rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 7- Demeaned Household Subsistence Consumption and Demeaned Net Migration for Work Only <!-- Start of picture text --> cairo<br>alexandria<br>ismailia<br>port saidsuezred sea giza qualiobia kafr el sheikh<br>north sinai<br>matrouh<br>south sinai damietta luxor new valley<br>bani suef<br>aswan<br>fayoumdakahlia<br>assiut garbeyyaqenamenoufia<br>menia sharkia beheira<br>sohag<br>-.2 -.1 0 .1 .2<br>dehhcpc<br>denetmig2 Fitted values<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> Source: Authors’ calculations using Labor Force Survey

# **The Model**

We ran a probit model where the dependent variable is a binary taking ‘1’ for the individual who migrated and ‘0’ for those who did not. The independent variables are a dummy for male; regional dummies (urban areas omitted); Lower Egypt, Upper Egypt, and Frontier governorates; education level dummies (illiterate is omitted); age (a continuous variable); dummy if the individual is working in agriculture sector (agrisec); dummy if the individual is unpaid family worker (unpaidfw ); governorate average for share of household food consumption from its own production (hhcpc); and GDP per capita of each governorate.

# **Data and Results**

We used the Labor Market Survey data conducted by the Central Agency of Public Mobilization and Statistics (CAPMAS). In the regression we used the cross-section data for the first quarter of calendar year 2010.The regression results confirm the earlier analysis that individuals with higher levels of education have a higher tendency to migrate. The likelihood of migration increases with higher levels of education, except for above intermediate and university graduates.

18
# Figure 10. Impact of policy improvements on trade costs

<!-- Start of picture text --> A. Reduction in overall trade costs associated B. Reduction in manufacturing trade costs<br>with policy improvements associated with policy improvements<br>Fraction of trade costs Fraction of trade costs<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2<br>0.2<br>0.0<br>0.0<br>Better Better Better Comprehensive Better Better Better Comprehensive<br>logistics border shipping reform logistics border shipping reform<br><!-- End of picture text -->

## Source: World Bank.

Note: EMDEs = emerging market and developing economies. A.B. Bars show the fraction of trade costs that would remain after policy improvements. Policy improvements assume that the average EMDE in the quartile of EMDEs with the poorest scores in the liner shipping connectivity index (LSCI) and logistics performance index (LPI) improves to match the score of the average EMDE in the quartile of EMDEs with the best score for liner shipping connectivity index (LSCI), logistics performance index (LPI). The comprehensive package assumes that all three scores are improved simultaneously. Data refer to 2018. Gray line indicates 1 for unchanged trade costs in 2018 among the sample of EMDEs scoring in the poorest quartile on these indicators.
.001||||
||(0.002)|(0.003)|(0.003)||||
|Routine cognitive||||-0.016***|-0.030***|-0.008**|
|||||(0.003)|(0.006)|(0.004)|
|Country-Year FE|X|X|X|X|X|X|
|Industry-Year FE|X|X|X|X|X|X|
|Sample|Total|Advanced|Developing|Total|Advanced|Developing|
|N|56,697|23,982|32,715|40,227|15,901|24,326|
|Adjusted R<sup>2</sup>|0.074|0.098|0.078|0.112|0.093|0.132|

_Notes_ : see Table 3. Results in columns 1-3 only include observations where the routine manual task intensity of the activity is above the mean, and results in columns 4-6 where the routine cognitive task intensity is above the mean. Accordingly, the regressions control for manual (columns 1-3) and cognitive (columns 4-6) routine task intensity of the activity. Routine task intensity constructed following Acemoglu and Autor (2011), see Appendix F.

# **5. Path-dependence in activity specialization and growth**

The cross-country average patterns found in the previous section are used to benchmark trade specialization patterns of individual countries in the vein of Coniglio et al. (2021), adapting their product-based methodology to the activity perspective. Coniglio et al. (2021) found that economic growth is weaker in countries with a higher degree of path dependence in their export specializations. We revisit this relationship using our activity export data. Following Coniglio et al. (2021) a country is denoted as “path-defying” when its new
Table B.1: Limitations in the budget execution data

||CE|MG|PB|PE|PR|RS|SP|Obs|
|---|---|---|---|---|---|---|---|---|
|_Incomplete temporal coverage_|||||||||
|Commitment|||||||||
|Verifcation|||✓|||||From 2009|
|Payment|||||||||
|_Has some municipality missing_|||||||||
|Commitment|✓||||✓|✓|✓|SeeFigure B.6|
|Verifcation|✓||||✓|✓|✓|SeeFigure B.6|
|Payment|✓||||✓|✓|✓|SeeFigure B.6|
|_Does not have unique ID_|||||||||
|Commitment||||✓|||||
|Verifcation|✓||✓|✓|||||
|Payment||||✓|||||
|_Has missing IDs_|||||||||
|Commitment|✓||✓|✓||✓|✓|SeeFigure B.2|
|Verifcation|✓||✓|✓||✓|✓|SeeFigure B.3|
|Payment|✓||✓|✓||✓|✓|SeeFigure B.4|

