rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Comparison to the literature in the manufacturing sector

||(1)<br>trade|(2)<br>trade|(3)<br>trade|
|---|---|---|---|
|RTA|0.02<sup>_∗∗_</sup>|0.29<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|
||(0.01)|(0.03)|(0.03)|
|Domestic trade|NO|YES|YES|
|Globalization control|NO|NO|YES|
|Observations|422656|425984|425984|

**Note:** All regressions use the PPML estimator with exporter-time, country-pair and importer-time fixed effects. Robust standard errors, clustered at the country-pair level, are in parentheses. _∗ p <_ 0.10,** _p <_ 0.05,*** _p <_ 0.01

and report the total effect of CAFTA-DR at the bottom of the table, by estimating equation (5). Results in column 1 suggest that CAFTA-DR had a negative effect on bilateral trade in goods among the member countries. While an average RTA increased trade in goods by 13 percent, the impact of CAFTA-DR is estimated to have been negative, decreasing trade by an average of 12 percent. The negative impact is observed consistently across sectors, despite differences in magnitudes ranging from 6.2 to 12.7 percent. The negative impact of CAFTA-DR on trade between member countries is puzzling but consistent with earlier studies that also fail to find a trade-promoting effect of the CAFTA-DR agreement (El Dahrawy Sánchez-Albornoz and Timini, 2021; Rojas Rodríguez and Matschke, 2023). In fact, a substantial share of RTAs have insignificant or negative impacts on trade flows, as shown in Baier et al. (2019).

To better understand the negative effect of CAFTA-DR, we investigate potentially heterogeneous impacts within the agreement. CAFTA-DR was one of the first RTAs between the U.S. and a small group of developing countries, acting as an agreement not only between the U.S. and Central America, but also among Central American countries and the Dominican Republic.<sup>14</sup> We, therefore, first disentangle the effect of CA trade with the U.S. and the intra-regional trade within Central America. Results in Column 2 of Table (3) show that the negative average effect of CAFTA-DR identified in Table (2) is driven by CA trade flows with the U.S. In fact, intra-regional trade is estimated to have increased

> 14In the remainder of the paper, Central America (CA) refers to signatories of CAFTA-DR excluding the U.S.: Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and the Dominican Republic.

13
6 each region villages were randomly selected among those with lowland for rice cultivation. Proximity to the rice mill was retained as a criterion for the selection of the project pilot villages to ensure sufficient market access, for buying inputs and selling their products. In total, 21, 20, and 19 villages were selected in Poro, Tchologo and Tonkpi, respectively.

Households were selected through a stratified random sampling, with each village a stratum. Prior to the survey, a census of all households within each selected village was conducted with the help of the village leaders. Subsequently, a list of rice growing households was compiled, among which only rice growing households with suitable lowland for rice cultivation were retained. From this final list, 15 rice growing households were each time randomly selected for the survey, resulting in a total of 1,446 rice growing households interviewed in all three regions. Sixty households that did not grow rice during the 2019 harvest season were removed. The final sample consists of 1,386 rice growing households.

The survey collected information on farmers’ socio-economic and demographic characteristics (e.g., age, sex, contact with extension agents, group membership, etc.), psychometric variables such as farmers’ information seeking attitudes and risk attitudes,<sup>5</sup> production related variables including the quantity of labor, fertilizers, pesticides, and equipment used as well as the total harvest and sales (including units and price). Special attention was given to the collection of detailed agricultural labor input data. This remains challenging and relatively uncommon in household surveys. Labor inputs were collected at the plot level in person-days<sup>6</sup> for

> 5 We follow Läpple & Rensburg (2011) to derive the information attitude and risk aversion measures. These variables are obtained from Principal Component Analysis of a set of items that describes the interest of the producer in information gathering, and the level of risk aversion. The individual items were measured using a five points Likert scale, <mark>in which responders specify their level of agreement to a statement going from: (1) Strongly disagree; (2) Disagree; (3) Neither agree nor disagree; (4) Agree; to (5) Strongly agree.</mark> For instance, a household with a high risk averse score indicates that the household is more risk averse.

