rafmacalaba/data-use-mentions-extended huggingface dataset:
Models 1.4 – 1.5 introduce one of the proxies for regulatory uncertainty–a measure of the variation in “bribe tax”. The variable is negative and statistically significant, as expected by **Hypothesis 2.a** , suggesting that unpredictable bribery is indeed likely to decrease the probability of investment by firms. This result comports with previous findings by Malesky and Samphantharak (2008) using firm-level micro-data from Cambodia and cross-national, macro designs used by Wei (1997) and Campos et al. (1999). It is also comports more broadly with the literature on regulatory uncertainty, which has almost always found that uncertainty in the enforcement of written regulations leads to declines in investment (Hallward-Driemeier and Pritchett 2015; Fabrizio 2012; Barrodale 2010; Engau and Hoffmann 2009; Lyons and Mayo 2005; Bittlingmayer 2001). Where our results differ, however, is that we examine not the difference between _de jure_ and _de facto_ written regulations, but the predictability of informal practices – specifically corruption – that are indelibly tied to them.

Similarly, Models 1.6-1.7 introduce our measure of raider attacks into our specification and the results indicate that raider attacks have a negative and significant effect on investment decisions. This finding indicates that as raider attacks increase, firms are less likely to invest and is consistent with **Hypothesis 2.b** , which suggests that a higher volume of raider attacks decreases the probability of investment due to uncertainty by firms about the sanctity of their property rights. Again, while this relationship comports with theoretical expectations ( _c.f._ North, Wallis, and Weingast 2009), this result is novel to the extent that we are unaware of any quantitative test of these expectations.

It is worth noting that in Models 1.5 and 1.7, we compare the effect of “bribe incidence” and “bribe tax” to our measures of regulatory uncertainty, variation in bribery and raider attacks, respectively. In both cases, our measures of regulatory
- 14 -

# **_5.1 An Overview of the Results_**

Table 5 presents estimates of MWRs for March of 1999, 2002, 2003, 2004 and 2005, using the cohortized version of the most updated mortality table for the annuitant population (the RV-04), and the risk-free yield curve.<sup>9</sup> The table shows the overall averages for each of the five years, the maximum and the minimum, and the averages for well defined categories, including type, age, gender, size of the premium, and the presence of guaranteed and deferred periods. It must be emphasized that these are MWRs computed for indexed annuities.

**Table 5: Money's Worth Ratios in March of 1999, 2002, 2003, 2004 and 2005** **<u>Computed with the Risk Free Rate and an Update Cohort Annuitant Table</u>**

||**March**<br>**1999**|**March**<br>**2002**|**March**<br>**2003**|**March**<br>**2004**|**March**<br>**2005**|
|---|---|---|---|---|---|
|All cases|0.978|1.079|1.036|1.064|1.062|
|- maximum|1.148|1.222|1.181|1.276|1.223|
|- minimum|0.755|0.872|0.872|0.876|0.706|
|Male Single Life|0.987|1.086|1.044|1.061|1.054|
|Female Single Life|1.009|1.111|1
- **b. By sector (private and public) and educational levels**

<!-- Start of picture text --> 500 449<br>403 407<br>400<br>299<br>277 279<br>300<br>200<br>100<br>0<br>Primary or less Secondary Post-secondary<br>Public Private<br>2015 Euros<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS. Nominal wages deflated with official CPI estimates from Kosovo Statistics Agency (Harmonized Indices of Consumer Prices; 2018)._

