rafmacalaba/datause-encoder-data huggingface dataset:
Table 8 System Estimation of Two Equations:Three Stage Least Square"

|Independent Variable: Growth||Independent Variable: Government Size||
|---|---|---|---|
|Dependent variables||Dependent variables||
|Constant|0.159***|Constant|-1.333***|
||(0.026)||(0.422)|
|Dummy for 1974-83|-0.015***<br>(0.003)|Dummy for 1974-83|0.036<br>(0.041)|
|Dummy for 1984-93|-0.017***<br>(0.004)|Dummy for 1984-93|0.089**<br>(0.043)|
|Initial Per Capita GDP|-0.019***<br>(0.003)|Per capita GDP|0.066<br>_(0.050)_|
|Initial Schooling|<br>0.003***<br>(0.002)|Age Dependency Ratio|0.318***<br>(0.111)|
|Population Growth|-0.335*"*<br>(0.165)|Urbanization Ratio|-0.002<br>(0.050)|
|Investment/GDP|0.006*'*<br>(0.003)|Relative Price of Government<br>Consumption|-0.697***<br>(0.064)|
|Government Size|-0.021***<br>(0.008)|Openness|0.136***<br>(0.041)|
|Quality of Bureaucracy|_0.056**_|Terms of Trade Changes|-0.851|
|(0 worst, I best)|(0.029)||(0.906)|
|Government Size * Quality of<br>Bureaucracy|0.022|Openness * Terms of Trade Changes|-1.422|
||(0.016)||_(1.015)_|
|Policy Distortion|-0.006***<br>(0.002)|Years Since Independence|-0.164***<br>(0.058)|
|Terms of Trade Changes|0.064*|Political Freedom|-0.012|
||(0.036)|(I high, 7 low)|(0.014)|
|Latin America Dummy|-0.016***<br>(0.003)|Federal Dummy|-0.073<br>(0.053)|
|Sub-Saharan Africa Dummy|-0.030***<br>(0.005)|Latin America Dummy|-0.199***<br>(0.043)|
|Socialist Dummy|<br>-0.015***|East Asia Dummy|-0.339***|
||(0.005)||(0.067)|
|Number of Observations|257||257|
|System R-squaredb|0.5581|||

*** significant at 1% level ** significant at 5% level * significant at 10% level

*Correlation among the error terms of the two equations is 0.02. The numbers in parentheses denote standard errors. The instruments are initial per capita GDP, initial per capita GDP squared and previous five year lagged averages of the following endogenous variables: investment/GDP; govemment size; govemment size * bureaucracy; policy distortion; per capita GDP; relative price of government consumption; openness; openness * terms of trade changes; and political freedom.

b System R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

43
point when polygyny in the communes in which they live rises by one standard deviation (Table 10). This result is consistent with Corno et al. (2020) who find that drought increases child marriage in bride-price countries. As Mali is a bride-price country Corno et al. (2020), polygyny can enable households to have many daughters, whose marriage can become a source of income during hard times. Indeed, our sample’s descriptive statistics show that polygynous households in Mali have nearly twice as many daughters aged 12 ́ 17 as monogamous households (Table 2), putting girls in polygynous households at further risk of lower human capital and individual agency.

# **6 Conclusion**

This paper extends the literature on risk coping in village economies by analyzing polygyny’s resilience effect in the aftermath of a drought shock. We study the interacting effect of polygyny—a marriage practice known to undermine development outcomes— and drought episodes, both current and lagged, in the context of rural Mali—a droughtprone country located on the polygyny belt in the West African Sahel region. For identification, we rely on the plausibly exogenous spatial and temporal variation in drought occurrences across rural Mali communes, accounting for potential serial correlation in our drought indicator and many commune-level as well as time-variant household-level confounders. We use the Standardized Precipitation-Evapotranspiration Index (SPEI) to construct our drought indicator for the rainy season, enabling us to control for a potential spatial correlation of drought episodes due to possible omitted weather variables.

