development Bank of El Salvador (BANDESAL) (source). Total of 600 mln. USD

- As of March 18, 2021, the government has expanded the coverage, to include informal sectors of the economy such as tourist transport, taxi drivers, artists , artisans, fishermen, among others, and also merchants of the Santa Ana market affected by the fire. (source, <u>source)</u>

- **Access to new credit:**

- For micro, small and medium-sized companies (MSMEs) affected by the COVID19 pandemic, max. 99 employees and max. 7 million USD in profits per year

- No collateral is needed, there is one year of full grace - no interest, no fees - and 3% annual interest

- Total budget for this component: 360 mln. USD (source)

- Also available for informal firms (as a “third pillar of FIREMPRESA”), loans for working capital, with total budget for informal businesses 100 mln. USD

- **Wage subsidies:**

- Subsidize 50% of the payroll, aimed at MSMEs registered as formal companies

- To access, the company needs to show that it has been affected by the pandemic, that it continues to operate, that it has registered with the Salvadoran Social Security Institute (ISSS)

- Total budget for this component: 140 mln. USD

## **Additional wage subsidies**

- Support to employers for the payment of contribution fees to SAP.

## **Fiscal relief**

- Fiscal incentives for investment in new productive projects, creation of angel capital funds and co-investment.

## **Deferral of payments**

- Mid-March 2020: Waiver for utility bills payments for individuals and legal entities directly affected by curfew enacted because of COVID-19 for three months. Utilities include electricity, water, telephone, cable, and internet.

- As of October 15, 2020, users of financial services can defer payments (source)

## **Other support**

- Simplification of procedures for MSME account

## **Support of individuals and families**

65
# **1. Introduction**

The issue of the increase in food prices has received renewed attention in recent months as the increase in prices worldwide has had large negative impacts on households (e.g., Ivanic and Martin, 2007; World Bank, 2008a and 2008b; IMF, 2008; Wodon and Zaman, 2008; Wodon et al, 2008). In Ghana, prices for rice, maize and other cereals increased by 20 to 30 percent between the last few months of 2007 and the spring of 2008. This has led the authorities as well as development partners to consider a range of compensatory measures that could help offset part of the negative impact on the poor of this increase in prices. However, at least from a conceptual point of view, the net impact of an increase in food prices on the poor is not obvious. Indeed, when discussing the link between rice and other cereal prices and poverty, a key issue is to assess the double and opposite impact that a change in prices can have through producers (who benefit from an increase in prices) and consumers (who lose out when the price increases).

The techniques for the analysis of the short term producer and consumer impacts of food commodity price changes are well developed in the literature. Early work in this area was conducted by Deaton (1989) using data from Thailand (see also Singh et al., 1986). Similar methods have been used in sub-Saharan Africa among others by Barrett and Dorosh (1996) for Madagascar, Budd (1993) for Cote d’Ivoire, and Loening and Oseni (2007) for Ethiopia. These are also the methods that we use in this paper. Most of these studies have found that food price increases tend to lead to an increase in poverty because the consumption effects dominate the production effects as many countries are net importers of food, at least in sub-Saharan Africa.

