rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 1. Summary Statistics</u>

||Mean|Std. Deviation|
|---|---|---|
|Migrant's Report|||
|Age in years|38.56|7.62|
|Years married|10.84|7.92|
|Years employed in Qatar|6.48|6.98|
|Household has other members working abroad (indicator)|0.03|-|
|Annual earnings (US$)|6406.9|2,364.7|
|Annual remittances sent to household net of fees (US$)|2900.4|1,563.2|
|Net annual remittance as a share of income|0.47|0.23|
|Household's annual medical expenditure as a share of migrant's income|0.08|0.16|
|Spends on temptation goods (indicator)|0.38|0.49|
|Aware of others having disagreement over remittance use (indicator)|0.49|-|
|Wife's Report|||
|Wife is employed in India (indicator)|0.13|-|
|Wife's years of education|11.68|2.75|
|Wife's report of husband's (migrant's) annual earnings (US$)|4807.3|1,799.6|
|Wife's report of annual remittances received from husband (US$)|2377.8|1,827.4|
|Household's monthly income in India (US$)|38.49|90.54|
|Household's monthly expenses in India (US$)|227.30|118.07|
|Migrant is the primary decision-maker for money matters (indicator)|0.19|-|
|Wife does not know husband's overseas expenses (indicator)|0.36|-|
|Constructed Variables|||
|Difference between wife and husband's account of annual earnings in Qatar (USD)|-1599.6|207.1<br>§|
|Ratio of wife to husband's account of annual earnings in Qatar|0.79|0.26|
|Difference between wife and husband's account of annual remittances (USD)|-522.7|165.1<br>§|
|Ratio of wife to husband's account of annual remittances|0.88|0.60|
|Difference between wife and husband's account of weekly hours worked**|-8.31|2.00 <sup>§</sup>|
|Ratio of wife to husband's account of weekly hours worked|0.90|0.28|
|Number of observations||108|

§ Statistically significant at the 1% level. ** Only 81 cross-reports of weekly hours were recorded. _Source_ : Authors’ analysis based on data from QSKF

# IV. REPORTING BEHAVIOR

We examine the reporting behavior of migrants using the discrepancy in the couple’s account of overseas income. The reported earnings ratio, denoted by  , is given by where _yw_ is the husband’s monthly earnings reported by his wife and<sup>_y_</sup> _h_<sup>is the monthly earnings</sup> 17
Hay et al., 2009 _PLoS Medicine_ Health in Action article <u>http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000048</u> Longnecker MP, Klebanoff MA, Zhou H, & Brock JW (2001) Association between maternal serum concentration of the DDT metabolite DDE and preterm and small-for-gestational-age babies at birth. Lancet, **358** (9276):110-114.

Longnecker MP, Klebanoff MA, Dunsonc DB, Guod X, Chenc Z, Zhou H, & Brock JW (2005) Maternal serum level of the DDT metabolite DDE in relation to fetal loss in previous pregnancies. Environmental Research, **97** (2): 127–133.

McGlynn KA, Abnet CC, Zhang M, Sun XD, Fan JH, O'Brien TR, Wei WQ, Ortiz-Conde BA, Dawsey SM, Weber JP, Taylor PR, Katki H, Mark SD, & Qiao YL (2006) Serum concentrations of 1,1,1-trichloro-2,2bis(p-chlorophenyl) ethane (DDT) and 1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene (DDE) and risk of primary liver cancer. J Natl Cancer Inst., **98** (14): 1005-1010.

MicrobiologyBytes , 2009 http://www.microbiologybytes.com/introduction/Malaria.html Pavuk M, Cerhan JR, Lynch CF, Kocan A, Petrik J, & Chovancova J (2003) Case-control study of PCBs, other organochlorines and breast cancer in Eastern Slovakia. J Expo Anal Environ Epidemiol., **13** (4): 267275.

Pesticide Action Network-UK. 2011. DDT Factsheet. http://www.pan-uk.org/ Ribas-Fito N, Gladen BC, Brock JW, Klebanoff MA & Longnecker MP (2006) Prenatal exposure to 1,1dichloro-2,2-bis( _p_ -chlorophenyl)ethylene ( _p_ , _p_ ′-DDE) in relation to child growth. Int J Epidemiol, **35** : 853– 858.

Roll Back Malaria, 2012. GMAP- the Global Malaria Action Plan. <u>http://www.rbm.who.int/rbmgmap.html</u> Settimi L, Masina A, Andrion A, & Axelson O (2003) Prostate cancer and exposure to pesticides in agricultural settings _._ International Journal of Cancer, **104** (4):458-461.

