rafmacalaba/data-use-mentions-extended huggingface dataset:
assume that most exertion of Political Influence will, at a minimum, have implications in a firm’s industry and location, and so we choose to use these fixed effects as our baseline.<sup>43</sup> To allow for the possibility that the analytical level that matters is the _intersection_ of location and industry, we also include a series of interacted fixed effects in Cols. 3–5. To avoid sparsity concerns, Col. 3 interacts economy-level fixed effects with 2-digit sector effects; Cols. 4 and 5 interact more disaggregated location fixed effects with those sector effects. The most saturated estimation is presented in Col. 5, which interacts all co-variates—with the exception of fixed effects—in **_X_** with the Political Influence score, allowing for these adjustments to change based on the estimated index score. All specifications use survey weights, and these are normalized to give each economy equal weight, as we are concerned with the average relationship of political influence within economies. Standard errors use two-way clustering following Cameron et al. (2011), either at the economy-sector or the location-sector level, depending on the assumption of the included fixed effects, and thus our assumption of the application of where firmlevel influence is relevant.<sup>44</sup> As an additional robustness check, in the Results Appendix B.5, we explore how these relationships differ based on underlying governance by interacting the Political Influence score

> competitors in firms’ main market, when the option ‘too many to count’ is assigned the maximum value.

> 40See Data Appendix for details.

> 41Note that throughout we use the term ‘firm’ for simplicity, but that the ES surveys establishments, meaning that an establishment can be part of a larger firm.

> 42Specifically, these are the regional strata determined by each economy’s survey design, often corresponding to known geographical aggregates, such as the Nomenclature of Territorial Units for Statistics (NUTS) in main European countries, though the aggregation
20

**Table 2.1.** Markets for new housing and secondary markets in Russia (price of one square meter of total floor space, fourth quarter, 1996.)

|Type of Housing|Price leve|l (thousand rubl|es)|
|---|---|---|---|
||Bottom|Top|Average|
|Low quality apartments<br>secondary market|734|4,314|1,969|
|Standard apartments||||
|newly constructed|1,220|4,493|2,794|
|secondary market|679|4,319|2,166|
|Ratio of primary/secondary market prices|1.8|1.0|1.3|
|Apartments with improved planning and design<br>newly constructed|882|6,046|2,934|
|secondary market|645|5,754|2,603|
|Ratio of primary/secondary market prices|1.4|1.0|1.1|
|Apartments in top-quality buildings||||
|newly constructed|1,613|11,987|4,644|
|secondary market|1,721|11,630|4,595|
|Ratio of primary/secondary market prices|0.9|1.0|1.0|

Source: Goskomstat _Socio-economic situation in Russia,_ No 12. Moscow, 1996.

These figures give an idea of the' environment in which the housing market is taking shape. Without question, the very high cost of housing in Russian is an important constraint, among others, affecting Russian households' behvaior as the attempt to deal with this issue.

# **2.2. Households' near-term plans and intentions to improve their housing conditions 2.2.1 General assessment of the housing situation**

Sociological surveys indicate that households' assessments of their housing conditions
countries will result in differences that are small relative to the confidence intervals. A useful rule of thumb is that the observed difference in estimates of governance between two countries is not statistically significant if the confidence intervals overlap. For example, comparing Guatemala with Zambia on Government Effectiveness (ge), it is clear from the graph that the 90 percent confidence intervals overlap. This means that although Zambia has a higher estimate of governance than Guatemala, the difference between the two countries should not be considered as statistically significant. In contrast, Mauritius has a much higher estimate of Government Effectiveness (ge) than either Guatemala or Zambia. For these comparisons, 90 percent confidence intervals do not overlap and the difference in estimates of governance can be considered statistically significant.

Looking more systematically across all six aggregate indicators and all years since 1996, 61 percent of all pairwise comparisons of countries within an indicator and year will result in estimates of governance that are significantly different in the sense that the 90 percent confidence intervals do not overlap. There are some differences across indicators and over time. For example, looking across the six aggregate indicators, the share of significant pairwise comparisons ranges from 52 percent (for Political Stability and Absence of Violence/Terrorism) to 70 percent (for Voice and Accountability), in part reflecting the considerably smaller number of data sources that feed into the former versus the latter (23 vs 10, see Annex 3). Looking over time and averaging across all six indicators, the share of significant pairwise comparisons increased from 52 percent in 1996 to 62 percent in 2005, again in part reflecting a steady increase in the number of WGI data sources over this period from 12 to 31 over this period (see Table 1). From 2005 onwards, the share of significant pairwise comparisons has fluctuated between 61 and 65 percent. This indicates that while margins of error are both substantial and important to consider when comparing countries, it also is possible to use the WGI data to identify many significant pairwise differences in estimates of governance
_fears could not be dismissed as imaginary.”_ Although the civil war ended 45 years prior, there are persistent ethnic tensions in Nigeria that occasionally escalate to localized violence. With limited exposure and information about other regions, it is easy for individuals’ perceptions of other ethnic regions to be defined by these events. Even when the specific “other” region is not violence-prone, individuals may feel it is unsafe. For example, an interview respondent described how he always felt scared of going to “the North” while growing up in Yorubaland. It was only after NYSC exposure that he was able to overcome his fear of all northern states.

