rafmacalaba/datause-encoder-data huggingface dataset:
# 4.4.1 Zero or unde...ned values for dependent variables

One of the most important features of our dataset is the high proportion of zero observations either for the size of diasporas in 2000 or for the ‡ows of migrants between 1990 and 2000. This naturally occurs in many migration datasets as there is almost none or minimal migration for many country pairs. Pooling the data across the two periods, we have zero values in about 31% of the observations for the stock of migrants and in around 36% for the ‡ows.

Our model is fully consistent with such large number of zero observations. Predicting a continuous number of emigrants, our model is an approximation of the "discrete-number" real world with Nij(h) 2 N. If ln [Nij(h)] < 0, less than one migrant wants to leave her country<sup>10</sup> . This means that the bilateral migration ‡ow is nil. The probability that Ni;j(h) = 0 is This case might arise for a number of reasons such as low wage di¤erentials, large distances, high migration or visa costs. In turn, those latter costs obviously depend on the size of the existing diaspora.

Large number of zero observations occurs frequently in other empirical studies in international economics such as gravity equations in trade models. In the estimation of models (10-11) by OLS for the size of migration ‡ows, the high occurance of zero values is likely to lead to inconsistent estimates. The use of a log speci...cation drops the zero observations from the sample which is likely to result in biased estimates of the impact of diasporas and other variables on the migration ‡ows and their selection. For instance, it might be the case that there are no migrants from country i to country j because migration costs are too high. In turn, migration costs might be too high because distance is too high and there is no diaspora. In this case, the exclusion of those observations leads to underestimation of the impact of the variables a¤ecting the migration costs such as distance, colonial links, linguistic similarities or diasporas.

The ...rst alternative is to use Poisson regression models that relies on pseudo maximum likelihood estimates, as advocated by Santos Silva and Tenreyro (2006) who show that the use of log linearization for gravity models leads to inconsistent estimates of the coe¢ cients (such as the one relative to distance). A ...rst reason, as mentioned before, is the exclusion of zero observations for the dependent variable. A second reason is that the expected value of the error will depend on the covariates of the model and hence will lead to estimation biases of the coe¢ cient. In order to address that, we carry out Poisson regressions of the models explaining the size of the migration ‡ows (i.e. models 10-11). The Poisson solution is nevertheless unfeasible for the selection and the concentration analyses. For the selection, the existence of

> 10In practice, some reported zeros might not re‡ect the actual absence of migrants. Due to con...dentiality and disclosure rules, some statistics o¢ ces report a zero when the diaspora size is lower than a threshold value. We are not able to distinguish these cases from "true" zeros.

18
|Table 2. Regression results: estimates of consumption equation(logcon|sumptionper capita)|
|---|---|
|Variable|Log Per Capita Consumption|
|agriculture is the main activity in the community|-0.190***|
||(0.031)|
|the community is connected to the national grid|0.051***|
||(0.017)|
|community with migration (people were coming or leaving community)|-0.297***|
||(0.045)|
|community benefited from new road during last 3 years|0.059***|
||(0.016)|
|household owns fridge|0.388***|
||(0.029)|
|household owns television|0.297***|
||(0.016)|
|household owns radio|0.049**|
||(0.025)|
|household owns motorcycle|0.136***|
||(0.041)|
|household owns bicycle|0.067***|
||(0.013)|
|household size|-0.102***|
||(0.005)|
|age of household head|0.004***|
||(0.000)|
|share of children 0-13 and elderly 60 plus in total household size|<br>-0.059***<br>|
||(0.019)|
|each member has a pair of shoes|0.202***|
||(0.011)|
|each member had two pairs of clothes|0.120***|
||(0.018)|
|at least one household enterprise|0.049***|
||(0.011)|
|head of household is not working|-0.097***|
||(0.013)|
|head of household is in subsistence farming|-0.048***|
||(0.013)|
|household head has incomplete primary education|0.104***<br>|
||(0.014)|
|household head has complete primary education|0.158***|
||(0.017)|
|household head has some secondary education|0.207***|
||(0.017)|
|household head has post-secondary education|0.417***|
||(0.025)|
|energy used for cooking is charcoal|-0.280***|
||(0.039)|
|energy used for cooking is firewood|-0.412***|
||(0.038)|
|community with migration*charcoal|0.197***|
||(0.046)|
|community with migration*firewood|<br>0.224***|
||(0.048)|
|agricultural community*household size|0.027***<br>|
||(0.005)|
|agricultural community*motorcycle|<br>0.196***|

