rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

Why do farmers continue to grow crops for local markets when crops for export markets are thought to be much more profitable? Several answers are possible: missing information about the profitability of these crops, lack of access to the necessary capital to make the switch possible, lack of infrastructure necessary to bring the crops to export outlets, high risk of the export markets (e.g., from hold-up problems selling to exporters), lack of human capital necessary to adopt successfully a new agricultural technology, and misperception by researchers and policymakers about the true profit opportunities and risk of crops grown for export markets.

We conduct a clustered randomized control trial with DrumNet, a project of Pride Africa, to evaluate whether a package of services can help farmers adopt, finance and market export crops, and thus make more income. The experimental design includes two treatments, one with credit and one without, and a control group. The intervention is a package of services. Thus, the design does not permit isolating the reasons for the failure, with the exception of credit. In addition to evaluating the impact of these packages, we examine whether there are heterogeneous treatment effects on the basis of prior experience growing export crops.

This experiment is motivated by a recent push in development to build sustainable interventions that help complete missing markets (e.g., the initiative launched jointly in 2006 by the Bill and Melinda Gates Foundation and the Rockefeller Foundation). Other similar interventions include the use of mobile phones to obtain real-time prices for fish in markets along the shore by boat owners returning with their catches (Jensen, 2007) and 2
Subsidies, Information, and the Timing of Children’s Health Care in Mali Anja Sautmann<sup>∗</sup> Samuel Brown<sup>†</sup> Mark Dean<sup>‡</sup> This version: July 2024 Keywords: demand for acute care; children’s health care; health care underuse and overuse; health care subsidies; community health workers; Mali JEL codes: I11, I12, O15

> ∗The World Bank, corresponding author, email: asautmann@worldbank.org.

> †Apple, email: sam.vin.brown@apple.com.

> ‡Columbia University, email: mark.dean@columbia.edu.
# I. INTRODUCTION

Turkey has, alone among the high-debt countries, managed to maintain a high growth rate after rescheduling its debt. Its real GNP grew by 5% on average since 1980. By comparison, countries with recent debt-servicing problems, grew at only 1.2% since 19811 , almost a four percentage point difference on average. At the same time, Turkey's debt-output ratio increased by an amount roughly similar to the increase in the debt-output ratio of the high-debt countries, from 28% at the end of 1980 to 56% at the end of 1986 (see Table 1).

in fact, it is surprising that Turkey's debt-output ratio did not increase a great deal more than it did in the higt.-debt countries. As a percentage of GNP, Turkey ran a much lower non-interest current account surplus than the high-debt countries did on average after their debt-crisis: -0.25 percent of GNP for Turkey over the period 1980-1986 versus +2.6 percent over 1982-1986 for the high-debt countries. This apparent inconsistency is explained by th. higher growth rate that Turkey managed to sustain. Turkey's debt-output ratio followed a path similar to that of the high-debt countries, not so much because of large trade surpluses, but because of its high output growth coupled with continued access to foreign financing.

This paper discusses two issues this experience raises. One, how did Turkey translate the extra breathing space continued access to foreign financing gave it into sustained high real growth? In particular, what was the

> 1/ Turkey's rescheduling exercise took place over the period 1978-80, before other debtor countries rescheduled their debt. Hence the shift if comparison period. The data for the high debt countries are taken from the IMF World Economic Outlook, October 1987. The WEO refers to this group as "Countries that experienced recent debt servicing problems". For brevity's sake, we refer to the same group as "high-debt" countries in this paper.

ChhibberDisk/chh4hbPr-",trklv Paer/ac:07-2F-88. Ic
<!-- Start of picture text --> Percent<br>5 mintra-wars = External wars<br>-5<br>-10<br>“15<br>-20<br>t t+1 t+2 t+3<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>10 mg intra-wars External wars<br>-5<br>-10<br>“15<br>-20<br>t t+1 t+2 t+3<br><!-- End of picture text -->
# **Figure 4**

First stage: effect of road prioritization on probability of new road by 2012 <!-- Start of picture text --> .6<br>.4<br>.2<br>−100 −50 0 50 100<br>Normalized population<br>New road by 2012<br><!-- End of picture text --> Notes: The figure plots the probability of getting a new road under PMGSY by 2012 against village population in the 2001 Population Census. The sample consists of villages that did not have a paved road at baseline, with baseline population within an optimal bandwidth (84) of the population thresholds. Populations are normalized by subtracting the threshold population.

42
Models of Land Tenure for Indigenous Peoples In or'<sup>1</sup> er to demonstrate the political and administrative obstacles and ecological consequences which have resultod from the above trends, it is necessary to describe the varying and sometimes overlapping land tenu;e models which have been applied to indigenous peoples in different national conteixts.

