rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6: Hedonic Price Regression Results, Urban Kigali, Spatial Error Model</u>**

||<br>**(1)**|<br>**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Log of parcel area in sqm|0.692***<br>(0.009)|0.665***<br>(0.009)|0.578***<br>(0.010)|0.558***<br>(0.009)|
|Log distance from the village to the CBD in km|-0.390***|-0.124|-0.203***|-0.069|
||(0.105)|(0.082)|(0.065)|(0.052)|
|Log number of workers at the cell level in 2014|0.108***<br>(0.021)|0.097***<br>(0.020)|0.070***<br>(0.019)|0.061***<br>(0.018)|
|Log of residential land at the cell level|0.037|0.020|0.010|0.018|
||(0.034)|(0.032)|(0.029)|(0.027)|
|Log distance to tarmac road in meters||-0.157***<br>(0.008)||-0.135***<br>(0.008)|
|Log distance to a primary school in meters||-0.053***<br>(0.020)||-0.046***<br>(0.018)|
|Log volume of buildings in m3|||0.169***<br>(0.005)|0.165***<br>(0.005)|
|Density of built up area of the village|||1.225***<br>(0.130)|1.092***<br>(0.127)|
|Log average height of buildings at the village in meters|||1.590***<br>(0.147)|1.623***<br>(0.142)|
|Log SD of building height at the village in meters|||-0.312***<br>(0.054)|-0.373***<br>(0.053)|
|Constant|4.665***|5.899***|2.654***|3.540***|
||(0.451)|(0.431)|(0.401)|(0.386)|
|Number of observations|12,592|12,592|12,592|12,592|
|Log Likelihood|-14,672.830|-14,494.370|-13,982.850|-13,835.030|
|<br>Wald Test (df = 1)|9,528.585***|5,194.450***|3,499.423***|2,296.457***|
|Lambda|0.905***|0.869***|0.834***|0.787***|

_Note:_ Dependent variable is the log of the sales price per parcel for residential properties in Kigali in 2013-16. The spatial weights are based on threshold distance of 435m at which each property has at least one neighbor. Alternative distance band weights (750m, 1,000m, 1,500m, and 5,000m) were used, but the one of 500m produced better fit using the likelihood ratio and AIC criteria. Standard errors in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 10%. The ‘spdep’ package of R was used for the estimation.

20
WPS4017

# **Do Subsidized Health Programs in Armenia**

# **Increase Utilization among the Poor?**

### Diego F. Angel-Urdinola and Shweta Jain<sup>∗</sup> World Bank

### <u>Abstract</u>

This article analyzes the extent to which the Basic Benefit Package (BBP), a subsidized health program in Armenia, increases utilization and affordability of outpatient health care among the poor. We find that beneficiaries of the BBP pay approximately 45 percent less in fees for doctor visits (and display 36 percent higher outpatient utilization rates) than eligible users not receiving the BBP. However, even among BBP beneficiaries the level of outpatient health care utilization remains low. This occurs because the program mainly provides discounted fees for doctor visits, but fees do not constitute the main financial constraint for users. Our estimates suggest that other non-fee expenditures, such as prescription medicines, constitute a more significant financial constraint and are not subsidized by the BBP. As a result, outpatient health care remains expensive even for BBP beneficiaries.

## World Bank Policy Research Working Paper 4017, September 2006

_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 benefited from valuable feedback from Aleksandra Posarac, Cem Mete, Panagiota Panopoulou, Emil Tesliuc, and participants to the ECSHD (Europe and Central Asia Human Development) Brown Bag Lunch series.
In order to further control for historical differences, Horowitz (2006) uses more homogeneous sub-samples, such as only OECD countries or only countries from the Former Soviet Union, and the negative relationship still holds. However, as directions for further research, he recommends empirical studies of income and temperature variations within large, heterogeneous countries, which would provide much more thorough control for historical differences.

This is exactly what we will do in the present paper. Using data from 1,829 districts in Peru<sup>2</sup> , we will estimate contemporary relationships between temperature and income as well as between temperature and life expectancy. While it is always dangerous to draw inferences about changes in time from cross-section estimates, we will use the estimated relationships to roughly assess the likely direction and magnitude of the effects of climate change in Peru.

Two different types of climate change will be assessed. First, the documented recent climate change in each of the 1,829 districts, as estimated from average monthly temperature series from 1948 to 2008 for all the Peruvian meteorological stations that have contributed systematically to the Monthly Climatic Data for the World (MCDW) publication of the US National Climatic Data Center.

