rafmacalaba/data-use-mentions-extended huggingface dataset:
Tanzania (HBS 2018) or Uganda (UNHS 2016), for example, include these questions in their questionnaire, but the data is missing for virtually all HHs.

Likewise, variables on cultural adequacy, a criterion outlined in the CESCR, are largely absent in HCES surveys. Further, cultural adequacy is too context specific for a study that aims at crosscountry comparability and largely depends on HHs’ preferences, which are often not captured in available micro-level data.

Finally, on affordability, we are limited by the dearth of information from HCES surveys on housing expenditure in a consistent manner. To account for this important element, we propose pursuing a twofold approach: first, estimate the adequacy of a given housing unit and the related housing deficit and then (in future research) assess if alternate adequate housing is affordable to HHs based on existing housing finance conditions, HHs incomes, and comparable house price data.<sup>7</sup>

# **_3.2 Data: Multi-Purpose HH Surveys_**

For the measurement of these seven adequacy dimensions and complying with our aim to construct a micro-founded _Housing Adequacy Index_ , we rely on the 64 countries’ most recent multi-purpose household surveys.

## 3.2.1 Household Consumption and Expenditure Surveys (HCES)

Our main data sources are household consumption and expenditure surveys (HCES) which are often carried out with support of the Living Standard Measurement Study (LSMS) program that was initiated by the World Bank Group in 1980. Many HCES, which are usually national representative surveys, collect data on a core set of questions on household consumption, expenditures, and income. The principal implementing agency of these surveys is usually the national statistical office (NSO). Most of these HCES have by now become multi-topic and multilevel surveys using several questionnaires to measure different aspects of living standards. While unique in their efforts to collect representative information on HHs’ living standards, HCES might unintentionally undersample unregistered or vulnerable populations which are often excluded from the standard census on which HCES
s demographic features and wealth. In addition, the team of investigators collected qualitative information in the form of household ‘histories’ of what decisions had been made in periods of drought-induced crises in the past, and why. Data on infrastructure and land use at various spatial and administrative levels completed the picture. Summary statistics of the sample villages are presented in Table 1.

These households were re-surveyed in the early months of 2010. Due to the pressure of time, the primary emphasis was on the immediate past _kharif_ season, when 11 of the 30 villages had an all-weather road, nine more than in 2001-02. The original household questionnaire was heavily redesigned so as to capture more fully the villagers’ activities in the domains of central interest, namely, trade in goods, education and health. The households’ individual members and their salient characteristics in 2004-05, a complete list of which was available from the previous survey round, provided both the basis for, 4
- <mark>Decker, R., Haltiwanger, J., Jarmin, R., & Miranda, J. (2014). The role of entrepreneurship in US job creation and economic dynamism.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 28(3), 3-24.</mark>

- <mark>Dunne, T., Roberts, M. J., & Samuelson, L. (1989). The growth and failure of US manufacturing plants. The Quarterly Journal of Economics, 104(4), 671-698.</mark>

<mark>Eslava, M., & Haltiwanger, J. (2016). Who creates jobs: the role of age and size in Colombia vs. the US.</mark>

- <mark>Engbom, N., and Moser, C. "Earnings inequality and the minimum wage: Evidence from Brazil." American Economic Review 112.12 (2022): 3803-47.</mark>

- <mark>Faggio, G., Salvanes, K.G, and Van Reenen, J. "The evolution of inequality in productivity and wages: panel data evidence." Industrial and Corporate Change 19.6 (2010): 1919-1951.</mark>

- <mark>Foster, L., Haltiwanger, J. C., & Krizan, C. J. (2001). Aggregate productivity growth: lessons from microeconomic evidence.</mark> _<mark>In New developments in productivity analysis</mark>_ <mark>(pp. 303-372). University of Chicago Press.</mark>

- <mark>Ferro, E, & Patiño Peña, F. (2023). Private Sector Performance Under the Pressure of SOE Competition – The Case of Ecuador. Mimeo.</mark>

- <mark>Gopinath, G., Kalemli-Özcan, Ş., Karabarbounis
probably had larger effects on households in larger and denser cities, as their agglomeration economies are hinged on connectivity and accessibility. Within a large city, workers in poorly connected neighborhoods in the suburbs and peri-urban areas may have found it difficult to access jobs and operate their businesses.

