rafmacalaba/data-use-mentions-extended huggingface dataset:
state-owned enterprises, with the remainder on-budget. The same study also finds that almost the entirety of infrastructure investment goes to just two sectors: energy (50 percent) and transport (45 percent).

Beyond these high-level estimations of expenditure and spending needs, little is known about infrastructure spending trends and patterns at the global level. This paper fills this gap by analyzing a new source of disaggregated infrastructure spending data covering some 75 countries over a period of 10 years, drawing from the World Bank’s novel BOOST database, complemented with data from relevant extra-budgetary channels. On this basis, the study aims to answer the following questions. What have been long term infrastructure spending trends across different types of countries? What are the patterns of infrastructure spending across sectors, functional categories, and institutional channels? Is there evidence of inefficiency, incoherence or cyclicality in infrastructure spending?

The rest of the paper proceeds as follows. The next section describes the data and methodology used to identify the infrastructure public spending trends, while the following one presents the results with a focus on roads and electricity spending. A fourth concluding section discusses the central findings and their policy implications.

# **2. Data and Methodology**

Public expenditure on infrastructure can be classified into three separate categories. The first category is on-budget expenditure, undertaken primarily by central government entities, such as ministries, which is reported in the budget itself and financed from domestic tax revenue mobilization. The second category is development expenditure provided by traditional and new foreign donors to governments and typically channeled outside of the public budget. The third category is off-budget expenditure, undertaken by corporatized or otherwise decentralized state entities, such as power utilities or Road Funds, which is not reported on the budget but can be found in the respective financial accounts.

The World Bank’s Public Expenditure Database BOOST compiles information on the level and composition of the first category of on-budget government expenditures for a growing number of emerging and low-income countries at quite a high level of granularity, currently around 75.
<!-- Start of picture text --> Upward mobile Downward mobile Quintile1 in both Quintile 5 in both<br>xxxx<br>_<br>ee<br>“ aN “ ‘ n<br>ae _| EO<br>So<br>oooo<br>$MV P Ge© @ $2 Ge© @ $ P Ge© © $2 Ge© @<br>ss oesYo &oe FOo oSSV SKSVo SSKos C2 KSNy SKKVANS SFos C3 KSMV SNKVk Sos SC2<br>Population shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>Noteand that thatfully 40% ofrandom the population dynamics wouldexperiences implyupward that 4%mobility, of the populand a tionnotheris 40%in a givendownward transitim o bility.n category,<br><!-- End of picture text -->
25 TRADE, TRANSPORT, AND TERRITORIAL DEVELOPMENT confirm that the GQ caused a spatial reorganization of manufacturing within the secondary region (See Table 14 in Appendix D).<sup>26</sup>

# **6. Conclusion**

In this paper, we explore how the interaction of external and internal trade costs affects the distribution of manufacturing activity across regions within India by examining the effect of two policy interventions during the period 1989-2009. These include the trade liberalization episode of the early 1990s and the construction and upgrade of the large scale Golden Quadrilateral (GQ) highway program during the 2000s. Using data from the organized manufacturing sector, we establish three key findings. _First_ , trade reforms in India helped disperse economic activity to secondary regions, but for the wrong reasons – incompetent manufacturers moved to these regions seeking protection from import competition. _Second_ , massive infrastructure investments in transport costs were successful in spreading manufacturing activity. Overall there was reallocation toward the primary region and only districts along the network benefited within the secondary region. _Third_ , economic activity in the interior region remained depressed and is in fact falling, irrespective of changes in external or internal trade costs. Our economic geography model of trade and production supports these results.

Reduction in transport costs may create spatial winners and losers. Reducing economic distance in the secondary regions and hinterland may possibly also lead to a decline in local activity. For remote locations, improved connectivity implies better access to markets, but also removes protection from outside competition for local producers. As our work suggests, activity in remote regions remains depressed even when connected by the GQ highways. Improvements in transport connectivity are often not enough to support economic development in less attractive locations. Complementary conditions such as the quality of local amenities, connectivity with ports and the strength of comparative advantage in the export sector are key factors in determining the gains across regions. Domestic policies that support spatial mobility of labor and investment in such complementary factors can mediate potential trade-offs between spatial efficiency and equity within countries, and
members, we focus on the primary economic activity of working age (10-65) members reported for the past 12 months. Where the members reported multiple economic activities, the highest total man-hour for the economic activity was used to arrive at the primary economic activity of the individual. We compare the economic activities of non-head members with that of the head of the household and construct a set of sectoral focus indicators for each working non-head members.

