rafmacalaba/datause-encoder-data huggingface dataset:
corruption”, defined as the percentage of the six transactions for which the firm experienced a bribe payment or request.<sup>5</sup> The firm-level responses to the bribery question cannot be directly used in the regressions as they are likely to be endogenous. That is, they could be affected by financial constraints faced the firm (reverse causality) and/or correlated with other firm characteristics that impact financial condition of the firm (omitted variable bias). For example, larger SMEs are likely to have higher ability to pay bribes. Therefore, public officials may target larger SMEs. Financial condition of the firm may also impact demand for bribes as cash rich firms may be targeted by public officials. One solution suggested in the literature is to proxy corruption experienced by a firm with the average level of corruption experienced by all other firms in the same “cell” (Amin & Soh, 2020; Aterido, Hallward-Driemeier, & Pagés, 2011; Fisman & Svensson, 2007). The cell is variously defined as the country, industry, country times industry group, etc. Note that the cell average does not include the firm in question thereby significantly reducing the chances of reverse causality and omitted variable bias. Additionally, using the cell average helps to control for measurement error if some firms refuse to answer or misreport incidence of bribery (Paunov, 2016). Thus, we follow this strategy and use cell averages of the incidence of petty corruption ( _Incidence of Petty Corruption_ ) and depth of petty corruption ( _Depth of Petty Corruption_ ) as our main explanatory variables, where the cell is defined at the country times industry level. Industry is at the 2-digit ISIC Rev. 3.1 level. All cells with fewer than 5 observations are excluded from the sample. In our baseline sample, there are 714 country-industry cells. The mean value of _Incidence of Petty_

> 5 These measures of corruption are available only for firms that solicited the public services previously mentioned. The ES methodology considers a refusal to answer a question on whether bribes were required or requested as an affirmative answer.

11
# **The Impact of Cash Transfers on School Enrollment: Evidence from Ecuador**<sup>*****</sup>

### **Hessel Oosterbeek**

Amsterdam School of Economics Tinbergen Institute

### **Juan Ponce**

## FLACSO-Ecuador

### **Norbert Schady**

World Bank JEL-codes: I38, I28 Key words: cash transfers, school enrollment, regression discontinuity, randomized experiment

> * We thank Arjun Bedi, Erik Plug and seminar participants in Madrid and Quito for their useful comments.
Over the period 2007 – 2012, Niger has experienced some monetary deepening, with the ratio of broad money to GDP increasing from 17 percent in 2007 to 26 percent in 2012. Nonetheless, the degree of monetization of Niger’s economy remains among the lowest worldwide and also well below the 41 percent average for Sub-Saharan Africa. The overall level of financial intermediation has continued to increase but remains low and the range of financial products available is still limited. Credit to the private sector increased fairly rapidly at an annual average rate of 23 percent. The main beneficiaries of credit are enterprises in the mining, telecoms and trade sector in urban areas. Small and Medium Enterprises (SMEs) and rural areas continue to have little access to credit.

# **_Debt_**

Following a significant reduction of Niger’s external debt through the Enhanced Highly Indebted Poor Country (HIPC) initiative and the Multilateral Debt Reduction Initiative (MDRI), public and publicly guaranteed (PPG) external debt has been rising quickly from 14.9 percent in 2007 to a projected 24 percent in 2012. Most of Niger’s external borrowing is on concessional terms to support its large development needs. A government guarantee for the government share of US$352 million in the loan for the construction of the petrol refinery has significantly added to Niger’s external debt. The planned refinancing through a concessional loan from the Exim Bank of China in 2013 will further increase Niger’s stock of public external debt as the entire loan amount would be on the government’s books, even though it is expected that 60 percent of the loan amount would be guaranteed by the foreign investor. Domestic public sector debt has declined from 9.2 percent of GDP in 2007 to 5.7 percent of GDP in 2012.

**Table 7: Public sector debt, percent of GDP, 2007 - 2012**

||**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|
|---|---|---|---|---|---|---|
|**Public sector debt (% of GDP) 1/**|24.2|22.0|27.2|24.0|29.9|29.7|
|o/w external (% of GDP)|14.9|15.1|18.9|17.0|22.4|24.0|
|o/w domestic (% of GDP)|9.2|7.0|8.3|7.0|7.5|5.7|
|Debt service-to-revenue and grants ratio (in percent) 2/|13.1|8.1|-1.2|7.2|4.0|8.0|
|Debt service-to-revenue ratio(inpercent)2/|18.2|10.7|-1.5|9.6|5.1|11.0|

_Source_ : IDA and IMF. Niger: Debt Sustainability Analysis. March 2013.

# **_The Extractive Industries Sector_**

Uranium and, more recently, oil are Niger’s main exports. In addition, the country also produces gold and is home to rich coal deposits whose use is at present limited to fueling a thermal power plant that provides energy for the uranium mines.