Indian Reserves (Brazil). **The** Brazilian Indian reserve system is the clearest case of a land tenure model that resulted from protectionist policies toward forest-dwellers. As far back as the 19208, the SPI established a series of Indian posts in the Brazilian backlands, which were meant to sedantarize and protect forest-dwellers from harmf'xl elements along the frontier. Recognizing the importance of land to the survival of these forest-dwelling groups, it negotiated with state governments and federal agencies to set aside reserve areas. These areas, according to Brazilian law, would be federally registered in the name of the SPI and eventually serve as a property base for the evolution of Indians from their status as forest-dwellers to that of settled agriculturalists (18).

In 1967, when the government disbanded the SPI and established the National Indian Foundation (FUNAI), it legally created a series of Indian parks and reserves, especially in the Amazon region where most of Brazil's remaining 220,000 indigenous people live. The legal basis for this reserve system was contained in the 1967 Brazilian Constitution and incorporated into the 1973 Brazilian Indian Statute (26,27).

The administrative process of land regularization involves three initial steps: identification of an area with an indigenous population, interdiction to legally protect the area from outside incursions, and delimitation of the boundaries based upon a set of technical studies. At this point, the proposed reserve is physically demarcated and then, with Presidential review and approval, officially recorded in federal and local land registries. The Federal government maintains title tt. the Indian lands, but the law provides that indigenous peoples will have "permanent possession" and "exclusive use" of natural resources.

The 1973 Indian Statute stated that FUNAI would follow this process and regularize all indigenous lands by 1978. This never occurred, and areas occupied
Table A14: Relationship between general management, energy management, and output (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.035|0.048|0.135|0.285<sup>_∗∗∗_</sup>|0.393<sup>_∗∗∗_</sup>|
||(0.103)|(0.063)|(0.090)|(0.067)|(0.099)|
|Energy|0.056|0.085<sup>_∗∗_</sup>|-0.032|0.109<sup>_∗∗_</sup>|0.080|
||(0.064)|(0.036)|(0.061)|(0.043)|(0.060)|
|No. Employees|0.640<sup>_∗∗∗_</sup>|0.644<sup>_∗∗∗_</sup>|0.625<sup>_∗∗∗_</sup>|0.957<sup>_∗∗∗_</sup>|0.843<sup>_∗∗∗_</sup>|
||(0.066)|(0.038)|(0.065)|(0.030)|(0.053)|
|Capital Stock|0.028|0.094<sup>_∗∗∗_</sup>|0.101<sup>_∗∗∗_</sup>|||
||(0.035)|(0.020)|(0.032)|||
|Materials|0.412<sup>_∗∗∗_</sup>|0.288<sup>_∗∗∗_</sup>|0.255<sup>_∗∗∗_</sup>|||
||(0.045)|(0.029)|(0.066)|||
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|420|1234|549|1509|854|
|Adjusted _R_<sup>2</sup>|0.801|0.817|0.793|0.624|0.589|
|df<br>m|43|47|53|43|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 56
The number of days during which patients reported symptoms before visiting a health center is on average 10 (median 5 days). Regional variations observed could be linked to relative access to health centers. For instance, the average duration of symptoms is three times longer in Salamat than in Tandjile (14 days vs. 4.6 days).

Table 5: Reasons for Visits and Symptom Duration

|||T|YPE||LO|CATIO<br>Other|N||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|Total|
|**Reason for the visit (%)**|||||||||
|Malaria|51.6|43.6|50.2|24.2|52.1|48.5|49.4|49.7|
|Diarrhea|21.4|17.8|22.5|9.1|16.8|20.8|22.0|20.8|
|Cough|11.6|4.0|4.1|15.2|10.1|11.3|8.8|9.5|
|<br>Weight loss|19.6|4.5|7.4|15.2|8.4|7.9|10.1|9.4|
|<br>Worms|8.2|7.9|15.9|3.0|9.4|6.5|10.1|9.3|
|Multiple Symptoms (%)|28.8|14.9|22.0|12.2|24.2|25.7|25.8|24.4|
|**Duration of symptoms**|||||||||
|<br>Number of days Average|10.0|11.2|10.7|10.8|12.9|11.2|9.2|10.3|
|<br>Number of days Median|5|5|6|7|5|6|5|5|

Source: Chad 2004 QSDS and authors’ calculations.

# **5.3 Costs of visits**

Table 6 presents total payments for a health visit reported by patients and the share of these costs represented by consultation fees, laboratory tests and prescribed medications. Overall, the average cost of a visit is 2,516 FCFA (US$5.07). Average payments are much higher in the capital but relatively similar in rural areas and other urban centers.

