rafmacalaba/datause-encoder-data huggingface dataset:
rates than the Dalits. Muslim boys however, do not display any significant difference in mortality rates compared to the Dalits. Upper-caste Hindus however, diplay lower mortality rates in this group, although this appears to be explained by their socio-economic characteristics.

This set of findings is consistent with the results from the child sample. Overall, all the results confirm that Muslims experience lower levels of female mortality. The difference between Hindus and Muslims is not attributable to differences in education, landholdings, wealth, rural-urban residence or state of residence. While Dalits may also display lower levels of female mortality, the Muslim effect appears to be stronger than the Dalit effect.

New research from immigrant communities in North America corroborates our finding on the distinctive demographic characteristics of certain religious groups. Recent work by Almond, Elund, and Milligan (2009), who study sex-ratios among immigrant communities in Canada, finds relatively normal sex-ratios in the Muslim and Christian community, but highly skewed sex-ratios among other religious groups.

We once again acknowledge that we can not rule out the possibility that unobserved variations in socio-economic status may indeed be driving these results. However, unless more national data become available, we must seriously consider the possibility that religion may indeed be a driver of preferences for males and females in India. The survival advantage of Muslim girls – as confirmed by our results from the child sample, woman sample and village sample – suggest that India’s “missing women” problem may be concentrated in certain religious groups. This has major policy-implications for it suggests that the status of women within particular caste- and religious-groups deserves greater analysis and attention from academics as well as policy-makers.

# **4 Conclusion**

In this paper, we use recent data from the 2006 National Family Health Survey to explore the relationship between religion and demographic outcomes in India. We find that fertility and demographic behavior vary not only across religious groups, but also across caste groups. A comparison of socio-economic variables suggests that Muslims are similar to Dalit (lowercaste) Hindus in that they are poorer and have more children, but unusually also exhibit 14
narrowly targeted at child ages and grades where, in the absence of the program, dropout rates are high (de Janvry and Sadoulet 2006).

In this paper, we construct a simple model of schooling decisions and how these respond to a child-specific CCT. We show that such a program will unambiguously increase school enrollment among eligible children, but will have an ambiguous effect on the school enrollment of their ineligible siblings. We then take the predictions of the model to data from a child-specific CCT in Cambodia. This analysis shows that the program significantly increased the school enrollment of eligible children, but left schooling outcomes for their siblings unaffected.

However, this need not have been so. Barrera-Osorio et al. (2008) analyze a program in Bogotá, Colombia. This program makes reasonably large transfers, equivalent to about 8 percent of expenditures for the median recipient household, conditional on the school enrollment of specific children selected for the program.<sup>23</sup> Barrera-Osorio et al. compare families in which two, one, or no children were selected into the program. They conclude that the program positively affected the school enrollment of recipients, but that this came, in part, at the expense of their siblings, who were more likely to drop out of school and enter the labor market. Similarly, Manacorda (2006) uses historical data from the United States to show that minimum working age laws that enabled a child of a particular age to join the labor market legally led to a reduction in their siblings’ labor participation and an increase in their siblings’ school participation.

Our simple model of two-child households can account for the results for Cambodia, Colombia, and the early-twentieth century United States. The model suggests that households will fall into one of three types. First, “poor” households who would send neither child to school in the absence of the program. If these households take up the program, they will enroll the eligible child, and perhaps the ineligible child if the transfer is large enough. Second, “middle-income” households who would send only one child to school in the absence of the program. If these households take up the program, they will

> 23 The amount of the transfer and its value as a share of expenditures for the average recipient household are not reported in the paper. We are grateful to Felipe Barrera for providing us with this detail.

25
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development, growth performance, infrastructure, and with its policies and institutions for trade, private sector development, and doing business.<sup>15</sup>

# **IV. Assessing the odds of achieving the MDGs**

Is it possible to link and simulate the impact of growth and policy to the likelihood of achieving the MDGs in a manner more rigorous and statistical than with graphical associations? Although formal econometric analysis, in principle, can isolate partial effects that are not apparent from the simple correlations in the previous section, there are caveats: the direction of impact between development outcomes as measured by the MDGs and the two basic drivers (growth and policy) can go both ways; the two drivers themselves are likely to be correlated; and some factors that affect the progress of MDGs are not readily measurable and available. Data constraints are also problematic. For these reasons, the findings in this section are specific to the approach and presentation of data taken; whether they would apply to other approaches or treatment of the MDG variables is uncertain and outside the scope of the paper.