Uranium was discovered in Niger in 1957 and production started in 1971 from the Arlit deposit and in 1974 from the Akouta deposit through a joint venture with a French nuclear power company. Total uranium production from these two mines was 4,351 tons in 10
# **Fig. 3: Business Formation Statistics**

<!-- Start of picture text --> 1800000<br>1600000 2006 2007 2008<br>2009 2010 2011<br>1400000 2012 2013 2014<br>2015 2016 2017<br>1200000 2018 2019 2020<br>1000000<br>800000<br>600000<br>400000<br>200000<br>0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51<br>Weeks<br>Cumulative New Business Applications<br><!-- End of picture text --> Source: This figure shows the cumulative new business applications that have a high propensity of turning into businesses with payroll, as reported in the Business Formation Statistics (BFS), United States Census Bureau. See `https://www.census.gov/econ/bfs/pdf/bfs_current.pdf` for further details.

8
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65
companies, and state procurement contracts (Ahram Online, various issues). A company owned by a former government official and a close friend of Hosni Mubarak, for instance, acquired a state-owned retail chain at a low price, while arranging for various regulatory protection measures in the domestic clothing market. And large politically connected firms in ceramics, cement, and steel owned by (former) government officials absorbed the lion’s share of the generous energy subsidies to industry while benefitting from exclusive licensing procedures to open new factories in these sectors.

Despite the regime changes in and after 2011, state-business relations in Egypt remain important. Acemoglu et al. (2015), for instance, identify a group of firms with politically connections to the Muslim Brootherhood, the (former) NDP, and the Military. They follow the group of connected firms over time from 2011 to 2014, including the latest regime change in July 2013, and document how their firm value varied with political events strengthening or weakening their political basis.

The results suggest that the established business elite initially mostly connected to the Mubarak regime managed to preserve both their economic power and strategic influence. Following the revolution in 2011, both the military and Muslim Brotherhood regimes supported a lenient stance towards the prosecution of some leading businessmen on corruption charges. Since 2014, most trials have been ended or terminated and several previous rulings have been revoked or relaxed. Therefore, even though the power groups have changed, the business elite remain strong, and the importance of privileges to the success of certain firms is likely to have remained similar. That is, large corporates continue to benefit from the political connections they had established in the past and dominate markets such as steel, cement, textile, and food markets. For instance, despite being charged (and later acquitted) for monopolistic practices Ezz Steel continues to enjoy a market share of 50 percent.

# **4. Data**

## **4.1 Establishment-level data**

The World Bank's Enterprise Surveys (WBES) are conducted in developing countries and cover information on establishment characteristics, income statement data, other establishment activities such as their efforts to adopt new technology or to obtain external finance, and establishments’ regulatory burden and government services obtained. The survey consists of a 7
21 For the B.ank itself, successful **adjustment** programs help restore a country's growth and improve its creditworthiness. Furthermore, the benefits of project lending are severely curtailed in highly distorted economies, and adjustment lending can therefore contribute to the success of project lending. Indeed, with distorted incentives, increased investment financing may inciease capital accumulation in the "wrong" activities, exacerbating the misallocation of resources. In the extreme, taking into account the need to service the debt, it could **even result in immiserizing investment.**

# IV. Main Conclusions of the First and Second Report on Adjustment Lending

The First and Second Reports on Adjustment Lending are two recen: comprehensive efforts at evaluating Bank experience with adjustment programs (World Bank 1988a and 1990a). We summarize below the main conclusions of these reports.

The main conclusions of the first report were:

**Success in Improving Performance.** Adjustment lending was moderately

successful in improving aggregate economic performance. Despite suffering more serious shocks, the 30 countries that received adjustment loans before 1985 performed better (increased growth more) on average thar, the 63 that did not. The performance was even better in the 12 countries with three or more adjustment loans before 1987 and in countries that were substantial exporters of manufactured goods. Adjustment lending appeared to have been relatively less successful in the highly indebted countries and in Sub-Saharan Africa. Detailed country studies corroborated the statistical findings. The report cautioned, however, that the conclusions were tentative, because it was
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> variables are as explained earlier. Although we can use only about 1200 panel households, these are located in almost 400 different communities, implying not only wide variation in the variables of interest but also a number of observations that is much larger than what is generally available in cross-country type regressions. This would, in particular, allow us to deal better with the problem of endogeneity and

> improve on the rather indirect evidence that is available in the literature (e.g. Collier 1999) by giving

> some quantification and comparing the impact of civil strife to that of other variables of interest.

## 4. **Results**

> Concerning the _causes_ of civil strife, our results support the general significance of the economic variables considered in Collier and Hoeffier (2000) even though additional factors, such as access to education and infrastructure, emerge as quite important. At the same time, we note that variables of high statistical significance are not necessarily the ones that are quantitatively the most important. In particular if looking at changes, this implies greater importance of policy-related variables such as infrastructure provision. In addition to providing insights into factors underlying civil strife, the regressions also

> demonstrate that higher levels of this phenomenon also reduce aggregate investment and drive individuals into greater reliance on subsistence and less integration into markets, implying that in communities

> affected by high levels of civil strife, both investment and diversification will remain stunted.

