rafmacalaba/datause-encoder-data huggingface dataset:
of trade with government. Because the dataset used for this study is crosssectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts.

Further, citizens may not be attributing goods and services to the government. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to various non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services.

Each of the indicators of perceptions of government performance is significant at the p _<_ 0.05 level. Food security is positively associated with a willingness to defer to the tax department. The difference in the predicted probability of deferring to the tax department for respondents who have never experienced food insecurity and for responden fts who have experienced food security at least once within the past year is 4 percentage points. A disinclination to pay taxes on the part of the hungry may reflect an inability to pay. It may also reflect a belief that government is not fulfilling its end of the fiscal contract. Satisfaction with government’s maintenance of water and sewage corresponds to a percentage point increase of 3.4 in the predicted probability that one will defer to the tax department. Similarly, satisfaction with government efforts to maintain roads and bridges corresponds to a percentage point increase of 3.0 in the predicted probability that one will defer to the tax department I find support for the hypothesis that citizens’ assessment of administrative competence is positively related to their acceptance of government’s right to make people pay taxes. There are two dimensions to administrative competence: the honesty of state agents; and the extent to which the bureaucracy can monitor and enforce tax regulations. I do not find any evidence of a relationship between citizens’ perceptions of the transparency of their local council in terms of providing information about its revenue and expenditures and their willingness to defer to the tax department. A perception that tax collectors are honest rather than corrupt corresponds to a percentage point 18
# Research Components of World Bank Education Projects. since 1982.

|Area|Country and Loan/Crediti<br>Number<br>l|Approved<br>|Research Components|Current Status of Studies,<br>including<br>Completed Documents/Data|
|---|---|---|---|---|
|LAC|Dominican Rep.<br>LN 2558-Do|1985|available<br>through SIED (Integrated<br>Information<br>System for Education)<br>[para.2.15<br>p.131.<br>Vocational<br>Training.<br>Study impact of training<br>on graduate's<br>employment, income and<br>performance (para.<br>2.12 p.6).|Project<br>cancetled.|
|LAC|F. Caribbean CR 1785-CRG|, 1987|Regionat Vocational and Technical Project.<br>Project includes: (i) establishment<br>ofaLabor<br>Market Information<br>System (para. 3.7 p.11).<br>(ii) Feasibilit,<br>studies aimed at cost-saving<br>inctude:<br>cost recovery on textbooks;<br>(iii) training program services;<br>(iv)<br>merging some technical<br>and skill courses<br>(due Dec 1988) [para. 3.10<br>p.131]|Referred to LAC files. No information<br>available.|
|LAC|Ecuador LW 2171-EC|1982|Second Vocational<br>Training.<br>Strengthening<br>of<br>educational<br>plamning management.Noresearch<br>components.|No information<br>supptied.|
|LAC|Guatemala LW 3003-GU|1989|Second Basic Education Proiect.<br>(i)Studies in<br>use of local technologies<br>in school<br>construction<br>(due June 1993);<br>(ii) evaluation of curriculum changes in<br>in-service<br>teacher training<br>program<br>(due Dec 1989);<br>(iii)<br>evaluate standardization<br>of student<br>evaluations<br>(due Dec 1989);<br>(iv) review alternative<br>approaches to improve<br>pre-service teacher training (due Dec 1990);<br>(v) develop new roles and responsibilities<br>for<br>supervisors (Octl990) [para 3.13 p.<sup>17 </sup>Anmex VI]|Project<br>not yet effective;<br>studies<br>not initiated.|

61
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

**Table 20. Dakar home electricity: logistic regression results for household access**

|Variable||Odds-Ratio|Standard error|p-value|
|---|---|---|---|---|
|Poor||0.544|0.104|0.002|
|Tenant||0.399|0.061|0.000|
|Locations|||||
|1. Nolivet/Barak||0.052|0.043|0.001|
|2. Arafat (base)|||||
|3. Hann||0.815|0.280|0.553|
|4. Dalifort||0.313|0.090|0.000|
|5. Santiaba||0.418|0.175|0.041|
|6. Taïf||0.505|0.160|0.034|
|7. Guinaw||0.709|0.234|0.300|
|8. Médina||0.475|0.144|0.016|
|9. Wakhinane||0.670|0.210|0.204|
|10. Diokoul||1.355|0.546|0.454|
||N<br>1,960||||
||U2<br>0.07||||