Snedeker SM (2001) Pesticides and breast cancer risk: a review of DDT, DDE, and dieldrin. Environ Heatlh Perspect, **109** : 35-47.

Sunyer J, Torrent M, Munoz-Ortiz L, Ribas Fito N, Carrizo D, Grimalt J, Anto JM, & Cullinan P (2005) Prenatal dichlorodiphenyldichloroethylene (DDE) and asthma in children. Environmental Health Perspectives, **113** (12):1787-1790.

Turusov VS, Day NE, Tomatis L, Gati E & Charles RT (1973) Tumors in CF-1 mice exposed for six consecutive generations to DDT. J Natl Cancer Inst, **51** : 983–997.

QCIL, 2011, Malaria Fact Sheet.

<u>http://www.qcil.co.ug/index.php?option=com_k2&view=item&layout=item&id=23&Itemid=73</u> 18
<!-- Start of picture text --> PolOcy, Research, and External Affairsi<br>De L ad .ernallonal Finance<br>WPS '717<br><!-- End of picture text -->

- This paper a' product of the Debt and Intemational Finance Division, International Economics Department -- is part of a larger cffort in PRE to determine the interaction between external and domestic finance in support of investment in developing countries. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sheilah King-Watson, room S8-045, extension 31047 (22 pages).

Assuming that li4uidity constraints exist in most developing countries, the majority of analysts believe that increasing real interest rates will raise the volume of lending and hence private investment.

Morisset, focusing on the demand for capital goods, argues that the positive effect on the domestic credit* market may be offset by the negative effect of a portfolio shift from capital goods and public bonds into monetary assets. He also demonstrates that a policy of financial liberalization could increase the public sector's demand for domestic credit, thus limiting the funds available to the private sector. This crowding out does not result from a change in the government's behavior but from a shift in the portfolio of private agents. Higher demand for bank deposits reduces the private sector's willingness to hold government bonds, so the public sector must finance a given budget deficit with more domestic credit.

His simulations for Argentina for 1961-82 suggest that the low response of private investors to changes in interest rate policy in those 20 years was attributable not to the low values of interest elasticities but to the interaction of the mechanisms allowed for in the model, which tends to neutralize the impact of such policies.

Morisset concludes that the effect of changes in interest rate policy on the demand for capital - goods is weak in Argentina and might affect the quality of private investment more than its quantity.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complex. An objective of thc series is to getthese findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
differences between MSMEs and large firm are statistically significant at the 1 percent significance level.

Overall, the CB results reported so far suggest that the relationship between access to finance and job growth is causal rather than merely correlated. Furthermore, CB introductions, especially with higher coverage, and the subsequent increase in access to finance appear to be better utilized by MSMEs who grow employment at a much faster rate than larger firms.

As an interesting aside, we delve deeper into the effects of CB introductions on corporate decision making. If firms have greater access to finance from the introduction of CBs, it is not hard to envision that this should manifest in the growth of the firm in other dimensions besides employment. An obvious effect would be on investment as firms finance lump-sum increases in capital. In Appendix Table A5, we present results replacing employment growth with total asset growth and tangible fixed assets growth, respectively, as the dependent variable in the specification of equation (2). These variables are only available for the Orbis sample and for a smaller sample of countries. We find that CB reform leads to increases in the growth rate of total assets and tangible fixed assets. In the disaggregated size categories, we get estimates that are in line with the pattern of results thus far – an effect among MSMEs and SMEs from financing (CB reform) that is much higher than large firms. These results suggest an obvious avenue for growth in employment among firms. It is conceivable that the increase in job growth from financing is working through an intermediate increase in capital. As firms finance capital accumulation, labor is hired to “work the machines” and maintain optimal capital-labor ratios.

24
the idea of widespread diffusion of contractual information among firms in the same location. If multiple equilibria are present, they seem to coexist within locations; thus, whereas some firms recognize that there are reputational consequences to contractual disputes, others in the same location do not.

# **<<B>>** _Firm performance and growth_

Thus far, we have focused on those business practices that may diffuse within networks. We have also investigated whether the above results are mirrored in labor productivity and growth rates, which are our measures of firm performance. The results for the specifications in which the dependent variable is defined as the absolute difference across firms in these performance indicators are shown in section 4 in the online appendix. For Ethiopia, we find little evidence that firms that are closer in the social-network sense have more similar performance. For Sudan, we find evidence that firms that share the same supplier have more similar performance than other firms. However, other results related to network links and geographical distance are weak. The overall conclusion from our analysis is that network links and geographical proximity are not strongly associated with any convergence in the performance of firms.