With these caveats and building on the empirical patterns, previously defined measures of MDG progress, and the basic drivers of progress identified in the literature (World Bank 2004), we introduce a simple and intuitive model that is suited to assessing the probability of a country falling into one of the three defined categories, linking performance to the two drivers. For a given development indicator associated with each MDG, the likelihood of a country being on target, close to the target, or far from the target is expressed as a function of:

- economic growth (annual per capita GDP growth, 1990–2009);

- recent quality of the policy and institutional framework approximated by the current CPIA, which assesses recent changes in policies and institutions and, by design, does not correlate with recent growth;<sup>16</sup>

- initial conditions (per capita GDP in 1990 and CPIA index in 1996); and

- controls (specific development indicators around 1990).<sup>17</sup>

The probability function across the different states of MDG performance is estimated using the multinomial logit model. Estimations are performed for each of the nine development targets under consideration using ―far from the target‖ as the reference group or base category. The

> 15 It is important to point out that these simple graphical patterns can be driven by more fundamental development factors, such as growth and institutions. The next section tackles some of these issues.

> 16 A study (IDA/DECVP 2007) found the correlation between contemporaneous CPIA and growth to be weak and the correlation between CPIA and future growth to be strong. The CPIA measures the level of policies, not the change; and it focuses on actual implementation, not just introduction or announcement. It is therefore backward looking. The inclusion of the 1996 CPIA is an attempt to capture the policy achievements close to the reference year in 1990, and the 2009 index will include the more recent record.

> 17 See annex table A8 for sources and definitions.

16
especially small states, have suffered much larger damages than suggested by the average effect. The average small state has suffered losses and damages from climate-related disasters of 5 percent of GDP per year, on average (World Bank 2023). These losses did not occur in a predictable pattern. Instead, it was not uncommon for the damages from a single climate-related disaster to cost a substantial portion of a country’s GDP, or even multiples of GDP in extreme cases.

The climate change scenario assumes that the number of climate disasters in 2022-30 will be higher than in 2011-21 in each country by the same amount as the increase between 2000-10 and 2011-21. On average, this means two disasters every three years in 2022-30, up from one every two years in 2011-21. The negative effect of the greater frequency of disasters on each country’s TFP growth is then estimated by multiplying the assumed increase in the number of disasters per year by the average impact of each disaster on TFP growth, as estimated by Dieppe, Kilic Celik, and Okou (2020).<sup>10</sup> In this scenario, both global and EMDE potential growth over 2022-30 would be almost 0.1 percentage point a year lower than in 2011-21.

# III.3 Recessions

With global output growth slowing sharply in 2022-23 amid tightening global financial conditions, there are risks of a global recession and of financial crises in EMDEs in the near term (World Bank 2023). In the past, slowing global growth and rising global financing costs have been associated with a significantly higher probability of currency crises and sovereign debt crises in EMDEs (Koh et al. 2020).

Recessions and financial crises have been associated with lasting reductions in potential output growth. Kilic Celik et al. (2023) show that national recessions between 1990 and 2021 have typically been associated with reductions of about 1.4 percentage points in potential growth even after five years.<sup>11</sup> Based on the econometric estimates presented in Kilic Celik et al. (2023) of the effect over different forecast horizons, recessions in EMDEs in 2023 could lower potential growth over 2022-30 by 0.7-0.9 percentage point per year globally, in EMDEs, and in advanced economies.

# III.4 Disappointing policies

The baseline scenario assumes that education and health outcomes will continue to improve in 2022-30 in line with their country-specific long-term trends. However, such improvements slowed over the 2010s (Dieppe 2020). An alternative scenario therefore assumes that such improvements continue, not at their historical average pace, but at the

> 10 Natural disasters have implications for output, productivity, and investment. The immediate effect might be damage to existing capital stock, followed by a rapid investment rebound in reconstruction. For the year as a whole, the net effect tends to be negligible. In contrast, output rebounds tend to be more muted than investment rebounds such that there are measurable output and TFP losses on an annual basis.

> 11 See Kilic Celik et al. (2023) for a review of the related literature.
been compiled.<sup>2</sup> The individual components of **F** are the “scores” for _common factors_ since they are common to several different measures. The KAM measures are thus predicted as linear combinations of the factors. The coefficients of the factors, called the _factor loadings_ , are the elements of **A**<sup>_′_</sup> . For example, consider the _i_ th measure (variable) _xi_ . It can be written as a regression model where _f_ 1 _, . . ., fk_ are the “exogenous” factors, and the coefficients _ai_ 1 _, . . ., aik_ are the “loadings” contained in the _i_ th column of **A**<sup>_′_</sup> . While _ei_ is given the interpretation of a regression residual, in fact it is made up of the measurement error in the measure _xi_ plus a “specific” factor that _xi_ does not share in common with other measures. Thus, each of the _p_ variables _xi, i_ = 1 _, . . . , p_ can be written as a regression model with the factors acting as the common “exogenous” variables weighted by the coefficients _ai_ 1 _, . . ., aik_ , and where _ei_ is the regression residual.