We link the identified resilience effect of polygyny to its positive impact on household fertility documented in the literature. Because polygyny raises fertility, it enables households in drought-prone rural communities to harness the size and composition of the family workforce to their resilience strategies in the context of imperfect rural labor markets. In that context, having a large family workforce, well-balanced in age and gender, enables households to leverage diversification of crop production and income sources as resilience strategies to the occurrence of drought shocks. Furthermore, in bride-price countries like Mali, polygyny also enables a household to have many daughters, whose 32
# Prhnary Commodity Prices and Macroeconomic Variables: A Long-Run Relationship

by Theodosios Palaskas and Panos Varangis Table of Contents

|1.|Introduction|3|
|---|---|---|
|II.|Long Run Relationships: Tests|5|
|III.|Integration and Co-integration Test Results|9|
||(a)<br>Integration Test|9|
||(b)<br>Co-integration Test Results|12|
|IV.|Error Correction Model|15|
|V.|Causality-Feedback<br>Tests|30|
|Conc|lusions|38|
|Anne|x A Actual Commodity Price Changes, Forecasts and<br>Error Bars (Based on Estimations of Table 5)|40|
|Anne|x B: Actual Commodity Price Change Forecasts and<br>Error Bars (Based on Estimations of Table 9)|46|
|Refer|ences|52|
Controlling for legal origin shows that the presence of a FOI law is correlated with good governance through mostly three measures in the KKZ: voice and accountability, and regulatory burden and sometimes with graft and government effectiveness as well (see Tables 14a-b).

# **Table 14a. FOIA, Legal Origin, and Governance**

||_DATASET:_|_KAUFMANN,KR_|_AAYANDZOI_|_DO-LOBATO_|_N_||
|---|---|---|---|---|---|---|
||How authoritie<br>r|sare selectedand<br>laced|Capacityof <br>implementso|the stateto<br>und policies|Respectofthe<br>citizensfo|stateandthe<br>r therules|
||Voice and<br>Accountability|PoliticalInstability<br>andViolence|Governnent<br>Effectiveness|Regulatory<br>Burden|Ruleof<br>Law|Graft|
|SocialistLegalCode|-.33**|-.02|-.60**|-.61***|-.48**|-.79|
||.(-2.0)|(,.13|(4.08)|(-3.75)|(-3.36)|(-5.82)|
|French LegalCode|.01|-.12|- -.07|-.10|-.28|-.33*|
||(-1l)|~<br>(-89)|(-.60)|(-1.05)|(-2.64)|(-2.86)|
|GernanLegalCode|.48*|.30|.22|-.30|.44**|.02|
||(1.74)|(1.14)|(.68)|(-1.50)|(2.33)|(.07)|
|Scandinavian LegalCode|*54***|.50***|.59***|-.09|.48***|.75***|
||(3.04)|(3.59)|(4.21)|(-.72)|(3.86)|_(5.05)_|
|Income|.38***|.54***|.47***|.36***|.57***|_.51***_|
||(5.47)|(8.26)|(7.62)|(7.41)|(10.9)|(8.6)|
|FOIA|.55*|.06|.31**|.24**|.10|.26*|
||(3.71)|(.52)|(2.34)|(2.55)|(.92)|(1.92)|
|Constant|-3.20***|.<br>4,54***|-3.93***|-2.82**|4.5**|4.06*|
||(-5.77)|(-7.93)|(-7.73)|(-6.81)|(-10.31)|(-8.93)|
|#obs|137|124|125|131|131|124|
|R'|.52|.53|.62|_.50_|.69|.69|

**Table 14b. FOIA, Legal Origin, and Governance**

_*DATA SET: ICRG, Averaged over the period 1984-1997_ <!-- Start of picture text --> ICRG Corruption Rule of Bureaucracy Contract Expropriation<br>._______ _______ Law Quality Repudiation Risk<br>Socialist Legal Code .61 .90** 1.34*** .17 .48 .86*<br>-(1.64) (2.29) (4.77) . (.4) (1.12) (2.76)<br>French Legal Code -.28 -.08 -.23 -.56** -.18 -.31*<br>(-1.51) (-.34) (-.92, (-2.3) (-99) (-1.86)<br>German Legal Code 1.12*** 1.05* 1.19** 1.30*** 1.27*** .88***<br>(3.42) (1.85) (2.32) (3.19) (6.04) (46<br>Scandinavian Legal Code .96** 1.60*** 1.30*** .99*** .71*** .53**<br>(3.98) (5.09) (4.5) (3.59) (2.8) (2.56)<br>Income .58V** .55*** .84* * .68*** .57*** .520*<br>(4.39) (3.28) (5.36) (4.21) (4.35) (4.91)<br>FOIA .79*** .95*** .67** 1.00*** .70*** .56**<br>(3.59) (3.39) (2.26) (3.5) (3.32) (2.97)<br>Constant -.83 -1.24 -3.17*** -1.92 -.53 .30<br>____________(-.88) (-1.01) (-2.82) (-1.64) (-.58) (.39)<br>#obs 94 94 94 94 94 94<br>- R 2 .62 .5 .58 .59 .59 .57<br>*indicates signifzcance at.) level, ** at .05 level and *** at .01 level.<br><!-- End of picture text --> These results hold under various permutations of the relationship; such as when state ownership of the media, various continent dummies and transparency are added. Controlling for 33
Policy Research Working Paper 10663