**To summarize, Kosovo’s labor market is characterized by high unemployment and informality rates, which must be taken into consideration for minimum wage policy design** . First, high unemployment rates could suggest high elasticity of labor demand, which may result in substantial job loss due to an increase in the minimum wage. Second, workers earning the minimum wage were on average younger, less educated, and employed in non-professional occupations. Given the high concentration of low skilled workers among minimum wage workers in Kosovo and given empirical evidence that labor demand elasticity was higher for low-skill occupations <mark>(Lichter, Peichl and Siegloch [2015] among others</mark> ), employment loss could be significant. However, even assuming no adverse effects of the policy (i.e., no job losses), because the employment rate was low (28.8 percent) and only about 8-9 percent of workers would be potentially affected by the policy assuming the baseline scenario (an increase to 250 euros), only 2.3-2.6 percent of the total population would gain from the minimum wage policy. When considering alternative minimum wage policies (see Table 1), the potential share of affected workers varies from 0.2 percent (Scenario 4: a rule based minimum wage accounting for core inflation and private sector wage growth
Furthermore, self-control is another factor that predicts fraud victimization (Schreck, 1999; Schreck et al., 2006; Reisig and Holtfreter, 2013). Individuals with lower levels of selfcontrol are more likely to become fraud victims because they are more likely to be shortsighted, and they find it hard to resist the temptation of promised immediate return with little inputs (Holtfreter et al., 2008; Van Wilsem, 2013).

However, most of the aforementioned results are correlational evidence, and are based on selected samples consisting of either limited number of victims or telephoneinterviewed older population. Alves and Wilson (2008) study the vulnerability of the elderly by using a sample consisting of 28 fraud victims. Titus and Gover (2001) and Mears et al. (2016) employ a telephone-survey data set to estimate the incidence and prevalence of fraud victimization. About 2,000 telephone respondents aged 60 and above form the sample used in Mears et al. (2016). Sample selection and insufficient sample size issues make the findings less convincing.

Several papers have examined the impact of credit accessibility on _crime_ from the perspective of criminal offenders. Garmaise and Moskowitz (2006) find that local bank merger induces poorer credit access and results in a large increase in crime. Cortés et al. (2016) find that cash grabbing crimes increase disproportionally after the crashing down of Ponzi schemes in 2008 in Colombia, and the effect is driven by limited access to credit. Aneja and Avenancio-León (2019) document that when there is a lack of access to credit, the deterring effect of incarceration on crime is smaller, and thus indirectly show credit constraints induce criminal behaviors. Palmer et al. (2019) find that financial assistance, on the other hand, can reduce criminal behavior. The total arrests in Chicago fell 1-2 years after temporary financial assistance to eligible individuals and households. This is likely due to greater housing stability associated with financial assistance. While this literature links credit constraints to crime, it does not address the relationship between credit
<sup>10</sup> In the absence of at least two comparable data points, it is impossible to analyze poverty or shared prosperity across time.

Figure 7 shows the distribution of countries in each region that report shared prosperity in the most recent World Bank global shared prosperity database, 2012 to 2017. Excluded are those countries that did not have two recent and comparable welfare aggregates. We distinguish between countries reporting improved shared prosperity (positive consumption or income per capita growth of the bottom 40 percent of population) and countries reporting deteriorating shared prosperity. The regions with the highest share of countries not reporting shared prosperity are Sub-Saharan Africa and MENA. Only four of 12 developing MENA countries with at least one international poverty rate also report shared prosperity, less than onethird of all countries, and much lower than the 80 percent of ECA countries that report shared prosperity.

> 10 Surveys being incomparable could be caused by improvements in survey design or methodology.

10
# **Figure 1. Interest payments and interest rates**

# **A. Interest rates and payments in advanced B. Interest rates and payments in EMDEs economies**

<!-- Start of picture text --> Percent of GDP Percent Percent of GDP Percent<br>2.0 Interest payments 4 1.9 Interest payments 8<br>Interest rates (RHS) Interest rates (RHS)<br>1.8<br>1.8 3 7<br>1.7<br>1.6 2<br>6<br>1.6<br>1.4 1<br>1.5 5<br>2010 2012 2014 2016 2018 2020<br>1.2 0<br>2010 2012 2014 2016 2018 2020<br>Sources: IMF; Kose et al. (2017); World Bank.<br><!-- End of picture text --> A. Nominal GDP-weighted averages of long-term nominal interest rates for up to 36 advanced economies and interest spending in percent of GDP for up to 37 advanced economies. Net interest payments are computed as a difference between primary balances and fiscal balances.

