rafmacalaba/datause-encoder-data huggingface dataset:
# **_Results_**

Results are presented in Tables 1 and 2. Following Ravallion (2013), we start with a “naïve” model where the growth of the income of the bottom 40 and bottom 20 is only explained by the average income growth and initial income level (Columns 1 and 2). The results when health is captured through child mortality are presented in Appendix E. Columns 3-11 include control and policy variables, first separately and then together, to see whether they can provide information on policies that affect the lower income groups beyond the overall growth rate of an economy. If the growth of the mean were to explain by itself the growth of the poorest, none of the coefficients would appear statistically significant besides overall growth.

A quick glance at the results in these two columns shows that this is not the case. In line with Dollar and Kraay (2002) and Dollar et al. (2016), we find that income growth at the bottom 20 and 40 percent is positively and statistically significantly associated with mean income growth. The short-run elasticity around 0.88 for the complete model is, however, not statistically different from unity.

Initial income levels are statistically significant and negatively associated with income growth of the various groups.<sup>15</sup> With fixed effects and mean income growth included, this result suggests that income shocks that are specific to a certain income group tend to fade out (and converge to a steady state income path) over time. Demographics also seem to matter beyond their impact on mean growth. The results suggest a pro-poor effect for the share of the working-age population, possibly reflecting the findings from many country analyses that sustainable reductions in poverty were often driven by labor market incomes (e.g. Azevedo et al., 2013). Finally, democratic accountability does not seem to be associated statistically significantly with income growth of the two poorest quintiles.

Turning now to our policy variables, one can observe that macroeconomic conditions do matter for the income growth of the two poorest quintiles. While the level of inflation does not appear to be statistically significant, inflation variations seem to be correlated negatively with income growth at the bottom of the income distribution. The poor do not seem to be affected by inflation any more than the average household (i.e. beyond any effect through the mean income growth), but they seem to be penalized to a greater extent by surprises in the inflation rate. In other words, their coping strategies seem to be as effective as those favored by richer households for steady inflation rates but they seem to be more vulnerable to sudden accelerations in price increases.

> 15 Note that in the fixed effects setup, this does not mean income convergence across countries but convergence of each country towards its own steady state. The convergence effect is also visible and significant when using random effects estimation.

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# **2. Conceptual Framework**

Public officials in developing countries are often viewed as unskilled, poorly educated and poorly motivated to perform their official tasks. The assumption is frequently made that public officials are primarily motivated to exploit any official privileges that their positions give them to engage in opportunistic behaviors. Given the poor performance of public bureaucracies in many developing countries, and the undeniable reality of the low public sector salaries, such an assumption may not be wholly unrealistic. Yet, there are many public officials who, despite such a poor incentive environment, may perform their tasks without shirking, stealing or subverting in spite of overwhelming incentives for opportunism. For every rent-seeking or poorly motivated public official, there may be another who is providing a valuable service under extremely adverse circumstances. There is perhaps more to be learnt from an examination of why some public officials perform against the odds than from reciting the standard litany of failing public services.

This paper starts from the premise that public officials' actions - and hence the performance of their organizations - depend upon the institutional environment in which they find themselves. In this way the analytical framework avoids the anti-government bias that pervades much of the literature on the public sector in developing countries, without attempting a defensive justification for poor performance.<sup>4</sup> The analytical model allows verification of the assumption that it is the institutional environment that impacts on the performance of organizations in the public sector rather than the waywardness or malfeasance of officials. Accordingly, this model suggests that reform interventions that improve any or all aspects of the institutional environment will result in higher perfornance of some or all of the areas in which performance is measured.

# **Figure 1. Analytical model for BNPP-funded surveys**

- Institutional environment: Performance: * policy credibility i * results focus * rule credibility * accountability . resource adequacy and * employee morale predictability

# _2.1 What is meant by institutional environment?_<sup>_5_</sup>

Institutions are the humanly devised constraints, or set of relational contracts that guide public officials' activities. They are made up of formal constraints (e.g. rules, laws, constitutions), informal constraints (e.g. norms of behavior, conventions, codes of conduct), and their enforcement characteristics. For public officials, formal rules are laid 4 See Tendler (1997) for a summary of these perspectives. This section was developed in collaboration with Yasuhiko Matsuda of LCSPS.