2
**Table 1: Summary Statistics and Tests of Randomization**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Variable_**|Full<br>Sample|Full<br>Sample|Control<br>Group|Handbook<br>Only|<br> <br>Handook<br>and Movie|Handbook<br>and|Handbook,<br>Movie, and|P-value<br>(A=B)|P-value<br>(A=C)|<br>P-value<br>(A=D)|<br>P-value<br>(A=E)|
||Mean|SD|(A)|(B)|(C)|Assistance<br>(D)|<br>Assistance<br>(E)|||||
|Respondent is Male|0.29||0.28|0.3|0.29|0.3|0.28|0.611|0.825|0.68|0.867|
|Respondent Age|45.27|11.31|45.22|45.27|45.28|45.16|45.38|0.959|0.951|0.951|0.866|
|Respondent Years of Education|9.39|3.78|9.1|9.52|9.36|9.42|9.55|0.185|0.446|0.327|0.174|
|Respondent Risk Preference Score|3.73|2.09|3.74|3.76|3.88|3.6|3.68|0.902|0.451|0.453|0.739|
|Respondent Time Preference Score|5.18|2.26|5.19|5.07|5.21|5.25|5.2|0.542|0.924|0.742|0.94|
|Shop Size (Sq. Meters)|13.22|12.34|12.67|12.77|12.84|13.82|14.03|0.908|0.851|0.248|0.287|
|Age of Firm|13.6|11.79|12.76|13.77|14.03|13.98|13.47|0.313|0.222|0.236|0.478|
|Firm has Tax ID|0.19||0.2|0.21|0.2|0.15|0.18|0.811|0.878|0.145|0.516|
|Total Number of Employees|2|1.22|2.03|2.05|1.9|1.99|2.04|0.837|0.218|0.708|0.919|
|Number of Customers on a Normal Day|49.33|43.32|45.77|50.1|47.22|51.8|51.79|0.253|0.602|0.085|0.099|
|Total Sales Last Month (USD PPP)|4786.16|4853.48|4454.37|4730.64|4840.55|4761.4|5139|0.5|0.371|0.467|0.114|
|Total Profits Last Month (USD PPP)|907.73|1185.09|889.58|961.1|926.78|825.25|934.66|0.496|0.723|0.524|0.674|
|Applied for Business Loan in Last 12 Months|0.18||0.2|0.17|0.15|0.22|0.17|0.374|0.171|0.438|0.374|
|Obtained Business Loan in Last 12 Months|0.16||0.18|0.15|0.14|0.18|0.14|0.3|0.2|0.926|0.2|
|Aggregate Score for Practices Covered in Handbook|0.35|0.18|0.36|0.35|0.36|0.34|0.36|0.383|0.884|0.243|0.99|
|Aggregate Score for Practices Not Covered in Handbook|0.18|0.13|0.18|0.17|0.17|0.18|0.19|0.254|0.445|0.681|0.757|

# **Chi-Square Test of Joint Orthoganality from Multinomial Logit (P-value):** 0.857

**Notes:** This table presents summary statistics for the baseline survey data. Columns 1 and 2 present mean and standard deviations for the full sample. Column 3 provides average values for the control group and columns 4-7 present average values by treatment status. Columns 8-11 present p-values for equality of means tests between a particular treatment group and the control group.
- Stiglitz, J. (2012), _The price of inequality_ , Penguin UK.

- Thakur, D. S. (1991), ‘Implementing educational policies in Sub-Saharan Africareview essay’, _Economics of Education Review_ **10** (4), 385–390.

- Thomas, D. (1996), ‘Education across generations in South Africa’, _American Economic Review_ **86** , 330– 334.

- Thorbecke, E. (2013), ‘The interrelationship linking growth, inequality and poverty in sub-Saharan Africa’, _Journal of African Economies_ **22** (suppl 1), i15–i48.

- Tomasevski, K. (2006), ‘Free or fee: 2006 global report’, _Copenhagen: Right to Education_ .

- UNESCO (1970), _Educational planning: a world survey of problems and prospects_ .

- UNESCO (2011), ‘Financing Education in Sub-Saharan Africa: Meeting the challenges of expansion, equity and quality’.

- Wooldridge, J. M. (2010), _Econometric analysis of cross section and panel data_ , MIT press.

32
# **5.3. Discussion**

Improving educational outcomes remains one of the key development challenges for Pakistan. While efforts to raise enrollment and retention rates must continue, the results reported here reiterate that enrollment and retention are only one measure of education. Enrollment and retention do not guarantee actual learning. Overall achievement is low and the gaps in achievement across schools are large. In contrast to popular writing on education in Pakistan and India, the levels of learning and the structure of the educational gaps are _very similar_ in the two countries; if corroborated by representative and standardized tests, this would suggest that increasing learning is a difficult challenge across both countries.

In terms of total learning, children in the third grade in Pakistan can barely read and write or perform the simplest arithmetic operations such as counting and addition. More difficult but equally crucial skills such as forming a complete sentence given a word and solving more complicated multiplication and divisions problems are out of reach of all but a few children. Households characteristics are associated with achievement along the dimensions expected—children from richer and more educated families perform better—but these differences largely vanish after controlling for all the household characteristics and, in particular, the school in which the child is enrolled.

The most striking gaps arise not between children but between schools in the same village. Private schools score significantly higher than government schools, even after controlling for characteristics of the student population. But there are large variations within government schools. Even in villages with a great number of poorly performing schools there is usually one school that performs well. While the best schools are not always private schools, the worst schools are almost exclusively government schools. These dismally performing government schools often record achievement levels so low that the pupils tested must have virtually no cumulated knowledge or skills after four years of education.