Written in this form makes it clear that factor analysis is a method of data-reduction. The method seeks to parsimoniously represent in a small set of variables ( _f_ 1 _, . . ., fk_ ) essentially the same information contained in a much larger set of variables ( _x_ 1 _, . . . , xp_ ). We will reduce the KAM data variables to their essential factors using two different factor-analytic methods.

The difference between model (1) and ordinary regression models is that the factors and coefficients are both unknown. That is, neither **F** nor **A**<sup>_′_</sup> is known and must be estimated. There is a fundamental indeterminacy in the model. If we (linearly) transform **F** and **A**<sup>_′_</sup> , respectively, as **F*** = **F C**<sup>_−_1</sup> and **A***<sup>_′_</sup> = **C A**<sup>_′_</sup> , then (1) is equivalently written as:

> 2 _p_ need not be fixed. Factor analysis of the KAM variables may be performed separately on subsets of the KAM variables. For example, each of the four pillars of the KAM data – (i) economic and institutional regime data, (ii) education and skills data, (iii) infrastructure data, and (iv) innovation potential data – may be distilled down to one or two factors.

5
Table 13. Contribution to the Pension System Among Self Employed in December 1999- January 2000 - Including Preference Variables (Men of Working Age)

||WithoutPref|erenceVariables|With Preferen|ceVariables|MEAN|
|---|---|---|---|---|---|
|CONT99|dF/dx|Std.Err.|dF/dx|Std.Err.||
|AGE|**0.009**|**(0.005)***|4.3E-04|(0.001)|39.89|
|HEAD|||||0.79|
|MARRIED|-0.019|(0.084)|0.024|(0.067)|0.68|
|SPCONT|||||0.21|
|YEDU|||||10.19|
|RURAL|||||0.19|
|FSIZE|0.001|(0.001)|7.OE-05|(3.1E-04)|12.73|
|WRKHRS|0.003|(0.003)|3.6E-04|(0.001)|52.50|
|CREDCON|||||0.34|
|SHYKIDS|||||19.68|
|SHKIDS|||||6.82|
|SHOLDM|0.010|(0.009)|0.001|(0.004)|2.23|
|SHOLDW|||||2.27|
|NYFEM|||||1.23|
|RESFAM|||||0.05|
|EXKIDS|||||2.85|
|LEDKIDS|||||6.00|
|FAMEX|||||0.48|
|GIVES|||||0.39|
|GETS|||||0.06|
|LRENT|||||7.34|
|COLATT|||||0.58|
|FINASST|||||0.00|
|WRKASST|-0.147|(0.087)*|-0.020|(0.051)|0.61|
|SLIFEX|||||34.69|
|LJUB|**-0.048**|**(0.031)***|-0.007|(0.021)|0.92|
|CONT98|**0.334**|**(0.166)*****|**0.046**|**(0.131)***|024|
|CONDEN|**0.278**|**(0.137)****|**0.054**|**(0.154)***|0.36|
|MPG|**-0.221**|**(0.144)***|-0.042|(0.118)|0.15|
|MPG*LRENT|(dropped, predic|tsfailureperfectly)|||1.10|
|TIMEPREF|||-0.017|(0.059)|0.44|
|RISKPREF|||**1.3E-05**|**(4E-05)***|3460.00|
|Intercept|**-0.752**|**(0.322)*****|**-0.127**|**(0.360)***|1.00|
|Numberofobs|62||60|||
|Loglikelihood|-16.121||-7.673|||
|LRchi2(10)|36.370|LR chi2(12)|52.130|||
|Prob >chi2|0.000||0.000|||
|PseudoR2|0.530||0.773|||
|obs.P|0.242||0.250|||
|pred.P|0.071||0.003|||