The least expensive visits are noted in NGOs and religious clinics, with an average cost of 1,500 FCFA. By contrast, visits to private clinics are by far the most expensive especially. in the capital where a visit to a private clinic cost on average close to 6 times more than in a public clinic.

10
enforceability of a ban on child labor is a moot point. While child labor bans were eventually introduced in most of Europe during the nineteenth century, enforcement was very difficult, and it has been argued that other factors (rising incomes, and technological change) were more important in reducing child labor (Nardinelli, 1980; Heywood, 1988). In most developing countries, it is far from obvious how a ban could be enforced, especially in rural areas.

Observations such as these have led to a search for other ways to reduce child labor while keeping the advantages to the poor of labor-intensive growth in developing countries. One seemingly appealing option is to make schooling more attractive to parents, and this has been favored by a number of observers.<sup>5</sup> An obvious policy instrument for this purpose is a targeted enrollment subsidy. Motivated by a desire to reduce both current and future poverty, cash or inkind transfers targeted to poor families - but conditional on their kids staying at school - have recently become popular in developing countries. If there is substitution between child labor and schooling then such programs will reduce child labor. Of course, the foregone income of - participants could then be high (though presumably less than the value of the subsidy) raising concerns about the efficiency of such programs as a means of reducing current poverty.

Both the arguments that "child labor reflects bad growth", and that "making schools cheaper can turn it into good growth" assume that child labor displaces schooling. This paper tests that assumption. To do so we study the effects on schooling and child labor of an enrollment subsidy in Bangladesh. We measure the extent of substitution by seeing how much the extra schooling induced by the subsidy comes out of child labor. We do not evaluate the programn, but only use it as means of identifying how much child labor displaces schooling.

s Including Grootaert and Kanbur (1995), World Bank (1995, Chapter 11; 1999), Psacharopoulos (1997), and Grootaert and Patrinos (1998).

3
Table A.6: Eect of a direct SLM training intervention on contact farmers' knowledge and adoption, basic specication

|Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|
|CFs' Knowledge and Adoption, unweighted||||
|Knowledge score<br>0.633<br>[0.173]|0.046<br>[0.049]|347|0.019|
|Number of techniques known by name<br>4.131<br>[1.626]|0.646<br>[0.571]|347|0.018|
|Number of techniques adopted on own plot<br>1.786<br>[1.309]|0.594*<br>[0.232]|347|0.023|
|Number of techniques adopted on any plot<br>3.738<br>[1.889]|0.752**<br>[0.244]|347|0.020|
|Sources: Contact farmer survey, 2010, 2012, 2013; Hous|ehold surve|y, 20|12, 2013.|
|Notes: Regressions include the following variables: a cons<br>an endline dummy, and district indicators.|tant, trea|tment|variables,|
|***, **, and * indicate signicance at the 1, 5, and 10 pe<br>CF=contact farmer; ITT=intent-to-treat eect; SLM=su|rcent criti<br>stainable l|cal lev<br>and m|el.<br>anagement.|

49
At the firm level, the relations for the first two are the same as on national level (Figures 2a and 2b). Yet for growth in TFP and capital /output share, we see a positive and significant correlation (Figure 2c), which is not the same at the national level.

**Figure 2a.  Growth in Technology and  Capital share in Value Added (at firm level)**

<!-- Start of picture text --> Growth in Technology Vs. Growth in Capital, firm, 2002-2005<br>-100 -50 0 50 100<br>Growth in Capital<br>100<br>50<br>0<br>Growth in Technology<br>-50<br>-100<br><!-- End of picture text --> Note: we limit TFP growth and Capital growth to be within [-100, 100]; the slope in the above graph is -.58, significant at 0.01 level.

**Figure 2b. Growth in TFP and Growth in Value Added Output (firm level)**

<!-- Start of picture text --> Growth in TFP Vs. Growth in Value Added Output, macro, 2002-2005<br>-100 -50 0 50 100<br>Value Added Output<br>100<br>50<br>0<br>Growth in TFP<br>-50<br>-100<br><!-- End of picture text --> The slope in the graph is .84, sig at 0.1 levels.

13
for standard explanatory variables commonly used in the literature. We find that services policies are an important determinant of growth performance; the coefficient estimates on our reform indices are all statistically significant. Section 4 concludes.