B. Nominal GDP-weighted averages of long-term nominal interest rates for up to 84 EMDEs and interest spending in percent of GDP for up to 150 EMDEs.

21
fall depends on the relative bargaining power of firms and workers in the economy. In the long term, the following happens: If workers have low bargaining power, increased product market competition causes real wages to rise. In contrast, if LMR is high, more competition can cause real wages to fall. In related work, Ebell and Haefke (2009) show that product market deregulation can decrease unemployment and increase real wages. As in Spector (2004), they clearly show how these effects depend on the semi-elasticity of wages with respect to firm surplus.

Fang and Rogerson (2011) propose a model, that highlights the endogeneity of workers’ labor supply decisions, which are characterized by a trade-off between consumption and leisure time. When product markets are deregulated, i.e., entry costs for firms are reduced, the employment effects of the reform are determined by whether the net returns to market work increase. This, in turn, depends on the precise implementation of the reform (for example, does the government redistribute the additional revenue obtained due to the charged firm registration costs?). How strong the increase or reduction in aggregate employment then depends on whether the substitution effect or the income effect dominates.

# Infobox: The OECD PMR Indicators

Since 1998, the Organisation for Economic Co-Operation and Development (OECD) has produced the Product Market Regulation (PMR) indicators. These are a comprehensive and internationally comparable set of measures for the degree to which policies promote or inhibit competition in the product market. The indicators are based on an extensive database, which is compiled by the OECD and relies on time-specific answers to a questionnaire that is sent to national authorities.

The information is quantified in a large series of individual indicators whereby the most competition-friendly value is zero and the least 6. Through a bottom-up approach, these indicators comprise the basis for broader PMR indicators at a sector-specific and economy-wide level. It should be noted that the OECD’s PMR indicators are comparable across countries due to their _de jure_ nature.
to competitors and negative price effects). Those effects appear to apply to overall employment even more than own industry. Beyond the first two rings, positive spillovers dominate and have very strong elasticities, especially at ring 2. In the right panel of Table 11 we explore this more with detailed own industry data, looking at precise rings based on point locations and trying both own industry employment and own industry count of firms as the scale measure. In both cases, we find significant negative, competition effects in the own ring, and positive 30
# 7. Conclusions

To make progress in promoting equality and improving the living standards of the population in Uganda, it is necessary to understand the effect of Uganda’s fiscal policy on inequality and poverty. This report uses the internationally comparable CEQ methodology to assess the individual and combined effects of taxes and public social spending, based on the microdata from the UNHS 2016/17 household survey. The main objective is to provide considerations for policy makers in terms of which fiscal instruments or, more specifically, which mix of fiscal instruments can contribute to reduce poverty and inequality.

Overall, our results show that Uganda’s fiscal system is modestly equalizing. As a whole, taxes and transfers in 2016/17 reduced inequality by approximately 3.23 Gini points. This result is comparable to Ghana, moderately lower than in Kenya and Tanzania, and much lower than the result observed for South Africa. The largest contributive factor to this equalizing effect is direct taxation (personal income tax or PAYE), followed by the education in-kind transfers (net transfers). This is not surprising, given the size of these fiscal interventions as a proportion of pre-fiscal income and their progressiveness.

Direct transfers and social protection programs more generally contribute to the redistributive effect and are simulated as highly concentrated among the poorest households. Nonetheless, their effect is rather modest due to the low coverage, scope and size of these programs. In this sense, expanding the coverage of social protections programs – in the case of NUSAF beyond the northern region, and in general, increasing the number of beneficiaries – should not only help to reduce household vulnerability but also lower inequality.