This paper aims to analyze such spatial heterogeneity in the COVID-19 impacts on urban household incomes. We look at the early days of the pandemic in Ethiopia for the analysis of between-city heterogeneity. We turn to Kinshasa, Democratic Republic of the Congo (DRC) to exemplify within-city heterogeneity. Our analysis primarily relies on panel household phone surveys—high-frequency phone surveys (HFPS)—that have been collected in many low- and middle-income countries since the COVID19 outbreak.<sup>2</sup> We focus on the first six rounds of the survey from April to October 2020 for Ethiopia and the first six rounds from June to December 2020 for Kinshasa. During these periods, urban households’ incomes experienced a gradual recovery from the initial shocks. In our Ethiopia analysis, we estimate two-way fixed-effects regression models to examine the changes in the probability of households experiencing income reductions across time and locations. For Kinshasa, we estimate the elasticities of the probability of income reductions with respect to mobility changes—which are captured by the Facebook movement data—and examine how the elasticities vary by locations, such as the distance from the central business district (CBD) and neighborhood density.

It is important to highlight the challenges and limitations of our empirical strategy. First, the HFPS data have limitations. The data were collected only from those who provided phone numbers in the baseline face-to-face surveys, which had been collected in June–September 2019 in Ethiopia and December 2018 in Kinshasa. While this is potentially a concern in case of rural households, many of whom do not own mobile phones, it is less of a concern for urban households with much higher phone penetration rates.

> 2 World Bank COVID-19 High Frequency Monitoring Dashboard

<u>https://www
planning to work but did not.  That is, a series of several consecutive days off would only appear if each day after the original illness shock, the individual was planning to go back to work but still could not.  As a result, the rate at which respondents report seven consecutive days of illness shocks in the tri-weekly data (0.52% of weeks) is considerably lower than the rate at which they report a week of missed work due to illness in the weekly data (4.3% of weeks).  This result further supports our primary focus in the empirical analysis on the respondents surveyed weekly.

> 8 More than half (58%) of the respondents are female.  This does not appear to reflect differential response rates by gender in the high-frequency survey, since a very similar percentage (57%) of adults in the household roster for the 2013 Labor Force survey from which the sample was drawn are female. The Ghana Statistical Service (2014) also reports that the 2010 Population and Housing Census found more females than males in Accra.

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employment, according to pooled regression analyses (Appendix Table 2, 3). However, there is some variation across countries (Figure 8, Appendix Table 2). In places like Indonesia and the Philippines, women were less likely than men to return to work at times of economic reopening, even conditional on sector of employment (Figure 8).<sup>23</sup> In other places such as Mongolia, differences between men and women in the likelihood of stopping work were only marginally statistically significant initially, but women were significantly less likely than men to return to work, thus enlarging the gender gap in work stoppages across time. As lockdown measures led to school closures, increased caregiving activities largely fell on women, presenting barriers to employment. In addition, in some countries, women were more likely to work in sectors that were harder hit by the pandemic such as retail, manufacturing, and tourism. Thus, in the longer-term, it is possible that women may face greater difficulty recovering from the pandemic.

> 23 Uneven recovery from work stoppages may be just one part of the gender story: Analysis based on the Indonesia Labor Force Surveys, however, shows that six months into the pandemic, women in Indonesia were more likely to join the labor force for the first time, largely in unskilled, informal, and agricultural employment (Halim et al., 2021).

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# **4.3. The Tradeoffs**

The possible tradeoffs between the two financing options warrant further research. Under the constant tax rate approach public and national saving increase and efficiency rises as a result of tax smoothing, but the government is left to manage large financial assets. Under the balanced budget tax revenue approach, private saving increases, efficiency rises as a result of low tax rates (during the next twenty years), and financial assets are managed by the private sector, but tax rate adjustments that may create uncertainty in the economy and be inequitable from an intergenerational perspective are necessary.

To examine intergenerational equity in the cost-benefit analysis of the different financing scenarios it may be advisable to apply the baseline assumptions and sensitivity tests to the New Zealand generational accounts (Kotlikoff 1995). A quick look at the tax cuts and tax increases under the balanced budget financing option suggests that the generation entering the work force at the end of this century would enjoy relatively low taxes, retire just in time to avoid the tax increases and enjoy a greater life expectancy with pensions as generous as the previous generation but financed from the higher taxes of following generation.

Options that would allow for tax smoothing at lower tax levels and without requiring the government to manage large public assets could be achieved by containing demographic pressures on future spending. The sensitivity tests and scenarios presented in the previous sections indicate that effective strategies for containing the fiscal effects of an aging population involve tight control over spending on health and pensions.