**Table 21. Nairobi piped drinking water: coverage insights**

|||# of EAs<br>with no<br>piped water||Coverag<br>|e (percenta<br>|ge of hous<br>Across|eholds with<br>constituent<br>|access)<br>EAs||
|---|---|---|---|---|---|---|---|---|---|
|Division|Number of EAs||Number of HHs|Across divisions|min|max|med|mean|sd|
|1. Central|10|4|198|24|0|75|23|24|25|
|2. Makadara|9|3|175|13|0|72|5|13|23|
|3. Kasarani|10|0|198|23|5|70|18|23|21|
|4. Embakasi|9|5|179|8|0|45|0|8|15|
|5. Pumwani|6|0|120|24|5|85|13|24|31|
|6. Westlands|3|1|60|38|0|85|30|38|43|
|7. Dagoretti|17|7|331|21|0|70|20|21|23|
|8. Kibera|24|7|494|11|0|76|5|11|17|
|ALL|88|27|1755|19|0|85|10|18|22|

45
arrangements of reserve management investment operations were collected systematically across central banks globally. In addition to questions on governance arrangements and investment policies, the surveys also included questions on asset allocation, portfolio management, risk management, performance and risk reporting, and transparency. Table 2 lists the survey variables we used to test our set of questions empirically.

**Table 2  Summary Statistics of RAMP Survey Variables**<sup>**14**</sup>

|Variable|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|<br>**Governance - Dummy var**|**iables**|||||
|Independent investment committee|105|0.2|0.4|0|1|
|Middle office reports to the board|105|0.9|0.3|0|1|
|Middle office reports to the investment committee|105|0.8|0.4|0|1|
|Back office, middle office, and front office are in the same department|105|0.3|0.5|0|1|
|Back office, middle office, and front office are in separate departments|105|0.3|0.5|0|1|
|Obligation to cover negative equity|93|0.6|0.5|0|1|
|<br> <br>**Governance - Continuous v**|**ariables**|||||
|Transparency of reserve management policies (degree of)|95|0.4|0.3|0|1|
|<br>**Composition of reserve portfolio - Co**|**ntinuous v**|**ariables**||||
|Number of eligible currencies|100|7.9|4.3|1|20|
|Number of eligible assets|97|6.8|2.8|1|14|
|Allocation to nontraditional assets (%)|71|10.1|17.0|0|72|
|Estimated risk of the portfolio|71|1.0|0.3|0.8|2.9|
|Investment horizon – Total tranched and untranched portfolio (months)|57|35.3|30.7|1|126|
|Duration – Total tranched portfolio (months)|52|22.4|18.9|1|84|
|Duration – Liquidity tranche (months)|64|7.8|12.6|1|76|
|Duration – Investment tranche (months)|62|32.1|31.5|3|180|

With respect to governance arrangements, we consider as independent variables those indicated in the RAMP survey results as differing across central banks and which could therefore influence central bank investment policies and risk taking. We use dummy variables for the following governance arrangements:

- (1) We consider an investment committee independent if it approves the investment management guidelines, that is, the specific investment rules for managing the portfolio.

- (2) We explore whether the middle office reports directly to the board.

(3) We also consider whether the middle office reports performance and risk metrics to the investment committee.

14. The survey question specifically asked whether the middle office reports to the board on performance and risk metrics and compliance with the investment management guidelines.

