rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2.1 Tetrachoric correlation catrix and eigendecomposition**

Table 5 presents the tetrachoric correlation matrices among the seven components of the job quality index for 2007, 2011, 2014, and 2021. As expected, the correlation coefficients are higher among the first four components, which assess a job’s regulatory and income dimensions. For example, in 2007, access to social security benefits exhibited a strong correlation (coefficient of 0.77) with minimum wage compliance, and this association remained consistent across the years. Similarly, minimum wage compliance and the incidence of relative underpayment showed a correlation coefficient above 0.85. On the other hand, compensatory moonlighting, job stability, and the incidence of overqualification demonstrated low levels of correlation with the other variables. Furthermore, most of the relationships between the components maintained similar magnitudes over the decade, suggesting a time-independent and structural correspondence between the components used to assess job quality.

Table 6 presents the results of the eigen-decomposition of the covariance matrix between 2007 and 2021. This table provides the eigenvalues associated with each principal component and allows for calculating the explained variance. Generally, it is observed that for almost every year, only the first three components had eigenvalues above one, which collectively accounted for 60 percent of the total variance. For further exploratory analysis, we present the rotated principal components (eigenvectors) for 2007, 2011, 2014, and 2021 in Table 7 (including only components with eigenvalues larger than one). Column (1) illustrates that across different years, the first principal component predominantly consists of variables such as access to social security benefits, minimum wage compliance, child labor, and relative underpayment. Consequently, the underlying factor behind this component could be interpreted as labor market formality or a rule of law dimension. Column (2) shows that labor stability and the incidence of overqualification have a higher weight in constructing the second component, suggesting a likely dimension related to career advancement and opportunities. Column (3) also reveals that the third principal component exhibits a stronger correlation with compensatory moonlighting, suggesting a latent factor associated with an income dimension.<sup>21</sup>

> 21Separate estimations of the JQI including an underemployment component (between 2007 and 2019) also had a higher weight of this variable jointly with compensatory moonlighting into the latent factor related to income.

15
Kabeer, Naila. 2003. _Gender Mainstreaming in Poverty Eradication and the Millennium Development Goals: A Handbook for Policy-Makers and Other Stakeholders._ London, Eng.: Commonwealth Secretariat.

- Lamont, Michèle, and Virág Molnár. 2002. "The study of boundaries in the social sciences." _Annual Review of Sociology_ 28: 167-195.

- Lamont, Michèle, and Mario Luis Small. 2008. “How Culture Matters: Enriching Our Understandings of Poverty”. Pp. 76-102 in The Colors of Poverty: Why Racial and Ethnic Disparities Persist, edited by David Harris and Ann Lin. Russell Sage Foundation.

- Lee, Ching Kwan. 1998. _Gender and the south China miracle: two worlds of factory women._ Berkeley: University of California Press.

- ______________. 2007. _Against the law: labor protests in China's rustbelt and sunbelt._ Berkeley: University of California Press.

- Lizardo, Omar, and Michael Strand. 2010. "Skills, toolkits, contexts and institutions: Clarifying the relationship between different approaches to cognition in cultural sociology." _Poetics_ 38(2): 205-228.

Lorber, Judith. 1994. _Paradoxes of Gender_ . Yale University Press.

- Mansuri, Ghazala, and Vijayendra Rao. 2012. _Localizing Development: Does participation work?_ . World Bank Publications.

Mauss, Marcel. 1973. "Techniques of the body." _Economy and society_ 2(1): 70-88.

- Mitra, Subrata K. 2006. _The puzzle of India's Governance: Culture, Context, and Comparative Theory_ . London; New York: Routledge.

- Moore, Barrington. 1967. _Social origins of dictatorship and democracy: lord and peasant in the making of the modern world._ Boston: Beacon Press.

- Pankaj, Ashok K., and Mahendra Prasad Singh. 2005. “The Changing Sociopolitical Profile of Local Political Elites (Mukhias) in Bihar: A study of the 1978 and 2001 panchayat elections.” _Contributions to Indian Sociology_ 39: 407-428.

Patterson, Orlando. 2014. “Making Sense of Culture.” _Annual Review of Sociology_ 40:1-30.

