rafmacalaba/datause-encoder-data huggingface dataset:
Despite the extensive recruitment that took place in 2010, the incumbents for the position of governor in Anjouan and Ngazidja both lost in the elections of December 2010. The governor of Anjouan ran for re-election in 2010, having won the governor race in 2008 after the removal of Bacar from power. Similarly, the governor of Ngazidja ran for re-election and lost. Some reasons for these outcomes are explored in the following section.

**_Chart 5.3  Recruitment of Present Civil Servants in the Government of Ngazidja by Year_**

<!-- Start of picture text --> 400<br>350<br>364<br>353<br>300<br>250<br>271<br>200<br>150 188 173<br>158<br>142<br>100 131<br>99 96<br>50 87<br>55<br>0<br><!-- End of picture text --> Source: Author database, 2011.

Note: 2011 data is incomplete as the questionnaire was completed in a rolling fashion over that summer.

As mentioned, one of the peak years for government recruitment was 2004, in which 780 civil servants joined. Nearly half of the civil servants recruited that year, 353 in total, joined the Ngazidja Government. Chart 5.3 above shows that after 2010, 2004 is the second largest year for recruitment for the Ngazidja Government. 2004 is also a peak year for the Union Government, again the second largest in the past decade with 257 civil servants recruited.

Upon the creation of autonomous island governments with the Constitution of 2001, Ngazidja presented a special case for devolution, due to the presence of the Union Government. In other words, with the new Constitution, the island territory would contain both the Union and Island Government, and not simply the Union government as before. The government of Anjouan contained staff that had already transferred authority. In Ngazidja, this had not occurred, as a number of civil servants were transferred from the Union government to the Island government only in the years after the new constitution, mainly 2003-2004.

Interviews with senior officials in the Ngazidja Island government suggest that the Union Government was resistant to transfer a large number of staff to the newly established island government. These senior officials describe the scene as “chaotic” decentralization, where staff were unclear as to whether they were moving or not, with many simply refusing to shift. The Ngazidja Island government therefore used the lack of transfers as a justification for further recruitment of civil servants; at the same time, in the Union Government, observers stated that the government similarly used the loss of personnel as a reason to recruit, such that they might be replaced. Combined, these observations point to a process of decentralization that included a disorderly transfer of staff and recruitment and served to duplicate many positions, leading to enormous growth of the civil service. As such, the hypothesis that these dynamics of decentralization, including the resistance of staff to transferring and the unplanned means by which it occurred, would lead to recruitment may indeed apply, especially in 2004, a topic that deserves further scrutiny in future research.

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_Table 4: Descriptive Statistics of the Global Corruption Barometer (2003)_

|**Number of**|**Number of**|**Questions of**|**Sample Size**|**Demographic**|
|---|---|---|---|---|
|**Questions**|**countries**|**Interest**||**Data controls**|
|6|47|‘Corruption is a<br>significant<br>problem in:<br>political life’<br>(no/yes,<br>slightly/yes<br>significantly)|40,838<br>(19,488) female<br>(21,390) male|Age,<br>Education<br>attainment.<br>Income level|

_Source: Transparency International, 2012_ Substantively, what is interesting about this question is that it is narrowly focused on corruption at the highest policy-making levels, thus it is most likely to capture the perceptions of corruption of senior politicians and bureaucrats. Of course, given the fact that the survey data (1) are focused on only one type of respondent (the general public) who may not experience some types of corruption directly (e.g. demands for bribes in exchange for business licenses etc.); and (2) do not combine other indicators of corruption to eliminate/reduce the effect of unrepresentative results, means that the question would be problematic as the primary dependent variable.

**Coding.** Over time, the questions in the GCB have changed. For the purpose of this paper, the questions of interest were asked of respondents in the 2003 survey. Specifically, respondents were asked whether ‘corruption had a not/significant/somewhat significant/very significant effect on (1) personal and family life, (2) the business environment, and (3) political life’ (Transparency International, 2011). Each answer received a percentage of respondents, was interval in nature, and could vary from 0%-100%. There is significant variation in the perceptions of corruption across the different policy domains.

**Over Time Variation.** The questions asked have varied by year and in order to ensure the most valid questions are used, the data from the survey are based on the 2003 iteration. Due to the fact that the questions and sampling strategy change over time, it is not always possible to compare the results in this manner.

