rafmacalaba/data-use-mentions-extended huggingface dataset:
|Public formal|4<br>1<br>**86**|9|18% (16%)|
||Unemployed|NA||0% (0%)|
||Out of labor force|6<br>1<br>**93**||70% (76%)|
||Private info|rmal<br>Private formal<br>Public formal|Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to women who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by age groups. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets Panel weights are used.

15
## 6.5 Do private schools want to partner with the government?

Within existing literature on education PPPs, the private sector’s motivation and reservations for partnering with the government have not received much attention. However, this aspect is critical to the success of these partnerships.

For the 2015 Morogoro school survey, private school providers were asked about potential partnership with the government. We randomize the delivery of these questions to better elicit underlying beliefs. Private schools were randomly selected to be asked in one of two ways: in a direct and personalized way (e.g. what are _your_ views?) or in a generalized way (e.g. what are the _average private school owner’s_ views?). Of the 87 private schools, 46 schools (53 percent) were given personalized questions and 41 schools (47 percent) were given generalized questions.

The personalized group expressed a higher likelihood of partnering with the government over the generalized group by 14 percentage points (53 percent in personalized vs. 39 percent in generalized). On the whole, slightly less than half (47 percent) of the private school owners who were interviewed were willing to partner with the government. Corruption was the most cited issue that would threaten the likelihood of a partnership, with 51 percent of all respondents reporting it as a major concern. Among the issues of most importance for a partnership, private school owners rated clear communication channels and clarity of accountability as the most important criteria for partnership.

# 7. Parental Demand

In 2017, as part of its annual Sauti za Wananchi survey, TWAWEZA collected information on households’ beliefs and attitudes towards the schooling of their children from a broad cross-section of Tanzanian households through mobile phone surveys. Of the 1,396 households surveyed which had at least one child in school, the vast majority (95 percent) had at least one child in a public school and 87 percent of households had at least one child in primary school. With a majority of households sending their
<!-- Start of picture text --> A Ye~ B<br>fia a cg <a<br>seme fi. actie cts vis”<br>re Mens at «if<br>- git ees SEY, SS ae<br>ie Saat Puta oes By F re<br>rs Nae e Age Me OO,<br>Tee other nia Age qe 4<br>q Pay Set Sete af peel ae<br>Log of county GDP in 2019 a Total storm exposure in our sample ‘ y<br>10.0 12.5, 15.0 17.5 20.0 0 5 10 15<br>Cc<br>2J£ :|: i : i<br>D1<br>—10<br>fo} ' :<br>D ' H<br>Cco O eeH ~ — H .<br>oO t :<br>xo[S) 'H ‘:<br> -10 : :<br>oO ' :<br>2oO2H': :H:<br>-20 H :<br>D Storm year Storm year Storm year,L1 Storm year,L2 Storm year,L3 Storm year,L4 Storm year, L5 Sum of lags<br>2—£ : 1 : i : i<br>D<br>=40<br>ct:<br>@20<br><@Hi :H<br><[S) :1 ::<br>®::<br>2i i<br>2 : :<br>-20 : :<br>Storm year Storm year Storm year,L1 Storm year,L2 Storm year,L3 Storm year,L4 Storm year, L5 Sum of lags<br>EF<br>=sy<br>e ¢ s ry<br>f uN vf # a Sues
br>1<br>.5<br>Density 0<br>1<br>.5<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Figure 4. Labor supply by age group and gender** (a) Women