Although an analysis of economic impact is beyond the scope of this paper, the evidence suggests that reducing these mental barriers to migration could have potentially significant economic growth effects for the country by increasing efficient allocation of labor. Bryan and Morten (2015) develop a structural model, which they estimate with data from the US and Indonesia, to show how lower physical and cultural migration costs are associated with higher productivity by facilitating the movement of labor to where it is most productive. In this paper, I provide evidence on a policy that potentially reduces these cultural costs.

# **6.3 National and Ethnic Identity**

A core aspect of national integration is for individuals to develop a sense of national identity, besides their ethnic identity. I ask participants about their sense of national pride, as well as ethnic pride: “ _Using a scale of 1-7 where 1 means “Not proud at all” and 7 means “Extremely proud”, how proud do you feel to be a Nigerian (Yoruba)?_ ”. I also ask about which of the two identities they identify more with: “ _If you had to choose between being a Nigerian and being a Yoruba, which of these two groups do you feel more attached to?_ ” where possible responses are Feel only Yoruba (0), Feel more Yoruba than Nigerian (1), Feel more
Health Survey (DHS).<sup>8</sup> Before 2007, the sibling history was collected only from female not from male respondents. The 2007 DHS surveys all ever-married women who are 15 to 49 years old and every third currently married man who is 15 to 54 years old. I limit the sample to the fertility preferences and histories of women whose partner has also been interviewed (7,792 couples). I exclude all mixed couples formed by one Muslim and one non-Muslim partner (0.79 percent of all observations). Indonesia has legal restrictions for inter-religious marriages and inter-religious couples are usually disqualified from Islamic inheritance (Buchanan 2010). I also exclude polygamous couples (1.3 percent of all observations) because the fertility of men who have multiple wives should be assessed across all wives, but only co-resident wives are interviewed. A final sample size of 7,657 couples and 20,145 child births is obtained.

In order to exploit the single cross-section of data available, I define pre- and postcompilation samples based on the year when the couples initiated child-bearing, and based on the year of birth of each child. I measure men and women’s son and fertility preferences as the ideal proportion of sons and the ideal total number of children. The ideal proportion of sons is a lower boundary of the degree of son preference because “children of either gender” is counted in the denominator but not in the numerator. Further, I calculate the actual proportion of sons and the actual number of children ever born from women’s birth histories. Since only single-marriage couples are included in the sample and only legitimate children can exclude male agnates, women’s birth histories can be confidently used to analyze the effects of the inheritance rule. Finally, I obtain the men’s total number of surviving siblings and an indicator for having at least one surviving brother nine months before the date of birth of each child ever born. If a brother was alive before or at the
to send homework by email, students to pay for some minutes to download this, and then work remotely and send back an email with this work.

We collected detailed time-use information (described in the next section), which enables us to determine not just whether students are doing any schoolwork, but also how much. Figure 1 provides histograms to show the distribution of time spent on schooling (on weekdays) for students with computers and internet access, compared to those without internet access. For students with technology, the mean (median) time spent on schooling on the past day is 4.4 (4) hours, compared to 3.6 (3.5) hours for those without internet access. However, we note substantial dispersion, with 23 percent of those without internet access doing no schooling, compared to only 9 percent of those with computer and internet access.

Table 3 examines the breakdown of time spent on schooling by different student characteristics. Overall, 14 percent of students did no schoolwork on the past weekday, 42 percent did 0.5 to 4 hours, and 44 percent did more than 4 hours. In addition to formal schooling, students could be pursuing other educational activities, such as reading for fun. However, the last column shows that only 4 percent of students did any reading that was not part of formal schoolwork in the past day. We see Grade 12 students spend more time on schooling than younger grades, and wealthier students spend more time on schooling on average than students from the lowest wealth quartile. However, the amount of time spent on schooling does not differ that much by ethnicity, region, or mother’s schooling level – grade, wealth, and technology access appear to be the main correlates of how much schooling students are doing.