10
with all positive youth behaviors in Chile. Poor parental response to misbehavior (such as hitting their child, insulting their child, or accusing their child in front of others) is significantly negatively correlated with all seven behaviors in Mexico. Social exclusion is negatively correlated with positive behaviors in Chile (Table 3b), while being socially excluded is positively correlated with school completion, starting work at an older age, safe sexual behavior and later age at first parenthood in Mexico (Table 3b).<sup>12</sup> Protective factors are also prevalent in the lives of young Mexicans and Chileans. More than 70 percent of Mexican youth live with both parents (Chilean youth were not asked this question). Mexican and Chilean youth report a high level of personal connections with caring adults. Mexican youth regularly discuss a wide range of topics with their parents including school, work, politics, and religion. While Chileans report good relationships with their parents, ten percent of Chileans also note important relationships with other adults. Fewer than 7 percent of Mexican youth are verbally or physically abused by their parents. Nearly 95 percent of youth Chileans believe in a god, and 66 percent of Mexican youth attend church with some frequency. School quality, a proxy for school connectedness, is high in Mexico. Youth are generally content with their schools in Mexico, with small percentages of youth reporting dissatisfaction in school attributes such as the physical environment, teacher preparedness, and teacher attendance (Table 2). However, Mexican youth trust government institutions (6.5% trust politicians, 16.1% trust judges, and 12.3% trust the police) less than they trust local institutions (66.2% trust teachers, 70.8% trust doctors, 15.9% trust shop owners, 12.5% trust union leaders, and 61.8% trust priests). Similarly, in Chile, youth trust local institutions more than government institutions (Table 2). Youth have positive outlooks towards life and the future. Nearly 70 percent of Mexican youth report that they are very happy (compared to 1.7 percent reporting they are not happy at all), In Chile, 87 percent of youth feel optimistic about future work possibilities.

> 12 The opposite trends in the two countries is likely due to the definition that “social exclusion” takes in each survey.  In Chile, social exclusion is defined as not having friends to spend time with, whereas the Mexico survey asks who the respondent spends his or her free time with.  If the respondent answers “friend” or “boyfriend/girlfriend”, he or she is not considered to be socially excluded.  However, most who were coded as socially excluded replied that they spend their free time with parents, siblings, and other family.  Thus, these respondents may have a rich social life such that they spend time with friends while in school or at their sports club while they are with family during their unstructured free time.

12
# **3.2.1 Cross-country firm and industry level studies**

Research establishes a robust correlation between improved access to finance and economic performance by exploiting firm- and industry-level heterogeneity. These studies address causality issues by identifying firms or sectors that are more likely to suffer from limited access to finance and then test whether financial development exerts a differential effect on the growth of these firms and industries. This research starts by observing that if financial underdevelopment prevents firms (or industries) from investing in profitable growth opportunities, it will not constrain all firms (or industries) equally. Firms that can finance themselves from retained earnings, or industries that depend less on external finance for technological reasons will be minimally affected, whereas firms or industries relying on external finance may be severely constrained. By also examining a specific mechanism though which finance might affect growth –ability to access external finance- both papers seek to provide evidence on the causal impact of finance on growth.

Whether using a firm- or industry-level approach, research suggests that improvements in the financial system boost economic growth. Specifically, Demirguc-Kunt and Maksimovic (1998) use firm level data from 8500 large firms in 30 countries and a financial planning model to predict how fast those firms would have grown if they had no access to external finance. And they find that in each country the proportion of firms that grew faster than this rate was higher, the higher the country’s financial development and quality of legal enforcement. Rajan and Zingales (1998) instead use industry level data across 36 sectors and 41 countries and show that industries that are naturally heavy users of external finance benefit disproportionately more from greater financial development compared to other industries. Natural use of external finance is measured by the finance-intensity of U.S. industries since the U.S. financial system is relatively free of frictions, so each industry’s use of external finance in the U.S. is assumed to be a good proxy for its demand.

Besides using cross-country, firm- and industry-level data to assess the causal linkages between finance and growth, researchers use these data to test whether financial development disproportionately benefits small firms because they face more severe credit constraints (Banerjee and Newman, 1993). In particular, Beck, Demirguc-Kunt, Laeven and Levine (2008) use the industry-level approach to assess this possibility, while Beck, Demirguc-Kunt and 32
# Sub-—Saharian Africa

