rafmacalaba/datause-encoder-data huggingface dataset:
# Farm Characteristics

## Input Use

Very few households spend on agricultural inputs to increase the productivity of cultivation (Figure 26a). For the four major crops, only between 20 percent (for sesame) and 36 percent (for sorghum) of households spend any money on pesticides or fertilizers. An even lower proportion spends a significant amount on these inputs. The most common input expense for farmers is farm repairs (except for millet, for which it is machine/equipment repairs). Labor costs constitute the second most common input expense. Input use by the poor is generally markedly lower than that of the non-poor (Figure 26b). A notable exception is fertilizer/pesticide use for sesame, which is used at a higher rate by the poor.

Figure 26: Agricultural Input Use in Sudan, 2014/15

- (a) Proportion of Households Using Given Input (Greater than 100 Sudanese pounds) by Crop

<!-- Start of picture text --> 60%<br>groundnut sorghum millet sesame<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text -->

- (b) Percentage Difference in Input Use by the Poor compared to the Non-poor

<!-- Start of picture text --> groundnut sorghum millet sesame<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15. _Note:_ SDG 100 = US$2.2 (June 2019).

Fertilizer/pesticide use is significantly lower for the poor compared to the non-poor (Figure 27). This is seen in all regions of Sudan, except for Kordofan. For Sudan as a whole, the poor are one-third less likely 29
2013). For instance, 90 percent of the informal firms in Sri Lanka are single-person enterprises (De Mel et. al. 2013), while 72 percent of all establishments in Rwanda, both formal and informal, employ one person, and 70 percent of Rwandan non-farm household businesses are owner-only businesses<sup>3</sup> .

**While the significant share of economic activity remains in the informal sector in developing countries (Schneider et al. 2010), what does the landscape of formal sector jobs look like?** Ayyagari et al. (2011) provides a comprehensive picture in this regard using the World Bank Enterprise Survey (henceforth WBES) data of 49,370 firms in 104 countries between 2006 and 2010. Their analysis suggests that while small firms (5-19 employees) are the smallest contributors to employment across countries (with approximately 16 percent of employment in the median country with a mean of 20 percent across the sample countries), the employment contribution of the small and medium enterprises (SMEs), with less than 99 employees, is comparable to that of large firms, with 100 or more employees. The SMEs’ mean and median employment share are 48 percent  and 45 percent, respectively, compared with 52 percent and 56 percent for the large firms. The large firms also appear to have the largest employment share in the United States (Haltiwanger et. al. 2010a, b).

Ayyagari et al. (2011) also find the employment share of small firms tends to be higher in low-income countries (LICs) than high-income countries (HICs). The small firms have the largest share of job creation (sample mean and median are 58 percent and 45 percent, respectively), and the job creation rates are the highest among small firms no more than five years old, while the large firms have the largest share of job losses. This, however, is in sharp contrast with the U.S. experience, where Haltiwanger et al. (2010b) demonstrate that small mature firms have net job losses. However, after controlling for firm age, they do not find any systematic relationship between firm size and growth in the U.S. Based on OECD data on net employment, Haltiwanger et al. (2010a) find that small firms account for higher pace of both job creation and destruction.

> 3 Based on Rwanda Establishment Census, 2011 and Rwanda Integrated Household Survey 2011.

4
developing countries. This will serve as background for the discussion that follows and help us relate firm characteristics to the structural characteristics of the economies in which they operate.

**_1. Concentrated Ownership and Separation of Cash flow and Voting Rights:_** Most large corporations in developing countries have controlling shareholders that are often firms controlled by other firms with wealthy families at the top of the chain. As shown in Table 1 adapted from La Porta, Lopez-de-Silanes, and Shleifer (1999), except in economies with very good shareholder protection (high antidirector rights), relatively few firms are widely held and most are typically controlled by families or the state.

Thus, the dominant form of business organization in developing countries is a large, pyramidal, family-controlled business group consisting of several (sometimes hundreds) of listed and unlisted firms (See for e.g. Khanna and Palepu, 2000; La Porta, Lopez-de-Silanes, Shleifer, and Vishny (henceforth LLSV), 1999a; Claessens, Djankov, and Lang, 2000 and Morck, Stangeland and Yeung, 2000). The controlling shareholders in these firms typically exercise control by holding voting rights far in excess of their cash flow rights through pyramidal structures (see Table 1) and cross shareholdings (La Porta, Lopez-deSilanes, and Shleifer, 1999; LLSV 2000). The agency problem in these companies is therefore not the failure of managers to serve shareholder interests as in a widely held corporation but rather the expropriation of minority shareholders by large controlling shareholders.