9
<!-- Start of picture text --> Number of Destinations per Exporter - GDPpc Entry Rate - GDPpc<br>MWI LAO YEM<br>BEL TZA<br>UGA CMR IRN<br>KHM<br>SWE BFAMLI DOM EST<br>MWI NERUGATZABFABGDMLI KENLAOPAKSENNICYEMCMR EGY MARGTMJORSLVECUALBPERIRNMKDBGRDOMCOLZAFCRIMUSLBNTURMEXCHL ESTPRT NZLESPKWT NOR BGDKHMKENPAKSENNIC MARGTMJORSLVECUALBPERMKDBGRCOLZAFCRIMUSBWA TURMEXCHL PRT NZLESP BELSWE NOR<br>BWA EGY<br>R2=0.37 BRA R2=-0.44<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Ln GDPpc Ln GDPpc<br>Exit Rate - GDPpc Survival Rate - GDPpc<br>MWI BGD<br>KHM PAK<br>YEM BRATUR<br>IRN EGY<br>LAO JOR ZAFCRI<br>UGATZABFAMLI KENLAO SENNICCMR MAR ECUALBPERMKDBGRDOM BWA MEXCHL EST ESP NOR BFAMLI KEN SENNIC MARGTMSLVECUALBPERIRNMKDDOMCOLMUSBWA MEXCHL PRT NZL BEL<br>KHM PAK EGY GTMJORSLV COLZAFCRIMUS TUR PRT NZL BELSWE MWI UGATZA EST ESP<br>BGD BRA R2=-0.36 CMR R2=-0.02<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Ln GDPpc Ln GDPpc<br>2<br>.5<br>1.5<br>.4<br>1<br>Entry Rate<br>.3<br>.5<br>Ln Number of Destinations per Exporter<br>0 .2<br>.6 .6<br>.5 .5<br>.4 .4<br>Exit Rate<br>Survival Rate<br>.3 .3<br>.2 .2<br><!-- End of picture text --> Notes: The measures plotted are based on measures in the country-year level dataset (file CY.dta) averaged across the 2006-2008 period for each country for each country except Kuwait for which data is for 2009 and Portugal for which averages are across the 2003-2005 period. GDP per capita is in current USD per inhabitant.

The panels in Figure 1 show that more developed economies have a larger number of exporters as well as larger average exporter sizes.<sup>38</sup> More developed economies are also characterized by more concentrated export sectors, in terms of higher shares accounted for by the top 5 percent of exporters. Exporters in more developed economies exhibit a more diversified portfolio in terms of products and destinations. However, more developed economies are less dynamic in terms of exporter churning. In such economies, larger pools of more productive firms may already be operating in the exporting sector and taking advantage of the profitable opportunities in markets abroad, making it less appealing to potential exporters to attempt an entry thus reducing entry rates. If those firms are well established, their probability of exit is also

> 38 A similar pattern is found for median exporter size.

24
The advantage of this approach is that while we do not have data on savings and consumption choices with skills, we do have data on household wealth and skills acquisition from, for example PISA to estimate the skills cost function. How this is estimated is discussed below. The disadvantage is that the capital stock, _K_ , is fixed; here we define it as the sum of all endowments, . KK= LL∫kkff<sup>kk</sup> (kk)ddkk By modeling education cost as a function of initial endowment of wealth, we can remove credit constraints and assume the following budget constraint: (h) − EEtt where (h) is the wage for a unit of labor with skill level cc1,tt + cc2,tt ≤(1 + rr _h_ , tt)kk+ ττ is the skills cost LL + wwtt function, is the redistribution of revenue from emissions tax receipts, and L is the total stock tt ee(h, kk, θθ) ww ee(h, kk, θθ) EEtt of labor which is assumed to be fixed. Because there is no credit constraint, the solution to the ττ LL ∂ ∂ (h) consumer problem can be characterized by the choice of _h_ alone where _h_ : = ~~;~~ (h,kk,θθ) optimal choices for 1 2 follow from _h_ . ∂∂h ∂∂h cc , cc Education cost, , is defined as (h−γ )<sup>2</sup> 0 1α 2α + 2 bb 3 . In equilibrium, ee(h, kk, θθ)∂ = ∂ (h) ~~,~~ and as a result, ee(h, kk, θθ) = γγaa (γγ + γγ∂ (h) 1(kk) + γγ0 1α (kk)<sup>2</sup> (h,kk,θθ) γγ2αθθ)<sup>−1</sup> 3 which is a linear function with wealth and wealth squared as independent ∂∂h ∂∂h h(θθ, kk) = ∂∂h γγaa (γγ + γγ (kk) + variables, In other words, the functional form of the skills cost function is based on typical, linear bb γγ (kk)<sup>2</sup> + γγ θθ) + γγ cognitive production function estimated with data on test scores (Todd and Wolpin 2003). Parameters and are changed to provide a counterfactual skills cost function as described below. They are assumed to be equal to 1 and 0, respectively, in the baseline case. aa bb γγ γγ This education cost is the monetized cost of an individual investing in skills. It might represent tuition payments or foregone earnings from attaining a higher level of education, but it would also reflect intangible costs including the disutility of effort invested by an individual into studying, for example. This cost whether tangible or intangible is monetized because of the equilibrium condition requiring that the marginal cost of skills equals the marginal benefits, which is the wage skill premium. As discussed below, in steady state, the skills cost function represents the efficiency of the education system to produce skills conditional on an individual’s level of family wealth and 8
# Table 3.6.

