rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: _Growth and initialfinancial depth, 1960-1989_

||Per Capita<br>Growth, 1960-1989|Per Capita Capital<br>Growth, 1960-1989|Productivity<br>Growth, 1960-1989|
|---|---|---|---|
|Constant|0.035***|0.002|0.034***|
||[0.001]|[0.682]|[0.001]|
|Log (Real GDP per|-0.016***|-0.004*|-0.015***|
|person in 1960)|[0.001]|[0.068]|[0.001]|
|Log (Secondary school<br>|0.013***|0.007***|0.011***|
|enrollment in 1960)|[0.001]|[0.001]|[0.001]|
|Government|0.07*|0.049*|0.056*|
|consumption/GDP in 1960|[0.051]|[0.064]|[0.076]|
|Inflation in 1960|0.037<br>[0.239]|0.02<br>[0.238]|0.029<br>[0.292]|
|(Imports plus Exports)/GDP|-0.003|-0.001|-0.003|
|in 1960|[0.604]|[0.767]|[0.603]|
|DEPTH (liquid liabilities)|0.028***|0.019***|0.022***|
|in 1960|[0.001]|[0.001]|[0.001]|
|R-Squared|0.61|0.63|0.58|

[P-values in brackers]

* significant at the 0.10 level, ** significant at the 0.05 level, *** significant at the 0.01 level. Observations = 57

_Source: King and Levine (1 993b)_
# **C Robustness Tests**

Table C.1. Impacts of Refugee Camps on Local Economic Development Restricting Sample to Closer and Further Grids Relative to Camps

||Night Lig|ht Density|Defores|tation|
|---|---|---|---|---|
||[0 to 63]|Sin*|Ha/_Km_<sup>2</sup>|Sin*|
||(1)|(2)|(3)|(4)|
|_A. Main Specifcation_|||||
|I(SID)_×_I(Post 2017)|0.676***|0.470***|0.003***|0.003***|
||(0.006)|(0.002)|(0.000)|(0.000)|
|FDR q-values|[0.001]|[0.001]|[0.001]|[0.001]|
|Observations|0.679|0.735|0.129|0.131|
|R-squared|35,880|35,880|26,312|26,312|
|Mean Dep. Variable|0.732|0.509|0.002|0.002|
|_Controls (All Panels)_|||||
|Grid FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the results of the main specification outlined in Equation 1 , restricting the sample to grids that are either closer to or further from the camps. The sample includes the 25% of grids that are closest to the camps and the 25% that are furthest from the camps. I(SID) represents an indicator variable equal to 1 if the grid is closer to the camp, and 0 if it is furthest. I(Post2017) is an indicator variable with a value of 1 for post-2017, marking the onset of substantial Rohingya inflows to Bangladesh. Dependent variables include: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Night light density Sin*: This variable is the inverse hyperbolic sine transformation of the original night light density variable, interpretable as a logarithm transformation. (iii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iv) Deforestation (Sin*): This variable represents the inverse hyperbolic sine transformation of the original deforestation variable, also interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid level in parentheses. False Discovery Rate (FDR) q-values are reported in brackets. Significance levels are denoted as follows:<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

61
<!-- Start of picture text --> 54%<br>88 of 90<br>53 of 98<br>studies<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4<br>Region in which Intervention Conducted<br>(n=188)<br>Africa<br>2% 6%<br>Asia<br>9%<br>2% Europe<br>6% Latin America<br>USA<br>Other<br>75%<br><!-- End of picture text --> There is a wide range of financial topics covered by financial education interventions in the literature which were grouped into six categories in this paper (Figure 5). Interventions which provide information on a variety of financial issues, coded as “mixed” interventions in the data, are the most common type and represent more than 40% of the total sample. Financial literacy programs in the U.S. are most likely to involve either multiple messages (mixed) or focus on savings and retirement. In developing countries 13
# Figure 5. International trade costs relative to domestic trade costs