Policies that offer further tax cuts in exchange for a reduction in future benefits are also likely to bring economic gain. For example, international evidence (section 2) suggests that the expected size of public pensions is negatively correlated with private saving. Thus, a lower expected level of NZS benefits is likely to induce the current generation to use any additional disposable income from tax cuts for private saving for retirement. Individuals could invest their funds according to their own risk preference. The resulting investment level is likely to be higher (
<br>smaller aircraft|100.0|28.3|50.6|42.7|42.5|32.8|
|Percent of carriers passing IATA/IOSA<br>audit|0|100|0|33.3|100.0|33.3|
|FAA/IASA audit status|No audit|No audit|No audit|Failed|No audit|Passed|

_Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data).

_Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. An HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent.

FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit. n.a. = Not applicable.

# **Challenges**

Since no airline in Botswana flies to Europe or the United States, it is neither audited by the U.S. Federal Aviation Administration (FAA) or ranked by the European Union (EU). But safety oversight is a challenge for Botswana. Air Botswana is not on the International Air Transport Association (IATA) registry, and a 2008 International Civil Aviation Organization (ICAO) audit of the safety oversight system found noncompliance with standard and recommended practices to be over 72 percent, compared to the global average of 30.8 percent for the year. Beyond Air Botswana, tourists flying on local charter flights may also be at risk.

The air transport market is concentrated: Air Botswana dominates, carrying over 75 percent of capacity, with an additional capacity of roughly 18 percent provided by South African Airlink Express.

> **4**
error is large, and the result is not significant. The rate of teacher absence was higher in SMS schools than in control, but the difference is statistically insignificant.

# **4.1.5 Infrastructure**

Turning to physical infrastructure, **Table 6** also presents transition probability comparisons for the addition of classrooms and toilets. In the comparison of means **(Table 5)** , we include estimations for other key school facilities: electricity, drinking water, and boundary walls at schools. We find no significant impacts from either the MEET or SMS intervention on any infrastructure outcome in either estimation.

In the point estimates **<u>(Table 10)</u>** <u>, we include all infrastructure variables. We find no impact from</u> MEET or SMS on any infrastructure variable which is statistically significant in comparison to control. However, in the case of SMS, a substantial impact is observed on the number of classrooms, with 0.23 additional classrooms at SMS schools; this is significantly greater than the level in MEET in equivalence testing.

# **4.1.6 Learning outcomes**

Finally, we present findings on the impact of the interventions on learning outcomes from standardized tests conducted during household surveys. Comparison of means **(Table 5)** shows no differences in test performance between any treatment group and the control for scores. **<u>Table 11</u>** 40
486|.497|{.505}|
|**If in relationship: Experienced any form of conjugal**<br>**violence**|.477|0.498|{.200}|
|**_B. Empowerment and Aspirations_**||||
|**Empowerment Index [0-100]**|17.1|15.2|{.776}|
||[20.6]|[20.7]||
|**Sexually active**|.715|.745|{.648}|
|**If active: Age at sexual debut**|14.6|14.6|{.061}|
||[2.17]|[2.02]||
|**If active: Uses contraceptive**|.434|.396|{.163}|
|**(any, excluding condoms)**|.496|.489||
|**If active: Ever used condoms**|.100|.084|{.351}|
|**Leisure activity: Men (weekly hours)**|5.00|5.73|{.501}|
||[5.43]|[5.36]||
|**Time use: Learning Activities (weekly hours)**|29.2|24.0|{.058}|
||[34.2]|[3.8]||
|**Unwanted sex over past year**|.107|.0805|{.076}|
|**Transactional sex over past year**|.037|.036|{.944}|
|**_C. Human Capital and Economic Activities_**||||
|**Skills: Literacy [0-100]**|22.3|21
the child and who married during the year of the child’s birth or the prior four years. We also utilize alternate definitions of expected dowry for robustness checks.

We construct a retrospective panel data set on dowries from the 2006 round of the Rural Economic and Demographic Survey (REDS). Note that, although the first set of interviews in the 2006 REDS were conducted in 2006, 84 percent of the interviews took place in 2008. In addition to data on marriage payments, REDS collected rich information on various forms of savings at the household level and on individual labor supply that we utilize. REDS has numerous advantages over other Indian data sets that have been used in the dowry literature that we discuss further in Section 2.

Our findings emphasize the crucial role of traditional cultural institutions like dowry in determining economic behavior. We find that the prospect of higher future dowry increases current savings. In particular, as expected dowry increases, FG families significantly increase per capita and total annual savings, both overall and relative to FB families. We also investigate the plausible channels that a household can use to finance the higher savings: (1) lower consumption, (2) increased income, and (3) increased debt. We do not observe any change in total consumption expenditure on items that REDS collected data on. However, we find that fathers of firstborn girls

> 5A son-biased stopping rule means that parents are more likely to stop childbearing after a male relative to a female birth.