Turkey:mNoinal Wages, Simple Averages XtL'LQay and Indexes)

|||M|anufacturing||_|Manufacturing|<br>and Mining|
|---|---|---|---|---|---|---|---|
|||All public with|Surveys|||||
|Year|Census<br>all establ.,<br>per employee|<br>private<br>establ.<br>> 10 persons<br> <br>engaged,<br>per person eng.|All<br>establishments<br>> 10 employee;<br>per employee|SMI,<br>per person<br>engaged<br>|**SII,**<br>per<br>insured|Censuses,<br>**all**establ.,<br>per employee|Manufacturing<br>surveys with<br> <br>mining<br> <br>census|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|1984|-|3,251.4|-|2,412.7|1,357.5|-|3,267.'2|
|1980|888.4|1,060.3|-|872.7|457.4|899-7|1,055.6|
|1970|"4.1|48.3|47.8|40.7|?5.8|43.8|47.4|
|1963|18.7|-|20.0|-|17.2|18.9|20.2|
|1960|_||19.0|15.7|13.9|-|18.8|
|1950|4.3|||||||
|||_|_|||||
||||Indexes|,<br>base yearI1||||
|1980- 84||3.07|-|2.76|2.97|_.10||
|1960-80|-||-|55.59|32.91|<br>-|56.15|
|1970-80|20.15|21.95|-|21.44|12.78|20.54|22.27|
|1963-70|2.26|_|2.39|-|2.08|2.12|2.?5|
|1960-70|-||2.52|2.5q|2.58||2.52|

a > 10 employees and > 10 hp.

Sources: Col.s 1, 2, 3 - Statistical Yearbook of Turkey, several issues. Col. 4 - World Bank from SPO. Including bonuses but excluding social security and pension funds payments. Col. 5 - My computations based on Statistical Yearbook of Turkey, several issues. Col. 6 - Statistical Yearbook of Turkey, several issues. Col. 7 - My computations based upon the data behind col.s 2, 3, and 6.
- 20 -

Figure 13. Number of days with power outages in Europe and Central Asia <!-- Start of picture text --> 250<br>350<br>Average (left scale)<br>Maxmum (right scale)<br>200 300<br>250<br>150<br>200<br>100 150<br>100<br>50<br>50<br>0 0<br>Source: BEEPS 2005.<br>Albania Georgia Tajikistan Kyrgyzstan Bulgaria Uzbekistan Ukraine Romania Yugoslavia Turkey Macedonia Belarus Kazakhstan Croatia Estonia Hungary Bosnia Armenia Moldova Russia Latvia Slovenia Poland Lithuania Czech Rep. Slovakia<br><!-- End of picture text -->

# **IV. EMPIRICAL MODELS AND DATA**

The following simple cost function is considered: _C_  _F_ ( _W_ , _Y_ ; _A_ ) where _W_ and _Y_ represent input prices and outputs, respectively. _A_ is a productivity or fixed cost parameter, which is assumed to be affected by the quality of public infrastructure and other unobserved factors.<sup>19</sup> Based on the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976; Fuss, 1977), a variant of the translog cost function is examined: where _C_ denotes the amount of total operating cost, _Y_ is an output proxy, and _Wi_ is the _i_ th input price. _Z k_ represents the _k_ th measure of infrastructure quality.

> 19 This potentially includes a variety of institutional and structural unobservables, which constitute a statistical error in the model.
order of magnitude is given / by the fact that hospitals rank consistently as long or short stay whatever ward is taken. In general, though, children and maternity patients have shorter lengths of stay than patients in the male and female wards. The variation in lengths of stay between hospitals suggests scope for reducing length of stay, notably in Rumphi but also in Salima, Mulanje and Chiradzulu.