- Planning Commission. 2002. _National Human Development Report 2001_ . Government of India, New Delhi.

- <mark>Platteau, Jean-Philippe, and Robert Peccoud (editors). 2010.</mark> _<mark>Culture, Institutions and Development: New Insights Into an Old Debate</mark>_ <mark>. Routledge.</mark>
<mark>raise firm performance and resilience to shocks (Bloom et al, 2007). Neither theory nor empirics offer a consensus on how long the sorting out process should take and for the reforms to come to fruition.</mark> _First_ , young plants benefit from connectivity and their scaling up drives the positive aggregate economic effects identified by Ghani et al (2016a). Despite falling exit rates across all ages of plants with proximity to the GQ, there is an average insignificant or negative effect on mature and old plants. Examining how the coefficients on the impact of the GQ evolve over time suggests that adjustment to the GQ shock requires not years, but decades and we expect that a longer sample period would reveal an even stronger divergence in performance between younger and older plants.

_Second_ , for young plants, in line with standard micro theory, the responsiveness to connectivity is blunted where factor and product markets are incomplete and where the enabling business environment shows a higher degree of distortion. Critically, then, building roads is not sufficient to stimulate local industry – other elements of the business climate, such as financial institutions must be in place as well. With the exception of plant capabilities, older plants generally show little such sensitivity to initial conditions suggesting that they have established alternative ways of resolving missing markets, and their performance is perversely _better_ in districts with higher distortions, possibly suggesting that, as in de Loecker et al. (2016), distortions may allow them to defend their market share while they can take advantage of cheaper inputs.

_Third_ , the young plants that respond most to connectivity tend to be more capital intensive, durable goods producers, linked to value chains but in activities that do not require close monitoring by the final producer, as might be associated with a kind of internal “offshoring” along the GQ.

_Fourth_ , in line with the Aghion et al. and the Bloom et al. managerial quality literature, both (district-level) pseudo panel as well as true plant-level panel estimations suggest that more capable firms can escape competition and take advantage of new opportunities. Strikingly, older plants in high literacy districts show a quantitatively similar positive response to GQ as that of the young plants, while it is those in low literacy districts that drive the aggregate negative trend for the cohort. This finding is broadly supported by the true plant-level panel analysis: young plants above median human capital and higher capital intensity drive the aggregate growth response while those below show no response to GQ; and below median older plants drive the contraction of the cohort while above median plants show no decline. Firm capabilities appear to be central to both the magnitude of the response to increased markets and competition, and to the composition of plants driving it.

2
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## **TABLE 3. OVERVIEW OF GOVERNACE VULNERABILITIES IN PUBLIC** **<u>FINANCIAL MANAGEMENT</u>**

|**Employee**<br>**compensation**|**Vulnerabilities**<br>• Absence of clear rules on hiring<br>• Absence of management controls, internal controls<br>• Absence or weakness in internal audit, external audit<br>• Absence of treasury payroll matching<br>• Absence of records, weak record record keeping<br>• Absence of management mandates for and review of<br>regular financial reports|
|---|---|
|**Goods and**<br>**services**|• Absence of nonpayroll expenditure controls<br>• Absence of inventory control, asset registry<br>• Weak procurement system<br>• Absence of management oversight and review of payment<br>and procurement practices|
|**Transfers**|• Cash or in-kind transfers<br>• Weak or no record keeping<br>• Absence of clear procedures for processing applicants<br>• Failure to follow procedures<br>• Absence of clear laws, regulations, rules for eligibility,<br>criteria|
|**Capital**<br>**expenditures**|• Absence of nonpayroll expenditure controls<br>• Absence of management oversight and review of payment<br>and procurement practices<br>• Weakprocurement system|

Source: Adapted from Dorotinsky and Pradhan (2007).

# **Measuring budget performance**

It is difficult to assess budget performance directly but some very useful process indicators exist based on the Public Expenditure and Financial Accountability (PEFA) framework. This uses a comprehensive set of 31 indicators to assess overall performance of public finance management systems (PEFA Secretariat 2005).<sup>7</sup> Indicators especially relevant to health are shown in Table 4.

