rafmacalaba/datause-encoder-data huggingface dataset:
we do not have a panel structure for teachers.

Due to budget limitations, the analysis of impacts and the calculation of rewards use the same tests. Given that the program is attached to the same test, there may be problems of degradation and inflation of the metric (Koretz, 2002; Neal, 2013). Nevertheless, we use a measure of learning outcomes that is different from the metric used to calculate the incentive payments. While the latter uses an IRT model to account for differences between grades, our estimation of impacts use a grade fixed-effects specification. In addition, as discussed above, the measures taken to minimize cheating mitigate this concern. Furthermore, it is unlikely that teachers were merely “teaching to the test” in the first year of implementation, since teachers had no experience with the tests.

At the time of baseline data collection, teachers’ mean monthly salaries were USD118, 73 percent of teachers felt that their salary was insufficient, 27 percent of teachers felt that they received insufficient recognition for their work, 67 percent were female, and 70 percent mentioned that they would choose teaching as a profession if they could choose over (Table A.6). There were also large disparities by gender. Female teachers reported to have larger classes, earn less, and were less satisfied with their earnings, but were also 10 percentage points _more_ likely to state that they would choose teaching again as a profession, if they had the chance to choose over.

In addition to the primary data collection, we also constructed a data set containing the total number of reported deaths due to Ebola for each prefecture by May 2014, the time of endline data collection. We combined the weekly reports on the total number of new cases by prefecture, provided by the WHO. The distribution of known deaths due to Ebola by May 2014 across prefectures is highly varied with the majority of cases were in one prefecture (168 cases in Gueckedou), and just over 65% of the prefectures had no known cases (Figure A.2).

We perform a range of tests to demonstrate the internal validity of the study.<sup>10</sup> The sample is balanced for a range of student and school-level characteristics, including student baseline learning, number of students assessed, and exposure to Ebola (Table A.7). Furthermore, school-level attrition in student assessment data is balanced (Table A.8), and the sample remains balanced if restricted to schools for whom we have midline and endline data (Tables A.9 and A.10, respectively). Finally, there is no _reduction_ in the number of students assessed at midline and endline in the respective treatment groups, suggesting that teachers did not strategically keep under-performing students from attending school on the day of assessment to inflate their scores (Tables A.11 and A.13).

> 10Section A.2 provides a more detailed discussion.

8
# **1. Reduced scope for protection**

WTO members have offered to reduce significantly legally bound levels of protection in goods and services. These cuts in bound rates are valuable because they limit the scope for future restrictive measures. Proposals under discussion would reduce the world average bound tariff for agricultural products from 40 to 30 percent and that for non-agricultural goods from 8 to 5 percent (Table 1).

**Table 1: Weighted Average Applied and Bound Rates Levied by WTO members**

||<br>**_Base_**|<br>**Applied Ra**<br>**_Formula_**|<br>**tes**<br>**_Formula_**<br>**_plus flex_**|<br>**_Base_**|<br>**Bound rate**<br>**_Formula_**|**s**<br>**_Formula_**<br>**_plus flex_**|
|---|---|---|---|---|---|---|
|**Total**|%|%|%|%|%|%|
|All countries|3.7|2.5|2.9|9.9|5.7|6.9|
|High income countries|2.5|1.4|1.7|5.2|3.1|3.8|
|Developing- non LDC|6.9|5.3|6.2|21.8|12.6|14.4|
|LDCs|11.1|8.7|11.1|na|na|na|
|**Agriculture**|||||||
|Allcountries|14.5|8.9|11.8|40.3|20.7|29.9|
|High income countries|15.0|7.5|11.0|31.9|13.5|20.2|
|Developing-non LDC|13.4|11.5|13.3|53.9|33.0|45.4|
|LDCs|12.5|12.2|12.5|94.1|51.6|94.1|
|**NAMA**|||||||
|All countries|2.9|2.1|2.3|7.8|4.7|5.3|
|High income countries|1.7|1.1|1.1|3.5|2.5|2.7|
|Developing-non LDC|6.4|4.8|5.6|19.1|10.9|11.8|
|LDCs|10.9|8.0|10.9|na|na|na|
|Source: Martin and Mattoo (20<br>Note: Country groups defined u|10).<br>sing World|Bankand UN de|finitions.||||

