rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: **Effects of Platinum Card on Credit Card Usage** **<u>(Transaction</u> Data)**

|Gold (30M CL) - Gold (20M CL)|0.009<br>[0.011]|0.008<br>[0.011]|-0.010<br>[0.009]|-0.009<br>[0.009]|0.011<br>[0.018]|0.012<br>[0.018]|
|---|---|---|---|---|---|---|
|Platinum (40M CL) - Gold (30M CL)|0.061***<br>[0.011]|0.053***<br>[0.012]|-0.005<br>[0.007]|0.000<br>[0.008]|-0.090***<br>[0.017]|-0.095***<br>[0.018]|
|Platinum (50M CL) - Platinum (40M CL)|0.011|0.015|0.009|0.007|-0.023|-0.017|
||[0.024]|[0.025]|[0.013]|[0.013]|[0.033]|[0.032]|
|Mean (Gold (CL 20M))|0.1<br>[0.0|05<br>07]|0.0<br>[0.0|54<br>06]|0.<br>[0.|673<br>012]|
|Controls|No|Yes|No|Yes|No|Yes|
|Number of clients:|||||||
|Gold (20M CL)|737|737|737|737|737|737|
|Gold (30M CL)|552|552|552|552|552|552|
|Platinum (40M CL)|1094|1094|1094|1094|1094|1094|
|Platinum (50M CL)|109|109|109|109|109|109|
|p-value (a)=(b)|0.008|0.020|0.708|0.549|0.002|0.001|
|p-value (a)=(c)|0.946|0.779|0.223|0.321|0.363|0.440|
|p-value (b)=(c)|0.085|0.195|0.391|0.665|0.094|0.049|

Notes: Column 1 reports raw comparisons of share of visible transactions for clients with different credit limits/type of card. Column 2 reports comparisons controlling for income, female dummy, muslim dummy, Jakarta dummy, and age. Columns 3 and 4 report results for online transactions, while columns 5 and 6 report results for share of retail transactions. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.
to markets and better infrastructure could enjoy higher wages relative to those located in other regions (Ravallion and Jalan, 1999; Jalan and Ravallion, 2002). The omitted variable biases resulting from the inability to control for the spatial sorting of unobserved attributes do not, however, apply to all households and all locations equally. The ability sorting and agglomeration economies may a ff ect the wages in sectors which are technology and innovation intensive. Evidence from developing countries suggests that only a small fraction of activities in urban centers qualify for such a categorization (Fafchamps and Shilpi, 2005). Similarly, because of the predominance of agricultural activities, di ff erences in the rates of return between rural areas across regions are likely to arise primarily from the di ff erences in public capitals and access to markets.

The spatial di ff erences in the rate of return to the same attribute can also be sustained in an equilibrium if migration is costly (Dahl, 2002; Kanbur and Rapoport, 2005; Bayer, Khan and Timmins, 2007). The cost of migration tends to vary across individuals and households as they face di ff erent levels of risks and costs. The migration costs are likely to be higher for the poorer and middle income households who face credit constraint as well as higher opportunity costs of disposing of existing assets. Various costs associated with migration are likely to pose no serious hindrance to the mobility of members of well-o ff households. Similarly, short-term migrations such as commuting and temporary migration of a member of the household involve less cost than the long-term and permanent migration of the entire household. Proximity can also in fl uence the formation of a migration network and through it, migration fl ows in subsequent periods (Kanbur and Rapoport, 2005). As a result, the di ff erence in returns to attributes will be smaller across areas which are in close proximity to each other.

Both locational sorting and migration literature thus suggests that returns to observed household attributes will vary across households depending on their position in the welfare distribution, and across regions depending on their relative proximity and locational characteristics. In this paper, returns to observed household attributes are estimated using the Machado and Mata (2005) quantile regression based decomposition technique. The estimation is carried out using household level data from two rounds of the Household Expenditure 2
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71.6 percent. By itself the _maquila_ premium accounted for 0.31 poverty points in the national poverty level (0.44 when allowing for gender-specific effects).

**Figure 3. Percentage of Total Poverty Headcount Attributable to Various Factors**

<!-- Start of picture text --> 2.50<br>2.00<br>1.50 gender wage premium<br>maquila wage premium<br>1.00 maquila employment<br>0.50<br>0.00<br>1991 1995 2001 2006 Average<br>change in the headcount ratio<br><!-- End of picture text --> <!-- Start of picture text --> 1991<br><!-- End of picture text --> <!-- Start of picture text --> 2001<br><!-- End of picture text --> _Source_ : Authors simulations, based on estimation results from specification 3a in table 6. _Note_ : Percentages are computed as the marginal difference in the poverty headcount shown by the cumulative effects displayed in table 8.