# **1. Shifts in the Structure of Services in Transition Economies**

The share of services in GDP and employment has grown significantly since 1990 in almost all transition economies. Compared to the high income OECD average in 1990—when the share of services in employment and GDP was around 63 percent—countries in Europe and Central Asia (ECA) lagged far behind: services accounted for 30-40 percent of GDP and employment. As of 2003, services shares had increased substantially. The greatest growth is observed in the Baltic States, which have almost converged on the OECD average of 68 percent in terms of GDP shares, although employment shares remain lower (Figure 1). The Central and Eastern European (CEE) countries that acceded to the EU in 2004 have also converged to a large extent. Much less progress has been made by the Central Asian countries, where natural resource-based activities continue to constitute a major share of GDP.<sup>1</sup>

**Figure 1: Changes in the Share of Services in GDP and Employment**

<!-- Start of picture text --> Share of Services Sector in GDP by Country Employment Share of Service Sector by Country<br>Group, 1991 vs. 2003 Group, 1991 vs. 2001<br>80 80<br>70 70<br>60 60<br>50 1991 50 1991<br>40 2003 40 2001<br>30 30<br>20 20<br>10 10<br>0 0<br>OECD CEE SEE FSU1 FSU2 FSU3 FSU4 OECD CEE SEE FSU1 FSU2 FSU3 FSU4<br>% %<br><!-- End of picture text --> Source: World Bank World Development Indicators. Note: CEE = Central and eastern European countries (Poland, Hungary, Czech and Slovak Republics, Slovenia); SEE = Albania, Bosnia and Herzegovina, Bulgaria, Croatia, FYRR Macedonia, Romania, and Serbia and Montenegro; FSU1= Estonia, Latvia and Lithuania; FSU2=Russia, Ukraine, Belarus, Moldova; FSU3 = Armenia, Azerbaijan, Georgia; FSU4 = Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan.

There is also a distinct pattern in labor productivity performance. The CEE, South-East European (SEE) and Baltic states register an increase in productivity, both overall and within services (broadly defined to include government).<sup>2</sup> Conversely, for those other countries where

> 1 See Figure 1 for the definition of country groups used in this paper.

> 2 Output data is measured in constant 1995 US dollars, as reported in the World Development Indicators.

3
**Tables and Figures**

Table 1: Categories of the data policy index and weights

|**Ca**|**tegories**|**Type of measures**|**Weighting**|
|---|---|---|---|
|1|Intellect|ual PropertyRights(IPR)|0.25|
||1.1|Restrictions related to the application process|0.20|
||1.2|Lack of clear copyright exceptions for the digital economy|0.20|
||1.3|Inadequately enforced of copyrights|0.20|
||1.4|Mandatory disclosure of business trade secrets|0.20|
||1.5|Mandatoryencryption standards that deviate from int. standards|0.20|
|2|Cross‐bo|rder data flows(CBDF)|0.25|
||2.1|Ban to transfer or local processing requirement|0.25|
||2.2|Local storage requirement|0.25|
||2.3|Conditional flow regime|0.25|
||2.4|Infrastructure requirement(residencyrequirements)|0.25|
|3|Domesti|c use andprocessingof data(DP)|0.15|
||3.1|Minimum / maximum period|0.25|
||3.2|Data protection law in place|0.375|
||3.3|Impact assessment (DPIA) or Appoint a data protection officer (DPO)|0.125|
||3.4|Government access topersonal data collected|0.25|
|4|Interme|diate liability (IL)|0.10|
||4.1|Safe harbor for intermediaries|0.60|
||4.2|Identity /monitoringrequirements|0.40|
|5|Content|access(CA)|0.15|
||5.1|Blocking or filtering practices|0.40|
||5.2|Discriminatory use of license schemes & Bans on cloud services|0.40|
||5.3|Other restrictions|0.20|
|6|Infrastru|cture & Connectivity (INF)|0.10|
||6.1|Maximum foreign equity share for investment in telecom|0.50|
||6.2|Anticompetitivepractices in the telecom|0.50|

Source: Authors’ using Ferracane et al. (2018) 17
# **6. Bibliography**

Abebe, G. T., Caria, S., Fafchamps, M., Falco, P., Franklin, S., Quinn, S., & Shilpi, F. (2017). Job fairs: Matching firms and workers in a field experiment in Ethiopia. World Bank Policy Research Working Paper, (8092).

Adoho, F., Chakravarty, S., Korkoyah, D. T., Lundberg, M. K., & Tasneem, A. (2014). The impact of an adolescent girls employment pro gram: The EPAG project in Liberia. _World Bank Policy Research Working Paper_ , (6832).

<mark>Ajayi, K. F., Dao, A., & Koussoube, M. E. J. (2022).</mark> _<mark>The Efects of Childcare on Women and Children: Evidence from a Randomized Evaluation inBurkina Faso</mark>_ <mark>(No. 10239). The World Bank.</mark> Aker, J. C., & Ksoll, C. (2016). Can mobile phones improve agricultural outcomes? Evidence from a randomized experiment in Niger. Food Policy, 60, 44-51.