# (b) Men

<!-- Start of picture text --> Labour supply by age group (women)<br>Young teenagers (Aged 15-19) Young adults (Aged 20-24)<br>.8446<br>.7007<br>.2993<br>.1554<br>0 1<br>Old-Young (Aged 25-29)<br>.5812<br>.4188<br>0 1<br>lsupply<br>Graphs by ages<br>1<br>.5<br>Density 0<br>1<br>.5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Labour supply by age group (men)<br>Young teenagers (Aged 15-19) Young adults (Aged 20-24)<br>.787<br>.4929 .5071<br>.213<br>0 1<br>Old-Young (Aged 25-29)<br>.6845<br>.3155<br>0 1<br>lsupply<br>Graphs by ages<br>1<br>.5<br>Density 0<br>1<br>.5<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 34
. Estimations report standard errors, clustered at the plant level. Agglomeration center is a dummy variable for plant location in top-five towns, which is defined as 1 if plant is located in any of the following cities/towns: Addis Ababa, Awassa, Bahir Dar, Dire Dawa and Mekele. Further indicators for distance from the nearest agglomeration centers are also considered. Industry concentration, measured through the Ellison-Glaeser index, and its interaction with location dummies are also included in full specification. These estimations consider the effects relative to towns that are more than 150 km from the nearest agglomeration center.
poverty line 6.6 percent higher among migrant-sending households, even after remittances are accounted for.<sup>5</sup> These estimates are reported in columns (6) and (7) of table (4).

Linking migration status to administrative statistics from the migrant’s home mahalla further clarifies the strong descriptive association between the local economic context and migration patterns. Table (5) reports a set of regressions on the same binary variable for a household having a migrant member and demonstrates the association with several indicators of well-being grouped by national quintile. Column (1) in table (5) can be interpreted as demonstrating that the greater the share of the resident population receiving social protection payments, the higher the share of households that send a migrant abroad. The comparison should be understood as being with respect to the bottom quintile: the top three national quintiles of social protection recipient areas are significantly, and increasingly, greater sources of migrants abroad. At the upper end of the distribution of the resident share working in small family enterprises (reported in column 3 of table (5)) there is likewise a significantly greater share of households that send members abroad for work.

**Table 5: Mahalla Administrative Records and Households with a Current Migrant Abroad**

|||**OLS: Current**<br>|**Migrant= 1, N**<br>|**o Migrant= 0**<br>|<br>|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Benchmarking against Quintile 1 of...|_Share of_<br>_residents_<br>_receiving social_<br>_protection_|_Share of wage_<
34 of expenditures (using data from both surveys). The estimated impact of social capital on _median_ expenditures is quite similar (slightly higher) than for _mean_ expenditures (whereas an effect that shifted the dispersion of log normally distributed incomes would affect these two differently). These findings suggest social capital appears to shift (natural log) expenditures upward without affecting the inequality of the distribution. It is possible that informal insurance increased incomes and the variance of incomes but that the variance increase in _incomes_ is just offset so as the variance of _expenditures_ is unchanged. However, the lack of association between expenditure inequality combined with the limitation that the data we have, which contain no direct evidence on intra household transfers or informal insurance, leaves the question open.

# Conclusion

Using a specially designed large scale survey (SCPS) to measure the degree and characteristics of associational activity, as a proxy for social capital, and trust among households in rural Tanzania, we find that a one standard deviation increase in the village social capital index (as would be caused by half the village joining one additional group with average characteristics) is associated with at least 20 percent higher expenditures per person in each household in the village. The link between the social capital index from the SCPS survey and expenditures measured in an earlier survey of _different_ households in the _same_ villages (HRDS) shows convincingly this effect is _social_ and operates at the village level. The social capital of a household's village is as important in determining the household's income as many of the household's own characteristics which receive a great deal of attention (e.g. schooling,
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We need also derive the initial stock of government infrastructure. Since there is no direct information on capital stocks, we have summed public fixed capital formation from 1970 to 1982, assuming F rate of depreciation of 5 percent, to arrive at a figure of 726.7 billion constant 1970 pesos.<sup>19</sup> Government current and capital expenditures for 1983-85 were taken to be their actual amounts.20 Foreign borrowing, which wee the actual values for 1983-85 of foreign borrowing by the banking system (Pasivos no monetarios con el sector externo),<sup>2</sup> 1 while amortization was also given its actual values. Government tax rates on capital and labor were taken as the effective rate of revenue collection on the respective factors in each year, 1983-85.22

- 19/ These are taken from Sistema de Cuentas Nacionales de Mexico (1979), Table 137, p. 238; (1982), Table 67, p. 104; (1985) Table 65, p. 20.