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The first factor on the RHS of (13) is the difference between the prospects of a good and a bad supplier, a measure of their heterogeneity; the second is the effect of quality differences on profit, a measure of the industry‘s characteristics (qualitysensitivity). Thus, the value of information, which in our setting drives the search for quality, is increasing in their heterogeneity and in the sensitivity of buyers to product quality.

# **3.2 More than three periods**

With more periods, the revision of beliefs (i.e. the difference between posterior and prior from one period to the next) becomes smaller over time as beliefs approach asymptotically zero or one, but how fast the process of revision converges depends, of course, on the parameters of the two processes. If the two distributions (good and bad) have similar parameters, it takes, in expectation, more time to tell apart the two types, which requires longer sampling. Figure 5 illustrates how the rate of convergence varies with the parameters. In the LHS panel the two distributions are characterized by sharply different parameters and beliefs converge after twenty periods; in the RHS panel, the two distributions have similar parameters and the beliefs take almost a hundred periods to converge.

<!-- Start of picture text --> Figure 5.<br>Random draws of Bernoulli processes in two cases<br>G B G B<br>A pair of draws with   0.8,   0.3 A pair of draws with   0.6,   0.4<br>0 20 40 t 60 80 100 0 20 40 t 60 80 100<br>delta px delta px<br>py<br>py<br>1<br>.5<br>0<br>-.5<br>-1<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Notes: The dotted blue curve gives<sup>_p_</sup> _tx_<sup>, the revised probability that</sup><sup>_x_is of the good type; the long-</sup> dashed red curve gives same thing for _y_ , and the plain black curve gives the difference between the 18
international organizations that need to be staffed. This creates institutional “space” and opportunities for the World Bank president to assert a more independent role vis a vis the American government. In short, World Bank presidents do not seek the same goals as American presidential administrations once they move outside of the domestic sphere and into the international one, and particularly after the distinct appointment tie is broken.

The insight that emerges from a comparison of these four cases with respect to the independence of the Bank from its largest shareholder, and with respect to domestic politics in the US, is that the American presidents have no doubt influenced the trajectory of the organization and thus global notions of development. Nonetheless, the circumstances accompanying weighted voting and thus the Bank's US executive director (ED) controlling the largest bloc of votes under American management have resulted in outcomes that are not easily categorized outside of their historical context.

To explore the meaning of the US-World Bank relationship as it relates to the selection of the president, this paper evaluates the influence of US politics through the prism of the selection and networks of four World Bank presidents on the thinking and practice of development policy in the World Bank during their tenure: Eugene Meyer, Eugene Black, Robert McNamara, and James Wolfensohn. The use of the historical narrative provides a good method to investigate this topic, and archival materials in particular, because they allow the researcher to examine different points of view from different documentary sources. These four cases are significant because the early clash between the Bank's first president, Eugene Meyer, and first US Executive Director (ED), Emilio Collado, combined with the desire of the US foreign policy establishment to tap New York capital markets to result in a weaker board than the Bank's Articles of Agreement might suggest. Nonetheless, the Bank’s transformation from a bank to a development agency can be associated with the desire of two of these presidents, Robert McNamara and James Wolfensohn, to seek a high degree of independence from the US government. These later presidents reinforced this institutional autonomy by creating and strengthening the Bank's internal research capabilities. The paper's approach is limited, however, in what light it sheds on views of development policy in other parts of the world during these years. However, many such explorations have been conducted elsewhere.<sup>4</sup> To demonstrate this feature of the US-World Bank relationship, the paper begins with an introductory section on the connection between the selection of the World Bank's president and 3
# **2.1.4 Fishery production**

Fish makes up approximately 17% of animal-sourced protein in the human diet worldwide (Mathiesen, A.<sup>`</sup> M., 2018). The fishery industry supports the livelihood of 12% of world population by creating 200 million jobs along its value chain. In the global trade system, 80 billion USD worth of fish is exported from developing countries and it plays a crucial role in promoting local economic development (Kelleher, Willmann, and Arnason, 2009).