<!-- Start of picture text --> Mozambique 03<br>Mali 01<br>Chad 04<br>Madagascar 03/04<br>Malawi 00<br>Gabon 00<br>Zambia 01/02<br>Uganda 00/01<br>Cameroon 04<br>Tanzania 04<br>Nigeria 03<br>Burkina Faso 03<br>Kenya 03<br>Benin 01<br>Namibia 00<br>Ethiopia 00<br>Mauritania 00/01<br>Eritrea 02<br>Ghana 03<br>Rwanda 00<br>0 10 20 30 40<br><!-- End of picture text --> Source: Demographic and Health Surveys.
# **Appendix Table I. 9: Non-household Unorganized Sector Log (Employment) & Log (Real Output)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Emplo|yment|Real O|utput|
||_Other-s_|_ector Employm_|_ent/Output Spi_|_llovers_|
|Horizontal Employment Spillovers from|||||
|Organized Sector|0.2106***|0.1002***|0.1569***|0.0590***|
||(0.0265)|(0.0239)|(0.0215)|(0.0206)|
|Forward Employment Spillovers from|||||
|Organized Sector|0.0721|-1.4315***|2.2393***|-14.4009***|
||(0.2504)|(0.5540)|(0.7828)|(2.7383)|
|Backward Employment Spillovers from|||||
|Organized Sector|1.0159***|-0.4545++|1.6015***|11.1751***|
||(0.1550)|(0.3390)|(0.4920)|(2.0298)|
||_Oth_|_er-sector Prod_|_uctivity Spillov_|_ers_|
|Average TFP of Horizontal-linked Organized|||||
|Plants|0.0434|0.0600|0.0852|0.0789|
||(0.0636)|(0.0602)|(0.1713)|(0.1647)|
|Average TFP of Forward-linked Organized|||||
|Plants|0.3071**|-0.5767**|0.8938**|3.8656***|
||(0.1396)|(0.2587)|(0.3962)|(1.0933)|
|Average TFP of Backward-linked Organized|||||
|Plants|0.3719***|-0.6056|-0.0448|4.2301***|
||(0.1343)|(0.5388)|(0.3639)|(1.1117)|
||_Own-se_|_ctor Employm_|_ent/Output Spi_|_llovers_|
|Forward Employment Spillovers from|||||
|Unorganized Sector|0.2158|0.5178|0.2369|0.4264|
||(0.2210)|(0.6204)|(0.2916)|(1.0942)|
|Backward Employment Spillovers from|||||
|Unorganized Sector|0.9580***|0.5703++|0.8018***|-1.2360*|
||(0.1457)|(0.4370)|(0.2255)|(0.6309)|
|Constant|-23.8416***|16.9023*|-108.4850***|73.2393*|
||(4.0094)|(10.0000)|(17.6922)|(40.4991)|
|Observations|5888|5888|5888|5888|
|R-squared|0.1151|0.2938|0.0854|0.2513|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2 and 4 include state-year and industry-year fixed effects while Columns 1 and 3 do not. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 62
The structure of the paper is the following. Section 2 contextualizes the trend of premature deindustrialization and what it implies for development opportunities. Section 3 analyzes what the onset of Industry 4.0 might imply for manufacturing-led development strategies in the future. Section 4 outlines why manufacturing was considered special for development in the past. Section 5 examines the role of the services sector as alternative source of productivity and job creation and whether countries still need to follow the conventional linear path of structural transformation. Section 6 concludes.

# **2. Premature Deindustrialization**

The literature on structural change during the 1960s documented canonical shifts of output and labor first from agriculture to industry, and later from industry to services in the structural transformation of today’s advanced economies (Kaldor, 1967; Kuznets, 1971). Recent trends, however, show that the share of manufacturing in employment and value added appears to be peaking at lower levels and at earlier levels of per capita GDP than in the past (figure 1). Controlling for population size and per capita GDP in a sample of 42 economies between 1950 and 2012, Rodrik (2016) finds a lower share of manufacturing in employment and value added over time, as reflected in the magnitudes of coefficients of decadal time dummy variables, which are negative and larger over time. Therefore, if industrialization is defined as an increase in the share of manufacturing in employment and value added, these results are indicative of “premature deindustrialization” (Dasgupta and Singh 2007). The following is important to highlight.