Low women’s participation in economic activities in Bangladesh, by international standards, as reported in household surveys requires some explanation. Some of the low participation is likely due to a range of factors from tradition to low bargaining power of women in the household and society (World Bank 2008). A part of low participation in economic activities may also be explained by low reporting of unpaid family work done by women in the survey. A recent survey seasonal economic activities in the _monga_ region as well as a national Labor Force Survey (LFS) suggest women’s participation in livestock rearing and other agricultural activities may be under reported in the current and previous HIES surveys (World Bank 2008, Mahadevan et al. 2012).

Table 1 shows the list of key variables and their descriptive statistics. The first set of variables are sectoral focus indicator for non-head members. The second set of variables include monthly per capita household consumption, access to credit and safety-net. The final set of variables are at the Upazila level: access to market, monsoon coefficient of variations, and historically flooded Upazilas.

4
<!-- Start of picture text --> Private informal 32 222 62 9% (11%)<br>Private formal 18 28 17 3 34 1% (2%)<br>Public formal 3 2 84 1 10 10% (10%)<br>Women<br>Unemployed 8 2 4 13 73 7% (5%)<br>Out of labor force 9 11 5 84 73% (73%)<br>Private informal Private formal Public formal Un-employed Out of labor force<br><!-- End of picture text --> _Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to individuals 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 by sex to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018. 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.

Figure 4 and Figure 5 show the transition matrices across employment statuses/sectors, by education, for men and women, respectively. Across all education groups, unemployed and OLF men, at baseline, tended to find jobs (mostly in the informal private sector). Though transitions to the informal private sector remain the most likely transition among men who were unemployed or OLF in 2012, regardless of their educational attainment, we note that the highest the level of education, the greater the share transitioning to the formal sector, be it in the private or public sector. Similarly, we also find that men’s education also matters for within-sector transitions, and particularly for transitioning out
FCS countries than for low income FCS countries. Overall, the results suggest the need to implement comprehensive macrofinancial and institutional strategies to improve SME financial inclusion in FCS countries.

The rest of the paper is organized as follows. Section 2 presents stylized facts on SME finance in FCS countries, including the SME financial inclusion index, a composite indicator that captures both access and usage of financial services of SMEs. Section 3 presents the methodology and data used in the analysis as well as the main results, highlighting the relative importance of variables for FCS countries compared to non-FCS countries. Section 4 offers some concluding remarks.

# **2. Stylized facts**

# **2.1. SME finance in FCS countries**

To characterize SME finance in FCS countries, this paper relies on WBES data. WBES is a firmlevel survey of a representative sample of an economy’s private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, competition, and various business performance measures. WBES are available for both large firms and SMEs and the survey is done typically about every four years per country though some gaps remain in the overall coverage. We focus in particular on WBES data related 3
# **Growing Markets through Business Training for Female Entrepreneurs: A Market-Level Randomized Experiment in Kenya**<sup>*****</sup>

David McKenzie, _Development Research Group, World Bank_ Susana Puerto, _Employment Policy Department, International Labour Organization_ _Keywords:_ Business Training, Spillovers, Microenterprise, Market Development _JEL codes:_ O12, O17, J16, L26.

* We gratefully acknowledge funding provided to this project from the International Initiative for Impact Evaluation (3ie), Private Enterprise Development in Low-Income Countries (PEDL), the International Labour Organization (ILO), the Strategic Research Program (SRP), and the World Bank Jobs Umbrella multi-donor trust fund. Human Subjects Approval was obtained from Innovations for Poverty Action (IPA, 13February-002) and the Maseno University Ethics Review Committee (MSU/DRPC/MUERC/000006/13). Authority to conduct research in Kenya was provided by the Kenyan Ministry of Science and Technology (NCST/RCD/14/013/553B). A preanalysis plan and the associated trial were registered on the AEA Social Science Registry on February 21, 2014 (AEARCTR-0000287). We thank Valerie Breda, Faizan Diwan, Bilal Hakeem, Mary Kamore, Virginia Losada, Jane Maigua, Grace Makana, Silvia Paruzzolo, and Miriam Wekesa for their contributions to the study design and implementation, ICRW for their qualitative work, and participants at various seminars for useful comments.
Rosenblaum’s (2015) findings on the unintended consequences of reform are supported by Bhalotra et al.’s (2017) analysis. Bhalotra et al. (2017) draw on NFHS data, and use a differencein-difference approach that exploits state-level variation in the timing of the inheritance law reform (and an indicator to capture the post-ultrasound period) to isolate the impact of inheritance reform on female feticide, female infanticide, and fertility stopping behavior. They find that following inheritance reform, SRB increased, female infant mortality (relative to male infant mortality) increased, and male-biased fertility stopping behavior increased. Furthermore, they find that stated son preference (measured as the desired share of sons among desired total births) increased in the post-reform period.<sup>17</sup> Given the negative impact of the reform on female fetal and child survival, Deininger et al. (2018) question whether some of the positive first-generation impacts Deininger et al. (2013) find translate into positive second-generation impacts. To examine this, they draw on three-generation individual data from the 2011 REDS follow-up conducted in Maharashtra, Uttar Pradesh, and Orissa, and they use a difference-in-difference strategy that exploits variation in cohort exposure to the law, state variation in implementation, and gender of the child observed. They find that postreform, first-generation female beneficiaries were more likely to complete primary education, brought more assets into marriage, and were more likely to have access to a bank account. These positive first-generation effects translated into positive second-generation effects as investments in the education and health of second-generation females (relative to male siblings) increased. At the same time, however, Deininger et al. (2018) observe that first-generation beneficiaries saw a reduction in the share of daughters born (i.e. the reform was associated with an increase in sex selective abortion among first-generation women).