We estimate both freshwater inland fisheries and marine production values using the FISHSTAT (FAO, 2009) data with a classification based on the fish production categories. The inland fishery production value is the result of disaggregating corresponding country level statistics in proportion to areas of inland water bodies in pixels. The distribution of inland water bodies is obtained from the ESA-CCI (Lamarche et al., 2017). Thus, the value of inland fishing production in each grid is calculated as follows: where _fishvali_ is the value of fishery production in pixel _i_ ; _freshvalx_ is the value of fresh fish production at the national level which is aggregated from FISHSTAT; _waterbodyi_ is the area of water bodies in pixel _i_ ; and _X_ is a set including all pixels _i_ that fall within the boundary of a nation _x_ .

The value of marine fisheries production is based on its proximity to fish landing ports weighted by a composite indicator of equal weight from the number of visits and sum of the vessel hold of fishing vessels. We use the port database from the World Port Index (National Geospatial-Intelligence Agency, 2019) and the number of port visits with a vessel hold of fishing vessels from Hosch et al. (2019) to create a composite variable as the prior based on the sum (for each port) of the number of visits (each event in the database) and total vessel hold at the port. The geographic coverage of the ports is calculated for each port using the minimum port distance provided in (Hosch et al., 2019). Any distance greater than 150km is calculated at 150km. The value of marine fishing production in each grid is calculated as follows: where _marinevali_ is the value of fishery production in pixel _i_ ; _portindexi_ is an equal weighted composite index of the number of visits in pixel and the total vessel hold in pixel _i_ ; and _X_ is a set including all pixels _i_ that fall within the boundary of a nation _x_ .

A map of global gridded fishery production value as a prior is shown in Figure 4.

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the (third) post‐harvest visit, farmer‐reported information on total plot‐specific maize production, non‐labor inputs and harvest labor inputs was solicited for all maize plots that were cultivated during the reference season. The post‐harvest visit was scheduled within a 2‐month period following the completion of each household’s harvest.

# **2.3. Key Measurement Domains and Methods**

# **2.3.1. Plot Area Measurement**

After walking the perimeter of a given plot with the plot manager to identify the boundaries, the enumerators re‐paced the perimeter and measured the area with a Garmin eTrex 30 handheld GPS device. The area was recorded on the questionnaire in square meters, and the raw GPS track outline was stored. The competing yield measures in our study are all anchored in GPS‐based plot area measurement. In MAPS II, the median plot size was 0.11 hectare (ha) (roughly one‐quarter of an acre), with 46 percent below 0.10 ha and 17 percent below 0.05 ha.

# **2.3.2. Soil Fertility Assessment**

Gourlay et al. (2017) provides details on the collection of soil samples at each plot location in MAPS I. The soil sample collection was not repeated in MAPS II partly due to budget constraints and partly due to the MAPS II preference for the plots that were on the same parcels that had a plot selected in MAPS I, as explained by Gourlay et al. (2017). In MAPS I, four samples of the topsoil (0‐20cm) were collected at random locations within each plot and were combined into one composite sample. A single deeper (sub‐soil) sample (20‐50cm) was collected from the plot center. All samples were shipped to the World Agroforestry Center (ICRAF) Nairobi office, and were subject to spectral soil analysis, with 10 percent of the top‐ and sub‐soil samples also analyzed with conventional wet chemistry testing. The key soil attributes that were measured include pH, texture analysis (sand, % clay, % silt), cation exchange capacity, electrical conductivity (EC), and the concentration of organic carbon (OC), total nitrogen (TN), and potassium.

Following Mukherjee and Lal (2014), a composite soil quality index (SQI) was calculated for each MAPS I plot. Multiple approaches to index construction were employed, including simple additive and weighted additive approaches, as well as a principal component approach and each were computed using topsoil (0‐20cm) and subsoil (20‐50cm) depths. Bivariate analysis of each index and crop cutting yield estimates (not reported) suggested that the principal component method using top‐soil properties was found to correlate more strongly with yield than other approaches, and thus, this index is used. Numerous versions of the principal component‐based soil quality index were constructed, using different combinations of soil properties. In this approach, 7
WPS3784 transport in particular. In contrast, residents of Gisenyi and Kibungo, better endowed in infrastructures, are more likely to be out of poverty.