A. Average trade costs in 1995 and 2019 <!-- Start of picture text --> B. Average trade costs in EMDE regions<br>Percent 1995 2019<br>250<br>200<br>150<br>100<br>50<br>0<br>EAP ECA LAC MNA SAR SSA<br>D. Average trade costs for agriculture for<br>EMDE regions in 1995 and 2019<br>Percent 1995 2019<br>400<br>300<br>200<br>100<br>0<br>EAP ECA LAC MNA SAR SSA<br>F. Average trade costs for EMDE regions for<br>manufacturing in 1995 and 2019<br><!-- End of picture text --> <!-- Start of picture text --> Percent 1995 2019<br>200<br>150<br>100<br>50<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> C. Average trade costs for agriculture in 1995 and 2019 <!-- Start of picture text --> Percent 1995 2019 Percent 1995 2019<br>300 400<br>250<br>300<br>200<br>150 200<br>100<br>100<br>50<br>0 0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br>E. Average trade costs for manufacturing in F. Average trade costs for EMDE regions for<br>1995 and 2019 manufacturing in 1995 and 2019<br>Percent 1995 2019 Percent 1995 2019<br>200 250<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0 0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br>s<br><!-- End of picture text --> Sources: Comtrade (database); ESCAP-World Bank Trade Costs Database; World Bank; World Trade Organization.

Note: EMDEs = emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Bilateral trade costs (as defined in the UNESCAP/World Bank database) measure the costs of a good traded internationally in excess of the same good traded domestically and are expressed as ad valorem tariff equivalent. Bilateral trade costs are aggregated into individual country measures using 2018 bilateral country exports shares from the Comtrade database. Regional and sectoral aggregates are averages of individual country measures. Bars show unweighted averages, whiskers show interquartile ranges. Sample in 1995 includes 33 advanced economies and 46 EMDEs (4 in EAP, 8 in ECA, 15 in LAC, 4 in MNA, 2 in SAR, and 13 in SSA). Sample in 2019 includes 23 advanced economies and 53 EMDEs (9 in EAP, 12 in ECA, 16 in LAC, 4 in MNA, 2 in SAR, and 10 in SSA).
and follow-up support. On average, managed PD was found to increase student test scores by 2.5 times (0.052 standard deviations) as much as general PD, and was at least as effective as the combined average of all school-based interventions. However, the analysis is based on relatively few studies, with just seven general PD studies and two managed PD studies. Nonetheless, this finding in support of specific and practical teacher training aligns with that of Walter & Briggs (2012) who, in a review of 35 evidence-based studies of teacher PD, found that concrete and classroom-based programs make the most difference to teachers.

Another U.S.-focused review found that professional development programs with significant contact hours (between 30 and 100 in total) over the course of six to twelve months were more effective at raising student test scores (Yoon, Duncan, Lee, Scarloss, & Shapley, 2007). But this review also draws on few strong studies: of the 1,300 studies included in this review only nine were identified as having pre- and post-test data and some sort of control group. Similarly, a 2014 review of professional development in mathematics found more than 600 studies of math PD interventions, but only 32 used any research design to measure effectiveness, and only five of those were high-quality randomized trials. As the authors concluded, “The limited research on effectiveness means that schools and districts cannot use evidence of effectiveness alone to narrow their choice” (Gersten, Taylor, Keys, Rolfhus, & Newman-Gonchar, 2014). As such, we look also to a wider range of evidence. For example, one recent US review which includes qualitative as well as quantitative studies concludes that teacher training is most effective when it focuses on “concrete tasks of teaching, assessment, observation and reflection” instead of abstract teaching concepts. Characteristics of effective programs included that (a) they were not a “one-shot workshop” but rather embedded in the curriculum, and (b) they were also “sustained” and “intense” (Darling-Hammond, Wei, Andree, Richardson, & Orphanos, 2009).