> 6We note that prenatal sex-detection has been illegal in India in public facilities since 1976 and in private facilities since 1994.

> 7For instance, parents who have more daughters may give less dowry per daughter while incurring the same total expense as parents with fewer daughters.

> 8See Section 3 for more details on why this is a reasonable way to define a marriage market.

2
Independent Evaluation Group (IEG) in 2014 concluded that women and female heads of households invest in livestock and agricultural tools as much as or more than men. However, the types of livestock or agricultural investment by women and men often differ (see e.g. Wheeler et al. (2012) for Ethiopia and Devereux and Ulrichs (2015) for Rwanda). Also, and particularly in relation to the reduction of future poverty, both Wheeler et al. (2012) and Devereux and Ulrichs (2015) show that the well-being and living standards of men and women are not defined by the same factors. In the Ethiopian case, for example, women defined wellbeing as the ability to feed their children, whereas for men well-being is most commonly associated with the accumulation of assets (Wheeler et al., 2012). Along this line, men and women are also exposed to different risks and face different constraints. Taking the case of the PSNP in Ethiopia as an example again, men often identify low wages as program-specific constraints to change, whereas women frequently mention conflicting time constraints as obstacles to overcome (Wheeler et al. 2012). Differences in needs and experiences offer one explanation of why the effects of poverty-based income transfers may lead to different results for men and women.

With this paper, we aim to advance the debate on how household characteristics and endowments contextualize access to and benefits from social assistance programs. We do so based on experiences from the Vision 2020 Umurenge Programme (VUP) in Rwanda. We are contributing to the existing literature and policy discourse in two ways. First, we use quantitative evidence from a statistically representative sample of VUP beneficiaries and a constructed comparison group to provide evidence of the effects of large-scale social protection programs in Sub-Saharan Africa – in this case from Rwanda in particular. Second, this paper uses qualitative data and information to take an in-depth look at a set of household characteristics, exploring how these shape the access to and gains from the VUP. This is a key
Policy Research Working Paper 8714

# **Abstract**

This paper uses a unique data set that captures the elimination of subnational regulatory barriers to firm entry and competition across 1,800 municipalities and matches it with establishment census panel data to estimate the impact on establishment productivity and markups. The elimination of local barriers that were inconsistent with national legislation was the result of legal reforms that strengthened the mandate of Peru’s competition authority. Legislative changes in 2013/14 empowered the competition authority to enforce the elimination of illegal, sector-specific subnational regulatory barriers to firm entry and competition, conditional on the existence of a precedence. The changes provide a unique quasi-experimental setting to identify the impact of enforcing competition within the controlled institutional environment of a single country. The paper finds that the elimination of subnational barriers to entry boosted the (revenue) productivity of establishments operating in reform municipalities and sectors relative to establishments in nonreform municipalities/sectors. But it did not raise the establishments’ markups, which, if anything, declined, suggesting that physical productivity improved. The paper provides a wide range of evidence supporting a causal interpretation of this finding. The results suggest that strengthening the mandate of institutions enforcing competition is critical to raise productivity.

This paper is a product of the Macroeconomics, Trade and Investment 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/research. The authors may be contacted at mschiffbauer@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
to all sibs in the same household in each year.<sup>17</sup> The school effects are less straightforward. In the present data, detailed information about the particular school each child attends is not provided. Nonetheless, we can identify the grade of attendance and the location of the household. Consequently, for each enumeration area (containing approximately 20 surveyed households), we categorize children into one of three school-grade categories based on their highest grade of completed or current schooling – namely: those attending grades 1-2; grades 3-4; and grades 5 or higher. For children who have never attended school, we hypothesize that the quality of schools (teachers) available in their locale may have an effect on their ability in numeracy and literacy. This may work directly, through the choice not to attend school, as well as indirectly through both sibling and peer effects – e.g., what other children learn can spill-over to nonattenders. In order to allow for these effects in the data, we allocate never-attenders to the

> 15  Due to the absence of equating or anchor items in the Uwezo tests, we cannot distinguish between changes in test difficulty over time and changes in (average) learning outcomes. However, from the perspective of a variance decomposition, standardization by year ensures that the variance contributions retain a consistent meaning in relation to the overall distribution of outcomes in each age cohort and year.

> 16 Details available on request from the authors.

> 17 The Uwezo surveys are cross-sectional in nature and no explicit attempt is made to track the same children over time.

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