**Notes** : This table presents the limitations in the budget execution data. ’Does not have unique ID’ refer to the cases where we were not able to build an identifier that uniquely identifies each row in the given table. Even if we manage to construct a unique identifier for most of the observations of a state-table pair, we may still have observations within
2011; Birn and Solórzano, 1999). Health improvements were concentrated among young children aged 0–5, with no detectable health improvements for elderly individuals similarly required to visit health clinics. Further, health improvements took time to materialize; we observe no improvements after 1.5 years, but 0.76 fewer sick days per month for 0–5 year olds after 2.5 years.<sup>2</sup> This suggests the importance of looking beyond very short-term impacts. Reductions in sick days were largest in villages with more baseline health workers per capita, consistent with improvements being sensitive to capacity constraints.

Overall, this evidence suggests a variety of mechanisms through which cash transfers may help to lift the burden of disease in Sub-Saharan Africa. We further show that these results are robust to adjustments for multiple hypothesis testing, estimation of linear as well as non-linear models, and both intent-to-treat and treatment on the treated estimates.

The remainder of the paper is organized as follows: Section 2 provides background information on health and the health care system in Tanzania, as well as the health conditions of Tanzania’s pilot CCT program. Section 3 describes the evaluation design, data, and outcomes of interest. Section 4 presents our empirical specification, the groups over which we examine heterogeneous treatment effects, balance tables showing the outcome of our randomization, and analysis of attrition. Section 5 characterizes our main empirical results and several robustness checks. Section 6 considers how our main impacts vary across different types of villages and households, and what this implies for the mechanisms likely driving treatment effects. Section 7 concludes.

# **2 Background**

## **2.1 Health care and health in rural Tanzania**

Tanzania is, in many respects, close to the Africa regional average in terms of health statistics. In 2012, 17.3 percent of the population contracted malaria versus 18.6 percent in Africa as a

> 2When we refer to illness in the last month, we are in
group.

In no country are smaller classes related to better performance in a statistically significant way. However, estimates of class-size effects are particularly prone to endogeneity bias. For example, if class size is allocated in a compensatory way, so that weak students are placed in smaller classes, results from observational data will be biased against finding class-size effects (cf., e.g., Wößmann and West 2004). By contrast, if strong performers are placed into

> 14 Note that in Colombia, national category 5 refers to “I do not know”.

18
MONGOLIA'S INFORMAL SECTOR Notes 62 76.As outlined in the employment section of this report, many kiosk operators do not consider themselves "'employed" and are not reflected in the official counts of employment. But some kiosk operators do consider themselves employed, so we can not infer that the jobs created by kiosks are completely omitted from the official figures on employment in distribution.

77.1f the assumptions for this estimate are satisfied, a 95% confidence interval is 5.55 billion togrogs i 17%.

78.Some of the data provided in this section was generated by qualitative interviews with managers of indoor markets, but most was drawn from a survey of 43 indoor markets in Ulaanbaatar conducted for the purposes of this study. The survey was much more modest than the other informal sector surveys. Individual participants were not interviewed except on a very casual basis. The goal of the survey, rather, was to get a sense of the types of activities taking place and generate rudimentary estimates of the level of employment within such indoor markets.

79.In addition to the large centers of _Dalai Eej, Durvan Uul,_ and the like, are many other buildings offering counters for rent, although those in the smaller buildings tend to be oriented toward consumer goods rather than food products.

80. Such estimates were based on various techniques, including the enumeration of official entrance receipts, weighted by the proportion estimated to crowd through without paying; visual sampling from nearby hills; counting traffic flows up and down the access roads. The estimates presented are for total number of visitors in a day, thus it should not be inferred that 1 00,000 people are ever there at the same moment.