The PEFA indicators are rated from 1-4 with + modifiers (4 indicating strongest performance).<sup>8</sup> Scores for three of the PEFA indicators relevant to health: (1) aggregate expenditure compared to original approved budget (in some countries there are large discrepancies suggesting that allocated resources do not reach service providers), (2) effectiveness of payroll controls (a particularly critical issue for health given the payroll costs in health are the largest among government sectors), and (3) availability of information on resources received by service delivery units.

> 7 At the time of writing, PEFA assessments have been carried out in approximately 100 countries, out of which roughly 40 assessments are publicly available. 8 The PEFA indicators are rated from A (best) to D with + modifiers, here we have converted them into numerical values for ease of exposition.

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BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE underspending of about $27.5 million worth of resources allocated to the sector. Lack of cost recovery via tariffs was a significant issue for Botswana’s power and water utilities; representing losses of $20.3 million in the power sector and $12.9 million in the water sector annually.

Fortunately the government took rapid action to tackle and correct most of these inefficiencies. Nowadays Botswana reports full execution of capital budgets, while tariffs schedules and levels for the power and water sectors have been aligned with costs. As a result of these sound policy decisions, the country has been able to recoup close to $68 million a year that was being lost to inefficiency in the space of just two years.

**Table 16. Botswana’s historical losses due to operational efficiency (as of 2008)**

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Underrecovery of costs|—|n.a.|20.3|Na.|12.9|33.2|
|Overstaffing|—|—|n.a.|—|n.a.|—|
|Distribution losses|—|—|0.4|—|0.0|0.4|
|Undercollection|—|n.a.|0.0|n.a.|1.3|1.3|
|Low budget execution|—|n.a.|0.0|27.5|5.5|33.0|
|Total|—|n.a.|20.7|27.5|19.7|67.9|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology. — = Not applicable. n.a. = Not available.

# Funding gap

As of 2008, no major funding gaps were found for basic infrastructure provision in Botswana. The one exception was the power sector, where spending allocations were falling short of requirements by some $305 million annually. Once again, the government has moved swiftly and decisively to address this funding shortfall for power, by raising capital for the $1.6 billion Morupule power generation project. As a result, the funding gap for that sector has now closed.

**Table 17. Funding gaps by sector (as of 2008)**

$ millions per year

||ICT|Irrigation|**Power**|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Spending needs|(70)|(5)|**(462)**|(107)|(141)|(785)|
|Existing spending|155|n.a.|**137**|256|268|816|
|Reallocation potential within sectors|0|n.a.|**0**|0|0|0|
|Efficiency gap|—|0|**21**|27|20|68|
|Funding gap||**n.a.**|**(305)**||||

_Source_ : Derived from Foster and Briceño-Garmendia (2009).

_Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. Figures based on annual averages for the period 2001–07. — = Not available. n.a. = not applicable.

35
The present study contributes to two strands of literature – women’s involvement in the labor market in developing economies, and the career progression of women managers and firm organization - by exploring the likelihood of female top managers in private firms across countries and its relationship to access to education. A representative sample of private formal (registered) sector firms in developing economies is used. The paper’s particular focus on developing economies is useful, given limited research for these economies on gender and high-paying jobs. The use of representative firm level data is also useful in that it allows for inferences beyond the sample under study to the broader economy.

Understanding what factors drive women’s presence in top managerial positions is important because gender disparity in labor markets continues to persist, especially at managerial positions (Claudia Goldin 2006; Alison Cook and Christy Glass 2013; Julie A. Kmec and Sheryl Skaggs 2014). Acknowledging that gender equality, especially for high paying and influential jobs, may have several positive economic and social outcomes (Duflo 2005; Lori Beaman, Esther Duflo, Rohini Pande, and Petia Topalova 2012), policy makers are now grappling with practical policy recommendations that may reduce the gender gap in managerial/leadership positions despite a countries’ economic, social or cultural orientation (World Bank 2011). A great deal of focus has now turned to women’s education for reducing gender disparities in incomes and labor markets (World Bank 2011, Esther Duflo 2012, Nitya Rao and Carolina Sweetman 2014).