# **(B) Measuring Corruption: Objective Indicators**

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Policy Research Working Paper 10842

# **Abstract**

Since 2020, many countries in Sub-Saharan Africa have experienced disruptions to agricultural activities due to the adverse effects of multiple global crises. Notably, the Russian invasion of Ukraine caused a surge in inorganic fertilizer prices, which had potentially significant impacts on Sub-Saharan Africa’s agriculture sector given that most countries in the region are net importers of inorganic fertilizers and the Russian Federation is the world’s largest exporter. Using high-frequency longitudinal phone survey data spanning four years from six Sub-Saharan African countries, this paper examines the dynamics of smallholder agriculture against the backdrop of these crises, with particular focus on prices, availability, and use of inorganic fertilizer, as well as the strategies employed by farmers to cope with high fertilizer prices and other accessibility constraints. The results show that inorganic fertilizer prices have increased in the region since 2020, forcing smallholder farmers to adopt coping mechanisms that are less productivity-enhancing, making them even more susceptible to future crises. Specifically, farming households reduced the quantity of inorganic fertilizer applied, by applying it at lower rates or to a smaller area. In some cases, households sold assets or borrowed money to cope with the high prices of inorganic fertilizers. This calls for policies to help smallholder farmers in the region to build strong support systems to be more resilient and better able to cope with the adverse effects of rising inorganic fertilizer prices during polycrises and related shocks.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at aamankwah@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
**<u>Table 9: Comparison of Performance Indices – Jalanidhi & KWA-BM Schemes</u>**

|**Performance Indices**|**Jalanidhi**<br>|**Schemes**<br>|**KWA Built**<br>**Sche**<br>|**& Managed**<br>**mes**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Availability & Reliability Index|0.14|0.99|‐0.57|0.85|
|Household Satisfaction Index|0.16|0.97|‐0.63|0.88|
|Household Cost of Service Index|‐0.17|0.70|0.66|1.61|
|Operation & Maintenance Index|0.05|0.97|‐0.18|1.12|
|Overall PerformanceIndex|0.07|0.96|‐0.27|1.11|

**<u>Table 10: Comparison of Performance Indices – Jalanidhi & Other Community Schemes</u>**

|**Performance Indices**|**Jalanidhi**|**Schemes**<br>|**Other Co**<br>**Managed**|**mmunity**<br>**Schemes**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Availability & Reliability Index|‐0.01|1.01|0.02|0.99|
|Household Satisfaction Index|0.02|1.02|‐0.04|0.97|
|Household Cost of Service Index|0.00|1.06|‐0.01|0.89|
|Operation & Maintenance Index|0.17|0.86|‐0.30|1.16|
|Institutional Sustainability Index|0.13|0.96|‐0.91|0.81|
|Overall Performance Index|0.13|0.96|‐0.76|0.99|

The relative performances are mixed when Jalanidhi schemes are compared with other community managed schemes. On average, Jalanidhi schemes perform better in areas of household satisfaction, household cost of service, O&M, institutional sustainability, and overall performance. Community managed schemes perform better in availability and reliability of service. However, for many of these indices, the differences in z-scores between these two groups are relatively small.

# 5.2 _Results from Propensity Score Matching_

A propensity score matching exercise was done to assess the performances of Jalanidhi schemes vis-àvis other approaches by using propensity scores to identify comparable treatment and control groups from the surveyed schemes. There were two comparisons done for the analysis. First, the performance of the decentralized demand responsive Jalanidhi schemes were assessed using the traditional supply driven KWA-BM schemes as control group. In the second analysis, Jalanidhi schemes were compared with the other community managed schemes. The PSM analysis was done using the ‘psmatch2’ command in STATA. Since ‘psmatch2’ does not provide the correct standard-errors and t-statistics for the estimates of ATT, the respective standard errors and t-statistics were estimated using bootstrapping. Balance tests were also carried out to check for comparability of treatment and control groups. The “Rubin’s B” and “Rubin’s R” statistics from balance tests indicate overall balance in the sample. The test results are reported in Table A1 and Table A2 in the appendix.

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**Figure 2. Distribution of firm-level markups, all sectors, 2008 and 2017**

<!-- Start of picture text -->
-2 0 2 4 6 8<br>Mark-up (ln)<br>2008 2017<br>.6<br>.4<br>kdensity lnmu<br>.2<br>0<br><!-- End of picture text -->

_Source:_ World Bank staff calculations based on SBS data set.

**In fact, data show that the rise in average markup has been driven by the ability of a small group of firms—the top decile firms—to increase their markups over time, especially after 2014.** Figure 3, panel a, shows that markup growth has been progressively uneven across firms, with firms in the top decile of the markup distribution increasing their average markup faster than the rest of firms. Firms in the 90th percentile increased their (salesweighted) average markup by 28.5 percent from 2008 to 2017, while the rest of firms—those below the top decile—experienced a completely opposite trend: their weighted markup contracted by 8.3 percent over the same period. This shows that the increase in the weighted average markup—of around 15 percent, as shown in figure 3, panel a—comes mainly from the group of firms in the top decile of the markup distribution. The divergence between the top decile and the rest of firms remains even when results are broken down by sectors; it is more pronounced in manufacturing, despite some volatility (figure 3, panel b), while in services this happens only after 2014 (panel c). The mining sector experienced the opposite trend (panel d), as most of the time (until 2016), the top decile group of the markup distribution reported lower markup growth than the rest of the firms in the data set.