# **4. Time-Use**

Our surveys took place during a period when schools were closed, and a nationwide shutdown (with exceptions for certain types of workers and businesses) was in place. When asked
Policy Research Working Paper 9184

# **Abstract**

Agglomeration boosts economic growth. A vast literature has empirically assessed the effects of agglomeration by estimating the city population elasticity on wages. This conventional approach is not necessarily suitable for analyzing urbanization at the early stage in developing countries, where a majority of urban workers engage in self-employment and/or informal jobs. Focusing on one of the poorest and largest among those countries, this paper sheds light on an aspect of urbanization and agglomeration: the transition in the mode of labor from self-employment/informal jobs to wage employment/formal jobs. Applying the instrumental variable approach to national labor force survey data sets, the analysis underscores several labor market transitions across space in urban Ethiopia. First, the town population size and the share of workers with wage employment are strongly correlated. The probability of engaging in wage work increases by 4.5 percentage points with a log increase in population size. Second, this relationship is particularly strong among disadvantaged workers, such as the female, young, and/or less educated population. Finally, the study documents higher labor force participation and lower underemployment in larger towns.

This paper is a product of the Poverty and Equity Global Practice. 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 snakamura2@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
control units of analysis as treated households is a conservative way to bound the direction of the main regression results because some of the households that moved in the post-shock period may have in fact lived in non-affected areas. Table 9 summarizes the effects on food and total household consumption per capita and poverty status estimated on this modified sampled.<sup>11</sup> All the findings hold (qualitatively and quantitatively) after the regressions are re-estimated on a sample where migrant households are reclassified as non-migrant units of analysis.

Finally, we exploited a different natural experiment as an additional robustness check. This natural experiment is generated by the severe floods that affected Zambezia, Nampula and Niassa provinces in central and northern Mozambique in early 2015. Weeks of heavy rainfall in December 2014 and January 2015 caused several rivers to swell, flooding several parts of districts in these provinces. The floods inflicted significant damage to crops as well as to public and private infrastructure, including roads, bridges, schools, health centers, telecommunication networks and housing. On January 12, 2015, the Government of Mozambique declared an institutional red alert for the affected areas of the country.

We run the triple difference model specified in Equation 1 to investigate if the negative effects identified for the floods in 2007, Cyclone Jokwe 2008 and the droughts in 2005 are observed again among the population exposed to a natural disaster that occurred a few years after. The empirical model for this event is implemented using data from the IOF survey 2014/15. Given the timing of the 2015 floods, we use as baseline the first wave of the survey (collected between August and October 2014) whereas the third wave (collected between May and July 2015) provides the post shock data point. Given the spatial part of the source of variation used for identification ( _Shock_ x _Cycle_ ), this analysis uses the cross-sectional rather than panel nature of the data.<sup>12</sup> The results, summarized in Table 10, show that the floods in 2015 also led
level of Digitalization that is less than the threshold.<sup>56</sup> This

> 53 Data on digitalization was collected from the UN E-Government Knowledgebase (available at https://publicadministration.un.org/egovkb/en-us/Data-Center). The variable is the sum of two variables, namely, the Online Service Index (OSI) and the Telecommunication Infrastructure Index (TII). The first variable measures the scope and quality of online services, while the second measures telecommunication connectivity. This information is published at country level every two years since 2008 (also for the years 2001, 2003, 2004, and 2005). Note that the database reports the variables E-Government Development Index (EGDI) and Human Capital Index (HCI), the latter measuring human capacity. OSI, TII, and HCI are composite indices that are normalized to lie in the unit interval, and then EGDI is calculated by taking their arithmetic average. The formula is as follows: E 3 (O n n + H n ) nnmmn iin nn nnmmn iin nn nnmmn iin nn DDEE=<sup>1</sup> EE + TTE EE

> 54 For instance, 4G coverage exceeds 93% of the territory in Morocco whereas the majority of rural consumers are equipped only with 2G and 3G handsets.

> 55 The null hypotheses of existence of any instantaneous, first lag, and second lag effects of Digitalization were rejected.

> 56 These countries are Algeria, Djibouti, Islamic Republic of Iran, Iraq, Lebanon, Libya, Morocco, Syrian Arab Republic, Tunisia, and Yemen.

31
subsidies by 50% would be sufficient to eliminate biological overfishing in the Southeast Pacific. Of course, each region has a different fraction of Chinese fishing hours and different baseline levels of overfishing status, so the calculations need to be repeated worldwide.

Our scenarios hold fishing by non-Chinese vessels constant. If other countries do not simultaneously reduce fuel subsidies or otherwise limit fishing (e.g., through fisheries management), then our calculations could be viewed as upper bounds on the changes in overfishing because it is possible that non-Chinese vessels would increase their fishing in response to the reduction in fishing by Chinese vessels. But viewed from the perspective of WTO negotiations, in which countries simultaneously reduce fisheries subsidies, then our calculations could be viewed as lower bounds in the sense that reductions in Chinese fuel subsidies would likely co-occur with reductions in subsidies by other countries. Our _ceteris paribus_ scenarios are intermediate to these two cases.