Importantly, some fiscal transfers in Uganda appear to be regressive and contribute to increased inequality. In particular, tertiary education transfers and, to a lesser extent, water subsidies and technical education transfers are disproportionately benefitting better off households. Households with access to the public water supply – usually non-poor and primarily urban households – capture current water subsidy benefits. This may suggest that non-poor users would agree
In this study, Nairobi and Dar es Salaam were found to have relatively medium levels of clustering in all three land classes, while Kigali and Addis have a wide range of dispersion (see <mark>figure X)</mark> . Commercial and Industrial appear to be the most clustered in Dar es Salaam and Nairobi, which is not surprising given the large industrial park that traverses Dar es Salaam, and the large commercial area in the center of Nairobi. In Addis Ababa, the commercial/Industrial clustering is quite low, and much lower than its irregular residential land. This is likely due to the high percentage of irregular residential land found in the city center, and the small pockets of industrial/commercial land spread throughout the city. In Kigali, the clustering of the commercial and irregular residential land is quite low, which is reflective of the lack of any large central irregular residential neighborhoods or commercial/industrial land.

# **Figure A2: Clustering Index (Moran’s I)**

<!-- Start of picture text --> Level of Clustering by Land cover Type<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Addis Ababa Dar es Salaam Kigali Nairobi<br>c. 2012 c. 2011 c. 2012 c. 2012<br>Regular Residential Irregular Residential Commercial/Industrial<br><!-- End of picture text --> _Note: high clustering = high Moran’s I value_ Data Source: Land cover Classification Calculations made by Author

# **Euclidian Distance from Residential to Commercial/Industrial**

To study the spatial relationships between land cover classes, an average neighborhood distance indicator was developed. Using the land cover 0.5x0.5km grid, the distance from each residential grid cell to each commercial/industrial area was
significant heterogeneity.<sup>20</sup> Similarly, results also show statistically significant estimates for females on “having an active project” both in 2011 and in 2014, but still without significant heterogeneity. The two sets of results are not entirely consistent and may be due to lower statistical power when performing heterogeneity analysis.

Lastly, there is no robust evidence with regard to differences in impacts by social capital (proxied by students who signed up individually or in pairs).

# **7. Impacts on broader employment outcomes**

Finally, we turn to analyzing impacts of the entrepreneurship track on broader employment outcomes beyond self-employment. Results show that there are no significant differences in overall employment status between individuals assigned to the entrepreneurship track and the control group (Table 10). While the estimates suggest a reduction in unemployment among men and women assigned to the entrepreneurship track, this reduction of unemployment is not counterbalanced by an increase in employment. Instead, it is in part explained by an increase in inactivity or studying (though it is not always statistically significant across different specifications), and in part by an increase in the probability of being a family helper. These results can be interpreted as consistent with the short-term increase in optimism and aspirations towards the future having receded by the time of the endline survey, and possibly with some graduates becoming discouraged.

Table 11 presents the impacts of the entrepreneurship track on employment characteristics such as hours worked, earnings, having a contract, being covered by social security, working in a large firm, and reservation wages. In line with short-term results one year after graduation (in 2011), the entrepreneurship track did not promote entry into higher quality jobs among participants. The only significant difference is the likelihood of employment at a large company. In MENA, smaller firms attach greater value to personal references and contacts and therefore “trust” when recruiting, while larger firms attach greater value to credential signaling. Smaller

> 20 The midline survey contains an indicator that is not presented
Rfst-Rc_ WOMX 1,8

# _u2u_ -in2ry f-indings

the iftAnemali microfirm seector is believed to be large, accounting for 20-40 percent of employment in many dcveloping countries. The literature tends to view the sector as the disad-vantaged sector of a seg-nented labor market, as existing to evade government regulations, or as constrained by lack of access to government services. Levenson and M.aloney offer a unique theoretical framework to analyze iinformality and microfirm growth behavior - one that emphasizes the entrepreneurial nature of infor-rmal firms and sees informality as a secondary characteristic.