The tiered formula under discussion for liberalization in agriculture involves larger cuts in the higher tariffs, and would, if implemented without exceptions, cut average bound tariffs almost in half—from 40.3% to 20.7%. But exceptions for “sensitive” and “special” products reduce the tariff cuts and allow many higher tariffs to be subjected to smaller cuts. For most developing countries, bound rates would, on average, still be much higher than currently applied rates. For 4
GFW vessel characteristics data and only match our vessel characteristics data to GFW fishing hours data on the following vessel identifiers: registry name, MMSI (called SSVID by GFW), call sign and IMO.

For vessels that do not match perfectly on these four variables between the two datasets, we develop a matching criterion with two indices: the vessel name as the primary fixed matching index and other indicators as the flexible matching parameter with the priority MMSI _>_ call sign _>_ IMO. This matching criterion means we first match vessels by name and MMSI, then match unmatched vessels by name and call sign, and finally match remaining unmatched vessels by name and IMO.<sup>12</sup> Figure B1 illustrates this process and states the number of vessels in each category.

We first apply this procedure to the 2,202 vessels whose names appear in only one RFMO database. Then we manually match the remaining 14 vessels. In total, we are able to match 1,496 vessels to the GFW fishing data. If a vessel-year has observations in GFW data in some months but not in others, we add rows for those months to our panel data and assign fishing hours = 0.<sup>13</sup> Finally, we subset vessels to those present in the publicly-available version of GFW’s fishing data so that our analysis is reproducible. This filtering reduces the number of matched vessels to 1,340, of which 1,234 have a gear with a gross tonnage discontinuity.<sup>14</sup> Table 1 reports summary statistics on our estimation sample, which we believe is the largest vessel-level database ever compiled on the attributes and fishing activities of the Chinese distant water fleet. These vessels are typically large, roughly 50 meters long on average, with engine power averaging 1,036 kW. Distant water fishing vessels fish on average 221 hours per month (297 for those fishing positive hours). The range of activity across vessels and months is large, with monthly fishing hours ranging from 1 to 751. 12This matching criterion is based on MOA regulations. First, the MOA mandates the use of the MMSI module in the AIS equipment of Chinese distant water fishing vessels (Ministry of Agriculture and Rural Affairs, 2021b). Second, the MOA requires vessels to report their call sign in their distant water fishing permits (Bureau of Fisheries, 2020), while reporting IMO is not mandatory (Ministry of Agriculture and Rural Affairs, 2020).

> 13Implicit in this procedure is that if a vessel-year does not appear in the GFW data, we do not add rows for that vessel-year to our panel data. The issue of when to add zeros only affects our estimates in Section 5.2.1, when the dependent variable is an indicator for positive fishing hours.

> 14Pacific saury vessels have only one ceiling of subsidized engine power: 2,200 kilowatts for vessels with gross tonnage above 1,400 (Table B1). All Pacific saury vessels have gross tonnage above 1,400, and there is no information on what the ceiling of subsidized engine power would be for Pacific saury vessels with gross tonnage below 1,400.

11
We can picture the situation as a multiple-input, multiple output situation at the center of which the measured structures _xi_ of the financial system sit: influenced by the infrastructural inputs _zj_ , influencing the outputs _yk_ . Other factors _wl_ will also be relevant. Examples of structures: banking depth, stock market turnover; examples of infrastructure: legal system, quality and style of regulation, informational infrastructure; examples of output: GDP growth, stability of output and employment, poverty. If the process can be written: then we can think of the function _gk_ as capturing the contribution of finance (structure and infrastructure) to the output _k_ . An overall objective function _W_ ( _y_ ) may be postulated to aggregate the outputs. Then the marginal contribution of a financial structure variable _xi_ to the objective function _W_ can be written If _f_ is linear in its two components, then this expression will be independent of the other non-financial factors _w_ .