At the very least, the findings point to the need for consistent evaluation of learning in Indian and Pakistani schools with some documentation of the learning-gaps across households and schools. The literature review highlights how different methodologies, the non-comparability of tests given at different points in time, and lack of uniformity in the data collected make it difficult to discern trends in learning over time and the changes in learning-gaps across households and tests. While this is increasingly recognized in both India and Pakistan (the ASER tests by Pratham in

- 23 -
- 42 -

# IkblJJ: Primary Employment in African Rural Nonfarm Enterprises (percent)

|C o u n t r y|Ru<br>Force<br>Nonfarm<br>Total|ral Labo<br>Employe<br>Enterpr<br>Male|r<br>d in<br>ises a/<br>Female|
|---|---|---|---|
|A. Primary Employment||||
|Benin (1961)|41|10|77|
|Cameroon (1976)|10|-|--|
|Chad (1964)|3|4|.1|
|Ghana (1960)<br>|27<br>|13|89|
|IvoryCoast, Bouake Region (1963)<br>|10<br>|--|--|
|Kenya (1970)|28|--|--|
|Malawi (1977)|11|17|4|
|Mali (1976)|8|--|--|
|Mauritania (1977)|21|--|--|
|Mozambique (1963)|9|19|1|
|Nigeria, 3-District<br>W.State (1966)<br>|63<br>|19|87|
|Rwanda(1978)|5|--|--|
|Senegal (1970-71)|18|--||
|Sierra Leone (1974, Males 1976)|14|19|--|
|Tanzania<br>(1978)|6|10|2|
|Togo (1970)|26|--|-|
|Uganda,4 villages<br>(1967)<br>|20|||
|Zimbabwe(1982)|20|--|--|
|B.<br>Secondary<br>Emoloyment||||
|Benin<br>(1961)|7|--|--|
|Chad (1964)|--|4|--|
|Western Nigeria,3states (1966)|--|20||
|Sierra Leone (1976)<br>|--|11|--|
|Tanzania<br>(1980)|17|--||
|Zambia, 2 regions|30|--|--|

## g./ See Note 3 for individual country definitions of rural.

Nigeria: NteU:r and Zwaring (1070) Rvnda: IRanda (1978) Sources: Beni: Rpubliqua du 0chomey (1904) Nigeria: NteU:r and Zwaring (1070) C_aroo: Repubique Unle du Cameroo (1076) Rvnda: IRanda (1978) Chad: Republique du Tchad (1966) Sasegai: Republique du Senegal (1973) aana: Gbane (1964) Sierra Leone: Halo (1976) frm Dyor.l.. et &I. Ivory Coast: Republiqu. de Cott d'Iveiri (1965) **Agregate** from Tbom (1083) Ieowa: W. (1972). cited in Cbute and Lledbols (1970) Tanznia, 1976: United Republic of TSnzania (1982 blaia: Malawi Governent (1980) Tanznia, 1980: Coller et a1. (1966) Hall: Republique du Hall (1960) Uganda: V'endt, et ai. (1972) Mauritania: Rpuiblique do Kuriteni. (1979) Z1ia: Ibdiund end Lendahl (1983) oxbaique: RUpubUca Popuir de Hosalque (1983) Zimabwe: Zibabw. (1965) Sasegai: Republique du Senegal (1973) Sierra Leone: Halo (1976) frm Dyor.l.. et &I. (1977 **Agregate** from Tbom (1083)
17 Table I