- 20/ These are taken from Sistema de Cuentas Consolidades de la Nacion (1985), Table 65, p. 20; (1986), Table 16, p. 16.

21/ See IndicAdores Economicos (1986), Tables I-4, 6,8 an I-H-24.

- 22/ See CIEMEX-WHARTON (1986), Tables 4, 15 and Sistema de Cuentas Consolidades de la Nacion (1985), Table 3.
10</sup> This variable captures the perceptions of citizens’ confidence in and adherence to rules within their society; it captures the quality of contract enforcement, property rights, policing, the courts, and the likelihood of crime and violence (Kaufmann, Kraay and Mastruzzi, 2010).

Data on religion are from the Pew Research Center (2015). For each country, the data show the percentage of the population that belongs to each of the following religious groups: Islam, Hinduism, Christianity, Judaism, Buddhism, folk religions, other religions, and unaffiliated. The Pew data set projects global religious trends from 2010 to 2050; we use the 2010 values.

# _2.2 Descriptive statistics_

The data displayed in Table 1 shows that, for the average firm in our sample, the WBL index score in the year the firm began operations was 65.9, implying that women had just under two-thirds of the rights of

> 8 A more detailed description of the index is available at https://wbl.worldbank.org/en/methodology and in Hyland, Djankov and Goldberg (2020).

> 9 https://datatopics.worldbank.org/world-development-indicators/

> 10 http://info.worldbank.org/governance/wgi/

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al (2014); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2014); Colombia: Melendez (2014); El Salvador: Beneke et al., (2014); Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Peru: Jaramillo (2013); Russia: Lopez-Calvo et al (2016); Sri Lanka: Arunatilake et al (2016); South Africa: Inchauste et al (2016); Uruguay: Bucheli et al (2014); Poland: own estimates based on the 2014 HBS.

Note: Old-age pensions are treated as deferred income for all countries shown. Marginal contributions are the <u>difference between the consumable income Gini coefficient with and without indirect taxes.</u> 23
statistics for each DISCO for each of the years from 2012 to 2016 of the percentage of villages in their area that have not yet been electrified. These statistics seem questionable in several cases. The percentages of total electrified villages fell in some cases or rose and then fell. The total number of villages covered by some DISCOS changed dramatically from year to year, and without a clear trend.

The picture for access is then one dominated by uncertain statistics. The estimate of 98 percent access for 2014 would indicate that the government had been extremely successful in bringing electricity to the population, while a value of around 70 percent in 2017, when the value for 1990 may have been around 60 percent, suggests modest progress. It is noticeable that there was no major rural electrification scheme or policy that could have explained an increase to 98 percent access and, given that the state-owned DISCOs have not been run efficiently, it would be strange if they had managed to increase the access rates so markedly with no government assistance. The indications are that the lower access figure is the more plausible and that utilities have made only modest progress in improving access.

On **affordability** there is limited material available. The retail pricing approach adopted in Pakistan involves an estimated revenue-required tariff determined by NEPRA, and a notified tariff (which is the one actually paid) determined by the government. The former takes into account

> “<sup>31</sup> In the Dark: How much do power sector distortions cost South Asia.?” 2016, World Bank Group.

> 32 https://saadiaqayyum.wordpress.com/2017/09/07/census-shows-the-real-state-of-pakistans-electrification/

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38 None of this means that poor consumers would not have been willing to pay more for a connection; a SEDAPAL survey in 1993 found that the average property value of houses in the _pueblos j6venes_ quadrupled after being connected to piped water (from US$1,542 to US$6,375). Moreover, poor consumers already paid high prices for water from vendors; a survey in December 1993 found that the average price was US$2.50 per M<sup>3</sup> .