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- Cuddington, J.T. and C. Urzua (1989). “Trends and Cycles in the Net Barter Terms of Trade: A New Approach.” _Economic Journal_ , 99: 426–442.

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- Eberhardt, M. and A. Presbitero (2021). “Commodity Prices and Banking Crisis.” CEPR Discussion Paper 15959. Center for Economic Policy Research, London.

- EIA, Energy Information Administration (2020). _Monthly Energy Review_ . April. Washington, DC: U.S. Energy Information Administration.

- Engel, E. (1857). “Die productions- und consumtionsverhaltnisse des königreichs sachsen”, Zeitschrift des Statistischen Bureaus des Königlich-Sächsischen Ministerium des Inneren, 8/9, 1–54 [“The production and consumption relations of the Kingdom of Saxony”, _Journal of the Statistical Office of the Royal Saxon Ministry of the Interior_ ].

- Engle, R.F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation.” _Econometrica_ , 50(4): 987–1008.

- Erdem, F.P. and I. Ünalmıs (2016). “Revisiting Super-Cycles in Commodity Crises.” _Central Bank Review_ , 16: 137-142.

- Erten, B. and J.A. Ocampo (2021). “The Future of Commodity Prices and the Pandemicdriven Global Recession: Evidence from 150 Years of Data.” _World Development_ , 137: 105164.

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- Fernández, A., A. González, and D. Rodriguez (2018). “Sharing a Ride on the Commodities Roller Coaster: Common Factors in Business Cycles of Emerging Economies.” _Journal of International Economics_ , 111: 99-121.

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- Fernández, A., S. Schmitt-Grohé, and M. Uribe (2020). “Does the Commodity Super Cycle Matter?” NBER Working Paper 27589. Cambridge, MA: National Bureau of Economic Research.

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decisions in a neutral and unbiased manner. This should be a priority for regulators in addressing demutualizations and oversight programs.

# **Conflict in regulating competitors**

**_Description:_** There are two types of conflict:

1. The conflict arising between an exchange and a participating broker-dealer that provides competing trading services and is subject to the exchange’s supervision. The primary example is an alternative trading system (ATS) that is a member or participant in an exchange. Large broker-dealers also compete with exchanges as liquidity providers, mainly by internalizing the matching of orders.

2. The conflict between an exchange and a listed company that provides any services in competition with the exchange. Conversely, an exchange may favour a company that it has a business relationship with. As exchanges broaden their businesses the number of these conflicts increases. The broader an exchange’s listing rules and regulation program, the greater the conflict is.

These conflicts can in turn arise in several areas including:

- Rulemaking that favours the exchange’s business or is biased against a competing business. Exchanges make policy and rules that can affect the interests of customers.

- Using investigation and enforcement powers in a biased manner.

- Making administrative decisions affecting firms that compete with the exchange in a biased or unfair manner.

- Predatory regulatory practices such as delays in responding to complaints or imposition of burdensome procedures.

- Improper use of confidential information about regulated firms for business instead of regulatory purposes.

**_Exchange view:_** Few exchanges outside the U.S. recognize conflict issues in regulating competing trading entities because ATSs are not a significant source of competition for order flow. Because of market structure and liquidity differences, internalization of order flow by ATSs and major dealers is not as big an issue in markets outside the U.S.

Australia has experienced broader potential for conflicts in this area because of the number of new business initiatives ASX has pursued, including share registry services, order routing and market data services, futures trading and clearing, a trading linkage with the Singapore exchange, and ASX World Link to provide access to US and other international markets.

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7

# 3.  Calculation of Poverty, Inequality, Migration and Remittance Variables

Annex Table A1 reports three different poverty measures. The first, the headcount index, set at $1 per person per day, measures the percent of the population living beneath that poverty line at the time of the survey.<sup>13</sup> However, the headcount index ignores the “depth of poverty,” that is, the amount by which the average expenditure (income) of the poor fall short of the poverty line.<sup>14</sup> We therefore also report the poverty gap index, which measures in percentage terms how far the average expenditure (income) of the poor fall short of the poverty line. For instance, a poverty gap of 10 percent means that the average poor person’s expenditure (income) is 90 percent of the poverty line. The third poverty measure -- the squared poverty gap index – indicates the severity of poverty. The squared poverty gap index possesses useful analytical properties, because it is sensitive to changes in distribution among the poor.<sup>15</sup> To measure inequality, Annex Table A1 uses the Gini coefficient. In the table this measure is normalized by household size and the distributions are weighted by household size so that a given quintile (such as the lowest quintile) has the same share of population as other quintiles across the sample.