# Unrestricted Estimates of Equation (20): Role of Size and Age

|Coefficient|(A)<br>Whole sample|(B)<br>With size<br>interaction<br>dunmies|(C)<br>With age<br>interaction<br>dummies|(D)<br>Including risk<br>premium|
|---|---|---|---|---|
|alI|0.107|0.205|0.218|0.203|
||(_1-73|(2.81)|(2.40)|(2.84)|
|a2|0.030|0.038|0.039|0.038|
||(3.60)|(3.94)|(3.81)|(3.93)|
|aL3|0.009||||
||(1.20)||||
|_oa4_|0.096||||
||_(0.!22)_||||
|a31||-0.038<br>|0.012<br>|-0.038|
||i___________ =|00.<br>-(0.79)|<br>(120)|-t0.80)|
|a30|_<br>_<br>_|0.022|0.022|0.022|
|||(2.25)|(1.77)|(2.28)|
|a41||-0.835|0.183|-0.755|
|._____.|______|-(0.38)|(0.41)|-(0.34)|
|a40||-0.884|-1.680|-0.864|
|||-(1.25)|-(1.46)|-(1.25)|
|WI|17.83|24.97|20.84|25.09|
||4|6|6|6|
|W2|4.86|10.255|5.876|10.38|
||2|4|4|4|
|Ml|-8.29|-7.530|-8.660|-7.51|
|Sargan/Hansen|23.68|24.549|24.549|24.76|
||21|22|23|22|

Instruments: gmm(I/K), gmm(I/K)<sup>2</sup> , gmm(Y/K), L/K, (L/K)<sup>2</sup> , D1(B/K), D1(B/K)<sup>2</sup> , DO(B/K), DO(B/K)<sup>2</sup> , 1/(1 +i), D1, all dated t-2 in levels. L represents liquid assets; D1 is the dummy for large (old) firms, DO for small (young) firms. Asymptotic 't' statistics are shown in parenthesis. WI: Wald test for joint significance of all the regressors. W2: Wald test for joint significance of financial variables. Ml: test for first order serial correlation, distributed as N(0, 1). The Sargan/Hansen test is distributed as a X2. Degrees of freedom are presented below the respective tests.

For small firins, the situation is very different. Here we find a _signiflcant_ positive coefficient on the quadratic term (a3O) which is consistent with a thodel where firms face both credit constraints and rising costs of borrowing. The coefficient on the linear term (a40) is negative as expected in this case, although not very precisely
# **Exporter Dynamics Database**<sup>*</sup>

## **Tolga Cebeci**<sup>**1**</sup> **Ana M. Fernandes**<sup>**2**</sup> **Caroline Freund**<sup>**3**</sup> **Martha Denisse Pierola**<sup>**4**</sup>

## **JEL Classification codes** : C81, F14.

**Keywords:** exporter dynamics, exporter growth, firm-level data.

* This database was conceived and initiated in 2009 by Caroline Freund and Martha Denisse Pierola with support of the governments of Norway, Sweden and the United Kingdom through the Multi-Donor Trust Fund for Trade and Development. We also acknowledge the generous financial support from the World Bank research support budget and the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank (www.worldbank.org/kcp). We are grateful to Mario Gutierrez-Rocha and Jamal Haidar for their contribution to this project, and to Aaditya Mattoo for useful comments and discussions. We thank Alberto Behar and Madina Kukenova for collaboration with the project early on and to Francis Aidoo, Paul Brenton, Maurizio Bussolo, Huot Chea, Julian Clarke, Alain D’Hoore, Pablo Fajnzylber, Elisa Gamberoni, Sidiki Guindo, Javier Illescas, Mehar Khan, Sanjay Kathuria, Josaphat Kweka, Erjon Luci, Mariem Malouche, Eric Manes, Yira Mascaro, Mamadou Ndione, Zeinab Partow, Stefano Paternostro, Jorge Rachid, Richard Record, Jose Guilherme Reis, Jose Daniel Reyes, Nadeem Rizwan, Liviu Stirbat, Dimitri Stoelinga, Daria Taglioni, Maria Nicolas, Erik von Uexkull, Cristian Ugarte, and Ravindra Yatawara for facilitating the access to the data for developing countries. We thank Lina Ahlin, Martin Andersson, Juan de Lucio, Emmanuel Dhyne, Luc Dresse, Cédric Duprez, Richard Fabling, Jaan Masso, Raul Minguez, Asier Minondo, Andreas Moxnes, Francisco Requena, Lynda Sanderson, Joana Silva, Priit Vahter, and Hylke Vandenbussche for providing us with measures for developed countries. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank.

1 Consultant, Trade and International Integration Unit, Development Research Group, World Bank (tcebeci@worldbank.org). 2 Senior Economist, Trade and International Integration Unit, Development Research Group, World Bank (afernandes@worldbank.org).

3 Chief Economist, Middle East and North Africa Region, World Bank and CEPR (cfreund@worldbank.org).

4 Economist, Trade and International Integration Unit, Development Research Group, World Bank (mpierola@worldbank.org).