# **<<B>>** _Heterogeneous diffusion and networks_

Now, we return to the points raised in section I that are related to the diffusion patterns across heterogeneous firms. We question whether the reason we find only limited evidence of diffusion is that the firms are too heterogeneous. To investigate whether the evidence for diffusion is stronger among pairs of firms in the same sector, we interact our network and distance variables with a dummy that tracks whether firms _i_ and _j_ belong to the same industrial subsector, and we add these new interaction terms to the baseline specification. To minimize the number of explanatory variables, the same industry dummy is interacted with a 25
Industry Restructuring and Regulation -Building a base for sustainable growth

# _2.1. 1 Cross country studies_<sup>_9_</sup>

Investigating the impact of infrastructure on growth has long been hampered by the lack of available data. A recent study by Canning (1999) attempts to start to redress this by considering information from 152 countries from 1950 to 1995. Some of the more relevant findings from this study are presented below. While this is an important step forward, it does still contain a significant flaw. Only physical stocks of infrastructure are available; it is not possible to measure the quality of the service being derived from those stocks. This problem should be kept in mind when considering the results of this survey.

Canning was able to consider the impact of infrastructure on economic growth from the period 1970 to 1990 (this sub-sample was chosen to ensure a sufficiently large number of countries had continuous data series). The results, shown in Table I below, are suggestive of the impact of infrastructure on economic growth. A noteworthy result of the study is that telephone lines are a significant explanatory factor while electricity capacity has a negative impact. Without further investigation, it is possible to build plausible explanations as to why electricity capacity could have a negative impact, however, these would be purely speculative.'<sup>0</sup>

|_Variable_|_ModelI _|_Model 2_|**_MO_**|
|---|---|---|---|
|Constant|1.148<br>(3.45)|1.061<br>(0.77)|3.028<br>(4.31)|
|Log GDP per capita 1970|-0.211<br>(3.89)|-0.303<br>(1.92)|-0.449<br>(4.23)|
|Log education per worker<br>1970|0.152<br>(1.67)|0.197<br>(1.60)|0.069<br>(0.78)|
|Average investment rate<br>1970-1990|0.036<br>(3.11)|0.029<br>(3.04)|0.030<br>(3.12)|
|Log telephone main lines<br>per capita 1970||0.226<br>(2.41)|0.169<br>(2.51)|
|Log paved roads per capita<br>1970||-0.012<br>(0.26)||
|Log electricity generating<br>capacity per capita 1970||-0.230<br>_(2.05)_||
|Log area||-0.062<br>(2.88)||
|Ratio urbanized 1970||0.185<br>(0.75)||
|Number of observations|88|58|72|
|R' adjusted|0.386|0.472|0.456|
|_Source:_Table 7, Canning||||

_Note:_ Heteroskedastic consistent t ratios in parenthesis Further evidence on the country level linkage between infrastructure and economic growth is provided in Box 1.

- 9 An overview of developed country studies on the links between infrastructure and economic growth was provided in Kessides (1993). Kwoka (1996) provides a review of studies on a sectoral basis, primarily from developed countries and split according to whether the focus is deregulation or privatization. D'Souza and Megginson (1999) provide an overview of the impact of all privatizations, infrastructure and non-infrastructure related, during the period 1990 to 1996.

- One explanation, for example, could be linked to a consideration of actual peak demand and its relationship with capacity. This may show capacity far outstripping peak demand and so imposing an additional burden on the economy rather than facilitating growth.

6
Kenyan National Provident Fund. 1989. Nairobi, Kenya.

Kohler, Peter and Hans Zacher. 1982. _The Evolution of Social Insurance 1881-1981._ New York: St. Martin's Press.

Knodel, John, Chanpen Saengtienchai, and Werasit Sttitrai. 1992. "The Living Arrangements of Elderly in Thailand: Views of the Populace." Population Studies Center, Comparative Study of the Elderly in Asia, Research Report 92-20. University of Michigan, Ann Arbor.

Kopits, G. Amieva, J., Ross, S. and Peter Saunders. Fiscal Affairs Department. 1992. "Indonesia: Blueprint for a Comprehensive Public Pension System." (July) Unpublished. Restricted. Washington D.C.: International Monetary Fund.