> 6 To measure the labor input provided by households for each agricultural activity, we use the concept of “persondays”. This information is collected by gender categories: men, women and children (over 5 years old) and for each farming activity. The number of person-days is calculated as follows. For each gender type (men, women, children),
3 OECD markets for African exporters is assessed<sup>to deternin: where analysis of the Uruguay</sup> Round's effects should focus. Second, detailed information on trade barriers<sup>maintained by</sup> the World Bank and UNCTAD is employed<sup>to assess the importance of tariffs (and tariff</sup> nreferences) facing African countries in their key<sup>cxport markets.</sup> This section employs a World Bank trade projection model to quantify the effects<sup>of MFN tariff cuts on African exports.</sup> Third, the study employs **an** inventory ef OECD countries'<sup>nontariff measures to assess</sup><sup>**the**</sup> incidence of these restrictions on African exports<sup>and the likely effects of their liberalization.</sup> The intention here is to determine whether or not<sup>liberalization of nontariff barriers could offset</sup> potential export losses African countries will experience<sup>due to erosion of tariff prefereiuces.</sup>

# The **Relative Importance of African Export**<sup>**Markets**</sup>

An important first question for any assessment<sup>of MFN tariff cuts relates to</sup> identification of the key markets for African exports<sup>since it is here that trade barrier</sup> liberalization will have its maximum impact.<sup>Table 1 provides relevant information by showing</sup> the value and share of individual African country<sup>exports destined for major groups of importing</sup> countries, i.e., -" developed countries, the EEC,<sup>North America, other Sub-Saharan countries,</sup> etc.<sup>4</sup> The major point that emerges from these figures<sup>relates to the overall importance of</sup> 4The statistics in Table I are mainly for 1988 and are based on information reported by African countries to the United Nations. While similar lata for<sup>most other countries is one to two years out of</sup> date, African trade statistics are typically less current<sup>and much less reliable. Yeats (1990) analyzed the</sup> quality of African trade statistics and concluded<sup>that "the data cannot be relied on to indicate the level,</sup> composition, or even direction and trends in African countries'<sup>trade." For this reason Table I must be</sup> used with caution. The United Nations and IMF publish<sup>more recent total African trade statistics, but</sup> these data do not provide details on component<sup>products or the destination of exports.</sup>
the Bank? We know even less about the incentives for learning among the Bank’s operational staff. Those incentives depend crucially on the value attached to WB research by staff for their work. Do the Bank’s practitioners value research for their work, and (if so) does this incentive to learn translate into greater familiarity and use of the Bank’s research?

This paper tries to throw new light on the demand for research by development practitioners within the World Bank. A key empirical question is how operational staff are mapped into the four groups identified in the following table. If the Bank’s operational staff highly value Bank research for their work, and the research done is relevant to their needs and accessible to them, then the bulk of staff will fall into the category ―functionally well-informed.‖ For this group, the incentive to learn comes with acquired knowledge. There may also be staff who are well-informed about research, but not because it matters to their work; they are ―independently well-informed.‖ And some value research for their work but face hurdles in accessing it, so their familiarity is low; these are the ―frustrated uninformed.‖ The remainder comprise those who do not see much value to Bank research for their work, and so do not seek it out; they can be said to be ―happily uninformed.‖

|||Incentive to learn: Perceived val<br>Low|ue of Bank research for own work:<br>High|
|---|---|---|---|
|||**“Happily uninformed:”**|**“Frustrated uninformed:”**|
|Knowledge:|L|Do not feel the need for research|Need to know more from research|
|Staff member’s|ow|and do not have a general interest|but cannot access or finds current|
|personal||in learning from new research.|research of little practical use.|
|familiarity with<br>the Bank’s<br>|Hih|**“Independently well-informed:”**<br>Do not need research for own|**“Functionally well-informed:”**<br>Research is an important input to the|
|research findings|g|work, but has a general interest in<br>learningabout development.|staff member’s workand<br> access to<br>relevant research is not aproblem.|

The paper uses a specially commissioned survey of the World Bank’s senior operational staff to determine how they are mapped to these four groups. The paper’s analysis of these data aims to better understand the demand for Bank research by the Bank’s practitioners—reflecting both their incentives to learn and the responsiveness of the supply of research to their needs.

# **2. The survey**

World Bank staff typically work in (overlapping) teams assigned to specific projects, each team having a ―Task Team Leader‖ (TTL) in the Bank’s terminology. The population for 4
Policy Research Working Paper 7181

# **Abstract**

This paper examines the correlates of success of development policy lending operations of the World Bank between 2004 and 2012. The paper uses a data set constructed of individual loan characteristics and ex-post loan ratings produced by the World Bank’s Independent Evaluation Group. Departing from the related literature, the paper focuses mostly on examining the impact of loan characteristics, reform program design features, and task team leader skills, among other variables, on intended development results, while still controlling for country characteristics. It finds that a variable used to reflect congruence or “line of sight” between the policy reforms supported and the results framework is a critical ingredient for success. Task team leader skills in general, and task team leadership by staff affiliated with the former “Economic Policy” department of the World Bank, also increase the chance of success. Conversely, a weaker set of supported reforms in these operations tends to reduce the chance of success. Reforms supported in the energy sector seem to reduce the likelihood of success, perhaps because of the inherent political difficulties of implementing reforms in this sector. The paper also draws important policy and institutional implications from these and other findings.