A third simulation captures the cumulative poverty effects of _maquila_ premia and employment. This simulation sets the wage of all _maquila_ workers equal to zero in order to produce a rough idea of how important the _maquila_ sector is for poverty alleviation in Honduras.<sup>16</sup> The results show that had there been no _maquila_ jobs in Honduras in 2001, the moderate poverty headcount would have been almost 2 percentage points higher on average (73.0 percent instead of 70.9 percent).

The results presented in table 8 are complemented by the presentation of the marginal contribution of each component ( _maquila_ , _maquila_ *women, and employment) shown in figure 3.<sup>17</sup> On average the _maquila_ sector accounts for almost 1.45 percentage point reduction in the poverty headcount, of which 1.00 point is attributable to employment creation, 0.35 points to _maquila_ wage premium, and 0.10 to the wage premium of women working in the _maquila_ sector.

These poverty effects seem rather small. One has to bear in mind, however, the limited impact of the _maquila_ sector for overall household income. Wages paid in the _maquila_ sector account for less than 4 percent of total household income in Honduras. Furthermore, the incidence of poverty among _maquila_ workers is lower than the national average (see table 3). Finally, as shown by the 16. This simulation is not a counterfactual of how the Honduras economy would have looked in the absence of a _maquila_ sector. Creating such a scenario would require estimates of the general equilibrium effects of the sector. Therefore this simulation should be seen as an upper bound estimate of the poverty reduction effects of the maquila boom.

17. The poverty effects attributable to the different components are equal to the marginal difference in the poverty headcount shown by the cumulative effects presented in table 8. There is an obvious problem of path dependency in our simulations.

16
This paper has responded to these observations by developing new approaches to measurement, as well as a simple theoretical framework that captures key features of education systems in Latin America. The first application of our new measurement approach used publicly available data from the school principal surveys conducted by PISA to construct a school management index spanning 65 countries. This PISA-based school management index can be well-validated against the more detailed (though also much more expensive) index based on the WMS. As such, it has clear value in settings where cross-country coverage is important, enabling researchers to study and compare the (within-country) correlation between management and student/school-level outcomes for a far wider set of countries than was previously possible.

Our second application used publicly available data from a national administrative survey to construct a school management index spanning all public schools in a single country: Brazil. This Prova Brasil-based index was also well-validated against the WMS. This second application has value in settings where within-country coverage, and the availability to merge with other administrative data sets, is important.

It is striking that both of our new school management indices confirm the strong positive correlation of school management scores with school-level student outcomes first reported in Bloom et al. [2015a]. A positive relationship holds for the global PISA sample, and in the census of schools in Brazil. Our theoretical framework, for the first time, formalizes the possible causal mechanisms in one of these regions: Latin America. We argued that strong people management practices may be improving student learning through a combination of teacher selection and incentive effects, and that schools could be encouraged to adopt practices that reward good performers, develop good performers, and create a good employee value proposition. Looking to operations management, we argued that strong operations practices may be improving student learning through a combination of teacher selection and household incentives, and that schools could be encouraged to adopt practices promoting quality of delivery in the classroom and adopt processes to review school performance and drive change. We also provided a suggestive set of evidence for these channels.

Improvements to management practices present an untapped opportunity for potentially large improvements in educational outcomes, particularly in cash-strapped regions of the world. One pos27
<!-- Start of picture text --> 10<br>——— pre-OOP consumption<br>— post-OOP consumption<br>8<br>Ho<br>a<br>_—<br>°<br>s 6<br>5<br>£<br>n<br>co<br>i=<br>oc<br>6S 4<br>=<br>|<br>77)<br>Cc<br>°<br>S)<br>aie ;<br>0<br>0 2 4 & 8 1<br>Cumulative proportion of population, ranked from poorest to richest<br><!-- End of picture text -->
implemented especially by women in the poorest households, who are less likely to have access to formal insurance schemes and are more economically vulnerable. These strategies come at the cost of lower consumption levels both in terms of household per capita food expenditures and wives’ nonfood private expenditures.

Hence, in this paper, we provide strong evidence that in non-nuclear households in developing countries spouses may act noncooperatively and invest in strategies aimed at keeping their resources out of the reach of other household members. More specifically, this article contributes to a better understanding of how polygamy and, even more, the potential risk of polygamy shape women’s saving and resource allocation decisions, which is crucial to being able to design adequate and well-targeted policies aimed at developing social protection for vulnerable socioeconomic groups, in particular women.