**_2. Capital Structure Choices:_** Debt finance, specifically bank finance, is the major source of external funding for firms of all sizes in developing countries (see Figure 1). The corporate bond market and organized securities markets are typically accessed by larger firms in need of long-term funding (e.g. Beck, Demirguc-Kunt, and Maksimovic, 2008).

Specific country and institutional factors (e.g. GDP growth, inflation, level of capital market development, investor protection, and taxation policy) explain systematic differences between leverage choices of developing country and developed country firms (e.g. Booth, Aivazian, Demirguc-Kunt, and Maksimovic, 2001; Giannetti, 2003; Fan, Titman, and Twite, 2010). Firms in developing countries have higher proportion of net fixed assets to total assets and also use less long term debt financing than firms in developed countries (Demirguc-Kunt, and Maksimovic, 1999). The lower amount of long-term financing per amount of fixed assets can be attributed to weak property rights and enforcement that reduces the value of collateral of firm assets and leads to lower investment in intangible assets (Claessens and Laeven, 2003).

**_3. Banks versus Markets:_** Both banks and markets have a role to play in providing access to finance and overall financial development. What matters for development is not whether a financial system is bank-based or market-based but the overall development of banks and markets (see Demirgüç-Kunt and Levine

5
**Figure 2.1 Container Traffic, 2008–12**

<!-- Start of picture text --> Vieux Fort Castries Woodbridge Campden Saint Saint Basseterre Long Point<br>Bay/Roseau Park George's John's<br>2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ Authors’ own elaboration on primary data. _Note:_ OECS = Organization of Eastern Caribbean States.

# **_Container Trade Imbalance_**

**A major issue relevant to container traffic is the balance of trade; a large number of containers are shipped back out of OECS ports empty.** With the exception of St. Lucia, full containers leaving the – islands make up only 5 13 percent of total container traffic (figure 2.2). St. Lucia’s numbers reflect the transshipment operations of Vieux Fort, where full containers are deposited and then picked up later by another vessel; such containers compose over 66 percent of all containers shipped from that port. Antigua’s data, meanwhile, may be skewed: shipping lines stored empty containers for free at the port until 2014, and a large number of empty boxes accumulated in the container yard. Overall, the disproportionate outflow of empty containers is seen as a key contributor to the high costs of shipping in the Caribbean.

**Figure 2.2 Percentage of Full versus Empty Containers in Total Flow, 2012**

_Source:_ Authors’ own elaboration on primary data. _Note:_ TEU = twenty-foot equivalent unit.

14
- 8 -

<u>Table 1. Improvement of privatized electricity distribution companies in Latin America</u>

|Country|Argen|tina|Chile|Peru|
|---|---|---|---|---|
|Company|EDESUR|EDENOR|CHILECTRA|Luz DelSur|
|Year privatized|1992|1992|1987|1994|
|Change in energy sales (%)|79|82|26|19|
|Change in energy losses (%)|–68|–63|–70|–50|
|Change in employment (%)|–60|–63|–9|–43|
|Change in customers per employee (%)|180|215|37|135|
|Change in net receivables (days)|–38|...|–68|–27|
|Changeinprovisionsforbad debts (% ofsales)|–35|...|–88|–65|

Source: Besant-Jones (2006).

On the other hand, if the government chooses to procure the public work from the outside, the level of efforts by the contractor may not be zero but still suboptimal, because the improved outcome will be partly shared by the government. In this case, it is assumed that a bargaining game over the ownership of the asset would take place. While the government is faced with a hold-up problem in the sense that there is no alternative option to procure this project, the contractor may have a positive reservation price of the object being delivered. Given the expected bargaining outcome, the contractor can select the optimal level of efforts, which would turn out less than the optimum.<sup>5</sup> _Proposition 3. In traditional public procurement from the outside, the level of efforts made by contractors is not zero but still remains suboptimal._ The model can be extended to include various elements that are not contractible ex ante and not enforceable by a third party, e.g., courts and regulators. For example, the quality of infrastructure work delivered or services provided may be non-contractible. Hart _et al._ (1997) discuss the cost and quality of services under the public and private provision, specifically questioning when a government should provide public goods in-house, given its imperfect ability to observe the agent’s effort toward cost reduction and quality improvement. In their model, a benevolent government, which owns a facility, can either hire a manager, i.e., a