The remaining variables in Annex Table A1 – migration as share of country population and remittances as share of country GDP – are of key importance to this study. Since these two variables must be estimated using some rather heroic assumptions, it is crucial to discuss each variable in turn.

In the absence of detailed records on international migration in the laborexporting countries, the migration variable in this study is estimated by combining data from the two main labor-receiving regions of the world: the United States and OECD
A one standard deviation increase in the overall business regulation index leads to a 3.4 percent productivity increase for the average manufacturing firm and a 1.7 percent increase for the average service firm. A one standard deviation increase in the tax index correlates to a 3 percent increase in manufacturing and 2 percent in services. A one standard deviation increase of the trade index leads to 3.16 and 2.10 percent increases for manufacturing firms and service firms, respectively. A one standard deviation increase in the employment regulation index raises labor productivity by 1.52 in manufacturing and 1.42 percent in services.<sup>42</sup>

# **5.3 EU15 South**

While the majority of the EU15 countries experienced positive productivity growth from 2003 to 2008, the southernmost countries—Greece, Italy, Portugal, and Spain—experienced a contraction. Given the results of the previous section on the importance of firm size, foreignaffiliation, and outward FDI for productivity, the answer may lie in their mix of firms.

**Figure 4 Share of Foreign Firms (% of total)  in EU15, 2008**

<!-- Start of picture text --> EU15 North EU15 Continental EU15 South<br>Sweden Finland Norway Belgium France Italy Spain<br>Firm<br>9 5 4 13 6 2 2<br>population<br>Employment 40 14 10 37 26 10 8<br>Value add 33 20 14 57 35 11 12<br>Value add<br>11 8 5 37 16 7 -9<br>growth<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on Amadeus.

Amadeus data show that less than 3 percent of Italian and Spanish firms are global headquarters (Figure 4). The distribution of firm size in the EU15 South is skewed towards microenterprises; very small, family-operated firms play a much greater role in the economies of Southern Europe than in the other developed economies of Western Europe. Microenterprises account for roughly one third of all value-added generated in the EU15 South and employ roughly half the workforce. When small and medium enterprises are added to microenterprises, they together employ four out of five workers in Southern Europe. These figures are nearly double those in the rest of Europe, where larger enterprises play a more significant role (Figure 5).

> 42 Results for the second sample are similar in sign and magnitude; See Appendix XI.

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We present results for three specifications. In specification I, X1i includes a constant and a set of control variables from the baseline application form, including age at first job, previous experience in self-employment, prior experience with projects, prior experience in helping an entrepreneur, and mother’s employment status. Standard errors are clustered by strata (university and gender). In specification II, Xi contains a constant and an expanded set of controls (X2i) including those from the baseline application form as well as additional variables from the baseline phone survey capturing behavioral skills (patience, willingness to take risk, impulsiveness, tenacity, and sense of achievement). This expanded set of controls reduces the effective sample size to 1,432 due to combined attrition in the baseline phone survey and the follow-up survey. Standard errors are again clustered by strata. Finally, results from specification III are obtained by using the same set of control variables as for specification I (X1i) and standard errors clustered by the governorate where students live at baseline.

# <u>4.3 Treatment on the treated (TOT)</u>

In addition to ITT estimates, we also present and briefly discuss treatment on the treated (TOT) estimates. TOT estimates capture the impact of the entrepreneurship track for the students who complied with their assignment to the treatment or control group. The last section has documented that not all students assigned to the treatment group remained in the program (and a few control students took up the training component of the entrepreneurship track). TOT estimates account for this noncompliance and isolate the impact of actually receiving the intervention for students who complied with their original assignment to the treatment or control group. In the empirical analysis, compliance for students in the treatment group is defined as _completing the business training and receiving coaching_<sup>_19_</sup> . TOT estimates are derived for a range of outcomes of interest by instrumenting actual completion of the entrepreneurship track with the randomized assignment to the entrepreneurship track.

The first stage isolates the effect of being randomly assigned to the entrepreneurship track on completing the entrepreneurship training and receiving coaching. The impact of the treatment on the likelihood of completing entrepreneurship training and attending coaching sessions can be estimated via OLS regression for the following equation:

> 19 Results are very similar when compliance is defined as _completing the business training_ only and are not presented here.

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