Taken together, these studies suggest that while reform in property rights is an important step towards improving the status of women and girls, it does not necessarily bring
No|No|No|Yes|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ : This table reports coefficients of proportionality calculated following the methodology in Oster (2017). Coefficients of proportionality represent the relative degree of selection on unobservables to selection on observables that would imply a true effect of the education of the group of 0. The first set of coefficients has been estimated using all the control variables in the set of observables; the second set of coefficients exludes the education of the head of household and the mean education level of the other group.

household and of the other group) are excluded, as it is unlikely that unobserved factors would be as correlated with the education of members from the same group as these variables, so they do not contain useful information about the relationship of unobservables to the treatment. In both sets of results the maximum possible _R_<sup>2</sup> is assumed to be 1, which renders the results conservative. What the table shows is that the relationship between unobserved factors and education of the group needs to be quite high— between 0.71 and 22.71 times the relation between observed factors and education of the group—to fully drive the correlation between education of the group and productivity. Most of the coefficients are higher than 1, which, as suggested by Oster (2017), can be interpreted as an indication that the true effect is nonzero. All the coefficients that are lower than 1 are estimated using the education variables in the set of controls, which, as argued earlier is very conservative as the unobserved characteristics are unlikely to be as correlated with the education of the group as these two variables. Moreover, the coefficients have been computed under the assumption that the variance in the productivity can be fully explained if the unobservables could be controlled for ( _R_<sup>2</sup
SEE Azerbaijan Poland Serbia and Mont. Slovak Rep. Czech Rep. Turkey Macedonia, FYR ECA Croatia CIS Moldova Slovenia Georgia CEE Bulgaria Uzbekistan Russia Kyrgyz Rep. Kazakhstan Europe Lithuania Armenia Latvia Romania US Estonia<br>Romania Georgia Turkey Kyrgyz Rep. Kazakhstan Armenia CIS Belarus Russia Moldova Ukraine Lithuania Croatia Slovak Rep. ECA Azerbaijan CEE SEE Czech Rep. Bulgaria Poland Hungary Estonia Macedonia, FYR Slovenia Latvia Europe<br><!-- End of picture text --> Source: World Bank, Cost of Doing Business Database (2003); and Bank staff calculations Figure 4.2: _Good access to finance is one of the main contributors to a good employment performance_ Source: World Bank, World Development Indicators (2002); IMF WP/99/54; and Bank staff calculations.

36
planations. In this section, we discuss and examine the extent to which these alternative factors could be driving our results. Importantly, the fact that we use community-level data in the main analysis makes it possible to account for competing explanations that operate at this level of aggregation. This would be unfeasible with individual-level data: since black people are highly concentrated in space, any factor influencing community-level income that is correlated with employment in mining might confound individual-level estimates; and since individual-level data lack precise geographic identifiers, these factors could not be accounted for in the estimation.

# **7.1 Access to infrastructure**

The period of analysis witnessed numerous government interventions aimed at addressing the historically uneven access to infrastructure. In line with historical accounts, Figure 8 documents large initial gaps between communities just-inside and just-outside the former homelands with regard to access to the electric grid, refuse, and piped water. However, this figure also documents considerable convergence in access and use of these infrastructure items over the period of analysis. If improvements in access to infrastructure are correlated with initial employment in the mining industry, our estimates would be biased.

In order to account for these factors we include the respective infrastructure variables for the years 1996 and 2011, both interacted with the former homeland indicator.<sup>26</sup> Table 6 presents the results, where from column 1 to 3 we include each infrastructure variable separately, and in column 4 we include all of them together (though these are not reported to avoid clutter). The corresponding estimates for the coefficients of those variables as well as our initial coefficients of interest are reported. Note that when accounting for changes in access to infrastructure, the point estimates for our coefficients of interest do not change substantially relative to our original estimates.