# _81 .Ardyn Erh,_ September 22, 1995.

82.There is a bit of a misnomer, deriving from direct translations. "Private numbers" means simply that the vehicle is registered to an individual. "Organizational numbers" means that a vehicle is registered to an organization (such as an
Policy Research Working Paper 5493

# **Abstract**

This paper uses panel data on the number of new firm registrations in 91 countries to study how the ease of registering a business and the magnitude of registration reforms affect new firm registrations. The authors find that the costs, days and procedures required to start a business are important predictors of the number of new firm registrations. However, they find that small reforms, in general less than a 40 percent reduction in procedures or 50 to 60 percent reduction in costs and days, do not have a significant effect on new registrations. They also find important synergies in multiple reforms of two or more business environment indicators. Finally, they show that countries with relatively weaker business environments prior to reforms require relatively larger reforms in order to impact the number of newly registered firms.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study entrepreneurship and growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ilove@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
rule is: where _FPMkt_ is the amount of resources allocated to state _k_ in year _t_ , and _θi_ is the FPM coefficient of municipality _i_ based on its population size.

Due to sample size restrictions, we limit our attention to municipalities with less than 50,940 inhabitants. These municipalities account for about 90% of Brazilian municipalities and 1/3 of the population. The provision of public goods and services in these locations is primarily financed by intergovernmental transfers from the federal and state governments _−_ local taxes represent only about 6% of fiscal revenue. The second column of Table 1 displays the FPM coefficients applied to each population interval. We focus our attention on the initial seven thresholds: 10,189; 13,585; 16,981; 23,773; 30,564; 37,356; and 44,148. The intervals between the initial three thresholds are equal to 3,396, whereas the intervals between the subsequent thresholds amount to twice as much (6,792). For symmetry, we exclude municipalities with fewer than 6,793 inhabitants. In light of these restrictions, it is important to emphasize that the results reported below are specific to the municipalities that are included in the sample, which excludes both very large municipalities and very small ones.

The coefficient _θi_ is assigned to a given municipality by _Tribunal de Contas Uni ̃ao_ (TCU) on the basis of population estimates for the previous year. These population estimates are calculated yearly by _Instituto Brasileiro de Geografia e Estat ́ıstica_ (IBGE), the national statistical institute. IBGE is independent from the government and employs a top-down approach in producing these estimates: municipal-level estimates must be consistent with the state-level estimates; the latter must in turn be consistent with population estimates of the whole country (which draw on birth and mortality rates, and net immigration between Censuses).

However, IBGE population estimates for a given year do not perfectly predict the FPM transfers that each municipality effectively receives in the subsequent year. As
<!-- Start of picture text -->
-2 -1 0 1 2<br>% Change in Employment<br>New exporters Always exported<br>.8<br>.6<br>.4<br>.2<br>Kernel Density for Exporters<br>0<br><!-- End of picture text -->

**Note: Vertical lines indicate the means. Source:** World Bank Enterprise Survey.

Table 5.4 estimates the probability of becoming an exporter (Column 1) and becoming a non-exporter (Column 2). The probability of a firm becoming an exporter is higher when the firm uses imported inputs and if the value of machinery assets is low. While this last result might be puzzling, it could reflect a change in the type of goods these new exporters are trading. New exporters may use less advanced technologies. In contrast, the probability that a firm stopped exporting is higher for firms with high value of machinery assets and low usage of foreign technologies. This might reflect a reduced ability to compete with international firms. An interesting fact is that the probability of stop exporting is higher among firms that were highly intensive in female labor. This might reflect the fact that female-intensive sectors, such as the apparel and textiles, were severely affected by international competition and were left behind by public support.

**Table 5.4. Probability of Becoming Exporter or Non-Exporter**

|**Dependent Variable:**|New exporter (yes =1)|Stopped exporting(yes =1)|
|---|---|---|
||1|2|
|VARIABLES|||
|New products (yes=1)|-0.586|0.508|
||(-1.57)|(1.19)|
|Value of machinery<sup>1/</sup>|-0.084
# **<mark>Figure 3. The proportion of enrolled school-age children and month of birth</mark>**

<!-- Start of picture text --> Panel A. All children Panel B. Sample of children age 1 and 2<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May June<br>Month of birth Month of birth<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Panel C. Sample of children age 2 and 3 Panel D. Sample of children age 3 and 4<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun<br>Month of birth Month of birth<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Panel E. Sample of children age 4 and 5<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun<br>Month of birth<br>Sample average within bin Polynomial fit of order 2<br>.2<br>.55<br>.5 .15<br>.45 .1<br>Child care attendance Child care attendance<br>.4 .05<br>.35 0<br>.4 .6<br>.3 .5<br>.2 .4<br>Child care attendance Child care attendance<br>.1 .3<br>.75<br>.7<br>.65<br>.6<br>Child care attendance<br>.55<br>.5<br><!-- End of picture text --> <mark>Note: The vertical axis gives the proportion of children attending child care,
as a result of a WTO negotiation will overstate the extent of the liberalization achieved in the negotiation. In such cases, new commitments may limit room for policy maneuver, but they imply little change in actual policy.