Much evidence from other high-income countries is also more qualitative. Narrative 7
<!-- Start of picture text --> —e— MENA - HIC Electronic<br>documents<br>=®=HIC (minus MENA) ef,<br>Intermediary <— Electronic<br>liability ¥ ¢ » | signature<br>'<br>1<br>VOa 1<br>iawss<br>: Online consumer<br>Cybersecurity ry ~*~ .<br>s, \7 protection<br>Se 7<br>S. ,Z<br>Data privacy<br><!-- End of picture text -->

<!-- Start of picture text --> —e— MENA - MIC Electronic<br>-©=MIC(minus MENA) 20cuments<br>Intermediary wR.SiSic Electronic<br>liability s? signature<br>. Online consumer<br>Cybersecurity .<br>protection<br>Data privacy<br><!-- End of picture text -->
and 2012, where there is a benchmark from comprehensive, spatially disaggregated price surveys, an important opportunity for assessing how well such “no-price” methods work in practice.

Our results show that spatial deflators and spatially disaggregated estimates of temporal inflation derived when the food Engel curve method is applied in Vietnam in 2010 and 2012 are poor proxies for the deflators obtained from two benchmark price indexes that rely on spatially disaggregated prices. Based on these benchmarks, substantial distortion in estimates of the level, location, and change in poverty and inequality would occur if Engel method deflators were used in Vietnam. This scope for potentially wrong inferences leads us to conclude that while Engel curve methods may be a useful tool, amongst several, for examining bias in temporal deflators, they are unlikely to proxy for the multilateral price indexes that would be calculated from spatially disaggregated price surveys. Even in the temporal context, a concern exists that the Engel curve method is recovering changes in the cost of living for an unknown household that could be anywhere in the income distribution. As such, deflators based on food Engel curves do not appear to provide reliable evidence needed to account for time-space differences in the cost of living, and there may be no substitute for large developing countries developing spatially disaggregated price surveys.

35
advancement in women's employment opportunities relative to men (Iregui-Bohórquez et al., 2021). In this challenging environment, women played a crucial role in the labor force by reaching new heights of participation and filling the gaps left by men affected by the war. Their increased participation demonstrated their resilience and underscored their contribution to sustaining the workforce during the cycles of violence.

**Figure 11: Effect of the male homicide rate on the female labor force participation rate**

<!-- Start of picture text --> 0.2<br>0.1<br>0.0<br>-0.1<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Male homicide rate<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

It is worth mentioning that at the beginning of the 2000s, the government, in response to the prevailing violence, deployed a strong military presence, although the impact of this militarization was complex and had mixed effects (Castro Torres and Urdinola, 2018). Since 2005, with the “Justice and Peace Program”<sup>11</sup> and later with the initiation of the peace agreement process in 2012, the male homicide rate decreased. There was a significant de-escalation compared to the violence experienced during the 1990s and 2000s. During this period the relationship between the male homicide rate and female labor participation continued to be positive although the confidence intervals widen, indicating increased uncertainty in this relationship.

# vi) **Female political participation**

Regarding the relationship between women's labor participation and their political participation, the overall findings suggest a complex and nuanced relationship. There are periods of positive and

> 11 The Justice and Peace Program was established by Law 975 of 2005. This program aimed to incentivize paramilitaries to demobilize and reintegrate into society. This law offered various incentives, including reduced sentences for individuals involved in human rights violations (Tribin, 2015).

20
a broader regional level (see Valente, 2014, 2015; Pivovarova and Swee, 2015; Akresh et al., 2012). Moreover, this study extends the literature on legacies of war by documenting the long-term eect of exposure to conict on women's nal adult height. Along with Akresh et al. (2018), which examines the impacts of the Biafran war using the variation in exposure to the war by ethnicity, this is among the rst papers to document the intergenerational transmission of the impact of early childhood conict exposure on second generation health.