Alfonsi, L., Bandiera, O., Bassi, V., Burgess, R., Rasul, I., Sulaiman, M., & Vitali, A. (2020). Tackling youth unemployment: Evidence from a labor market experiment in Uganda. Econometrica, 88(6), 23692414.

Alibhai, S., Buehren, N., Frese, M., Goldstein, M., Papineni, S., & Wolf, K. (2019). Full esteem ahead? Mindset-oriented business training in Ethiopia. _Mindset-Oriented Business Training in Ethiopia (June 17, 2019). World Bank Policy Research Working Paper_ , (8892).

Alibhai, S., Cassidy, R., Goldstein, M., & Papineni, S. (2022). Evening the Credit Score? Impact of Psychometric Loan Appraisal for Women Entrepreneurs.

Arouna, A., Michler, J. D., Yergo, W. G., & Saito, K. (2021). One size fits all? Experimental evidence on the digital delivery of personalized extension advice in Nigeria. American Journal of Agricultural Economics, 103(2), 596-619.

<mark>Bandiera, O., Buehren, N., Goldstein, M., Rasul, I., & Smurra, A. (2019).</mark> _<mark>Empowering Adolescent Girls in a Crisis Context</mark>_ <mark>(No. 32115). The World Bank Group.</mark> Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., & Sulaiman, M. (2020). Women’s empowerment in action: evidence from a randomized control trial in Africa. _American Economic Journal: Applied Economics_ , _12_ (1), 210-59.

<mark>Baird, S., McIntosh, C., & Özler, B. (2011). Cash or condition? Evidence from a cash transfer experiment.</mark> _<mark>The Quarterly journal of economics</mark>_ <mark>,</mark> _<mark>126</mark>_ <mark>(4), 1709-1753.</mark> <mark>Bass, J., Bolton, P., Murray, S., Cole, G., Robinette, K., Poulton, C., ... & Annan, J. (2014). Study of</mark> <mark>effectiveness of a social-economic intervention for sexual violence survivors in Eastern DRC.</mark> Bastian, G., Bianchi, I., Goldstein, M., & Montalvao, J. (2018). Short-term impacts of improved access to mobile savings, with and without business training: Experimental evidence from Tanzania. Documents de travail, 478.

Berry, J., Karlan, D., & Pradhan, M. (2018). The impact of financial education for youth in Ghana. World Development, 102, 71-89.

25
assume the _normative_ model is the _positive_ model. That is, just assume that the chooser actually behaves as if he/she were optimizing. While this assumption is heroic, it is implicit (and sometimes explicit) in nearly all educational production function research<sup>7</sup> .

_C)_ A new positive model of expenditure allocations Very few people would accept the optimizing model as a complete or accurate description and in fact the production finction has had very little empirical success in explaining school performance. However, since the intuitive foundations are so clean and attractive the difficulty in rejecting the optimizing model is proposing a specific, concrete, alternative against which to judge it. Therefore, before turning to the empirical evidence we propose a very simple alternative model, which embeds the optimizing models as a special case.

Suppose the person allocating inputs accepts a certain budget as fixed and maximizes a weighted average of the utility of school output _S(X)_ and teacher's utilities T(X) (expressed in units of test scores). The implied objective function is: _C(X)_ = (I-a)*S(X) + _a*(X)_ 7 To estimate and interpret the cost function as the dual of the production function requires the assumption of maximization (Jimenez, 1986, Jimenez and Paqueo, 1996, James, King and Suryadi, 1996).

10
# **The Belt and Road Initiative: Economic, Poverty and Environmental Impacts**

Maryla Maliszewska<sup>1</sup> and Dominique van der Mensbrugghe<sup>2</sup> JEL: F14, F15, C68, Q56, I32, R41.

Keywords: Belt and Road Initiative, Computable General Equilibrium, Poverty, Environment and Trade.

> 1 Macroeconomics, Trade & Investment, The World Bank Group, 1818 H Street NW, Washington DC 20433 (mmaliszewska@worldbank.org). We are grateful to Michele Ruta and Gladys Lopez-Acevedo for comments and suggestions and to Maria Pereira for excellent research assistance. The poverty estimates under section 4.1 have been provided by Israel Osorio-Rodarte and Rakesh Ramasubbaiah.

> 2The Center for Global Trade Analysis (GTAP), Department of Agricultural Economics, Purdue University, West Lafayette, IN 47906 (vandermd@Purdue.edu).