Second, we will use the predictions of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC4) climate models to simulate the possible effects of projected future climate change in Peru.

The rest of the paper is organized as follows. Section 2 describes the data sources and provides descriptions of the key variables. Section 3 estimates the cross-district relationships between climate and human development, controlling for other key variables that also affect development. Section 4 analyzes past climate change for 24 meteorological stations across Peru, and estimates average trends in temperatures and precipitation. Section 5 uses the results from sections 3 and 4 to simulate the effects of past climate change on income and life expectancy in each of the 1829 districts in Peru. Section 6 summarizes the climate changes that are expected for Peru during the next 50 years, and section 7 simulates the likely effects of these changes on incomes and life expectancy. Section 8 concludes.

# **2. The data**

The data used for this paper consist of both cross-section data and time series data. The district level cross-section data base, which was used to estimate the relationship between climate and development in Peru, was constructed using data from many different sources. Table 1 lists the variables, their definitions, and the sources of the information.

> 2 There are 1831 districts in Peru, but we don’t include the districts of Mazamari and Pangoa of the Satipo province in Junín, because the local authorities didn’t accept to participate in the 2005 Census, thus we do not have social and economic data for these two districts.

4
by the private sector also has implications for quality is a matter of conjecture, although Jimenez et. al. (1995) argue that this is plausible.'<sup>8</sup> What accounts for the very large sheepskin effects found in the Philippines? Here, too, I discuss three possibilities: Credentialism, differences in the actual amount of learning acquired by students who complete the last year within a given level, rather than any intervening year, and discontinuities in ability associated with discontinuities in the cost of education across levels.

Credentialism and signaling are the most-frequently cited explanations in the literature for the presence of measured sheepskin effects in the United States. Referring to the original Hungerford and Solon (1987) article, Belman and Heywood (1991, p. 720) write that "the finding that a diploma had value independent from the accumulated years of education indicated that in addition to any role it plays actually making workers more productive, education also credentiates workers as more productive". In the Philippines, where information in the labor market is likely to be far less accurate than in the United States, credentialism of this sort may be even more important. One reasonable question might be what kinds of employers reward credentials. Separate estimation of discontinuous spline functions for government and private sector employees for the full sample of men show a university graduation effect that is more than twice as large in the private sector (.343, significant at the 0.1I% level) as in the public sector (.153, significant at the 5% level). This is similar to the pattern found in the United States (Heywood 1994), but different from that found in some African countries, where sheepskin effects

> **18** Moreover, a large fraction of students currently attending university in the Philippines are enrolled in technical subjects, such as engineering, which are likely to be associated with high rates of return (World Bank 2000, pp. 15-16).

19
**Table 1: Proposed** **<u>Governance Indicators for Health in Lewis and Pettersson (2009)</u>**

Budget and resource management _PEFA Indicators track budget credibility, comprehensiveness, transparency, execution, reporting, recording, and external audits and scrutiny_ Discrepancy between public budgeted health funds and the amounts received by health <u>providers</u> Irregularities associated with government payroll for health workers Differences in prices paid for similar medical supplies/equipment across health facilities Human resources Frequency of illegal side payments/bribes influencing hiring decisions and of payments for particular assignments _Existence of licensing requirements and of continuing educations programs and their operation_ Fraction of physicians or nurses contracted for service but not in site during the period(s) of observation _Types of incentives and accountability mechanisms facing_ _<u>public providers</u>_ Institutional providers _Incentives and accountabilities in hospital payments_ Average length of stay and bed occupancy rates Informal payments Frequency of illegal charges for publicly <u>provided health services</u> Perceptions of corruption and institutional quality Fraction of households, experts or public officials perceiving corruption in health; relative ranking of health sector on corruption index The Country and Policy Institutional Assessments (CIPA) for health Note: Governance determinants are italicized. Other items are governance performance measures. Source: Adapted from Tables 1 and 2 in Lewis and Pettersson 2009.

The list of five governance performance indicators proposed by WHO 2008 is a good starting point because it is parsimonious yet covers the full range of important public health care service functions (financial management, personnel management, drug management, client relations, and regulation). In addition, these indicators are attractive because they can be measured quantitatively and objectively and can be interpreted consistently. If sufficient resources were available to expand beyond these five governance indicators, then items from Lewis and Pettersson 2009 could be considered as additional candidates.