Kopits, George. 1992. "Social Security." Tanzi, Vito, Editor. _Fiscal Policies in Economies in Transition._ Washington D.C.: International Monetary Fund.

Kuddo. 1994. "Some corrections considering the World Bank Report..." data based on official governrent statistics.

Lim, Chong Yah et. al. 1986. "Report of the Central Provident Fund Group." (April) _Singapore Economic Review._ Lindgren, Jarl. 1990. "Demographic and Socio-economic Aspects of Population Aging in Finland." Paris, France: CICRED.

Liu, Lillian. 1991. "Social Security for State-Sector Workers in the People's Republic of China: The Reform Decade and Beyond." _Social Security Bulletin_ (October) 54(10): 2-16.

Lok, C.C., and M.R. Lok. 1985. "A Note on social Security and Private Savings in Singapore." _Public finance,_ 299-304.

Lora, E., Hernando Zuleta and Loredana Helmsdorff. 1992. "Viabilidad Macroeconomica y Financiera de un Sistema Privado de Pensiones." _Coyuntura Economica_ (April) XXII(1): 75-95.

Madagascar: Rapport au governement sur la situation actuelle et les perspectives d'evolution de la protection sociale. Geneva, 1991.

Maddison, Angus. 1987. "Growth and Slowdown in Advanced Capitalist Economies: Techniques of Quantitative Assessment." _Journal of Economic Literature_ XXV: 649-698.

Marquez, Gustavo. 1992 "El Seguro Social en Venezuela." Washington D.C.: Banco Interarnericano de Desarrollo.

-. 1992. ? "El Sistema de Seguros Sociales en Venezuela. Gestion Fiscal y Distribucion del Ingreso en Venezuela." Caracas, Venezuela: IESA.

133
Wilmsmeier, G., Tovar, B., Sanchez, R. (2013). The evolution of container terminal productivity and efficiency under changing economic environments in Iberoamerica. Mimeo.

- World Bank (2013a) Doing Business Indicators. World Bank. Retrieved from: <u>http://data.worldbank.org/ data-catalog/doing-business-database.</u>

- World Bank (2013b) World Development Indicators. World Bank. Retrieved from: http://data.worldbank.org/.

- World Economic Forum (2013). Global Competitiveness Report 2012–2013. Retrieved from http://www.weforum.org/ issues/global-competitiveness.

23
<!-- Start of picture text --> Na [ontitcenngmentI ! . '<br>Nin RF<br>~ ! iy / \/<br>2 <=" Now \ INA .<br>i i/o<br>i \ i<br>| y i<br>1 '<br>1 1 1<br>o I 1 1<br>os | pete saymert up to sof<br>a j H 1H<br>oO I! |] Secape| contains<br>I 1 1<br>—-—- Treatment | | H<br>a || Control 1 ! !<br>o<br>2019m1 2019m4 2019m7 2019m10 Fpgomtate 2020m4 2020m7 2020m10<br><!-- End of picture text -->

<!-- Start of picture text --> co Fiat contahmen1 — |1 iNa!<br>ANI Lin ih<br>\ ' it~ | a<br>NTO ' tiny aft<br>~7 \ ‘| a on<br>8 \W<br>i Y i<br>+ ' |<br>w 1 Colfipctiess cayment up to Sdeurag<br>I 1 1<br>| H H<br>1isN ' | 1; Secepe1 containment<br>1a\ i]<br>iW 1<br>to ||—-—- Treatment 1 We H<br>—— Control I 1 '<br>2019m1 2019m4 2019m7 2019m10 Fpgomtate 2020m4 2020m7 2020m10<br><!-- End of picture text -->
formulate when they envisage trade reforms: “What will be the effect of liberalization reforms such as tariff reduction on wage differences between various occupations and industries?” For import liberalization, we use the World Bank measure of unweighted average tariff (variable _tarf_ ) rate which covers the period from 1980 to 2000, includes 144 countries, and provides 1,255 observations (county/years) in total. The list of countries and number of country/years are shown in Annex 1 (Table 3). Over this period, the average tariff rate (calculated across the available countries) has been reduced from 28 percent to about 10 percent. Figure 3 how the distribution of average tariff rates by countries has shifted leftward with the median, mean, and the standard deviation all significantly less today than in the early and mid-1980s.