In a linear approximation then, a composite financial development indicator would be a weighted average of the various components: So much for the algebra. Let me illustrate with some examples which will serve to illustrate the diversity of the data sources being employed in the recent literature to dig behind the aggregates and try to uncover the mechanisms that are at work in linking finance to wide economic goals.

I will take three particular infrastructural dimensions: legal, regulatory and ownership, and look at just a handful of papers that illustrate the way in which infrastructure interacts with the financial system itself in influencing outputs along – again – three major dimensions, growth, stability and distributional aspects.

# _Legal infrastructure_

In their highly influential 1997 and 1998 papers, La Porta et al. (LLSV) assembled a database on the major distinguishing characteristics of legal systems in different countries as they impact financial contracts and control. In essence the major distinctions lie in the relative protection that is provided to a firm’s managers, controlling shareholders and other insiders as against outsider financiers including both creditors and minority shareholders. Fairly systematically, the relative protection formally granted correlates 15
categories werc created. The grcatcr homogcncity of the rsulting industry categorics is an addledd bencfit of this approach. This is because if an individual's human capital investmcnt has cxtcrnal effects upon his co-workers, this is cxpected to occur morm within a homogencous grour of workers; for cxamplc, within a group of manual workers in any given industry.

In the second step the coefficicnts of the industry dummies (b) arc regressed on industry characteristics (Ij) -- including avcragc human capital variables by industry, as follows: bi = CO + cAI, + eib If the coefficients of the average human capital variables turn out to be statistically significant, then we can conclude that human capital has external effects over and above the effects on the individual.

The explanatory power of regressions with both control variables and industry dummies, only control variables and only industry dummies are shown in Table 3. Comparing the R' of equations 1, 2 and 3 we can conclude that industry effects explain between 1.3 and 35.3 percent of wage variation.

The coefficients of the industry dummies (wage differentials) from the full regression (column 1, table 3) with their t-statistics as well as the cell sizes for each industry category are presented in Table 4. Approximately half of the wage differentials are statistically significant or nearly statistically significant at the 5 percent level.

9
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**Table 3. Incidence of Poverty by Region and Relative Contributions (2000-2004)**

||**20**<br>P0|**00**<br>C0|**20**<br>P0|**04**<br>C0|
|---|---|---|---|---|
||Incidence|Rel.<br>Contribution|Incidence|Rel.<br>Contribution|
|National|51|100|47|100|
|Rural|66|80|59|75|
|_Senegal River Valley_|_77_|_35_|_66_|_17_|
|_Other Rural_|_60_|_45_|_57_|_58_|
|Urban|28|20|29|25|
|_Nouakchott_|_29_|_12_|_26_|_13_|
|_Other Urban_|_27_|_8_|_33_|_12_|

_Source:_ GIRM (2006)

**_B. Trends and Likelihood:_ a low probability of achieving MDG1** . During 2001-2006, non-oil growth did not make a significant dent on poverty.<sup>12</sup> Between 2000 and 2004, the overall poverty incidence declined from 51 to 46.7 percent, the net effect of a decline from 66 to 59 percent in rural areas, and an increase from 28 to 29 percent in urban areas. Over the same period, the Gini coefficient remained at 0.39, indicating that the moderate decrease in poverty was not accompanied by a comparable decline in inequality. If the rhythm of poverty reduction continues to follow the historical trend, the incidence of poverty will stay well above the very ambitious PRSP-2 target and the corresponding MDG (Figure 2).

**Figure 2. MDG 1: Current Situation and Trends**

<!-- Start of picture text --> 0.60<br>0.52 0.57 0.56 0.56 0.55 0.55 0.55 0.54 0.53 0.53 0.52 0.51 0.50 0.49<br>0.48 0.47<br>0.45<br>0.44 0.43<br>0.41<br>0.39<br>0.37<br>0.36 0.35 0.33<br>0.31<br>0.29<br>0.28 Past performance 0.27 0.25<br>Government plan<br>Projected status quo<br>MDG-1<br>0.20<br>1990 1995 2000 2005 2010 2015<br>Source: Authors’ calculations.<br><!-- End of picture text -->

**_C. Financial Needs:_ 0.9 percent of GDP on average per year.** The financing gap for poverty reduction is the widest of all considered in this paper, but reaching this goal would equally yield the biggest developmental impact. The cost of achieving this target for the period 2008-2015 amounts to 1.5 percent of GDP on average per year, corresponding to US$ 275.6 million in total. Once the allocation of government expenditure is taken into account, the country needs additional total financing of 0.9 percent of GDP on average per year (Figure 3), equivalent to a total of US$ 162.2 million (Table 4). For a detailed, yearly cost breakdown, see Annex 1.

