rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table C.5 First-stage regressions, West Bank and Gaza</u>**

||**2007-**|**2009**|**2009-**|**2011**|**2011**|**-2017**|
|---|---|---|---|---|---|---|
||**2007**|**2009**|**2009**|**2011**|**2011**|**2017**|
|Head`s age|0.005|0.006***|0.005***|0.002|-0.000|0.012***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Head is female|0.226**|0.206***|0.226***|0.133***|0.141***|0.160***|
||(0.11)|(0.05)|(0.06)|(0.05)|(0.05)|(0.06)|
|Highest education level is primary|0.177**|0.220***|0.214***|0.218***|0.210***|0.260***|
||(0.07)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|
|Highest education level is secondary|0.276***|0.344***|0.346***|0.339***|0.341***|0.373***|
||(0.09)|(0.04)|(0.04)|(0.04)|(0.04)|(0.05)|
|Highest education level is tertiary|0.585***|0.607***|0.606***|0.602***|0.606***|0.518***|
||(0.08)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|
|Urban|-0.034|0.014|0.012|-0.064**|-0.070**|-0.240***|
||(0.05)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|Refugee|-0.327***|-0.039|-0.034|-0.314***|-0.314***|-0.538***|
||(0.07)|(0.05)|(0.05)|(0.04)|(0.04)|(0.05)|
|_cons|8.288***|8.388***|8.457***|8.718***|8.828***|8.381***|
||(0.15)|(0.07)|(0.07)|(0.08)|(0.08)|(0.08)|
|adjusted R2|0.08|0.09|0.09|0.09|0.09|0.11|
|N|962|2944|2938|3288|3229|2815|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

83
We then explore within the same gravity setup how patterns of trade creation and trade diversion vary across bilateral pairs depending on whether there are resource rich or resource poor. This could be done only for PAFTA as this is the only trade agreement within MENA involving both resource rich and resource poor countries.<sup>8</sup> PAFTA is also one of the well-functioning regional trade agreements in MENA. Indeed as argued by Hoekman and Zarrouk (2009), intra-PAFTA trade barriers have substantially come down since the entry into force of the agreement.<sup>9</sup> The gravity equation becomes:

> where _RR_ and _RP_ capture whether the importer or the exporter is considered as a resource rich or resource poor country respectively. As before RTAintra _ijt_<sup>_k_= 1 if i and j</sup> belong to the same RTA _k_ in t, otherwise 0, and RTArow _ijt_<sup>_k_= 1 if i but not j belongs to</sup> the RTA _k_ in t, otherwise 0. The intra variables of PAFTA are then interacted with _RRi_

> and _RPi_ , as well as _RR j_ and _RPj_ to explore the degree of heterogeneity on trade creation within MENA depending on whether the importer and exporter are resource

> rich or poor. Then _β1_ captures trade creation between rich resource countries in

> PAFTA; _β2_ when the importer is resource rich and the exporter is resource poor

> within PAFTA; _β3_ when both PAFTA countries are resource poor, and _β4_ when the importer is resource poor, but the exporter is resource rich within PAFTA.

> 8 According to World Bank’s classification resource poor countries in PAFTA include Tunisia, Morocco, Lebanon, Jordan, Egypt, Sudan, West Bank of Gaza and Djibouti. Resource rich countries can be divided into two sub-categories. GCC Oil exporters include UAE, Saudi Arabia, Qatar, Oman, Kuwait and Bahrain. Developing Oil Exporters include: Yemen, Syria, Iran, Iraq, Libya and Algeria.

> 9 Although as argued by them and Chauffour (2011) there is still some important work left in terms of nontariff barriers.

8
<!-- Start of picture text --> 2000 to 2005<br>5<br>°<br>3gs e e<br><!-- End of picture text --> <!-- Start of picture text --> 2000 to 2005<br>“<br>cyi}-<br><!-- End of picture text --> <!-- Start of picture text --> $<br>&8 e =e °<br>ia<br><!-- End of picture text --> <!-- Start of picture text --> =<br>Boy2 : . .<br>3 * %e e<br><!-- End of picture text --> <!-- Start of picture text --> 8. e ° e<br>°<br>-2 -1 0 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> . . 2<br>o<br>-2 -1 oO 1 2<br>Normalized distance around 10km threshold (in km}<br><!-- End of picture text --> <!-- Start of picture text --> 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 2002 2003 2004 2005 2006 2007 2008 2003 2010<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> o<br>3<br><!-- End of picture text --> <!-- Start of picture text --> °<br>Co<br><!-- End of picture text --> <!-- Start of picture text -->
|<br>r |<br><!-- End of picture text -->