Nepal experienced a decade-long violent civil conict between 1996 and 2006, which resulted in more than 13,000 fatalities, destroyed considerable infrastructure, hindered delivery of basic services, and generated pervasive and strong feelings of fear, insecurity, and stress among its citizens. I use the Informal Sector Service Center's (INSEC) records of conict victims to create a casualty-level data set with exact geographical locations (villages) and dates of incidents. I merge the villagelevel conict intensity data with the 2016 Nepal Demographic Health Survey (NDHS), which is a nationally representative survey of the female population aged 15 to 49. I limit the analytical sample to women who were either born before or who were in utero at the start of the war in February 1996 to assess the lasting impact of conict on height. In 2016, these women were old enough (20 to 49) to be sampled in the ever-married NDHS 2016 women sample and had documented survey responses about their children that can be used for second-generation analysis. Limiting the sample to women born before the conict's start date also reduces potential confoundedness through selective fertility and migration (further discussed in empirical strategy section 4). I rely on the biomedical literature that height development in human beings is characterized by rapid growth during the rst three years of life, followed by lower level of constant growth and then a secondary growth spurt during adolescence (Figure A.5), and classify women into three treatment cohorts, namely: ages 0 to 3, 4 to 8, and 9 to 15 at the start of the war in February 1996. While women in age cohort 0 to 3 would have been exposed to conict throughout their entire three stages of growth, the age cohorts 4 to 8 and 9 to 15 would have been exposed only in their latter stages of growth. I dene women ages 16 to 21 in 1996 as a comparison group because they would have passed their pubertal ages and attained full adult height by the time of the conict's start in 1996. I also include women ages 22 to 29 as a

> on women's employment (Menon and van der Meulen Rodgers, 2015). Libois (2016), on the other hand, using conict measurement at more detailed geographical area (distance from the conict sites) nds signicant negative immediate impact on consumption and income. Failure to capture the substantial conict heterogeneity across villages within district, thus, may explain the little or no impact of conict at the district level.

2
Policy Research Working Paper 5710

# **Abstract**

Can communal heterogeneity explain persistent educational inequities in developing countries? The paper uses a novel data-set from rural Pakistan that explicitly recognizes the geographic structure of villages and the social makeup of constituent hamlets to show that demand for schooling is sensitive to the allocation of schools across ethnically fragmented communities. The analysis focuses on two types of social barriers: stigma based on caste affiliation and female seclusion that is more rigidly enforced outside a girl’s own hamlet. Results indicate a substantial decrease in primary school enrollment rates for girls who have to cross hamlet boundaries to attend, irrespective of school distance, an effect not present for boys. However, low-caste children, both boys and girls, are deterred from enrolling when the most convenient school is in a hamlet dominated by high-caste households. In particular, low-caste girls, the most educationally disadvantaged group, benefit from improved school access only when the school is also casteconcordant. A policy experiment indicates that providing schools in low-caste dominant hamlets would increase overall enrollment by almost twice as much as a policy of placing a school in every unserved hamlet, and would do so at one-sixth of the cost.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group; and the Poverty Reduction and Equity, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hjacoby@worldbank.org and gmansuri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Cashin, P., K. Mohaddes, and M. Raissi. 2012. "The Global Impact of the Systemic Economies and MENA Business Cycles." Working Paper 12/255, International Monetary Fund, Washington, DC.

Chandy, L., and B. Seidel. 2016. "Donald Trump and the Future of Globalization." Brookings Institution Up Front, November 18.

Chahrour, R., S. Schmitt-Grohé, and M. Uribe. 2012. "A Model-Based Evaluation of the Debate on the Size of the Tax Multiplier." _American Economic Journal: Economic Policy_ 4 (2): 28–45.

Chellaraj, G., Maskus, K. E. and A. Mattoo. 2008. "The Contribution of International Graduate Students to U.S. Innovation," _Review of International Economics_ 16 (3): 444–62.