One may argue that access to education is more likely to play a key role in developing economies than in developed economies, since access to education is not as big an obstacle for women in developed economies. For instance, the ratio of women to men in tertiary enrollment in high income countries in 2012 is 126 percent, while the corresponding figure for low income economies is 65 percent (World Development Indicators). For the case of developed countries, the literature on women’s careers and firm organization has rigorously explored the various firm characteristics as well as social attitudes and cultural factors affecting women’s career 3
10 countries as in developed countries. If anything, the relationship in developing countries may even be negative (Frick and Rodriguez‐Pose, 2016) (see further below).

This differential effect on poverty of migration to small towns versus big cities is an important phenomenon which needs to be explored and interrogated further, with cross‐country and country specific data. It is starting to attract attention (Dorosh and Thurlow, 2013; 2014; Datt, Gibson, Murgai and Ravallion, 2016; Sekkat, 2016). One such country specific exercise, for Tanzania, a country in which the composition of urbanization has some expected and perhaps some striking features, is particularly illustrative. The capital city, Dar es Salaam, had a population of 4.5 million according to the 2012 census. It constitutes 10% of the national population, it is the largest urban agglomeration by a huge margin, and it has grown dramatically over the past 50 years, driven largely by in‐migration. Yet as Figure 5 (taken from Wenban‐Smith and Ambroz, 2014) shows, the composition of urbanization throws up some interesting patterns. In 2012 Dar accounted for about one‐third of the urban population. But it also accounted for about one‐third of the urban population half a century ago. Further, towns other than district capitals (average population around 20,000) account for a growing proportion of the urban total. There is surely a lot of migration into Dar, but movement out of rural areas is as much to small towns as to the capital city.

A unique lens on the poverty impacts of migration from rural areas to the city versus small towns is provided by the Kagera Health and Development Survey (KHDS) which tracks migrants from this remote rural region in North‐West Tanzania. While it is only focused on out‐migrations from one region, we have information on 4,339 individuals, first interviewed in their baseline communities in the early 1990s and then re‐interviewed nearly two decades later in 2010. The data set is unique not only with respect to its long time frame, but also because it has tracked migrants to rural areas, towns and cities (Beegle, De Weerdt and Dercon, 2011). There was a considerable amount of income growth and poverty reduction in the KHDS sample over its 18‐year span and Christiaensen, De Weerdt and Kanbur (2015)
# What Are the Poverty and Inequality Impacts of Fiscal Policy in Turkey?

P. Facundo Cuevas Leonardo Lucchetti Metin Nebiler<sup>♣</sup> World Bank World Bank World Bank _Keywords_ : fiscal policy, incidence, social spending, transfers, taxes, poverty, inequality, Turkey _JEL classification: H22, I38, D31_

> ♣ Authors’ contact information: <u>fcuevas@worldbank.org; llucchetti@worldbank.org; mnebiler@worldbank.org.</u> Special thanks go to Stephen Younger, Nora Lustig, Gabriela Inchauste, Matt Wai, Luis-Felipe Lopez-Calva, Habib Rab and Auguste Tano Kouame for their generous suggestions and guidance throughout the study. We also gratefully acknowledge feedback from Turkey’s Revenue Administration, Ministry of Treasury and Finance, and Central Bank on earlier drafts of the paper, and from seminar and workshop participants at Turkey’s Ministry of Treasury and Finance, the Strategy and Budget Office of the Presidency, Middle East Technical University, Oziegyn University, and the World Bank. All remaining errors are our own.
# **Appendix 3: Long-Term Effects and Exclusion of Non-Reverse Items in S/E Skills Index**