# **Abstract**

Closing the gender digital divide by ensuring equal access to and benefit of the internet may reduce economic inequalities and close the gender gap in employment by providing new economic opportunities and facilitating access to market information. This paper estimates the impact of digital inclusion, measured by the Inclusive Internet Index on the female-to-male labor force participation ratio, while controlling for other economic and social factors. Using data from the World Development Indicators, the Economist Intelligence Unit database, and the World Bank’s Women, Business and the Law database for 13 countries in the Middle East and North Africa region for four years (2018 to 2021), a pooled cross section dataset is constructed. The model is estimated using generalized least squares to control for heteroskedasticity. The results show that an inclusive internet environment would reduce the gender gap in the labor force. Other key drivers include the structure of the economic growth, norms, and gender roles in the society. These results are relevant for the United Nations Sustainable Development Goals agenda, mainly goals 5 and 10.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at racha.ramadan@feps.edu.eg.

_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
variation (or heterogeneity) in standard errors for the entire sample of impact evaluations. This is not surprising given that 90 percent of the impact evaluations were RCTs. To the contrary, the variation in correlations of outcome effects (pcorr) is generally low (or homogenous) across subgroups and especially so for sanitation and water supply.

Smaller sample sizes may overestimate effects while larger sample sizes increase sensitivity; thus, meta‐ analysis generally improves statistical power by increasing sample size. There is no correlation between sample size and outcome effects in the publications included in this review (Figure A4 in Appendix A), suggesting that studies with smaller sample sizes are not exerting undue influence on overall effects, and that the minimum requirements for internal and external validity have been met.

# **4.2 Bias**

Evidence of potential regional bias was detected in two areas, North America and South Asia (Table 2). South Asia represented the largest subset of studies; however, the estimate was imprecise and the standard error relatively high and therefore unlikely to have exerted significant bias on the pooled outcomes. On the contrary, the subset of studies from North America was limited to six well‐designed evaluations that estimated the effects of WASH interventions on diarrhea, and these may have disproportionately influenced the pooled outcomes (Table 2).

**Table 2. Heterogeneity Estimates by Region**

||East Asia<br>and Pacific|Europe<br>and<br>Central<br>Asia|Latin<br>America<br>and the<br>Caribbean|Middle<br>East and<br>North<br>Africa|North<br>America|South Asia|Sub‐<br>Saharan<br>Africa|
|---|---|---|---|---|---|---|---|
|Slope||||||||
|(Precision)|2.986***|0.660|2.904***|1.796**|1.487***|0.975|3.053***|
||(0.966)|(0.472)|(0.935)|(0.882)|(0.312)|(2.068)|(0.992)|
|Bias|‐0.245|‐0.149|‐0.0111|‐0.171|‐0.256***|1.222**|0.134|
||(0.190)|(0.0899)|(0.222)|(0.159)|(0.0598)|(0.536)|(0.265)|

_Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database. _Note:_ Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

Finally, Figure A5 (in Appendix A) illustrates evidence of comparability between publications given the number of views per publication and limited outliers. The size of the circles indicates the number of views and the closer and more overlapped the circles, the higher their comparability.

# **4.3 Effects Trends**

At least two different studies per outcome are needed to perform a meta‐analysis. Thus, only the results for 13 of the 21 outcome indicators were included in the meta‐analysis. Table 3 lists the estimated effects and types of interventions for the remaining eight outcomes. In terms of risk differences, studies assessing access to sewerage and incidence of shigellosis infection showed the highest effects, +0.20 and ‐0.22, respectively. Among risk ratios, behavior change and child weight gain were the only statistically significant outcome effects with a reported mean higher than 1. One study measuring cholera reduction showed the third‐highest risk ratio coefficient of 0.48, which was statistically significant.