This paper is a product of the Country Economics Unit, Operations Policy and Quality Department (OPCS). 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 pmoll@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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
# **2. THE EVENTS METHODOLOGY**

Cross-section growth studies have identified that some public expenditures tend to be “productive” in the sense of being growth-enhancing. In the “standard growth regression” framework, average GDP per capita growth over a decade or more is regressed on a set of “standard” control variables augmented by the inclusion of public expenditure variables.<sup>4</sup> From the perspective of this study, among the more interesting recent developments in these exercises, Kneller _et al._ , 2000, Bose _et al._ , 2003, and Adam and Bevan, 2005, using dynamic panel, have argued persuasively that one should take into account both the sources and the uses of funds _simultaneously._ Taken together, these recent cross-country studies find that capital expenditure, as well as spending on education, health, transport and communication can be favorable to growth when the government budget constraint is taken into account (see World Bank, 2007 - Annex 2). We retain from these studies that any analysis of fiscal performance should incorporate the government budget constraint to provide for a meaningful evaluation of the correlates of public expenditures and its relation with growth.

Moreover, as pointed out in several studies (e.g. Easterly et al., 1993), growth tends to be highly unstable in low-income countries. This makes it more difficult to unveil the relation between growth and its fundamentals. Moreover, as noted by Hausmann, Pritchett and Rodrick (2005) “ _standard growth theory, whether of the neoclassical or the endogenous variant, suggests that our best bet for uncovering the relation between growth and its fundamentals is to look for instances where trend growth experiences a clear shift_ . [...] _If instead we lumped together data on growth without paying attention in these turning points, we would be averaging out the most interesting variation in the data._ ” Here we focus on events that are restricted to GDP growth and primary spending growth _acceleration_ (an increase in annual GDP per capita growth of at least 2 percentage points and sustained for at least 5 years and on the fiscal side to an increase in primary fiscal expenditure). Rather the fiscal event captures a change in fiscal stance that likely includes a discretionary component that meets certain qualifications seeking to exclude increases in fiscal expenditures that could be destabilizing.

As a point of departure, an “events” methodology is purely descriptive. It approaches the robustness problem of the correlates between growth and fiscal expenditures by focusing on turning points in the data, the turning points being defined at discretion. Using threshold values to define events is also a way to

> 4 Perotti (2007) reviews critically the contributions of the production function and growth regressions emphasizing that the endogeneity of public investment combined with the lack of good instruments casts doubts on the robustness of the results. These criticisms apply to the events methodology as well when interpreting results.

4
# **4 Analysis**

In this section, we report intent-to-treat (ITT) estimates of the impacts of the mechanization treatment on households assigned to the treatment group. Treatment assignment was random within strata, so the impacts of the intervention on a given outcome _Yi_ can be measured using the following regression: where _Yi_ represents outcome _y_ for household _i_ , Treat _i_ is an indicator variable equal to 1 if household _i_ was assigned to receive oxen in the first wave, **Xi** is a vector of unbalanced demographic variables, and _δstratum_ is a series of strata fixed effects. As treatment was assigned at the individual/household level, we report heteroskedasticity-robust standard errors.<sup>13</sup> In addition, we examine impact heterogeneity by whether households live in districts where women are more or less involved in oxen tasks. Specifically, we run the following regression: where High _j_ is an indicator variable equal to one for households living in districts where the share of oxen tasks conducted by women within the district exceeded the median across all districts. We restrict to control households for this calculation, since our goal is get as close as we can to capturing baseline-level norms. As a robustness check, we-run this analysis using pre-program Demographic and Health Survey (DHS) data to divide districts into ones that are more conservative (below-median women’s decision-making power over their health care, making large household purchases and visiting family and friends) versus less conservative, as described in Section 3.2. We confirm that results are unchanged.

## **4.1 Household-level impacts**

Table 4 presents intent-to-treat impacts on agricultural yields and area cultivated, showing that traction oxen increase yields and lead farmers to bring a larger area under cultivation.<sup>14</sup> Panel A of Table 4 focuses on treatment effects for first season agricultural outcomes, showing that assignment to the oxen treatment arm led to significant increases in agricultural output, even though the oxen were distributed mid-season and recipient producers were not notified that they would receive oxen before they made their planting decisions. Producers increased cotton production by 7%, yielding an equivalent increase in cotton revenues.<sup>15</sup> Notably, the total market value of all crops did not increase, with the increase in cotton production partially

> 13For individual-level outcomes with more than one observation per household, such as children’s education and health, and wives’ initial labor outcomes, we cluster standard errors at the household level.

> 14As noted in Section 3.3, above, non-compliance within both the treatment and control groups suggests that our estimates represent a lower-bound for the treatment effects of the matching grant for the oxen.

> 15The cotton society-dominated market structure yields little variation in the price farmers receive for their cotton, with the median reported sales prices within communities in the sample varying by less than 2%.