Among the household characteristics that affect poverty, occupation appears to be the single most important variable. The results suggest that someone who earns a wage in the non-farm sector has a substantially higher chance of not being poor and that a selfemployed non-farm worker is also much more likely to be non-poor. On the other hand, being an agricultural worker (4 percent of rural households) implies a higher probability of being poor. However, these three categories represent only 4 and 8.5 percent of rural households, respectively. About 76 percent of household heads are farmers. Being a farmer helps many households escape from poverty but only insofar as the comparator category is households without occupation (7 percent are in other sectors or have no occupation).<sup>3</sup> The other household characteristics that significantly affect poverty include household size, education and the level of wealth, reflected in terms of land and livestock holdings. Households with a low dependency ratio, large land and livestock holdings and a more educated head are more likely to be out of poverty. The result regarding land is interesting given a proposed revision to the land law in Rwanda that seeks to increase the feasibility of larger land plots. Also of importance here is the role of cooperatives, which can successfully bring small scale farmers together to allow for more efficient utilization of land, improved access to credit and better dissemination of information. The key message here is that a reorganization of the rural sector, which leads to the more effective use of agricultural land, is likely to have a substantial effect in reducing poverty.

# **3. The Poverty Impacts of Lower Rural Transportation Costs**

_Direct short run narrow effects_ The analysis in this section shows that allowing and improving access to markets through better rural infrastructure, especially rural transport, has a strong potential for reducing

> 3 These results are consistent with those of Dabalen, Paternostro and Pierre (2004) on the returns to participation in the nonfarm sector in Rwanda.

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21 39. All BOP variables have now been defined. The complete system of equations is shown in Table 3. The behavior of the balance of payments system, as a whole, is uncomplicated. Most of what happens is already predetermined by the rnlationships in the national accounts. The only tricky part is the feedback loop between interest charges and the size of the CAPFIL. 17

- **17** There is an additional feed-back loop betfeen the balance of payments and the national accounts which .-ught to be discussed. An earlier footnote indicated that imports of consumption goods, when under the maximum marginal savings constra.tt, are actually linked to gross national savings rather than gros.e domestic savings. The latter do not include factor service income or transfers. This is an important omission because factor service income includes interest payments which expand or contract with the amount borrowed. If the MAXMSR is used, then the effects of a real resource gap (M > X) will be exacerbated by the interest charged on the resulting external borrowing.
|**Table 9: Impacts on Business Profits**|**and Sales**|**Pro**|**fits**|**Rev**|**enues**|
|---|---|---|---|---|---|
|**Study**|**Gender**|**% increase**|**95% CI**|**% increase**|**95% CI**|
|Berge et al. (2012)||||||
||Male|5.4%|(-20%, +38%)|**31.0%**|(-4%, +79%)|
||Female|-3.0%|(-23%, +22%)|4.4%|(-23%, +22%)|
|Bruhn and Zia (2012)|Mixed|-15%|(-62%, + 32%)|n.r.|n.r.|
|Calderon et al. (2012)|Female|**24.4%**|(-1%, 56%)|**20.0%**|(-2%, +47%)|
|De Mel et al. (2012)||||||
|Current Enterprises|Female|-5.4%|(-44%, +33%)|-14.1%|(-68%, +40%)|
|Potential Enterprises|Female|**43%**|**(+6%, +80%)**|**40.9%**|(-6%, +87%)|
|Drexler et al. (2012)||||||
|"Standard"|Mostly Female|n.r.|n.r.|-6.7%|(-24.5%, +11.2%)|
|"Rule-of-thumb"|Mostly Female|n.r.|n.r.|6.5%|(-11.4%, +24.4%)|
|Giné and Mansuri (2011)|Mixed|-11.4%|(-33%, +17%)|-2.3%|(-15%, +13%)|
||Male|-4.3%|(-34%, +38%)|4.8%|(-14%, +27%)|
||Female|n.r. (a)|n.r.|n.r. (a)|n.r.|
|Glaub et al. (2012)|Mixed|n.r.|n.r.|**57.4% (c)**|n.r.|
|Karlan and Valdivia (2011)|Mostly Female|17% (b)|(-25%, +59%)|<br>1.9%|(-9.8%, +15.1%)|
|Mano et al. (2012)|Male|54%|(-47%, +82%)|22.7%|(-31%, +76%)|
|<br>Valdivia (2012)||||||
|General training|Female|n.r.|n.r.|9%|(-8%, +29%)|
|Training+ technical assistance<br>_Notes:_|Female|n.r.|n.r.|**20.4%**|(+6%,37%)|