# **4.2 Approaches to modeling changes in policy**

There have been two basic approaches followed in CGE modeling of changes in policies, once prevailing polices have been somehow benchmarked. The first approach involves a focus on cross-border delivery of services. This includes not only modes 1 and 2 under the GATS, but also mode 3, to the extent this involves cross border sales through affiliates. For example, from the U.S. data we know that exports through affiliates are a prominent aspect of cross-border trade. Consistent with this observation, there is also evidence that FDI restrictions in services do lead to less cross border trade as well.<sup>18</sup> A common approach in CGE modeling of barriers to cross-border trade in services involves using estimates

> 17See e.g. Dee and Hanslow (2001), Hoekman and Konan (2000, 2001) for early CGE applications analyzing the importance of whether services policies generate waste (red tape) or rents.

> 18See for example Table 7 in Francois and Hoekman (2010).

18
beneficiaries, 58 percent relied on mosque announcements for information, 47 percent relied on TV, and only 10 percent mentioned radio or newspaper (Mott McDonald, 2014).

# **VI. Conclusion**

Although electricity subsidies were introduced as a form of social safety net in Pakistan, the analysis of household survey and billing data in this paper demonstrates that they continue to be regressively targeted, and that many poor households remain exposed to high bills especially in the summer months. Subsidies are often introduced in the name of helping the poor, but implemented in isolation from other social protection programs, mainly due to the mapping of implementation responsibilities within government. Going forward, subsidies and their reforms should be part of the broader dialogue on social protection in Pakistan, and considered as one of a set of antipoverty interventions.

The qualitative assessment findings indicate that electricity is central to the lives of low‐ income and lower middle income households and that households struggle with electricity costs and they resort to coping mechanisms such as reducing necessary expenses on food, health and child care to afford electricity bills. Low‐income households, especially _katchi abadi_ residents and BISP beneficiaries, use illegal electricity (called the _kunda_ system) as a way to cope with increasing electricity costs. Despite efforts to improve service delivery, load shedding and electricity outages still negatively impact on households’ economic and social well‐being. Women are more affected by load shedding and by the household’s efforts to manage electricity expenses because they are on average the main users of electrical appliances and spend more time in the home. The majority of consumers do not trust their bills, and believe that their bill amounts do not reflect their actual consumption. Almost none of the respondents were aware that the government provides electricity subsidies. Overall, the negative attitude of respondents toward the electricity service is a result of frustration with unreliable supply and perceptions of poor sector governance. Despite struggling to afford electricity, respondents appear willing to pay higher prices provided service quality improves and governance problems are
16 such complex interaction between village-level inequality and group organization; perhaps most interestingly for our current discussion, in more unequal villages, individuals are less likely to report trust in the community, and more likely to report conflict of interest. There is more rigorous and detailed evidence from India on the role of historic institutions of inequality in influencing breakdowns in collective action for broad public goods (Banerjee and Iyer, 2005; Andersen, 2011), dissolution of social cooperation (Fehr, Hoff, et al, 2008), and weak performance of government poverty alleviation programs (Besley et al, 2004).

Ethnic fragmentation and polarization in many parts of Africa have been blamed for the lack of organization by civil society to generate political institutions in the public interest (Easterly and Levine, 1997; Alesina, Baqir and Easterly, 1999; Montalvo and Reynal-Querol, 2010). Miguel and Gugerty (2005) find that community contributions to public schools and quality of school infrastructure are lower in communities with greater ethnic diversity. In experimental work to examine why ethnicity matters in lowering contribution to public goods, Habyarimana et al (2007) conclude that it is not because of greater altruism towards co-ethnics, or greater sharing of common preferences, but rather because of greater power of social sanctions within ethnic networks. That is, social networks among co-ethnics make reciprocal exchanges easier to achieve.

Perhaps because it facilitates such reciprocal exchange, ethnic identity in Africa may be used to sustain clientelist political competition, enabling the type of quid-pro-quo interactions of provision of private benefits for political support that allow politicians to get away with larger rents and lower public goods. Consistent with this view of ethnicity as an instrument of clientelist strategies, Eifert et al (2010) find that self-reported ethnic attachment in the Afrobarometer surveys is greater when the question is asked nearer to elections.

Table 1 provides further data on reported participation in groups for 18 African countries covered by the most recent round of the Afrobarometer survey around 2008. On average, across all countries, 26 percent report being