<!-- Start of picture text --> é<br>3 |<br>a<br><!-- End of picture text -->

<!-- Start of picture text --> IMI<br>=1 =.05 0 05 a<br>Estimated discontinuity in spraying<br><!-- End of picture text --> <!-- Start of picture text --> dt<br>=4 =.2 0 Ps a<br>Estimated discontinuity in cultivation<br><!-- End of picture text -->
Adaptive Experiments for Policy Choice: Phone Calls for Home Reading in Kenya Bruno Esposito<sup>∗</sup> Anja Sautmann<sup>†‡</sup> Latest version here.

_Keywords:_ adaptive experiments; multi-armed bandits; education technology; early literacy; Kenya _JEL codes:_ C11, C93, I25, O15

> ∗Development Economics Research Group, World Bank, email: bespositoacosta@worldbank.org

> †Corresponding author. Development Economics Research Group, World Bank, email: asautmann@worldbank.org.

> ‡The authors thank Tim Sullivan and Clotilde de Maricourt at New Globe and Peter Bergman for connecting us with them, as well as Grant Bridgman, Christine Vorster, and Faith Kibuswa at Uliza. Isaiah Andrews, Jiafeng Kevin Chen, Daniel RodriguezSegura, Adam McCloskey, and Kelly W. Zhang were extremely generous in sharing their expertise. We thank Kathleen Beegle, Daniel Bj ̈orkegren, Esther Gehrke, Andrew Foster, Maximilian Kasy, David McKenzie, Robert Pless, and seminar participants at the World Bank and Rochester University for helpful comments and feedback. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are ours.
<!-- Start of picture text --> ° °<br>o .<br>ro)<br>©'<br>.. . —<br>° = | ‘<br>oO r . ie ". . o's:* t. a "2t S*hseged3 e 5 trPir fie<br>" 1 \"etl ‘ie |<br>E fee MS: "rybpe '<br>ol. . ! 6 . . . * | ] | .<br>; . ; 3 Pai he Si ut “Hh; i<br>ve OES! io ee oe Be * a:<br>. e ‘ s r .<br>i : . : Hae 1<br>fo | .<br>=f<br>2019m1 2020m1 2021m1 2022m1 2023m1<br>Date<br>* CH4Anomaly =——— Monthly Mean Anomaly<br><!-- End of picture text -->
employment . As seen in Table 25, most of the migrant workers are females migrating mainly for family reasons (Table 24), Eventhough their share has declined during this period, most of them have a primary to middle school level education, but the share of those with secondary and higher level education has also increased. Most of the migrants are engaged in agriculture related activities, Eventhough the share has declined. Most of them migrate from rural forest areas followed by other urban areas.

**Table 25: The characteristics of migrant workers in Ghana (in percent).**

||**1987**|**1988**|**1991**|
|---|---|---|---|
|**Sex**||||
|male|36.12|47.49|48.68|
|female|63.88|52.51|51.32|
|**Educ. level**||||
|no education|1.74|1.00|14.6|
|primary|22.34|22.83|30.5|
|middle|63.20|60.95|40.3|
|secondary|8.41|9.32|7.1|
|higher|0.82|0.95|2.3|
|TTA/Others|3.49|4.96|_5.1_|
|**Occupation**||||
|Professional/|0.76|0.97|1.04|
|technical||||
|Admin./ managerial|_3.51_|4.91|3.31|
|clerical|2.60|3.77|2.63|
|sales|18.76|17.37|15.97|
|service|3.69|4.69|3.31|
|agricultural|70.24|67.53|66.93|
|production|0.44|0.76|6.81|
|**Locality(5)**||||
|Accra|12.21|14.26|11.75|
|Other Urban|29.48|28.40|27.57|
|Rural coast|13.75|14.95|14.28|
|Rural forest|33.39|32.24|35.26|
|Rural Savannah|11.17|10.14|11.14|

36
Fields G. S. (1990), "Labor Market Modeling and the Urban Infonnal Sector: Theory and Evidence," in OECD, _The Informal Sector Revisited,_ Paris.

Flatau, P. R. and Philip E. T. Lewis, Segmented Labour Markets in Australia, _Applied Economics,_ 25, 285-294.

Gregory, P. (1986), _The Myth of Market Failure: Employment and the Labor Market in Mexico,_ Baltimore: World Bank and Johns Hopkins.