By international standards, however, these lengths of stay are already relatively low. It is very unlikely that case-mix differences would explain all the variation in length of stay, especially between Kasungu, Mulanje and Salima, though Rumphi does have a regional role.

Bed occupancy rates and lengths of stay therefore explain some of the unit cost variation between hospitals, notably the low cost per inpatient day in Rumphi, but not all of it. For example, Nkhata Bay and Mulanje have very similar bed occupancy rates and fairly similar lengths of stay, but Mulanje is over 50% more expensive than Nkhata Bay in terms of cost per inpatient and 20% more expensive per inpatient day. There is clearly a difference in the efficiency with which hospitals use resources that is independent of occupancy rates or length of stay. In order to explore this further, it is important to distinguish those costs which can be locally controlled and those over which the hospital has no control. For example, the source of heat for kitchen and laundry (mains electricity or wood) and the technical efficiency of the equipment strongly influence cost and cannot be changed locally. However, other costs are more susceptible to local control. Of these, those which varied most were food and drugs. For example, the detailed analysis of drugs' sunnlies to OPn and wardq %howed 'onniderahlp variations in practice between hospitals, which seem unlikely to be related simply to the characteristics of patients. The cost of drugs per new outpatient varied between KO.5S and K1.84. IX one hospital, 18
WPS3754

# **Reaching out:**

# **Access to and use of banking services across countries**

## **Thorsten Beck, Asli Demirguc-Kunt and Maria Soledad Martinez Peria**

**Abstract:** This paper (i) presents new indicators of banking sector penetration across 99 countries, based on a survey of bank regulatory authorities, (ii) shows that these indicators predict household and firm use of banking services, (iii) explores the association between the outreach indicators and measures of financial, institutional, and infrastructure development across countries, and (iv) relates these banking outreach indicators to measures of firms’ financing constraints.  In particular, we find that greater outreach is correlated with standard measures of financial development, as well as with economic activity.  Controlling for these factors, we find that better communication and transport infrastructure and better governance are also associated with greater outreach.  Government ownership of financial institutions translates into lower access, while more concentrated banking systems are associated with greater outreach. Finally, firms in countries with higher branch and ATM penetration and higher use of loan services report lower financing obstacles, thus linking banking sector outreach to the alleviation of firms’ financing constraints.

### World Bank Policy Research Working Paper 3754, October 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors are with the World Bank’s research department.  We would like to thank Ross Levine for insightful discussions and Jerry Caprio, Stijn Claessens, Augusto de la Torre, Xavi Gine, Patrick Honohan, Leora Klapper, Anjali Kumar, Inessa Love, Susana Sanchez and seminar participants at the World Bank and the Inter-American Development Bank for useful suggestions.  Hamid Rashid and Andrew Claster provided excellent research assistance.

1
We focus on the effect of government support on the following main aspects of firms’ operations: workforce, hours of operations, and expectations. In particular, we use the following four outcome variables available in COV-ES: (i) percentage change of permanent full-time workers since December 2019, and three binary outcome variables, (ii) whether the firm decreased its total number of temporary workers since the onset of the pandemic, (iii) whether the firm decreased its total hours of operation per week compared with the same month one year ago, and (iv) whether the respondent anticipates falling in arrears on outstanding liabilities within the next six months of the date of the COV-ES interview.

To alleviate further concerns about selection bias and endogeneity, we use two methods, matching, and panel estimation. For matching estimation, we use a rich set of information about each firm from the baseline ES to match firms that received government support in COV-ES with the ones that did not. In this process, we take into account details of support measures of each country. For example, when matching firms in the Georgia data, we forced the matching algorithm to perform exact matching within the sectors of hotels and restaurants because the Government of Georgia provided a specific set of support to firms in these sectors. For panel estimation, we use both rounds of COV-ES in fixed effects regression analysis which addresses selection bias stemming from the time-invariant characteristics of firms. In these specifications, we also include a long list of controls from COV-ES to account for time-varying aspects. Panel regressions also enable us to use COV-ES questions that break down the government support that firms received into separate categories, such as cash transfers, fiscal relief, and wage subsidies (with many firms receiving combinations of support types).