12
# **References**

- Abadie, A., Athey, S., Imbens, G. W., and Wooldridge, J. (2017). When should you adjust standard errors for clustering? NBER Working Papers 24003, National Bureau of Economic Research.

- Altunbas ̧, Y., Kara, A., and Marqu ́es-Ib ́a ̃nez, D. (2010). Large debt financing: Syndicated loans versus corporate bonds. _The European Journal of Finance_ , 16(5):437–458.

- Arezki, R., Bolton, P., Peters, S., Samama, F., and Stiglitz, J. (2017). From Global Savings Glut to Financing Infrastructure. _Economic Policy_ , 32(90):221–261.

- Bas ́ılio, M. S. (2014). The determinants of multilateral development banks’ participation in infrastructure projects. _Journal of Infrastructure Development_ , 6(2):83–110.

- Bird, G. (2007). The IMF: A Bird’s Eye View of Its Role and Operations. _Journal of Economic Surveys_ , 21(4):683–745.

- Bird, G. and Rowlands, D. (2007). The imf and the mobilisation of foreign aid. _The Journal of Development Studies_ , 43(5):856–870.

- Blended Finance Taskforce (2018). Better Finance; Better World. Consultation Paper.

- Bruche, M., Malherbe, F., and Meisenzahl, R. R. (2017). Pipeline Risk in Leveraged Loan Syndication. CEPR Discussion Paper 11956, Center for Economic and Policy Research (CEPR).

- Cameron, A. C., Gelbach, J. B., and Miller, D. L. (2011). Robust Inference With Multiway Clustering. _Journal of Business & Economic Statistics_ , 29(2):238–249.

- Carey, M. and Nini, G. (2007). Is the corporate loan market globally integrated? A pricing puzzle. _The Journal of Finance_ , 62(6):2969–3007.

- Carter, P., Van de Sijpe, N., and Calel, R. (2018). The Elusive Quest for Additionality. Working Paper 495, Center for Global Development, Washington DC.

- Cecchetti, S. G. and Kharroubi, E. (2015). Why does financial sector growth crowd out real economic growth? Working Paper 490, Bank of International Settlements, Basel.

- Cerutti, E., Hale, G., and Minoiu, C. (2015). Financial crises and the composition of cross-border lending. _Journal of International Money and Finance_ , 52:60–81.

19
province, illustrating that the spatial distribution of poverty differs across markedly across provinces. Poverty rates are highest in the Northern Province and Free State, although the depth of poverty is highest in the Free State and Eastern Cape<sup>5</sup> The poverty gap (i.e. the amount that is needed annually to wipe out poverty through a perfectly targeted transfer to the poor) in 1995 was about R15 billion, or about 4% of GDP. In the case of the Northern Province, the poverty gap amounts to 21% of the Gross Provincial Product.

How has poverty changed since 1993? A 1998 resurvey in KwaZulu Natal of the households included in the SALDRU sample indicates that, in the immediate post-apartheid period, both the depth and the level of poverty increased. This was accompanied by a widening of the income distribution (Carter and May, 1999). While a few households close to the poverty line, endowed with relatively high levels of productive assets, were able to take advantage of the more favorable policy environment and move ahead, a much greater number of households -especially those with low asset endowments- fell behind. This suggests that strategies to increase households' access to productive assets will be a key element of any strategy aimed at sustainable poverty reduction.

# **3 The land reform program in the context of the RDP strategy**

To understand the character of the land reform program, its objectives, and the way in which these were to be implemented, we discuss this program within the overall context of the Reconstruction and Development Program (RDP) launched by the ANC government in 1994. Looking at the key RDP programs jointly allows not only to appreciate the amount of resources involved but also to highlight potential linkages between them.

In 1994, the first democratically elected government of South Africa committed itself to the RDP as policy framework to promote a fundamental transformation of the social, economic and moral foundations of South African society (ANC, 1994; GNU, 1994). Even though a number of institutional and substantive changes were introduced<sup>6</sup> in the context of the adoption of the Growth, Employment and Redistribution strategy (GEAR), poverty reduction and asset redistribution continued to remain at the 5Comparing different data sets and poverty measures, Leibbrandt and Woolard (1999) have confirmed that this ranking of provinces is robust.

One important change was that implementation of the RDP shifted from a specialized program office to become a function of tine departments. This implied that, instead of a coordinated policy framework that would link the program's different elements together in an attempt to exploit their synergies (as was attempted with the original RDP White Paper), implementation was increasingly guided by sector-specific White Papers, policy documents and legislation. This also implied that monitoring of the different elements became compartmentalized in individual line ministries, focusing more on the achievement of physical targets rather than the degree to which their coordinated activity contributed to achieving overall policy goals.

7