Most of these indicators, with some refinement, could be collected periodically to assess changes over time and across countries in ways that permit meaningful comparisons. Four of the indicators can be measured by cross-sectional surveys, the one exception ______________________________________________________________________________________

-19-
**Figure 6: Third principal component**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Component 3<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

# **B. Analysis of individual variables on the** **_f_lfp_r_**

In this section, we examine the effect of some individual variables on the _f_lfp_r_ in Colombia using a TVEM method. Their correlation with the principal components dictated the choice of variables. It is worth mentioning that both long and short-run factors can affect female participation in the labor market. In the long run, better education, reduced fertility and infant mortality, changes in women's marriage decisions, and changes in social norms can affect the evolution of female participation. In the short run, the _f_lfp_r_ responds to changes in economic activity.

# **i) Fertility rate**

Colombia experienced the demographic transition since the end of the 1960s from high fertility rates (6.2 children in 1967) to low fertility rates (1.8 in 2018). The reduction in the fertility rate was mainly the result of greater education for women, access to contraceptive methods, and the decline in infant mortality (Iregui-Bohórquez et al., 2021).

Based on the coefficients obtained through the TVEM, our analysis suggests that the relationship between fertility and women's labor participation varied over time from 1960 to 2018 (Figure 7). The results show a strong negative marginal effect from the fertility rate to the female labor participation. During 1960-1969, we observed a strong negative relation between fertility and women's labor participation, signaling that a reduction of one child per woman during this period was associated with an increase of 5.63pp in the _f_lfp_r_ , on average. Therefore, any reduction in fertility rates during this period was crucial for empowering women economically by enabling them to participate more 12
WPS4264

# **Financing of the Private Sector in Mexico, 2000-2005: Evolution, Composition and Determinants**

by

## **Constantinos Stephanou and Emanuel Salinas Munoz**<sup>∗</sup>

**Abstract:** The objective of this paper is to describe the evolution, composition and determinants of financing to the non-financial private sector in Mexico between 2000 and 2005. Supported by the macroeconomic environment and financial system reforms, total financing to the private sector (particularly consumer credit) increased relative to GDP, while accessibility and affordability generally improved. Equity issuance did not play an important role during the period under consideration. Although the supply of financing shifted towards domestic non-bank providers, commercial banks remain the primary source of funding. Significant progress was made in cleaning up bank loan portfolios and in strengthening financial system soundness and infrastructure. The prospects for continued private sector financing growth remain very positive, but financing is not spread out evenly across all market segments. The authors conclude with some policy implications to further facilitate deeper and broader financing of the private sector.

**Keywords:** Mexico, debt financing, equity financing, private sector financing, consumer finance, housing finance, bank lending.

### World Bank Policy Research Working Paper 4264, June 2007

_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._

> ∗ Constantinos A. Stephanou (cstephanou@worldbank.org) is a Financial Economist in the World Bank’s Latin America and the Caribbean Region, and Emanuel Salinas Munoz (esalinas@worldbank.org) is a Financial Economist in the World Bank’s Europe and Central Asia Region. This paper is drawn from the 2006 Financial Sector Assessment Program (FSAP) Update that was undertaken by a joint World BankIMF mission. The authors would like to thank the Mexican authorities for their excellent cooperation, and express their gratitude to Augusto de la Torre and Alfredo Leone for helpful comments and suggestions.

1
Table 11: Robustness checks, restricting the analysis to Iraq. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

|(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|
|Age<br>0.012***|-0.077**|-0.014**|-0.000|
|(0.001)|(0.034)|(0.006)|(0.005)|
|Age squared<br>-0.000***|0.001**|0.000***|0.000**|
|(0.000)|(0.000)|(0.000)|(0.000)|
|No education<br>0.019***|0.519***|-0.030|-0.450***|
|(0.004)|(0.119)|(0.021)|(0.026)|
|Primary or preparatory<br>0.032***|0.491***|0.049**|-0.420***|
|(0.004)|(0.128)|(0.022)|(0.025)|
|Secondary<br>0.017***|0.373***|0.033*|-0.284***|
|(0.003)|(0.104)|(0.017)|(0.020)|
|Rural<br>0.009***|0.257***|0.009|-0.027*|
|(0.003)|(0.084)|(0.015)|(0.016)|
|Oil prices<br>0.013***<br>(0.000)||||
|Diplomatic exchange|-0.246**<br>(0.104)|||
|Confict||0.047***<br>(0.013)||
|Illegal status|||-0.255***<br>(0.070)|
|Legal status|||0.177***<br>(0.048)|
|Observations<br>14,879|1,955|2,071|7,897|
|Parental controls<br>Yes|Yes|Yes|Yes|
|Decades of migration dummies<br>Yes|Yes|Yes|No|
|Labor market controls in Egypt<br>No|No|No|Yes|
|Predicted log wage: Non-mig.: 1.515; Illegal mig.=|1.384; Legal mi|g.=1.727||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return, and is restricted to returnees from Iraq (the country with the highest share of return migration). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