12
# **Table of Content**

|1. PREMISES ..................................................................................................................... 4|
|---|
|Initial conditions ......................................................................................................... 4|
|Institutional transition and transitional reforms.......................................................... 6|
|Growth and the labor market ...................................................................................... 8|
|2. GROWTH IN SEGMENTED LABOR MARKETS.................................................... 13|
|The 'dual' labor market.............................................................................................. 13|
|Dual performance...................................................................................................... 13|
|3. GROWTH CONTRAINTS IN THE FORMAL SECTOR........................................... 17|
|Economic barriers..................................................................................................... 18|
|Financial barriers ...................................................................................................... 19|
|Institutional barriers.................................................................................................. 20|
|Internal barriers to enterprises .................................................................................. 21|
|Multivariate analysis................................................................................................. 22|
|4. FROM SUBSISTENCE TO GROWTH IN THE SMALL INFORMAL SECTOR.... 24|
|The rise of self-employment ..................................................................................... 25|
|Informality ................................................................................................................ 26|
|Explaining labor market flows.................................................................................. 28|
|5. JOB CREATION POLICIES IN SEGMENTED LOW INCOME COUNTRIES....... 32|
|References......................................................................................................................... 37|
|ANNEX - Regression Tables............................................................................................ 39|

3
|(9)<br>(10)|-0.138_∗∗∗_<br>-0.021<br>(0.0100)<br>(0.0273)|-0.347_∗∗∗_<br>-0.123<br>(0.0814)<br>(0.2507)|0.070_∗∗∗_<br>-0.025<br>(0.0229)<br>(0.0634)|X<br>X|133<br>133|0.27<br>0.25|112954<br>112954|4<br>5|ucated, as well as 3 digit|etherlands, Poland, Spain,|e owns at least 20% equity|y frms without any|ions but with a 40% equity|h the same last name as the|n 20% equity has a manager||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(8)|-0.132_∗∗∗_<br>(0.0087)|-0.492_∗∗∗_<br>(0.0982)|0.096_∗∗∗_<br>(0.0211)|X|133|0.15|112954|3|t is college ed|many, Italy, N|that last nam|tions, but onl|main specifcat|managers wit|with more tha||
|(7)|-0.069_∗∗∗_<br>(0.0233)|0.149<br>(0.2541)|-0.100<br>(0.0686)|X|126|0.11|61228|2|pulation tha|Finland, Ger|gement and|ain specifca|that in the|the share of|gest owner||
|(6)|-0.137_∗∗∗_<br>(0.0090)|-0.511_∗∗∗_<br>(0.1058)|0.101_∗∗∗_<br>(0.0218)|X|133|0.17|112954|1|a, share of po|vina, Estonia,|d under mana|that in the m|analogous to|re defned as|s FM if the lar||
|(1)<br>(2)<br>(3)<br>(4)<br>(5)|-0.098_∗∗∗_<br>-0.101_∗∗∗_<br>-0.096_∗∗∗_<br>-0.111_∗∗∗_<br>-0.030_∗∗∗_<br>(0.0033)<br>(0.0082)<br>(0.0031)<br>(0.0035)<br>(0.0098)|-0.159_∗∗_<br>-0.169_∗_<br>-0.158_∗∗∗_<br>-0.104_∗∗_<br>-0.211_∗_<br>(0.0653)<br>(0.0942)<br>(0.0601)<br>(0.0414)<br>(0.1054)||X<br>X<br>X<br>X<br>X|133<br>126<br>133<br>133<br>133|0.17<br>0.11<br>0.15<br>0.27<br>0.25|112954<br>61228<br>112954<br>112954<br>112954|1<br>2<br>3<br>4<br>5|ations include controls for log frm age, regional log gdp per capit|xed effects. Full sample consists of Bulgaria, Bosnia and Herzogo|ine. In specifcation 1, a frm is defned as FM if one family is liste|shareholders. In specifcation 2, the FM defnition is analogous to|a shareholder are included. In specifcation 3, the FM defnition is|ip (rather than 20%). In specifcation 4, FM is a continuous measu|r with more than 20% equity. In specifcation 5, a frm is defned a|ring in their title.|
|Dependent: FM|ln_L_|Trust|Trust_×_ln_L_|Controls|No. Clusters|_R_2|_N_|Specifcation|Notes: All specifc|SIC and country f|Switzerland, Ukra|held by individual|company listed as|cutoff for ownersh|largest shareholde|with a CEO-like st|

36
_Latin America and the Caribbean._ Development in the Americas, Washington DC: IADB. Khan, H. (2016). The Linkage Between Political Trust and the Quality of Government: An Analysis. _International Journal of Public Administration_ ,39(9), 665-675.