12 During 2001-2006, non-oil growth averaged around 4 percent per annum, driven mainly by activity in the tertiary sector of the economy (telecommunication, transport). Non-oil growth is forecasted to increase to 4.6 over 2007-2012, in line with historical trends (IMF 2007).
<!-- Start of picture text --> Maternal Work & Child Development: Number of Findings by Outcome<br>Behavioral Cognitive Schooling Health Weight Height<br>10-<br>9-<br>8-<br>Fa23o 7-6- findingLJ Negative<br>33E5 4.5- ywPositive<br>1 a<br>i Le a<br>neg' null+pos' neg' null+pos' neg' null+pos' neg' null+pos' neg1 null+pos' neg' null+pos'<br>40 findings from 26 papers<br>Source: Authors' systematic review of papers 2002-2022<br>Weight category includes BMI measures<br><!-- End of picture text -->
# 2. Trends and spatial patterns of input use

The analysis in this paper uses data from about 3,000 households across Uganda, interviewed in the nationally representative Uganda National Panel Survey (UNPS) from 2005/6 to 2013/14. The Living Standards Measurement Study (LSMS) and Integrated Surveys on Agriculture (ISA) is a household survey program that supports the design and implementation of surveys such as the Uganda National Panel Survey (UNPS), and ensures comparability with other surveys being carried out under the LSMS‐ISA project in Sub‐Saharan Africa.

The UNPS has data on household characteristics, household consumption and income from a variety of income sources. It also contains a rich agricultural module. Five rounds of the UNPS are used in this analysis comprising data collected in 2005/6, 2009/10, 2010/11, 2011/12 and 2013/14. Households can be matched across rounds using a unique household identifier. The attrition in the UNPS was quite substantial between 2005/6 and 2009/10, but was moderate in the following two rounds (see Table 2.1). For the 2013/14 wave, the panel was rotated, and the survey lost a substantial portion of its original 2005/06 sample. For summary statistics, we use the sample of households engaged in agriculture weighted with cross sectional household weights. For the analysis of input adoption, we consider all households engaged in agriculture that were present in all five rounds of the UNPS: 1,108 households in total.

**Table 2.1. Attrition in the UNPS by wave**

||Sample|Original sample<br>retention|Split‐off<br>HHs|Total|
|---|---|---|---|---|
|2005/06|3,123|100|0|3,123|
|2009/10|2,607|83.5|367|2,974|
|2010/11|2,564|82.1|305|2,869|
|2011/12|2,356|75.4|479|2,835|
|2013/14|1,320|42.2|1,799|3,119|

_Source: Uganda Bureau of Statistics_ <u>(2013)</u> Maize, beans, matooke and cassava are the four most important crops as a share of total crop income for Ugandan smallholder farmers. Table 2.2 provides a description of the type of crops grown by the households for two of the latter years of the analysis. Maize and beans are universally important— comprising 10 percent or more of crop incomes in all regions in both years. Matooke is important in all regions except the Northern region, and cassava is important in all regions except the Western region.

For the most part, crops produced for household, domestic and regional consumption dominate crop income. Coffee is important for some households, and has been traditionally important for Uganda. Sunflower produced for commercial production has increased in importance in recent years, particularly in the north, but it is still a relatively small share of crop income and is also not considered further. Even though food crops dominate crop income, crop sales are important and increasingly so. The share of household income coming from crop sales has increased considerably, particularly for the bottom 40 percent: from 60 percent in 2005/06 to 72 percent in 2011/12 (Figure 2.1).