Figure 3. Distribution of countries’ average tariff rates in the periods 1980-88 and 1995-2000 <!-- Start of picture text --> 0 20 40 60 80 100<br>x<br>before 1988 after 1995<br>.06<br>.04<br>.02<br>kdensity aa/kdensity bb<br>0<br><!-- End of picture text --> Note: average tariff rate for a country over a period (1980-88 or 1995-2000) represents one observation. Number of countries is 106 for the first and 132 for the second period.

The reduction has affected both rich and poor countries. The average tariff rate in poor countries (defined as those with GDP per capita less than $9,000 at international 30
Muthén, B. O. 2004. _Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data._ In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 345–370). Thousand Oaks, CA: Sage Publications.

Nguyen, Trang. 2008. “Information, Role Models, and the Perceived Returns to Education.” Unpublished manuscript.

O’Cummings, Mindee and Susan Therriault. 2015. “From Accountability to Prevention: Early Warning Systems Put Data to Work for Struggling Students.” AIR Early Warning Systems in Education Program. Washington, D.C.: American Institutes for Research (AIR).

Oreopoulos, Philip and Kjell Salvanes. 2011. “Priceless: The Nonpecuniary Benefits of Schooling.” _Journal of Economic Perspectives_ 25(1): 159-184.

Patrinos, Harry and George Psacharopoulos. 2004. “Returns to Investment in Education: a Further Update.” _Education Economics_ 12(2): 111-134.

Pearson, Karl. 1901. “On lines and planes of closest fit to systems of points in space.” _Philosophical Magazine_ , Series 6, 2(11): 559-572.

Rogosa, David. 2005. “Statistical Misunderstandings of the Properties of School Scores and School Accountability.” _Yearbook of the National Society for the Study of Education_ 104(2): 147-174.

Sara, Nicolae-Bogdan, Rasmus Halland, Christian Igel, and Stephen Alstrup. 2015. “HighSchool Dropout Prediction Using Machine Learning: A Danish Large-Scale Study.” _The European Symposium on Artificial Neural Networks (ESANN) Proceedings 2015_ .

Sheskin, David. _Handbook of Parametric and Nonparametric Statistical Procedures_ . Boca Raton: Chapman & Hall / CRC Press, 2004.

Stuit, David, Mindee O’Cummings, Heather Norbury, Jessica Heppen, Sonica Dhillon, Jim Lindsay, and Bo Zhu. 2016. _Identifying Early Warning Indicators in Three Ohio School Districts_ . Washington, D.C.: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educaitonal Laboratory Midwest.

United Nations. 2015. _The Millennium Development Goals Report_ . New York: United Nations.

World Bank. 2016. EdStats. Retrieved from: http://datatopics.worldbank.org/education/ 20
# Table 13: Program Impact on Child Participation in Fetching Water

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School||||||||||
|Meals|0.049|0.017|0.084|0.045|0.000|0.102|0.071|0.077|0.073|
||[0.026]*|[0.036]|[0.036]**|[0.030]|[0.040]|[0.042]**|[0.054]|[0.079]|[0.068]|
|Dry Rations|0.055|n.a.|0.096|0.058|n.a.|0.112|0.048|n.a.|0.075|
||[0.031]*|n.a.|[0.036]***|[0.036]|n.a.|[0.042]***|[0.056]|n.a.|[0.062]|
|Follow-up|-0.031|0.004|-0.066|-0.043|0.001|-0.089|0.002|0.026|-0.025|
||[0.019]|[0.026]|[0.025]***|[0.021]**|[0.029]|[0.030]***|[0.037]|[0.056]|[0.046]|
|Girl|0.330|||0.311|||0.403|||
||[0.013]***|||[0.014]***|||[0.026]***|||
|Constant|0.075|0.160|0.347|0.082|0.158|0.342|0.445|0.435|0.865|
||[0.020]***|[0.028]***|[0.027]***|[0.022]***|[0.029]***|[0.028]***|[0.037]***|[0.061]***|[0.036]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.24|0.19|0.20|0.22|0.17|0.19|0.30|0.28|0.14|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether child has fetched water the week before the survey.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

36
<!-- Start of picture text --> 3 — 2019<br>=— || —— 2020<br>— 2021<br>ie<br>|| 2022<br>O°<br>2023<br>2amfe) || —<br>2<br><x<br>Se)<br>a8 :<br>oO<br>o<br>dD)<br>ag<br>®+<br>2<br>is<br>E<br>oO<br>1-Jan 1-Feb 1-Mar 1-Apr 1-May 31-May<br><!-- End of picture text -->