# **4.1 Determinants of civil strife**

Results on determinants of civil strife are reported in table 2. To avoid spurious conclusions, we use both a zero/one indicator for the presence of civil conflict at the community level in a probit regression as well as the share of community members reporting to have been affected as a measure of the severity of such conflict for a tobit specification. As noted earlier, information on the level of civil conflict in 1992 was obtained retrospectively in the 1999/2000 survey. This implies that the independent variables needed for the 1992 regression are available only for the 371 "panel" communities that were included in both 19
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17

# <u>3.1.3 Employment</u>

_The poor and the non poor display similar employment-to-population ratios, but differences emerge when taking into account education levels_ . As shown in Table 8, 42.5 percent of the non poor are employed compared to 41.5 percent for the poor and 40.9 percent for the extremely poor. Employment-to-population ratios are close for the poor and the non poor with no education, standing at respectively 45.2 percent and 46.2 percent (Figure 7). But for individuals with some schooling the gap widens. Indeed, 30.7 percent of the poor with some primary schooling and 28.5 percent of those secondary schooling are employed, whereas these figures stand respectively at 35.2 percent and 35.7 percent for the non poor.

**Table 8. Employment-to-Population by Poverty Status, Gender, Living Area, and Age Group**

**(in %)**

||_All_|_Non Poor_|_Poor_|_Extremely Poor_|
|---|---|---|---|---|
|_All_|42.1|42.5|41.5|40.9|
|_By gender:_|||||
|_Male_|65|66.2|63.4|61.9|
|_Female_|21.8|21.4|22.4|22.2|
|_By living area:_|||||
|_Rural_|42.7|42.8|42.7|41.3|
|_Urban_|41.2|42.2|38.5|39.1|
|_By age group:_|||||
|_15-24_|21.6|20|23.5|21.8|
|_25-54_|53.2|54.4|51.6|51.6|
|_55-64_|54.9|50.9|60.4|64|

Source: EPCV 2004. Figures are displayed in percentage.

_While employment-to-population does not seem linked to poverty for females, it is lower for poor males as compared to non poor ones_ (Table 8) _._ Indeed, 66.2 percent of non poor men are employed compared to 63.4 percent for the poor and 61.9 percent for the extremely poor. As men’s labor force participation is roughly similar for the poor and the non poor (Table 6), this difference is mainly due to higher unemployment rates among the poor (Table 7). Female’s employment-to-population rate appears to be only weakly correlated to poverty: 22.4 percent of poor women are employed against 21.4 percent of the non poor. Yet, poor women tend to participate more in the labor market than the non poor, as they can probably not afford not to work or look for a job (Table 6). Their higher participation rate is counterweighed by the higher unemployment rate they face compared to non poor women (Table 7).
Table 3: The Determinants of Policy Delay

|Dependent Variables|Delay Interna|tional|Delay Dom|estic|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log(GDP per capita)|0.054|0.044|0.014|0.055|
||(0.056)|(0.045)|(0.063)|(0.047)|
|Log(Population)|0.081***|0.090***|0.076***|0.083***|
||(0.026)|(0.021)|(0.022)|(0.022)|
|Average Temperature|-0.144|-0.015|-0.670|-0.870|
||(0.590)|(0.524)|(0.488)|(0.551)|
|Middle Distance2China|0.096|-0.078|-0.093|-0.104*|
||(0.130)|(0.072)|(0.095)|(0.061)|
|Top Distance2China|0.149|-0.050|-0.073|-0.007|
||(0.156)|(0.123)|(0.146)|(0.111)|
|Elderly|1.351|1.216|2.170***|1.718**|
||(1.488)|(0.830)|(0.829)|(0.851)|
|Obesity|-2.247**|-1.907***|-1.107|-0.515|
||(1.059)|(0.607)|(0.900)|(0.653)|
|Urban share|0.514*|0.388*|0.284|-0.052|
||(0.279)|(0.232)|(0.224)|(0.236)|
|SO2|-0.005|-0.014|-0.062|-0.068|
||(0.046)|(0.048)|(0.042)|(0.049)|
|Democracy|-0.110|-0.106|-0.205**|-0.288***|
||(0.092)|(0.085)|(0.097)|(0.090)|
|Out-group Trust|0.587|0.457|0.035|0.007|
||(0.452)|(0.429)|(0.393)|(0.436)|
|Individualistic|0.123|0.059|0.689|1.040|
||(0.594)|(0.678)|(0.633)|(0.724)|
|Missing Trust Indicator|0.034|0.078|0.052|-0.040|
||(0.092)|(0.091)|(0.059)|(0.102)|
|Missing Indiv. Indicator|0.025|0.007|0.048|0.114|
||(0.082)|(0.094)|(0.078)|(0.104)|
|λ||3.217***||5.003***|
|||(0.651)||(0.864)|
|ρ||-4.536**||-5.208***|
|||(1.899)||(1.298)|
|Continental Dummy|Yes|Yes|Yes|Yes|
|Obs.|140|140|140|140|
|Adjusted R<sup>2</sup>|0.311||0.158||

Note. The results are similar when adding controls of longitude and latitude. *, **, and *** indicate statistical significance at the 10, 5, and 1 percent levels.

28