<mark>95% CI denotes 95 percent confidence interval. Impacts significant at the 10% level or more reported in</mark> **<mark>bold.</mark>** <mark>n.r. denotes not reported.</mark> (a) They look at an aggregate sales and profitability measure and find no significant impact for either gender. (b) Impact on profit from main product.

(c) Calculated as difference-in-difference calculation. Study reports difference in log sales is significant at the 1% level. Profit increases are scaled as a percentage of the control group mean to enable comparability. When multiple rounds are used, longest-term impacts available are reported.

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||PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|---|
|WPS0|Cote d'lvolre's<br>Vocational and<br>Technical Education|Christlaan<br>Grootaert|<br>June 1988|R. Vartanlan<br>34678|
|WPS20|<br>Imports and Growth in Africa|Ramon Lopez<br>Vinod Thomas|June 1988|61679|
|WPS21|Effects of European VERs on Japanese<br>Autos|Jaime de Melo<br>Patrick Messerlin|June 1988|S.Fallon<br>61680|
|PS22|Methodological<br>Problems in Cross-<br>Country Analyses of Economic Growth|Jean-Paul Azam<br>Patrick Guillaumont<br>Sylviane Guillaumont|June 1988|E. Zamora<br>33706|
|WPS23|Cost-Effective<br>Integration<br>of<br>Immunization<br>and Basic Health Services<br>in Developing<br>Countries: The Problem<br>of Joint Costs|A. Mead Over, Jr.|July 1988|N. Jose<br>33688|
|WPS24|World Bank Investments<br>in Vocational<br>Education and Training|John Middleton<br>Terri Demsky|July 1988|W. Ketema<br>33651|
|WPS25|A Comparison<br>of Alternative<br>Training<br>Modes for Youth in Israel: Results<br>from Longitudinal<br>Data|Adrian Ziderman|July 1988|W. Ketema<br>33651|
|WPS26|Changing Patterns in Vocational<br>Education|John Middleton|July 1988|W. Ketema<br>33651|
|WPS27|Family Background<br>and Student<br>Achievement|Marlaine E. Lockheed<br>Bruce Fuller<br>Ronald Nyirongo|<br>July 1988|R. Rinaldi<br>33278|
|WPS28|Temporary Windfalls and Compensation<br>Arrangements|Bela Balassa|June 1988|N. Campbell<br>33769|
|WPS29|The Relative Effectiveness<br>of<br>Single-Sex and Coeducational<br>Schools<br>in Thailand|Emmanuel Jimenez<br>Marlaine E. Lockheed|||
**Table 13. Allowing for Persistence**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Constant|-3.13<br>(-4.03)|-0.725<br>(-1.04)|0.076<br>(0.12)|
|Lagged dependent<br>variable|0.571***<br>(10.44)|0.881***<br>(33.84)|0.905***<br>(55.02)|
|Dummy fuel > 25%|2.19***|1.28***|0.447|
|exports|(5.42)|(3.17)|(1.63)|
|Dummy ores and|0.203|0.240|0.167|
|metals > 10%<br>exports|(0.78)|(0.96)|(0.83)|
|Hard peg dummy|0.533<br>(1.07)|-0.313<br>(-0.54)|-0.280<br>(-0.99)|
|Political stability|0.0271**|0.0565***|0.0397***|
|(0 to 100)|(2.51)|(3.35)|(3.41)|
|Time trend|-0.0097|0.0231|0.0377*|
|(=0 in 2000)|(-0.50)|(1.27)|(1.75)|
|n|1,342|1,358|1,344|
|R-squared|0.423|0.846|0.874|
|RMSE|3.88|3.76|3.21|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

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