> 5 Under the assumption that the agent’s cost function of effort (or investment) is convex, it can be shown that the socially optimal level of efforts, which maximizes the benefit from quality improvement by efforts minus agent’s cost associated with those efforts, would exceed the level of efforts selected by the agent under the incomplete contract context (Hart, 1995).
**Figure 12: Women have lower job opportunities than men in Middle East & North Africa region:** Unemployment and youth unemployment rates in selected countries

<!-- Start of picture text --> (a) By gender (b) By gender among youth<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Females Males Females (15-24 years) Males (15-24 years)<br>Source: World Development Indicators (2012)<br>Figure 13: Proportion of total inequality attributable to inequality of opportunity<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Unemployment (%) Unemployment (%)<br> percent<br>Denmark Germany Finland Slovakia Norway Netherlands Austria Egypt Czech Rep Latvia Sweden France Slovenia UK Poland Greece Estonia Ghana Hungary Italy Belgium Guinea Spain Ivory Coast Lithuania Ireland India-Rural India-Urban Madagascar Portugal Colombia Ecuador Peru Panama Brazil Guatemala<br><!-- End of picture text --> _Note_ : The plotted values are between group shares of total inequality measured by the mean-log deviation (Theil-L). Theil-T is used in the case of Ghana, Guinea, Ivory Coast and Madagascar _Source:_ Adapted from Ferreira, F.H.G. (2012), “Inequality of Opportunity Around the World: What Do We Know So Far”.

Opportunity gaps in different stages of life add up to systematic differences in the earnings of adults with different circumstances. Figure 13 makes a comparison of the share of inequality in income that is due to inequality of opportunity across a selection of countries featured in five different studies (adapted from Ferreira, 2012). Inequality of opportunity is measured here as the share of overall inequality that is associated with between-group differences, where each group is defined by a unique set of circumstances 21
<!-- Start of picture text --> Kernel density propensity score - BEFORE matching<br>©<br>=<br>N<br>=<br>co<br>Oo<br>—<br>N<br>Oo<br>0 1 2 ES 4 5 6 aT 8 9 1<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density of propensity score - AFTER matching<br>lO<br>> a<br>oO<br>NoOf\<br>0 ra 2 S) 4 s 6 TL 8 9 1<br>———<br>_ Treatment ——W Control<br><!-- End of picture text -->
borrowers, the 1990s have seen a sharp rise in net resource flows to the private sector in recipient countries.

Portfolio equity flows have been the most rapidly growing but volatile component of private capital flows in the 1990s, increasing more than thirteenfold from a mere $3.5 billion in 1989 to $47 billion in 1993 and subsequently falling sharply to $22 billion by 1995. The surge of portfolio equity flows in the 1990s has helped integrate the emerging stock markets into global capital markets, but it also brings concerns about the volatility of these flows and their sustainability. Private capital flows, however, remain concentrated in a few developing countries, most of which are in Latin America and East Asia (Figure 2 and 3).

**Figure 2**

# Geographical Distribution **Of** Private Capital Flows, 1990 - 1994

<!-- Start of picture text --> East Asia and Pacific 43%<br>South Asia 4%<br>Sub-Saharan Africa 1%<br>Europe and Central Asia<br>North Africa and 20%<br>Middle East 3%<br>Latin Americ a nd the<br>Caribbean 30%<br><!-- End of picture text --> _Source:_ World Bank Data and **staff** estimates.

5
Financial Regulation and Government Revenue calculations highlight that such savings lie between 1.5% and 2.6% of the tax revenue and between 11% and 19% of the primary deficit. Differently from the previous macro-oriented literature, we are able to track the effect of the policy by having access to monthly bank balance sheets and by verifying the bank mechanics behind the policy change. Specifically we observe that banks comply with the policy and start purchasing these new bonds as prescribed. This leads to an increase in safe asset holding, enhancing the banking system safety, and it also slows down year-on-year profit growth from 11% to 2%, in a context where banks’ return on equity lies at 55%.