When considering the estimates for the correlation between our infrastructure variables and income growth, we find that our results are consistent with the existing evidence. These estimates have to be interpreted with caution since there may be other unobserved heterogeneity related to the
in groundwater**

We use the groundwater arsenic (As) data set from the national-scale survey of groundwater in Bangladesh (National Hydrochemical Survey) that was conducted jointly by the British Geological Survey (BGS) and the Department of Public Health Engineering (DPHE) (BGS and DPHE, 2001). According to the national hydrochemical survey, nearly 90% of these surveyed wells have an intake depth of <150 mbgl and are considered shallow wells (see Supplementary Texts for further details). Approximately 25% of these shallow tubewells were drawing groundwater with As concentrations >50 μg/L (Bangladesh drinking water standard); nearly 4
|1.78|2857|1.94|2281|1.94|1454|1.48||0.35|
|2000|2190|1214|1.77|2847|1.93|2264|1.92|1462|1.47||0.34|
|2005|2208|1224|1.77|2847|1.93|2260|1.91|1468|1.47||0.32|
|2012|2199|1254|1.73|2776|1.87|2196|1.83|1452|1.42||0.28|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, 3 and 4, and National Sample Survey rounds 38, 43, 50, 55, 61, 66, and 68. _Note_ : TEE is Total Energy Expenditure, REE is Resting Energy Expenditure, and AL is activity level. Columns 1-3 report population means (using sampling weights). Columns 4-9 are population means for subsets of the population. Column 10 is the share of the children aged 14 and under in the population. The reported years correspond to the last year covered by National Sample Survey “thick” survey rounds (38, 43, 50, 55, 61, 66, 68).

31
tructure improvement, provision of school materials, teacher training, and school-based management (Garcia-Moreno, Gertler, & Patrinos, 2020; Murnane, Willet, & Cardenas, 2006; Santibanez, Abreu-Lastra, & O’Donoghue, 2014; Skoufias & Shapiro, 2006). Our study focuses specifically on the parental involvement component (Apoyo a la Gestión Escolar). A previous study of AGE used quasiexperimental methods to estimate the effect of the program (Gertler, Patrinos, & Rubio-Codina, 2012). This paper uses data from two randomized control trials conducted by the government of Mexico to understand the effectiveness of different program components – information, grants at the extensive margin, and grants at the intensive margin – in improving parental involvement in schools.

The rest of the paper proceeds as follows. In Section 2, we present relevant background details of the study setting and the experimental design. We describe our data sources in Section 3, introduce our conceptual framework in Section 4, and present our empirical strategy in Section 5. In Section 6, we present results on the effect of each intervention on parental involvement in school, parenting behavior at home, teacher behavior in school, and children’s educational outcomes. In Section 7, we explore trust between parents and teachers as a mechanism for understanding the effectiveness of parental involvement programs. Section 8 concludes.

# **2 Background and Study Design**

In 1996, the Government of Mexico established _Apoyo a la Gestión Escolar_ (AGE), a parental involvement program targeting parents of children enrolled in primary schools. The program has two key components: (1) financial grants to parent associations and (2) information provision to parents through parent associations. The grant is provided annually, ranging from USD 500 to 700 depending on the size of the student population. Parent associations can decide how to use these funds for school infrastructure, supplies, and activities. These funds are not permitted to be used towards increasing teacher or principal salaries.<sup>2</sup
in 1984 to 26.5 million in 1994 and further to 33 million in 2005—and is projected to increase to 81.9 million by 2030. The industrial sector has much potential to create the many jobs that will be required in the coming years. But the descriptive statistics suggest that many cites attract capital-intensive firms, and that there is scope to increase the contribution of labor-intensive manufacturing. The move towards capital-intensive manufacturing could be a function of whether Ethiopian workers have the right skills – whether acquired through traditional academic and/or vocational education. The right mix between the two is subject to debate – for instance, Biavaschi et al (2012) argue that school education is the foundation and that vocational education and training is an add-on component only. Shaorshadze (2012) argues that Technical and Vocational Education and Training (TVET) in Ethiopia has been mostly supply driven. Figure 16 presents shows that the cities with high levels of school education also have high levels of vocational education. This very likely because in the both are provided by the public sector. This is unlike in successful countries such as Germany, where vocational education is supported by the private sector and is complementary to traditional schoolbased education. In a developing country like Ethiopia, given that policy makers might have to make cost-based choices, research (Tsang 1997) indicates that secondary vocational education is more expensive than secondary academic education, indicating a larger role for private sector involvement.<sup>8</sup>

**Figure 17: School education versus vocational education**

<!-- Start of picture text --> 80 8.0<br>70 7.5<br>7.0<br>60<br>6.5<br>50 6.0<br>40 5.5<br>30 5.0<br>4.5<br>20<br>4.0<br>10 3.5<br>0 3.0<br