**Figure 1: Peak Manufacturing Share of Total Employment (1950-2012)**

<!-- Start of picture text --> 50<br>Peak share of manufacturing employment<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>5 6 7 8 9 10<br>Log GDP per capita (2010 constant USD)<br>LIC LMIC UMIC HIC<br><!-- End of picture text --> Source: Hallward-Driemeier and Nayyar (2017) Note: The sample covers the period 1950-2012 3
and services employment, and the horizontal and vertical axes provide the median value for each index. The graph demonstrates a negative correlation, such that higher diversity is associated with reduced specialization, but also confirms that the correlation is modest.

We next evaluate the degree to which specialization and diversity patterns are stable over time (Henderson, 1997). When looking at the manufacturing sector by itself, our earlier study found very little persistence—even at the frequency of a decade, there was almost no correlation in the longitudinal values of districts. Tables 2a and 2b show a stronger degree of persistence of Indian specialization and diversity levels when also modeling services in the local industrial base. This persistence is still modest in overall scope, but nevertheless noticeably stronger. For the specialization index in Panel A of Table 2a, there is a 0.2-0.4 correlation of district values across adjacent surveys that are about five years apart. This correlation over a short time period is somewhat stronger than for manufacturing alone.<sup>4</sup> Across 2000→2010, or about ten years in duration, the correlation remains economically and statistically meaningful at 0.2. This is significantly larger than the rapid change in specialization levels observed for manufacturing alone for 2000→2010. Panel B shows even greater persistence for the diversity index, and Table 2b looks pretty similar when we isolate urban areas of districts.

Pausing for a moment on the persistence evaluation, Table 2c compares the specialization and diversity index values derived using both sectors with that evident in manufacturing and services data only. We observe two broad patterns. First, our indices using the combined data closely correlate with each sector individually. The connection is a bit tighter on the specialization index to the services sector, while the link is a bit tighter on the diversity index to the manufacturing sector. These patterns were foreshadowed by Figures 1a and 1b, and the most important point is that neither sector is dominating the index design. Second, the negative correlation noted earlier between specialization and diversity index values holds within each sector, while being uncorrelated across sectors. We further discuss these correlation and sectorlevel values below when modeling them as regression covariates.

Tables 3a-b show an alternative approach for measuring persistence. We develop a transition matrix to follow cohorts of districts over time. In Panel A of Table 3a, we start by

> 4 In the longer series for manufacturing alone, the stability of index values across five-year intervals increases with time. This connects to the broader stability we observe for 2000-2010 with the combined data.

13
**Figure 1:  Frequency of “Yes” Responses**

<!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>mean of bribegov mean of votepm mean of late mean of lesstaxes mean of bribepolice mean of officephone mean of corrbenefit mean of lied mean of bribepermit mean of bribemost<br><!-- End of picture text --> For four of the questions, responses are less than 0.5, including three politically sensitive questions on taxes, bribing police, and our main question on bribing to obtain business permits. For the question on tax evasion the mean response is 0.3811, indicating that at least 23% of firms are providing false responses to this questions. Turning the questions on bribing the police and paying bribes for permits, at least 6% of respondents are providing false responses. This is assuming zero tax evasion and bribery (a very bold assumption that contradicts other measures of corruption in Bangladesh). If we assume that at least 50% of firms paid some sort of bribe, as much as 35% of firms are providing false responses.

13
**Fiscal Risks from Early Termination of Public-Private Partnerships in Infrastructure**<sup>1</sup>

_Matías Herrera Dappe Martin Melecky Burak Turkgulu World Bank World Bank World Bank_

# JEL: E62, H54, L33, O18

Keywords: Public-private partnerships, early termination, infrastructure, fiscal risk, survival analysis, value at risk 1 This paper was peer reviewed by Clive Harris, Lead Infrastructure Specialist; David Duarte, Senior Public Private Partnerships Specialist; and Alvaro Pedraza Morales, Research Economist, all of the World Bank. It was prepared as a background paper for the Infrastructure Vice-Presidency’s Flagship _Fiscal Costs and Risks from Infrastructure in an Era of High Debt Vulnerabilities_ and presented at the corresponding authors’ workshop, held July 12, 2021, where it benefited from feedback provided by the workshop participants. We are particularly grateful to Vivien Foster, Teresa Ter-Minassian, and Hans Timmer for their helpful comments and suggestions. We also thank Antonella Bonacina and Dmitry Lesnichiy for their excellent research assistance.