We believe our results are important and encourage additional research on the linkages between financial regulation and government revenue, especially on a variety of issues that are fundamental in several countries (i.e., optimality, welfare trade-offs, implementation, etc.). Indeed, we strongly believe there is generally insufficient empirical evidence on which policies low-income countries should pursue to boost financial systems and which trade-offs are encountered in their implementation.

# **References**

- Ag ́enor, Pierre-Richard, and Peter J. Montiel. (2008). ”Development macroeconomics”. Princeton University Press.

- Aizenman, Joshua, and Nancy Marion. (2011). ”Using inflation to erode the US public debt.”Journal of Macroeconomics 33, no. 4 : 524-541.

- Bacchetta, Philippe, and Ramon Caminal. (1992). ”Optimal seigniorage and financial liberalization.” Journal of International Money and Finance 11, no. 6: 518-538.

- Borensztein, Eduardo, and Ugo Panizza. (2009). ”The costs of sovereign default.” IMF Staff Papers: 683-741.

- Brock, Philip L. (1984).”Inflationary finance in an open economy.” Journal of Monetary Economics 14, no. 1: 37-53.

- Brock, Philip L. (1989). ”Reserve requirements and the inflation tax.” Journal of Money, Credit and Banking 21, no. 1: 106-121.

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|control for structure|ndustry/Price<br>GDP/Industry<br>_↓_<br>_↓_<br>GDP<br>P||0.04<br>0.75<br>(0.10)<br>(0.57)<br>_−_0_._15_∗∗_<br>_−_1_._17_∗∗∗_<br>(0.08)<br>(0.45)<br>_−_0_._05<br>_−_0_._23<br>(0.10)<br>(0.39)||3.46<br>1.43<br>0.30<br>0.26<br>1.00<br>5_._42_∗_|at the 1%, 5%, and 10% level<br> Standard errors are reported in|
|---|---|---|---|---|---|---|
|With|GDP/Price<br>I<br>_↓_<br>Industry||-1.57_∗∗∗_<br>(0.33)<br>_−_1_._32_∗∗∗_<br>(0.22)<br>_−_1_._57_∗∗∗_<br>(0.27)||2.44<br>0.15<br>8_._00_∗∗_|otes signifcance<br>anger causality.|
|hout control for structure|Industry/Price<br>GDP/Industry<br>_↓_<br>_↓_<br>GDP<br>Price||-0.01<br>0.66<br>(0.11)<br>(0.54)<br>_−_0_._17_∗∗_<br>_−_1_._17_∗∗∗_<br>(0.08)<br>(0.43)<br>_−_0_._07<br>_−_0_._64<br>(0.09)<br>(0.27)||16.42_∗∗∗_<br>3.36<br>0.04<br>1.13<br>0.35<br>2.33|nger causality.<br>_∗∗∗_, _∗∗_, and _∗_den<br>squared statistic for the test of Gr|
|Wit|GDP/Price<br>_↓_<br>Industry||-1.70_∗∗∗_<br>(0.34)<br>_−_1_._40_∗∗∗_<br>(0.19)<br>-1.52_∗∗∗_<br>(0.25)||4.64_∗_<br>1.49<br>6_._11_∗∗_|ection of Gra<br>sents the chi|
|Income group||Long run|High income<br>Upper middle income<br>Lower middle income|Short run_†_|High income<br>Upper middle income<br>Lower middle income|Note: _↓_denotes dir<br>respectively. _†_ repre<br>parentheses.|

24
Policy Research Working Paper 5997

# **Abstract**

Information, education, communication and interventions based on behavioral-change communication have had success in increasing the awareness of HIV. But these strategies alone have been less successful in changing risky sexual behavior. This paper addresses this issue by exploring the link between action and the intention to change behaviors. In Africa, uncertainty in the lives of those at risk for HIV may affect how intentions are formed. Characterize this uncertainty by understanding the reasons for discrepancies between intentions and actions may help improve the design of HIV-prevention interventions. Based on an incentivesbased HIV prevention trial in Tanzania, the longitudinal dataset in this paper allows the exploration of intended strategies for changing sexual behaviors and their results. The authors find that gender, intervention groups and new positive diagnoses of sexually transmitted infections can significantly predict the link between intent and action. The paper examines potential mediators of these relationships.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ljpackel@gmail.com, wdow@berkeley.edu, ddewalque@worldbank.org, zach.isdahl@ gmail.com and yammtz@yahoo.com.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Table 6.2 IV estimated impact on enrollment and child development outcomes (Nonpoor children at baseline)</u>**<sup>**a**</sup>