First, they assume that inforinal firms in developing countries bave dvnarniics similar to firms in industrial countries: entrepreneurs have uinobserved, differing cost structures that determine the&r long-run size and survival - strocru:i-es rhat tihev can only discover _by_ going into busiress.

that ensure property rights, pool risk, or enforce contracts become more important as a firm grows, and the entrepreneur will be willing to pay for them through "taxes" in a way that was not the case as a small firm. The combination of these assurnptions generates several of the stylized facts emerging from cross-sectional data and identified in existing mrdels -informal firms tend to) remnailn small and have high rates of mortality 5 and lower productivity -- without recourse to government-induced distortions ia labor or product markets. Further, the framework predicts _that firms_ whose cost structures dictate that they should expand will make the transition to formality as they grow. Using detailed observations fromn Mexico, Levenson and Maloney find their view consistent with patterns of formality and growth of .nicrofirrns.

Secornd, informality can be thought of as a decision to participate in societal instituLticns. Access to mechanisms This paper-a product of the Poverty Reduction and Economic Management Sector Unit, Latin Arn erica and the Caribbean Region--- _is_ part of a larger eZfort in the region to understand the structure of labor markets in developing countries. Copies of the paper are available free from the World Bank. 1818 _H_ Street NWV, Washington, DC 20433. Please contact Tania Gorriez,
# **I.  Introduction**

More than a billion people around the world still lack access to electricity. For untold others who are nominally connected to the grid, access to electricity is often uneven and unreliable, characterized by frequent, long‐lasting power outages.<sup>2</sup> Outages often occur because of technical failures. In many developing countries, they also reflect the efforts of utilities to cope with persistent power shortages through load shedding (or rolling blackouts). According to business surveys, firms in about a third of developing countries experience at least 20 hours of power outages a month on average. The situation is even worse in South Asia, where firms report almost one outage a day, with an average duration of 5.7 hours.<sup>3</sup> Providing access to reliable electricity is vital for ending extreme poverty and boosting shared prosperity. Many studies have examined the welfare effects of grid connections on households (for example, Dinkelman 2011; Khandker, Barnes, and Samad 2012 ; Lipscomb, Mobarak, and Barham 2013; Banerjee et al., 2015, and Chakravorty, Emerick, and Ravago 2016). While earlier analyses focused on credibly estimating the causal relationship between household welfare and a binary variable of electricity access, few have taken into account whether the “connected” household actually receives an adequate level of service.

Going beyond basic connectivity is essential to understanding the effects of modern energy in the developing world. Even where electrical wires are present, children cannot study in the evenings if there is no power to provide light—nor can people keep their businesses open. Poor‐quality electricity service also means that households must continue to rely on costly backup services. And because consumers often pay a fixed monthly charge regardless of their actual consumption, long power outages increase their effective electricity tariff. All this suggests that models ignoring the quality of electricity service may underestimate the net benefits associated with reliable electricity supply. A recent World Bank study concluded that
The source of short term interest data is OECD and Oxford Economics for China, Japan, Indonesia, Malaysia, the Philippines, Singapore and Thailand. For others the lending rate is used as a proxy for the short term interest rate and they are collected from the IMF’s IFS data set<sup>3</sup> .

In the individual VARX country models, all the four variables are included both as en3Except Brunei, Lao PDR and Vietnam, for which data were not available.

8
Figure 8: State- Level Education Deprivation <!-- Start of picture text --> child 15-17<br><!-- End of picture text --> <!-- Start of picture text --> child 10-14<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. States are sorted by the highest deprivation rate of the oldest child category.

Figure 9: State-Level Education Deprivation by Region <!-- Start of picture text --> a) b) c)<br>Educational Deprivation - by State Educational Deprivation - by State Educational Deprivation - by State<br>in North Central - MICS in North East - MICS in North West- MICS<br>70%<br>80% 80%<br>70% 60% 70%<br>60% 50% 60%<br>50% 40% 50%<br>40% 30% 40%<br>30% 30%<br>20% 20%<br>20%<br>10% 10%<br>10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Educational Deprivation - by Educational Deprivation - by State Educational Deprivation - by State<br>State in South East - MICS in South South MICS in South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50