Harman, H.H. (1976), _Modern Factor Analysis,_ 3rd ed, University of Chicago Press, Chicago.

Harris, J.R. and M. P. Todaro (1970), "Migration, Unemployment, and Development: A Two Sector Analysis," _American Economic Review,_ 60:1, 126-142.

Hart, K. (1972), _Employment, Income and Inequality: A Strategy for Increasing Productive Employment in Kenya,_ Geneva, ILO.

Jovanovic, B. (1982), "Selection and Evolution of Industry," _Econometrica,_ 649-670.

Kim, J. and C. W. Mueller (1978), _Statistical Methods and Practical Issues,_ Sage Publication, Beverely Hills.

Levenson, A.R. and W. F. Maloney (1997), "The Informal Sector, Firm Dynamics and Institutional Participation" Mimeo, University of Illinois and the Milken Institute.

Loayza, N.V. (1994) "Labor Regulations and the Informal Economy," Policy Research Working Paper 1335, World Bank, August.

Lopez-Castano, H. (1990) "Inestabilidad Laboral y Ciclo de Vida en Colombia," (Labor Instability and the Life-Cycle in Colombia) _Coyuntura Economica,_ 20:1, p 173-191.

Lucas, R. E., Jr. (1978), "On the Size Distribution of Business Firms," _Bell Journal of Economics,_ 9:2, 508-23.

Maloney, W. (1996) "Are LDC Labor Markets Dualistic?" University of Illinois Working Paper.

Maloney W. (1997) "Labor Market Structure in Mexico: Time Series Evidence on Competing Views" University of Illinois Working Paper Marcouiller, D. V., Ruiz de Castilla and C. Woodruff (forthcoming), " Formal Measures of the Informal Sector Wage Gap in Mexico, El Salvador, and Peru", _Economic Development and Cultural Change._ 40
| POLICY RESEARCH WORKING PAPER 2991

# Abstract

The authors use a large data set from Ethiopia that differentiates tenure security and transferability to explore determinants of different types of land-related investment and its possible impact on productivity. While they find some support for endogeneity of investment in trees, this is not the case for terraces. Transfer rights are unambiguously investment-enhancing.

The large productivity effect of terracing implies that, even where households undertake investments to increase their tenure security, this may not be socially efficient. In Ethiopia, government action to increase tenure security and transferability of land rights can significantly enhance rural investment and productivity.

> This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to assess the impact of land policy on equity and productive development. Copies of the paper are available free from the World Bank,

> 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandez, room MC3-542, telephone 202-473-3766, fax 202-522-1151, email address mfernandez2@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. Klaus Deimnger may be contacted at kdeininger@worldbank.org. March 2003. (28 pages)

> _The Pohcy Research Working Paper Seres dissemPnates the fRndgs_ ea _of ork in progress to encourage the excaange of deas about development tssues. An objectivse of the series Is to get the findings out quickly, even if the presentations are less than fully polisbed. The_

> _papers can7y the names of the auithors and should be cited accordingly. The findings, interpretatiois, and coniclusions expressed in this paper are entirely those of the authors. They do not niecessarnly represent the vetvw of the World Bank, its Execuitive Dtrectors, or the couintrnes they represent_