We re-estimate models using a restricted sample as an additional check on selection bias. In particular, we try to exclude from the analysis the firms that were not eligible to receive the support and are likely not appropriate comparators to those that did. In forming this restricted sample, we also exclude firms that report no need for such support or lack of awareness about its existence. Even after these steps, we consider that concerns about selection bias probably remain to some extent. We thus avoid commenting on the estimated size of the effectiveness of government support and instead focus on whether it is above zero, validating this focus through discussion about the expected directionality of these biases across our estimated models.

3
Vyas, S., and C. Watts. 2008. “How does economic empowerment affect women's risk of intimate partner violence in low and middle income country settings?: A systematic review of published evidence.” _Journal of International Development_ 21 (5): 577-602.

Wang, C.E., L.S. Vittinghoff, and Z.M. Rong. 1996. “Effects of family planning with husband participation on pregnancy and induced abortion rates in urban China.” Abstract of paper presented at annual meeting of the American Public Health Association, New York Warner, R.L. 1991. “Does the Sex of Your children Matter? Support for Feminism among Women and Men in the United States and Canada.” _Journal of the Marriage and Family_ 53: 1051-6.

Warner, R.L., and B. Steel. 1999. “Child Rearing as a Mechanism for Social Change: The Relationship of Child Gender to Parents' Commitment to Gender Equity.” _Gender and Society_ 13: 503-17.

Washington, E. 2004. _Family matters: How daughters affect their legislator fathers’ voting on women’s issues._ Working paper, Yale University.

Washington, E. 2008. “Family Socialization: How Daughters Affect Their Legislator Fathers' Voting on Women's Issues.” _American Economic Review_ 98 (1): 311-32.

West, C., and D.H. Zimmerman. 1987. “Doing Gender.” _Gender and Society_ 1 (2): 125-51.

World Bank. 1999. _PAD for Rajasthan District Primary Education Program._ Washington, DC: World Bank.

Yount, K.M., R. Langsten, and K. Hill. 2000. “The effect of gender preference on contraceptive use and fertility in rural Egypt. _Studies in Family Planning_ 31 (4): 290-300.

37
# **Table A11. Associations between SCP Quality Determinants and Academic Outcomes** **_Using Program-Level Data for all Survey Countries_**

|_Dependent variable:_|_Dropout_<br>_Rates_|_Extra time-to-_<br>_graduate_|
|---|---|---|
||(1)|<br>(2)|
|_Costs_|||
|HEI scholarships are used by some students|1.438<br>(0.876)||
|Loans b banks are used b some students|-1.284|2.595*|
|y y|(0.834)<br>|(1.555)|
|HEI has received funding from government|1.309*<br>(0.739)<br>||
|HEI has received funding from private sector|1.466*<br>(0.815)||
|||-0987**|
|Annual tuition (2019 PPP USD, log)||.<br>(0.427)|
|_Training and curriculum_|||
||-2.111**||
|Curriculum is fixed|(0.899)||
|Offers remediation classes during the program|-1.003<br>(0.782)||
|Years since last update to curriculum|-0.0439<br>(0.111)<br>||
|Enrollment trends are very important to update program|-1.122<br>(0.703)||
|||-7.358***|
|Promotes work under hardship or pressure||(2.236)|
|Test required for graduation||1.988<br>(1.623)|
|Thesis or research project required for graduation||3.153*<br>(1.778)|
|HEI perception of labor market is very important to update||<br>5.069***|
|program<br>||(1.646)<br>|
|Analyze student performance to solve problems more than||-2.547|
|once per year||(2.358)<br>|
|Collect student satisfaction data more than once per year||-1.135<br>(1.582)|
|_Link with productive sector_|||
||-0.667|-3.652**|
|Industry helps with student evaluation or curriculum design|(0.790)|(1.752)|
|Industry lends or provides equipment to program for student|<br>-1.769***||
|<br>training|(0.670)<br>||
|Somebody in charge of industry relations|-1.507<br>(1.198)||
|HEI i d f b ii|<br>-0.817||
|trans stuents or jo ntervews|(0.876)||
||-2.198**||
|HEI provides job market information|<br>(1.036)||
|||1289|
|HEI has an employment center||.<br>(1.485)|
|_Faculty_|||
|Number of faculty (log)|||
|||-00524**|
|Percent of faculty working in the industry||.<br>(0.0205)|
|Percent of facult with SCP deree|-0.0132|<br>-0.0546**|
|y g|(0.0126)|(0.0266)|
||<br>0.00629||
|Percent of faculty with graduate degree|(0.0127)||
||0.0217*||
|Percent of faculty working in the industry|<br>(0.0118)||
|Research skills are very important when hiring faculty|-1.096<br>(0.801)||

83