REFERENCES Anderson, S., and J. M. Baland. 2002. “The Economics of Roscas and Intrahousehold Resource Allocation.” _The Quarterly Journal of Economics_ 117 (3): 963–95.

Antoine, P. 2002. “Les complexités de La nuptialité: de la précocité des unions féminines à la polygamie masculine en Afrique.” DIAL working paper DT/2002/06, Paris.

Ashraf, N. 2009. “Spousal control and intra-household decision making: An experimental study in the Philippines.” _The American Economic Review_ 99 (4): 1245– 77.

Becker, G. S. 1974. “A theory of marriage.” In Theodore W. Schultz. _Economics of the Family: Marriage, Children, and Human Capital_ , 299–351. University of Chicago Press, Chicago.

Boltz, M., K. Marazyan, and P. Villar. 2016. “Income Hiding and Informal Redistribution: A Lab-in-the-field Experiment in Senegal.” Working paper. PSE working papers 2015-15, Paris. <halshs-01157710v2> Boltz-Laemmel, M., and P. Villar. 2013. “Les liens des migrants internes et internationaux à leur ménage d’origine: portraits croisés de familles étendues sénégalaises.” _Autrepart_ 67–68.

Bove, R., and C. Valeggia. 2009. “Polygyny and Women’s Health in Sub-Saharan Africa.” _Social Science & Medicine_ 68 (1): 21–29.

Browning, M. 2000. “The Saving Behaviour of a Two-person Household.” _Scandinavian Journal of Economics_ 102 (2): 235-251.

Castilla, C., and T. Walker. 2013. “Gender Roles and Asymmetric Information: NonCooperative Behavior on Intra-Household Allocation.” Unpublished manuscript, PDF, July 2013.

22
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NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 7. Optimal and existing fuel levy in select Sub-Saharan African countries**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing Optimal for maintenance<br>US cents per liter<br><!-- End of picture text --> _Source_ : Gwillliam and others, 2008.

In Niger current capital spending falls well below what is needed to clear the rehabilitation backlog and maintain the existing network. Taking into account the current distribution of network conditions and working toward a target of clearing the current rehabilitation backlog within a fiveyear period and maintaining the network, it is possible to produce detailed estimates of the rehabilitation and maintenance requirements for Niger’s road network. The current levels of capital expenditure in Niger are not high enough to address the rehabilitation problem: they fall 65 percent below the rehabilitation norm, one of the biggest deviations among West African countries.

**Figure 8. Spending on road maintenance and rehabilitation in Niger and other Sub-Saharan nations**

<!-- Start of picture text --> 60%<br>40%<br>20%<br>0%<br>-20% Nigeria Niger Senegal Cote Liberia Ghana Benin<br>d'Ivoire<br>-40%<br>-60%<br>-80%<br>Maintenance Rehabilitation<br>Source: Gwilliam and others 2009.<br>Note: Analysis of adequacy of road maintenance spending can only be<br>performed for the primary network under federal jurisdiction.<br>Spending as percentage of norms<br><!-- End of picture text --> Spending on maintenance is around 70 percent below what is required to properly maintain Niger’s road network (figure 8). Road maintenance funding paradoxically declined after the creation of CAFER<sup>6</sup> , the road fund created in November 1999; a lack of realism in maintenance programming has led to an overcommitment of CAFER’s resources. A lack of resources and the accrual of substantial arrears in recent years have been two main obstacles to the proper maintenance of the road network and the development of a genuine private-public works subsector (World Bank 2008c). Additional funding will be needed despite implementation of road user charges, in particular for secondary and tertiary networks; 89

> 6 Caisse Autonome de Financement de l’Entretien Routier.

15
<!-- Start of picture text --> Figure 18: Fiscal and External ‘Space’ Prior to the Global Downturn<br>8<br>MIC SAR<br>6<br>4<br>2 Ind Npl<br>Bgd<br>Lka<br>0<br>-2<br>Pak<br>-4<br>-6<br>-8<br>-10<br>-6 -4 -2 0 2 4 6<br>Fiscal space index (pre-downturn)<br>External space index (pre-downturn)<br><!-- End of picture text --> _Note_ : Axes are drawn at median levels Relative to a set of lower middle income and middle income countries, prior to the global downturn, South Asian countries ranked well below the median on a fiscal space index (save for Nepal) (see Figure 18).<sup>8</sup> However they did somewhat better on the external space index reflecting, for instance in India, an accumulation of reserves and low private external debt. In terms of the indices countries far in the N-E quadrant should generally have the ability to run a countercyclical fiscal policy while those deep in the S-W quadrant would not. Prior to the crisis S. Asia fell in the “undefined” zone. Though Pakistan and Sri Lanka were arguably closer to the “no space” zone they appear to be firmly there after the global downturn has intensified.