The cost to the poor could have been made affordable through subsidies, by including the connection costs in the variable water tariff charged to all consumers, or by extending longer term, lower interest loans for connections.<sup>31</sup> However, none of these arrangements were contemplated in the concession agreement, and would not have been easily added. Any efficient provider, public or private would have wanted to reduce the already large cross subsidies in Lima's tariffs. Industrial tariffs were 130 percent larger than residential. An efficient operator would have also wanted to raise metering and change per meter prices closer to marginal cost, not only to assure a reasonable return on investment, but also to keep the demand for water down so as to reduce the need for further costly expansion. It was therefore inevitable that poorer consumers would face substantially higher prices, as indeed occurred under public ownership later, reducing the political benefits of the concession.

Not only would price increases reduce the net benefits from the concession for lower income consumers, the size of the projected benefits from investment was open to

> 3 Loans were in fact offered to water companies and consumers to finance construction of primary networks and to cover investments in secondary networks and costs of connection through a government fund financed by a Social Tax, FONAVI. FONAVI channeled about US$1,000 million to the water sector at moderately favorable interest rates, out of which 75% went to customers. However, customers failed to repay the loans
service, customers, quality, transparency and process improvement.

Social norms or social capital are key factors in understanding the motivation for compliance in transition countries and other regions. Moreover, social capital seems to be an important determinant of economic phenomena like macroeconomic performance. For example, Knack and Keefer (1997) find, in a cross-sectional analysis, a strong and significantly positive relationship between social capital variables (civic duty) and economic growth. Schaltegger and Torgler (2007), using data for a synthetic panel of Swiss cantons over the 1981–2001 period, show that accountability enhances fiscal 54
Each firm produces a differentiated product with the standard Cobb-Douglas production function: s s s s 1−ααss ααss where s stands for firm-specific productivity; YY = AA LLs is the firm's laborKK s is the firm’s capital; and is the sector-specific capital share. AA LL ; KK Each firm maximizes current profits: ss αα s s s s s s where s s is the firm's value added (which is the firm's revenue minus the cost of intermediate YYs KKs ππ = (1 −ττ )PP YY −ww LL −(1 + ττ )RRKK inputs), and s and _R_ are the cost of one unit of labor and capital, respectively. The term denotes firm-specific output distortions that reduce firms' revenues. The firm-specific "capital" PP YY YYs distortions, which raise the cost of capital (relative to labor), are denoted as ww . ττ HK differentiate two productivity measures: TFPQ, which captures “physical productivity”; and KKs ττ TFPR, which captures “revenue productivity”: <u>s s s</u> T s = ~~1−α α~~ and T s = ~~1−α α~~ s s s s YY PP YY PPTT LL αssKK αss PPRR LL αssKK αss In the absence of distortions, TFPR should not vary across firms within each sector. This is so because more capital and labor are allocated to firms with higher physical productivity (TFPQ) such that their higher output results in a lower price, s ; this occurs up to the point where T s is equalized for all firms in the sector. On the contrary, it is normal for T s to vary across firms PP PPRR because different firms may have different productivity levels. PPTT Assuming monopolistic competition and CES technology, HK derive T s in terms of revenue data as: PPTT T s s σσ ~~,~~ σσ−1 s<sup>YYs</sup> s<
|0.742|1.045|
|---|---|---|---|---|
||[0.142]***|[0.178]***|[0.035]***|[0.169]***|
|Output × _De Jure_|**-0.390**|-0.006|0.000|**-0.318**|
||[0.151]***|[0.186]|[0.000]|[0.191]*|
|Observations|963|945|945|965|
|Countries|22|21|21|22|
|**_Developing Econo_**<br>Output|**_mies_**<br>0.835<br>[0.023]***|0.766<br>[0.058]***|0.875<br>[0.074]***|0.802<br>[0.015]***|
|Output × _De Jure_|-0.031|**-0.139**|**-0.144**|**-0.052**|
||[0.028]|[0.059]**|[0.068]**|[0.024]**|
|Observations<br>|2,870<br>|1,376<br>|1,785<br>|2,870<br>|
|Countries|66|43|45|66|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. “IMF” refers
framework for a survey of public servants that we then use to guide our measurement choices.