_Table A3.1: Effects on GPAs of Different Roma Students in Long-Run (Z-Scores)_

|||Gen|der|Acade|mic Year|Pre-|Treatment Achieve|ment|
|---|---|---|---|---|---|---|---|---|
||Average<br>(1)|Male<br>(2)|Female<br>(3)|Sixth Grade<br>(4)|Seventh Grade<br>(5)|First Tercile<br>(6)|Second Tercile<br>(7)|Third Tercile<br>(8)|
|_Panel A: GPAs, Short-Run (2015/2016 Q4)_|||||||||
|Treatment 1|-0.015|-0.009|-0.029|0.022|-0.034|-0.063*|0.037|-0.048|
|“Student Self-Learning”|_(0.028)_|_(0.034)_|_(0.036)_|_(0.034)_|_(0.035)_|_(0.036)_|_(0.035)_|_(0.055)_|
|Treatment 2|0.055***|0.056***|0.063***|0.003|0.108**|0.006|0.076***|0.083***|
|“Teacher Delivery”|_(0.017)_|_(0.020)_|_(0.023)_|_(0.026)_|_(0.045)_|_(0.043)_|_(0.028)_|_(0.024)_|
|N|1,161|611|550|573|588|388|391|382|
|N Control|476|254|222|228|248|189|146|141|
|N Treatment 1|262|142|120|136|126|84|105|73|
|N Treatment 2|423|215|208|209|214|115|140|168|
|_R2_|_0.913_|_0.908_|_0.923_|_0.920_|_0.917_|_0.521_|_0.403_|_0.862_|
|_Panel B: GPAs, Medium-Run (2016/2017 Q1+_|_Q2)_||||||||
|Treatment 1|0.109**|0.085|0.162***|0.162**|0.065|0.108*|0.079|0.174*|
|“Student Self-Learning”|_(0.045)_|_(0.053)_|_(0.060)_|_(0.074)_|_(0.042)_|_(0.060)_|_(0.053)_|_(0.090)_|
|Treatment 2|0.166***|0.179***|0.170***|0.232**|0.113***|0.126*|0.177***|0.210***|
|“Teacher Delivery”|_(0.056)_|_(0.060)_|_(0.054)_|_(0.093)_|_(0.028)_|_(0.065)_|_(0.048)_|_(0.057)_|
|N|1,045|551|494|521|524|349|352|344|
|N Control|426|229|197|207|219|170|132|124|

37
that the timing of the values of variables in our baseline econometric specifications does not affect our results.

# **_3.1 Bribes and Tax Evasion_**

One of the concerns with self-reported measures on corruption and tax evasion is whether reliable data can be collected on illegal activity. However, Fisman and Svensson (2007) note that with appropriate data collection techniques, surveys<sup>16</sup> now have been able to elicit detailed information from firm managers on corruption. With theES surveys, given the sensitive nature of the data, government officials are not directly involved in data collection<sup>17</sup> nor are they given any raw data or any information that allows them to identify the responses of individual firms. Thus, firm names and their identities are confidential information. Furthermore, the surveys are conducted by the World Bank in partnership with the local private sector such as independent chambers of commerce or business associations that the local firms have confidence in. In addition, questions on bribes and tax evasion were phrased indirectly in the ES surveys. Consistent with established and approved survey methods, the firms were asked about the behavior of a typical firm rather than the firm itself, to avoid implicating the respondent firm with illegal activity.<sup>18</sup> Other established survey methods were also used to increase data accuracy. Corruptionrelated questions were asked at the end of the interview when the interviewers had presumably established credibility and trust with the respondent and multiple questions were asked on bribe payments. In addition, we performed survey reliability tests by examining answers to the questions across two different points in time or across an equivalent set of firms. Specifically, for a smaller sub-sample of firms over 27 countries, we have additional variables on bribe payments

> 16 Fisman and Svensson (2007) rely on the Enterprise Survey for just one country, Uganda.

> 17 The World Bank does coordinate with the national statistics agency where possible to obtain the sample frame and other information.

> 18 Indirect questioning where subjects are asked about likely responses of a “typical subject” has been used extensively in other fields such as psychology, marketing, and criminology to counter social desirability bias where respondents over-report good behavior and underreport bad behavior. Fisher (1993) and Johansson-Stenman and Martinsson (2006) show that indirect questions elicit more honest responses to normative statements (those with social norms) than direct questions. Other studies show that indirect questioning yield a better reflection of what people actually did when they were not being scrutinized by an interviewer (e.g. Lusk and Norwood, 2009a, b) and that people’s predictions of others were a significantly more accurate predictor of actual future behavior than people’s statements about themselves (Epley and Dunning, 2000)

13