To combat the economic and social downturn, South Asian countries have announced fiscal packages to boost their economies. The fiscal stimulus has been largely targeted towards building social and physical infrastructure as well as strengthening the social safety nets. Infrastructure spending can provide an effective counter-cyclical stimulus in South Asia. The South Asia region enters the current global crisis with very low endowments of infrastructure stock and services. Counter-cyclical public spending in infrastructure is an effective tool both to provide the foundation for rapid recovery and job creation, and to develop a robust economic platform for long term growth.

> 8 The median refers to data for lower middle income and middle income countries and so does not strictly apply to Bangladesh and Nepal. Also in these two countries the relatively large availability of grants gives the impression of higher fiscal and external space. The fiscal space index reflects levels of public debt and fiscal deficits over the last five years, while the external space index reflects external private debt, reserves, and current account deficits.

32
|Sabah ForestryTechnical Assistance Project||
|---|---|
|Sabah ForestryTechnical Assistance Project||
|Sabah ForestryTechnical Assistance Project||
|National ForestryResearch and Development Project||
|National ForestryResearch and Development Project||
|National ForestryResearch and Development Project||
|National ForestryResearch and Development Project||
|National ForestryResearch and Development Project||
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Sabah Land Settlement and Environmental Management Project|Syed Husain|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project|Omporn Regel|
|Polytechnic Development Project<br>|Omporn Regel|
|Primaryand SecondaryEducation Project(02)||
|Primaryand SecondaryEducation Project(02)||
|Primaryand SecondaryEducation Project(02)||
|Primaryand SecondaryEducation Project(02)||
|Primaryand SecondaryEducation Project(02)||
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Health Development Project|L. Richard Meyers|
|Sarawak Power Project||
|Sarawak Power Project||
|Sarawak Power Project||
|Sarawak Power Project||
|Sarawak Power Project||
|Sarawak Power Project||
|Sarawak Power Project||
|RISDA - Rubber||
|RISDA - Rubber||
|RISDA - Rubber||
|RISDA - Rubber||
|RISDA - Rubber||
Policy Research Working Paper 8151

# **Abstract**

The ease with which workers can move between sectors has a strong impact on the effects on labor markets of shocks such as changes in world prices or migration flows. This paper introduces an approach to labor mobility with frictions under which worker capabilities (their efficiencies in different sectors) depend on their sector affiliation. If workers in sector a move to sector a’, their efficiency shortfall due to a capability misfit compared to what is needed in a’ (and possessed by workers already in a’) is measured by a proximity parameter, 0 ≤ proxa,a’ ≤ 1. If proxa,a’ < 1, the efficient quantity reaching a’ is below the physical quantity. In this setting, profit-maximizing producers are willing to pay the same wage per efficiency unit irrespective of worker origin and thus pay less efficient workers a lower wage per physical unit. This approach to labor mobility is tested in a static CGE model that is applied to an illustrative sub-Saharan African dataset with sector proximities defined using the approach of the product-space literature. Simulations of positive export price shocks show that, the higher the proximities, the stronger the labor reallocation and the welfare gains.

This paper is a product of the he Development Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hlofgren@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
informal sector may have its own niche in financing very small firms, we find no evidence that it scales up and is an efficient substitute for bank financing for most firms.

# **7. Conclusion**

With one of the largest and fastest growing economies in the world, China provides many puzzles. More recently, it has been singled out as a counterexample to the findings of the finance and growth literature: while Chinese financial systems and institutions are underdeveloped, its economy, particularly its private sector, has been growing very fast. One explanation for this observation has been that China has alternative mechanisms and institutions that play an important role in supporting its growth and these alternative mechanisms are good substitutes for standard corporate governance mechanisms and financing channels (Allen, et al. 2005).

In this paper we take a closer look at the financing of 2,400 Chinese firms and their performance, using a detailed firm level survey. We find that in China, private firms’ use of formal financing channels is comparable to its use in other developing countries, and it is financing from these sources that is positively associated with firm performance. Contrary to earlier findings, fund raising from informal channels is not associated with faster firm growth. To the extent that there are measurable benefits of informal financing, they arise only when retained earnings is classified as informal financing, as in some earlier studies. These findings have broader implications for the role of informal versus formal financial systems. Our results contradict the belief that nonstandard financing mechanisms provide effective substitutes to formal financing channels in promoting growth. They suggest that the role of informal relationship based 44