Produced by the Research Advisory Staff
# Figure

|Figure 1.GDP Growth Rate (percent) and Inflation(annual average percent)...........ccccssseereeed|
|---|
|Figure 2. Incidence of poverty in Vietnam inRural-Urban areas............ssessesscscsseseseesessseesseeeaceneseeed|
|Figure 3. Strategic PlanningFramework inVietnam ............eesssesesseseeeeceseeeesesacarescateceacrsanseeneaeeeeeeO|
|Figure 4. Faster-growing regions havereducedpoverty MOSt.............:ssssssssscesssscceseseeecsetsssssseseestetseee<br>lhS|
|Figure 5. Povertyreductionprojections under different growth SCenari0S..............:csesseseecessereeeeeeeeee4|
|Figure 6. IlliteracyRate of 15-24 year-olds in SelectedCountries, 1999.00...<br>eessseecesesceeesteeseeeee LO|
|Figure 7. Trends inPrimaryNetEnrolment Rates, 1993-1998,byincomequintile...Lb|
|Figure 8. PrimaryCompletion Rates forselectedcountries andregion, 1997 0.0...ceseeeecesesseeereeeed7|
|Figure 9. Girls as apercentage ofboys inprimaryandlower secondary education: Levels in<br>1993-98 and targets for 2005 00... eeecssesessesecesecsseseescceeesseesessscessssseseeseesteessesesssetateeeeeeeLO|
|Figure 10. Under 5 Mortality—progressmadeduring 1990-1998 andprojections for2010 and|
|Figure 11. Under 5 Mortality—progressmade during 1990-1998 andprojections for2010 and|
|Figure 12. Malnutrition: progressmade during 1990-1998 andprojections for2010 and 2015.........27|
|Figure 13. Maternal mortality—progressmade during 1990-1998 andprojections for2010 and|
|Figure 14. Contraceptive Prevalence rate progress andtargets1990-2015<br>oo...<br>eeseseeeteeteeteeene dl|
|Figure 15. Trends inthecompositionofVietnam's Forest Cover...........ccccssssessescesseseesseeteeeseeeneeeeedS|
|Figure 16. Marine catch inVietnam 1991-2001oeecsessessesecseeneseessesssssescesseessesoneeseseseeesesseeseeesDo|
|Figure 17. CountryComparison ofAccess to SafeWater, 2000 (percentage ofpopulation) .............34<br>Figure18Distributionof householdwell-beingrelativetopovertylineinVietNam..............000.36|
- Duflo, E. (2012). Women Empowerment and Economic Development. _Journal of Economic Literature_ , 50(4):1051–1079.

- Flabbi, L., Macis, M., Moro, A., and Schivardi, F. (2019). Do Female Executives Make a Difference? The Impact of Female Leadership on Gender Gaps and Firm Performance. _The Economic Journal_ , 129(2012):2390–2423.

- Folke, O. and Rickne, J. (2020). All the Single Ladies: Job Promotions and the Durability of Marriage. _American Economic Journal: Applied Economics_ , 12(1):260–287.

- Friedemann-Sanchez, G. (2006). _Assembling Flowers, Cultivating Homes_ . Lexington Books, Lanham, MD.

- Getahun, T. D. and Villanger, E. (2018). Labour-Intensive Jobs for Women and Development: Intra-household Welfare Effects and Its Transmission Channels. _Journal of Development Studies_ , 54(7):1232–1252.

- Heath, R. (2014). Women’s Access to Labor Market Opportunities, Control of Household Resources, and Domestic Violence: Evidence from Bangladesh. _World Development_ , 57:32–46.

- Heath, R. and Jayachandran, S. (2018). The Causes and Consequences of Increased Female Education and Labor Force Participation in Developing Countries. In Averett, S. L., Argys, L. M., and Hoffman, S. D., editors, _The Oxford Handbook of Women and the Economy_ . Oxford University Press, New York, NY.

- Heath, R. and Mobarak, M. A. (2015). Manufacturing growth and the lives of Bangladeshi women. _Journal of Development Economics_ , 115:1–15.

- Heß, S. (2017). Randomization inference with stata: A guide and software. _Stata Journal_ , 17(3):630–651.

- Hoffman, M., Kahn, L. B., and Li, D. (2018). Discretion in Hiring. _The Quarterly Journal of Economics_ , 133(2):765–800.

- Imbens, G. (2015). Matching Methods in Practice. Three Examples. _The Journal of Human Resources_ , 50(2):373–419.

- Imbens, G. and Kalyanaraman, K. (2012). Optimal Bandwidth Choice for the Regression Discontinuity Estimator. _Review of Economic Studies_ , 79(3):933–959.

35
Policy Research Working Paper 5535

# **Abstract**

This paper draws lessons from an original randomized experiment in Malawi. In order to understand why roads in relatively good condition in rural areas may not be used by buses, a minibus service was subsidized over a six-month period over a distance of 20 kilometers to serve five villages. Using randomly allocated prices for use of the bus, this experiment demonstrates that at very low prices, bus usage is high. Bus usage decreases rapidly with increased prices. However, based on the results on take-up and minibus provider surveys, the experiment demonstrates that at any price, low (with high usage) or high (with low usage), a bus service provider never breaks even on this road. This can contribute to explain why walking or cycling is so widespread on most rural roads in Sub-Saharan Africa. In terms of policy implications, this experiment explains that motorized services need to be subsidized; otherwise a road in good condition will most probably not lead to provision of service at an affordable price for the local population.