'"*""'statistically significant at 1%; "**" at 5%; and"" at 10% 52
10

# FIGURE 3

<!-- Start of picture text --> Evolution of Subsidies|<br>Scenario B<br>7C .. _ 30<br>70 -~~~~~~~~~~~~~~~~2<br>60~~~~~~~~~~~~~~~~<br>50.<br>20 l ISS l I total subsidies in nominal terms: 449 Billion Forints _ 10<br>l ~~total subsidies in real terms : 183 Billion ForintsT<br>1989 2025<br>-a- bond rate (%) subsidies<br>||Dependence of Subsidies<br>on Bond Rate Scenarios|<br>100-<br>90-<br>- 80 _<br>U 7 0 -<br>50_<br>SQ40. sci1 dditonal subsidies:_<br>8 6 ~~~~~~~98 Billion Forints In nominal terrns,<br>3 0° / 1 Bilslion Forints in real terms (1/1989)_<br>10 20-~~~~~~~~~~~22 S A a<br>0<br>1989 2025<br>LScenario A addit. subs.(Sce.B)<br><!-- End of picture text -->
scale, or a negative relationship between the scale of farming and yields in the second and third models, and for the first two years of the fourth model. The estimates from the fourth model suggest a notable shift in the observed frontier characterizing the production technology, from decreasing in the previous years to constant returns in 2008. These findings suggest that with the wider and more uniform adoption of technology, there appears to be no technology related advantage or disadvantage associated with farm size in terms of conditional yields. However, since farm size is also included in the inefficiency function in these models, a valid comparison with the basic model of how production varies with the scale of farming requires accounting for the marginal effect of farm size in the inefficiency function.

The estimates of the inefficiency function show a strong and statistically significant positive impact of farm size on inefficiency in all the three models; that is, all models show production efficiency falls with size. This is an important result indicating that large farms are more technically inefficient than small farms.

Next, to establish the farm size-productivity relationship, the marginal effects from both the frontier and inefficiency components of the SPF are combined to obtain the adjusted elasticity of output with respect to farm size. These effects are given in Table 3 for each of the four models. The first row shows the scale elasticity from the SPF (coefficient on farm size) for each model. For the extended model, the scale elasticity is shown for each of the three years (using the coefficients from the direct and interaction terms). The estimated coefficients show a notable shift in the production frontier from decreasing returns in the first two years to constant returns in 2008 (the frontier scale elasticity is insignificantly different from zero).

The second row of Table 3 shows the marginal impact of farm size on E(U) or the marginal impact on mean inefficiency, derived using the approach proposed by Wang (2002).<sup>15</sup> Adding this marginal impact to the scale elasticity from the SPF, reported in the third row, shows how productivity varies with farm size. The results show a consistent and statistically significant inverse relationship between farm size and conditional yields in all models (and for all three years in the extended model) except the basic model.

> 15 The marginal effects are readily calculated in Stata (see Belotti et al., 2013).

18
26

**Table 6. Marginal budget shares on expenditure for non-remittance and remittancereceiving households, Guatemala, 2000**

|_Expenditure_<br>_Category_|_Households receiving_<br>_no remittances_<br>_(N=5665)_|_Households receiving_<br>_international_<br>_remittances (from_<br>_USA) (N=593)_|_Percentage change_<br>_(No remittances vs._<br>_international_<br>_remittances)_|
|---|---|---|---|
|Food|0.386|0.330|(-14.77)|
|Consumer goods,<br>durables|0.203|0.229|+12.81|
|Housing|0.183|0.187|+2.18|
|Education|0.031|0.049|+58.06|
|Health|0.023|0.023|---|
|Other|0.173|0.177|+2.31|
|**Total**|1.000|1.000||

Notes: In the dataset, 1063 households receive international remittances (from Guatemala) and results for these households are not reported here. Some figures do not sum to unity because of rounding.

Source: Adams (2005b: Table 5)
WPS4148

# **Regional disparities in labor market performance in Croatia: The role of individual and regional structural characteristics**

## Xubei Luo

## _<u>Abstract:</u>_

The labor market performance in Croatia failed to keep pace with the moderately good overall macroeconomic development in the last few years. Youth, the less well-educated, and women face more difficulties in getting a job with a decent salary. A large part of the difference in regional labor market performance is associated with the difference in the human capital endowment. With a stagnant total employment rate, the large disparities in employment and earnings across individual groups and regions have become one of the concerns for the long-term sustainable development of the economy.

Using Labor Force Survey (LFS) data from 2002-2004, this paper studies the labor market performance in Croatia at the national and regional levels. The results show that both one’s individual characteristics (including age, education and gender) and where he or she works plays a role in his or her employment and earnings. Regional differences in employment and earnings are reduced to a large extent when accounting for differences in individual characteristics. The simulations shed light on the effectiveness of the nation-wide education policy and regional specific labor market policy, and suggest that improving human capital endowment and adjusting labor market structure are both important to rebalance regional development and enhance total welfare.

## Key words: Employment; Labor market performance; Return on education.

JEL classification: R23, J31, J71

## World Bank Policy Research Working Paper 4148, March 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._ 1