Chow, J., F. Jaumotte, S. Park, and Y. Zhang. 2015. "Spillovers from Dollar Appreciation." Working Paper No. 15/02, International Monetary Fund, Washington, D.C.

Chui, M. K., I. Fender, and V. Sushko, 2014. "Risks Related to EME Corporate Balance Sheets: the Role of Leverage and Currency Mismatch." _BIS Quarterly Review_ 2014: 35-47. Council of Economic Advisors. 2015. Economic Report to the President. Washington, DC: Council of Economic Advisors.

Corsetti, G., A. Meier, and G. J. Müller. 2012. "Fiscal Stimulus with Spending Reversals." _The Review of Economics and Statistics_ 94 (4): 878–895.

De Grauwe, P., and Y. Ji. 2016. "International Correlation of Business Cycles in a Behavioural Macroeconomic Model." CEPR Discussion Paper 11257. Centre for Economic Policy and Research, London.

De Hoyos, R. E., and L. Iacovone. 2013. "Economic Performance under NAFTA: A FirmLevel Analysis of the Trade-Productivity Linkages." _World Development_ 44: 180-193.

Dées, S., and A. Saint Guilhem. 2009. "The Role of the United States in the Global Economy and Its Evolution over Time." Working Paper Series 1034, European Central Bank, Frankfurt. Dées, S., and I. Vansteenkiste. 2007. "The Transmission of US Cyclical Developments to the Rest of the World." Working Paper 798, European Central Bank, Frankfurt.

Desai, M. A., C. F. Foley, and J. R. Hines. 2009. "Domestic Effects of the Foreign Activities of US Multinationals." _American Economic Journal: Economic Policy_ 1 (1): 181-203.

Devereux, M. B., and S. Shi. 2013. "Vehicle Currency." International Economic Review 54 (1): 97-133.

Druck, P., N. E. Magud, and R. Mariscal. 2015. "Collateral Damage: Dollar Strength and Emerging Markets’ Growth" Working Paper 15/179, International Monetary Fund, Washington, DC.

28
17

**Figure 1**

<!-- Start of picture text --> MEXICO<br>Composition of Public Expenditures<br>50.00%<br>45.00%<br>40.00%<br>35.0<br>g30.00% 4,,0<br>'625.00% p.<br>20.00%<br>15.00% ra rs<br>10.00%<br>5.00%<br>0.00%<br>le° 1411 Iq" {040§1 lqiL le:t l el l le lq 141 iqq lq" 41 l ql 10 I0<br><!-- End of picture text --> Source: Banco de Mexico, Indicadores Econ6micos, various issues.

Fortunately, the constraints facing Mexico in this regard are not quite as paralyzing as they may seem at first sight. For a start, it has to be kept in mind that the preceding parameter estimates reflect average behavioral relationships across countries and as they pertained over a particular period in time. To the extent that Mexico can improve the quality of its public investments, both relative to the cross-country average and relative to its own past performance, there is hope that its public investment program could make a stronger contribution to growth. In this context, an improvement in quality means paying closer attention to the choice of public investments so that it complements private economic activity (thereby resulting in less crowding out and more crowding in of private investment) and results in a more efficient use of public capital (thereby raising its rate of return). Stated differently, if public investments are carried out in the same manner as before, then its impact on growth is anticipated to remain very limited. Only by improving the process of identifying and preparing investment projects, with more attention to social rates of return, can a more significant growth impact be achieved.

# F. **Concluding Summary**

The preceding analysis provides limited support to the claim that the decline in public investment since the 1 970s contributed in some measure to the decline in Mexico's total factor productivity growth. However, it also suggests that increases in public investment will not automatically translate into faster output and productivity growth. One reason why a positive relationship between more public investment and faster growth cannot be taken for granted is the high crowding out effect that appears to have characterized public investments in the past. Another is that the positive relationship between public investment and productivity growth appears to have