This paper is a product of the Transport Department, 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://econ.worldbank.org. The author may be contacted at graballand@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
# **_2.2 Initial Impact of the Transition on Economic Performance_**

The initial moves from plan to market have lead to four economic outcomes that we find repeated in country after country, across Central and Eastern Europe and Soviet countries:

- _A significant decline in output_ linked to the disruption of the old system of economic coordination by the state. This output decline was accentuated by the disruption of trade patterns among these countries that were traditionally dictated by governments under the old CMEA trade arrangements, popularly known as the Comecon.

- A _sharp increase in open inflation_ following the initial price liberalization after decades of price controls. The impact on consumers is not entirely negative given trade-offs between this open inflation and the end of the shortages and queues which had become increasingly long prior to the communist collapse.

- An important _movement of resources across sectors_ of the economy. These shifts are the consequence of the move from state-planned decisions to market-driven decisions, both on the internal and external markets. Resource transfers also reflect the important distortions that existed between socialist domestic prices and actual prices on the world markets. These distortions were especially important in the energy sector and have had a large impact on housing construction and operation.

- Important _gains in private sector output_ with the new encouragement of private sector activities and the shifts to private ownership. However, these gains are usually not well recorded by official statistics since the information network of the old statistical bureaus focused on state enterprises and public activities. A significant source of these private sector gains has been the rapid emergence of private regional trade flows with Western Europe and of new links with the rest of the world.

Output decline has been particularly severe in the housing sector. Housing is a sector where the state had achieved almost total dominance through monopoly control of access to land, financing, and the supply and price of materials, especially in the larger cities. With the disruption of state controls, very high inflation and falling wages, the initial years of the transition have been marked by a massive collapse of housing output. Figure 1 shows the decline in the number of _completed_ new housing units in seven countries representative of Group 1 (Czech Republic, Poland, Hungary, and Slovakia), Group 2 (Bulgaria, Romania), and Group 3 (Russia). To facilitate comparisons, annual housing production in these seven countries is standardized on the national production level in year 1986, the last year of economic stability in many of these countries.<sup>4</sup> 4 1 am grateful to Dr. Margarete Czemy of the Austrian Institute of Economic Research (WIFO) for providing me with the latest data on Eastern Europe and her own evaluations "Trends in Eastern Europe: 1995/96" prepared for the arnual Euroconstruct Outlook Conference, London, June 1995.

4
obtaining the 10-90 gaps in the residualized productivity and technology sophistication variables, we follow the same procedure as before to determine the contribution of technology sophistication to the dispersion in TFPR.

Table 11 reports the results from the development accounting exercises. The first three rows correspond to the productivity regressions reported in the first three columns of Table 8. In the baseline specification with country and sector fixed effects, differences in technology sophistication account for 23% of the differences in productivity and 24% of the differences in TFPR across establishments. Excluding country effects allows us to study these contributions both within and across countries. In this case, differences in productivity account for 26% of productivity differences and 31% of TFPR differences across establishments.

# **6.3 The Agricultural Productivity Gap**

Cross-country differences in productivity are roughly twice as large in agriculture than in non-agricultural sectors (Caselli, 2005). The FAT dataset is consistent with this so-called agricultural productivity gap as the gap between the (log) productivity of establishments in the 90<sup>_th_</sup> and 10<sup>_th_</sup> deciles is 5.91 in agriculture, compared to 4 in services. This implies that the 90-to-10 productivity ratio is 6.75 times (i.e. _exp_ (1 _._ 91)) larger in agriculture than in services.

To study the role of technology sophistication in the agricultural productivity gap, we reestimate the productivity regression (9) separately for each one-digit sector. The estimates, reported in Table 12 show a strong positive association between technology sophistication and productivity across all three sectors. However, the coefficient of technology sophistication varies significantly across sectors, being largest in agriculture and smallest in services.

Rows 4-9 of Table 11 report the contribution of technology sophistication to the dispersion in productivity and TFPR across establishments in each sector. Technology sophistication accounts for 50% of the dispersion in TFPR in agriculture, 30% in manufacturing and 28% in services. Within countries, it accounts for 33% of TFPR dispersion in agriculture, 26% in manufacturing and 24% in services. We find similar contributions to the cross-establishment dispersion in productivity (see column 1 of Table 11).

Differences across sectors in the contribution of technology sophistication to cross-establishment dispersion in productivity have implications for the agricultural productivity gap. In agriculture, technology sophistication accounts for a 90-10 log-productivity gap that is 1.05 points larger than in services. This means that over half of the agricultural productivity gap (1.05 out of 1.91 log-points) can be accounted for by differences in technology sophistication across establishments.

31