45
which is less than £ since y>O. That is, financially integrated economies exhibit GDP growth correlations which are smaller than those of Solow residuals. This model therefore predicts that financial integration reduces the degree of synchronization of business cycles, and that one should expect countries 'o fall somewhere in the lower-left quadrant of Figure 1 as financial integration becomes more prevalent.

The intuition for this surprising result is quite simple: In financially integrated countries, international booms not only produce the interest-rate and wage effects, but they also raise domestic income since the home economy shares in the good times experienced by the rest of the world through financial risk-sharing agreements. This risk-sharing effect induces agents to buy more leisure, thereby reducing employment. In our stylized model, this risk-sharing effect on employment exactly cancels out the wage effect. Since we still have the capital outflow that a foreign boom creates, GDP growth correlations fall below those of Solow residuals.

It should be apparent that the existence of a risk-sharing effect is a robust result, since it will apply to any model in which the labour supply decision is affected by the level of income (that is, for any reasonable utility function excluding the quasilinear case). What is not robust here, however, is the result that the risksharing effect exactly offsets the wage effect. This is a special property of combining Cobb-Douglas preferences with permanent shocks. If either of these two restrictions is relaxed, there is no reason to expect the two effects to cancel each other. For instance, by choosing preferences with a high (low) elasticity of substitution between leisure and consumption, we would make the wage effect large (small), without altering the size of the risk-sharing effect.<sup>15</sup> Also, by assuming that shocks are transitory and/or anticipated (so that they can be 'smoothed' over time),

**5** The CES utility function works nicely in this regard. 20
plots, this quality differential would not depend on the difference in landholdings across the plots’ owners; all leased plots would be of the same (bad) quality.

What could create an identi fi cation problem is that the degree of adverse selection in the leasing market might depend on the land endowment of the lessor. Suppose, in particular, that there are diseconomies of scale in self-cultivation over the relevant range of landholdings due, for example, to the cost of supervising hired labor. At the margin, it may pay for a landowner to lease out some of his good plots in this case, even at the disadvantageous market equilibrium rent. Since larger landowners would fi nd it necessary to lease out a greater proportion of their good plots, there could be a positive correlation between a plot’s quality and the landholdings of its owner.

The question, of course, is whether this argument is empirically relevant. We can directly check this because we have data on the total area cultivated by each tenant’s landlord. In the nearly 800 tenancies for which these data are complete, two-thirds of the landlords cultivate no land at all and about 90 percent cultivate less than half of their land. The average landlord cultivates 11 percent of his owned area, a fi gure that rises to just 15 percent for ‘small’ landlords (owning less than 100 kanals = 12.5 acres). Such uniformly limited landlord cultivation leaves little scope for variation in the degree of adverse selection.

Nevertheless, if the degree of adverse selection does in fact vary systematically with landholdings, then land rents must adjust in equilibrium; this is because a landlord’s holdings are easily observed by potential tenants. Thus, as these tenants realize that small landowners lease out worse land on average than large landowners, the rent that they would be willing to pay to a small landowner for an observationally equivalent plot must decline. Land rent should then be positively related to the landholdings of the landlord, conditional on the information that the tenant has about the plot.<sup>18</sup> Table 1 presents an analysis of land rent (i.e., log of cash + value of in-kind rent) based on the 198 fi xed rent tenants in our sample with complete information. We control for plot characteristics, including the value of the plot reported by the tenant, as well as for village fi xed effects to capture local leasing market conditions. Neither the log of the landlord’s landholdings, in column (1), nor the small landowner dummy, in column (2), are signi fi cantly related to land rents. Thus, we fi nd no evidence that the degree of adverse selection in the leasing market varies with our key instruments. Which is not to say that adverse selection is unimportant; we still need to correct for its possible in fl uence

> 18 The same argument would apply to the terms of sharecropping contracts, but empirically it is much harder to measure the ‘rent’ accruing to the landlord in this case.

11