> 30This portion of the analysis uses fishing hours by all Chinese-flagged fishing vessels, as identified by publicly available data from GFW.

35
above pre-recession norms (see Figure 2).

Figure 3 shows the evolution of the employment rate. The long run employment rate followed an upward trend, increasing from 52 percent in the period 1984-1999 to 55.8 percent in the period 2000-2013.

Informality has been a persistent phenomenon over the last two decades: regardless of the definition of informality used,<sup>6</sup> approximately one of every two workers are considered informal. Figure

> 5Labor market statistics are constructed using data from Colombian household surveys. These surveys are repeated cross-sections carried out by the Colombian Statistics Department (DANE) on employed and unemployed individuals, and include: Encuesta Nacional de Hogares (ENH) for the period 1984:QI to 2000:Q2, Encuesta Continua de Hogares (ECH) for the period 2000:M1 to 2006:M12, and Gran Encuesta Integrada de Hogares (GEIH) for the period 2007:M1-2013:M6. Informality statistics are constructed using the Informality module in the surveys, available every two years before 1996, and yearly afterwards. The statistics presented in this section are for all urban workers.

> 6 The following definitions will be used in the paper:

> **“Social Protection” (SP) Informality Definition** : A worker is considered informal if either of the following two conditions hold:

> _• Health Affiliation_ : Is not affiliated to a health plan, or if affiliated does not make any contributions to the system (either because is part of subsidized regime or beneficiary of the contributory regime)

> _• Pension Affiliation_ : Not affiliated to a pension fund

This variable is primarily a proxy for non-compliance to labor regulations in Colombia.

**“Firm Size and Occupation” (FSO) Informality Definition** : A worker is considered informal if the following two criteria hold:

4
responsible for their loss in SWB and think that under different leadership they would have been better off. Since the Gallup World Poll does not include a voting variable and some of the demographic variables of interest to us, we again use the data from the Brazilian Electoral Study and a logistic regression to link the likelihood to vote for Bolsonaro to a similar set of variables as those included in the analysis of SWB change in section 4.4.

**Table 8: Factors Associated with the Bolsonaro Vote – Logistic Regression**

|**Panel A**|**Dependent**<br>**variable:**<br>Voted for|**Dependent**<br>**variable**:<br>Voted for|**Dependent**<br>**variable:**<br>Voted for|
|---|---|---|---|
||Bolsonaro in<br>Second Round of<br>Presidential<br>|Bolsonaro in<br>Second Round of<br>Presidential<br>|Bolsonaro in<br>Second Round of<br>Presidential<br>|
||Elections|Elections|Elections|
|Economic situation country||||
|Improved|1.09||1.08|
|Same|Ref.||Ref.|
|Worsened|0.59**||0.70#|
|Personal economic situation||||
|Improved (Upward)|0.62**||0.78|
|<br>Same<br>|Ref.||Ref.|
|Worsened (Downward)|0.84||0.91|
|Perceived corruption|1.17#||0.95|
|<br>Against income redistribution|1.07||1.13#|
|<br>Immigrants bad for economy
on the economic decisions of recipient households, using a natural experiment among Indian households that benefited from a large nationwide debt relief program. In particular, I test whether –similar to a bankruptcy settlement– the bailout acted to restore access to formal credit among beneficiary households and led to economically meaningful changes in investment and productivity. My analysis takes advantage of an experiment

> 1For evidence on barriers to household risk-management see also Cole et al. (2012) and Gin ́e et al. (2008)

> 2USDA Economic Research Service Briefings, http://www.ers.usda.gov

> 3See also Robert (2012) who uses the natural experiment and survey data underlying this study to test the impact of wealth on subjective well-being.

> 4“Waiving, not drowning: India writes off farm loans. Has it also written off the rural credit culture?” _The Economist_ , July 3, 2008.

> 5For the theory of moral hazard and credit rationing see Stiglitz and Weiss (1981) and Jaffee and Russell (1976). Karlan and Zinman (2009) provide empirical evidence on moral hazard and adverse selection in an emerging credit market.

1
region, compared to other regions of Guatemala. The percent of all migrant households receiving any remittances in the past 12 months was 53 percent in 2012 (down from 82 percent in 2008) and total real remittances for the receiving households also declined slightly. Finally, the 2012 household survey indicates that while the percent of households hiring in labor was the same as in 2008, the wages paid had been reduced by about one-quarter, consistent with depressed local wages and demand. Even after substantial recovery in the United States, these patterns suggest persistent economic effects of the crisis in Huehuetenango, where households pursuing migration strategies may have been less able to recover as quickly.