||IVMode|l:Baseline|IVMod|el:Midline|IVMod|el:Endline|
|---|---|---|---|---|---|---|
||Actua|l Batch 1|Actual|Batch 1|Actua|l Batch 1|
||Coeff|Robust SE|Coeff|Robust SE|Coeff|Robust SE|
|**Enrollment in ECED Programs**|||||||
|Was child enrolled in project playgroups in survey year (1=Yes)|0.157***|(0.052)|0.195***|(0.066)|0.272***|(0.069)|
|Was child enrolled in non-project playgroups in survey year (1=Yes)|0.033|(0.039)|0.050|(0.051)|0.052|(0.052)|
|Was child enrolled in Islamic Kindergarten in survey year (1=Yes)|-0.015|(0.017)|-0.050|(0.032)|-0.032|(0.053)|
|Was child enrolled in Kindergarten in survey year (1=Yes)|-0.004|(0.024)|-0.128**|(0.064)|-0.139*|(0.078)|
|Was child enrolled in any ECED programs in survey year (1=Yes)|0.167***|(0.054)|0.006|(0.058)|-0.011|(0.029)|
|Was child enrolled in Primary school in survey year (1=Yes)|NA|NA|0.014|(0.011)|0.003|(0.003)|
|Cumulated number of months of project playgroup|1.628***|(0.518)|2.889***|(0.914)|3.888***|(1.106)|
|Cumulated number of months of non-project playgroup|0.307|(0.384)|0.711|(0.580)|0.666|(0.619)|
|Cumulated number of months of Islamic Kindergarten|-0.147|(0.174)|-0.664|(0.469)|-0.681|(0.811)|
|Cumulated number of months of Kindergarten|-0.062|(0.237)|-1.493*|(0.765)|-2.788**|(1.346)|
|Cumulated number of months of any ECED programs|1.726***|(0.544)|1.443|(0.972)|1.086|(1.202)|
|Cumulatednumberof months of Primary school|NA|NA|0.143|(0.113)|0.145|(0.693)|
|**Standardized Child Development Outcomes**|||||||
|EDI: Physical Health & Well-Being|0.140|(0.143)|0.036|(0.113)|-0.075|(0.123)|
|EDI: Social Competence|-0.127|(0.104)|-0.141|(0.108)|-0.225**|(0.105)|
|EDI: Emotional Maturity|-0.064|(0.135)|-0.069|(0.102)|-0.148|(0.109)|
|EDI: Language & Cognitive Development|0.038|(0.071)|-0.005|(0.094)|-0.008|(0.039)|
|EDI: Communication & General Knowledge|-0.062|(0.054)|-0.113*|(0.062)|0.026|(0.121)|
|SDQ: Emotional Symptoms|-0.145|(0.134)|-0.052|(0.100)|-0.148|(0.129)|
|SDQ: Conduct Problems|-0.122|(0.117)|0.040|(0.107)|-0.033|(0.114)|
|SDQ: Hyperactivity & Inattention|0.042|(0.130)|0.018|(0.109)|0.117|(0.127)|
|SDQ: Peer Problems|0.075|(0.148)|0.177|(0.112)|0.071|(0.119)|
|SDQ: Pro-social Behavior|0.095|(0.125)|0.013|(0.120)|0.011|(0.101)|
|SDQ:Total Difficulties|-0.082|(0.154)|0.055|(0.101)|-0.025|(0.136)|

Notes: *** p<0.01, ** p<0.05, * p<0.1;<sup>a</sup> IVs are two dummy variables indicating original batches 2 or 3 with original batch 1 as a reference group. Each row is the result of a separate regression. Models are estimated separately for baseline, midline and endline. Controls include: child’s age, gender, household size, household wealth and mother’s education level. Robust standard errors are clustered at the village level. Sample size for each year varies from 551 to 554.

35