This requirement is particularly important given the comparative nature of the data we are harmonizing. By making public much of the survey data we – and governments – have collected, we have faced the challenges of integrating surveys that were not always implemented with integration in mind. By including a core module of questions consistent across settings and enumerating it to a similar set of central administrators in our own surveys, the scope of integration is increased. Towards this end, we have developed a conceptual framework to define the topics and questions we include in this core module.

<mark>The conceptual framework of the Global Survey reflects its dual aims: to better understand how public administrations work, and to provide actionable evidence to governments to improve management of public servants. To this end, the core questionnaire of the Global Survey contains measures which reflect core scholarly interests in comparative public administration on the one hand, and government interests on the other.</mark> <mark>As further detailed in Mikkelsen, Schuster, and Meyer-Sahling (2021), from a scholarly perspective our core questionnaire follows public administration literature in focusing on assessing ‘ideal types’ of modes of governing - “the actual operating modes and administrative arrangements by which rulers govern” (Roth 1968, 156). We distinguish and measure in the GSPS indicators related to three ‘ideal types’: patrimonialism, Weberian bureaucracy, and (new) public management.</mark><sup>1</sup> <mark>Weber (1978) coined the term ‘patrimonialism’ for an administrative system in which rulers treat the state as private property and govern it as ‘patrons’ through informal connections with – and loyalty and reciprocity of – public employees (their ‘clients’). Patterns of (neo)patrimonial rule persist in many countries in the global south in particular (cf. Fukuyama 2004), underscoring the conceptual importance of patrimonialism for a global
|POLICY RESEARCH WORKING PAPER 1999

# Summ.rary findings

A long tradition sees the smail firm sector as a holding pattern for workers queuing for jobs in the formal sector of a segmented labor market. An alternative "entrepreneurial" view suggests that many workers prefer self-employment to salaried jobs. These competing views can be resolved if the sector is heterogenous.

Using factor and cluster analysis, Cunningham and Maloney generate a typology of the sector by taking advantage of a Mexican data set on micro-firms that offers information on a broad range of small firm characteristics. The methodology permits divisions to emerge from the data without the a priori imposition of a theoretical structure.

The data break into several distinct groups, broadly characterized as highly profitable and dynamic young firms, older firms that have stabilized at a small size, and young firms that act as an employer of last resort. Those in the last group, comprised of older entrepreneurs with low levels of education, are the most likely to cite that they started their firms because they were unable to find a salaried job.

In general most of the firm owners in all groups stated that they chose self-employment over formal sector employrment in order to be independent, collect higher earnings, or follow family tradition. These survey responses are supported by the finding that income distribution adjusted for human capital is composed of two subdistributions, with the "underperforming" distribution comprising only 14 percent of the sample.

The factor analysis also implies that firm owner characteristics and firm size or profitability may not be correlated. For example, young workers who we might think are forced into the small firm sector due to inability to enter the formal job market do not necessarily earn less or have less capital than older entrepreneurs. Furthermore, a distribution of the earnings residual factor shows that very few firms, regardless of the firm owner's age, are earning below their expected profits.

The data suggest that the small size of informal firms may not necessarily result from limited access to financial institutions or a desire to evade labor or tax
Sara and Andreas Kotsadam** , “Resources and Intimate Partner Violence in Sub-Saharan Africa,” _World Development_ , 2017, _95_ (July), 211–30.

- **Cullen, Claire** , “Method Matters: The Underreporting of Intimate Partner Violence,” _The World Bank Economic Review_ , 12 2023, _37_ (1), 49–73.

- **, Sarthak Joshi, Joseph Vecci, and Julia Talbot-Jones** , “Female Empowerment and Male Backlash: Experimental Evidence from India,” _Working Paper_ , 2024.

- **Dhar, Diva, Tarun Jain, and Seema Jayachandran** , “Reshaping Adolescents’ Gender Attitudes: Evidence from a School-Based Experiment in India,” _American Economic Review_ , 2022, _112_ (3), 899–927.

- **DHS** , “Rwanda Demographic and Health Survey 2014-15’,” 2016.

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