Klein, G.R. & Regan, P.M. (2018). Dynamics of political protests. _International Organization,_ 72(2), 485–521.

Klein, G., Cuesta, J. & Chagalj, C. (2021). The Nicaragua Protest Crisis in 2018–2019: Assessing the Logic of Government Responses to Protests. _Journal of Politics in Latin America_ , 14(1), 5583.

Kumagai, S. & Iorio, F. (2020). Building Trust in Government through Citizen Engagement. Washington, DC: World Bank.

Latinobarometro Corporation (2021) Latinobarómetro Report 2021: Goodbye to Macondo. Latinobarometro, Santiago de Chile.

Lavezzolo, S., & Ramiro, L. (2018). Stealth democracy and the support for new and challenger parties. _European Political Science Review_ , 10(2), 267-289.

Le Cessie, S. & Van Houwelingen, J. C. (1992). ridge estimators in logistic regression. _Journal of the Royal Statistical Society: Series C (Applied Statistics)_ , 41(1), 191-201.

Lee, Y. (2021). Government for Leaving No One Behind: Social Equity in Public Administration and Trust in Government. SAGE Open.

Lee, Y. & Schachter, H. (2019). Exploring the Relationship between Trust in Government and Citizen Participation. _International Journal of Public Administration_ , 42:5, 405-416.

Levi, M. & Stoker, L. (2000). Political trust and trustworthiness. _Annual Review of Political Science_ , 3(1), 475–507.

Mattes, R., & Moreno, A. (2018). Social and political trust in developing countries: sub-Saharan Africa and Latin America. In E. M. Uslaner (Ed.), Oxford Handbook of Social and Political Trust (pp. 357-382). Oxford University Press Muhlberger, P. (2018). Stealth Democracy: Authoritarianism and Democratic Deliberation. _Journal of Public Deliberation_ , 14(2).

Mullainathan, S. & Spiess, J. (2017). "Machine Learning: An Applied Econometric Approach." _Journal of Economic Perspectives_ , 31 (2): 87-106.

Nannicini, T., Stella, A., Tabellini, G. & Troiano, U. (2013). Social capital and political accountability. _Am. Econ. J. Econ. Policy_ , 5, 222–250.

Neblo, M. A., Esterling, K. M., Kennedy, R. P., Lazer, D. M. J., & Sokhey, A. E. (2010). Who wants to deliberate—And why? _American Political Science Review_ , 104(3), 566–583.

Newman, B. J., Johnson, J., & Lown, P. L. (2014). The “Daily grind”: Work, commuting, and their impact on political participation. _American Politics Research_ , 42(1), 141–170 OECD (2018). Institutions to make the state deliver in Latin America and the Caribbean. In Latin American Economic Outlook 2018: Rethinking Institutions for Development, OECD Publishing, Paris, https://doi.org/10.1787/leo-2018-8-en.

29
50 context. How well the Cameron approach performs remains unclear, as the relevant reports omit validation exercises.

Deaton (1987). This study contains several models of interest to us, particularly because the applications are in a developing country setting. One model, based on information from a travel survey of 800,000 Indian people, explains trips per household by mode at the state rather than the individual level as a function of per capita household expenditure, an urban dummy, and household size. Aggregate analysis was necessary because data at the individual level were unavailable to the researcher. Of much interest to us, Deaton found that trips to work and school are 82.2% of all urban trips; and a sizable fraction of urban trips (34.8%) are on foot, with another 19.0% by bicycle.

Another model, applied to the city of Jaipur, India using a travel survey undertaken in 1983, is much more sophisticated. Deaton estimates directly the relationship between income and demographic variables on the one hand and travel time, distance, and travel expenditures, on the other. A special feature of the model is that the available modes are represented by a continuum represented by speed of travel.

The survey of 6300 households collected data for the previous day on home-based trips, by mode, distance and time per trip; household characteristics, and household expenditures. Unfortunately, no data were collected on fares or other travel costs. Descriptive statistics show that students and workers generate one round trip per day and such trips are about 90X of all trips. This information is the evidence for suggesting that a preferred model may not need equations for estimating non-work trips and number of trips.