4
# 7. **Recommendations for Poverty Reduction**

Residents of the seven communities were asked to recommend a response to their poverty. Several interesting lessons can be drawn from the responses summarized in Table 5. First, respondents believe that solutions are at hand, and that certain elements related to poverty can be addressed. This finding contradicts the old 'culture of poverty' theory and which argues that the poor accept their situation and transmit norms and values related<sup>to</sup> living in poverty from generation to generation.

Second, the expressed solutions are quite practical. Responses do not call for major land reforms or simply income transfers. Families do not ask for 'more land and animals'<sup>as</sup> one might have expected after their own poverty definitions showed above. Rather, families overwhelmingly suggest measures which will increase the productivity of available land and human resources. Infrastructure (48 percent) and training emerge as very important suggested solutions.

Third, the families distinguish between exogenous and endogenous factors<sup>they view as</sup> important to better living conditions and do not only expect 'help' to come from the<sup>outside.</sup> Three of the seven communities give organization of the community itself a high<sup>importance in</sup> overcoming poverty.

**Table 8. Recommendations for Responding to Poverty.**

|_Rank_|_Melan_|_Apunag_|_Maca Chico_|_Jatun Era_|_Bellavista_|_MembrilUal_|**_Vdlano_**|
|---|---|---|---|---|---|---|---|
|1|Credit for<br>land,<br>animals, and<br>inputs|Credit for<br>land and<br>animals;<br>roads|Training|Credit and<br>training|Employm.<br>genetation|Services<br>(water,<br>latrines)|Roads|
|2|Services<br>(latrines,<br>electricity,<br>water)|Community<br>organization|Roads,<br>bridge,<br>irrigation|Organiz.|Credit for<br>land,<br>animals, and<br>inputs|Employm.<br>generation|Services<br>(water,<br>health)|
|3|Communal<br>meeting hail|More work|Services<br>(water,<br>latrines)|Roads,<br>communal<br>meeting hail,<br>telephones|Community<br>organization|Roads|Communal<br>meeting<br>haU|
|4|Communal<br>store|_|Church,<br>clinic,<br>school|_|Improved<br>economic<br>conditions|Schools|Credit|
|**_5_**|Training|||||||

Source: Field Interviews for Rural Qualitative Assessment (1994).

28
geo-referenced a total of 941 road projects, and matched these data geographically to data on population density, precipitation, ruggedness, distance to the nearest city of more than 100,000 people, and distance to the nearest port.<sup>3334</sup> Appendix S.3 provides detailed information on how we spatially linked the road work activity data with the additional data. Summary statistics for these variables are shown in table S.14, and results from estimated unit cost regressions using these disaggregated data are shown in table 8.

Columns (1)–(2) in table 8 show results for a specification similar to our baseline models in tables 4–5, the only difference being that disaggregated data on geographical characteristics and population density are used and that estimation is based on a smaller sample of countries. The conflict and corruption results are in fact stronger when we use the disaggregated data on geography and population density to measure these controls. There is thus no evidence that the significant effects on conflict and corruption are an artefact driven by aggregation of these control variables. The population density variable is statistically highly significant and the coefficient estimate is positive, albeit smaller, than in the baseline regressions above. The relationship between the geography variables and unit costs is generally weaker than what was found previously. Rainfall is statistically significant at the 10% level only, in column (2). Ruggedness, distance to large city, and distance to port are all statistically insignificant. In column (3) we add country-time fixed effects, hence the corruption and conflict variables must be excluded from the specification due to collinearity. The results change only marginally as a result, suggesting a weak correlation between country-time level unobservables and the remaining regressors. The inclusion of the fixed effects implies that the estimated coefficient on population density falls to 0.005 and is no longer statistically significant. The geographical variables are mostly insignificant. In all three specifications reported in table 8, the coefficient on the dummy indicating whether the road is longer than fifty km remains negative, and it is highly statistically significant in column (3). Thus, the evidence of increasing returns to scale in road construction is robust to unobserved heterogeneity in the form of country-time fixed effects. We further

> 33We are very grateful to an anonymous referee for suggesting this approach.

> 34These are the countries with the highest number of work activities for which we have information on the section, maintaining a geographical balance between Africa and Asia.

22