rafmacalaba/datause-encoder-data huggingface dataset:
Finally, the legal rights index from Doing Business (LEGAL_RIGHTS), is positive and very significant (although in an unclustered regression only), suggesting that the life sector develops faster under a supportive legal framework.<sup>11</sup> Models (5) and (6) show that financial development is strongly associated with LLIFE even when LGDPPC is controlled for. A 1-percent increase in the log of private credit to GDP (LPC) is associated with a 1.12 percent increase of LIFE. This finding implies that deeper credit markets spur personal loans, which often requires life insurance as collateral. Additionally, financial development in general can help insurance companies conduct better risk and asset and liability management which can increase the supply of insurance. Interestingly, the inclusion of LPC renders INFLATION insignificant in Model (5). This could be driven by the fact that inflation is detrimental to financial development. Indeed, INFLATION and LPC are negatively correlated (rho=-0.4). Together, these findings suggest that inflation exerts influence over life insurance activity in part via its strong impact on credit market development. In addition, Model (6) suggests that deeper private bond markets help insurance companies manage their assets more efficiently. Note, however that the number of countries in the sample falls significantly since bond market data are typically only available for more developed countries.

Lastly, Model (7) displays regression results that test for the statistical significance of individual factors in all categories to address a potential confounding variable problem. Although the sample size decreases significantly, the regression shows that the main results continue to hold with coefficients of the same order of magnitude. Interestingly, LINCOMETOP20 and MUSLIM have now become statistically significant in clustered regressions as well.

_Non-life premium regression results_ Next, we turn to a regression analysis of non-life insurance development in Table 6. The dependent variable is the log of non-life premiums to GDP (LNONLIFE). Depending on the

> 11 Admittedly, none of the legal indicators from Doing Business captures with accuracy the quality of the legal framework in the insurance sector.  However, it can be argued that countries that have stronger creditor rights also tend to have a more supportive legal and regulatory framework in the insurance sector.

19
ENESTYC 2001 and 2005, and dates of participation if any. The ENESTYC and EIA were linked through an identification code constructed by INEGI. This dataset provides information on the treatment for the years before the intervention and after the intervention.

The ENESTYC-EIA panel is conformed of near 2,600 firms, of which around 1,600 firms reported having participated in one or more programs (the treatment group) and 1,000 stated that they had never participated in any programs (the control group). CIMO-PAC, the CONACyT program and the environment program were the three most commonly used by firms in the treatment group (Table 15). Firms are characterized as either currently participating in a program or having participated in the past, with the former category having more respondents.

**Table 15. SME Program Participation**

|**SME program**|**Number of**<br>**Participating**<br>**Firms**|**Percent of**<br>**Total**<br>**Sample**|**Currently**<br>**Participating**|**Not Participating**<br>**Now, Participated**<br>**in the Past**|
|---|---|---|---|---|
|CIMO|282|10.96|142|140|
|COMPITE|60|2.33|23|37|
|CRECE|38|1.48|16|22|
|FIDECAP|8|0.31|6|2|
|FAMPYME|10|0.39|6|4|
|MEX-EX|36|1.40|18|18|
|PATCI|10|0.39|4|6|
|PMT|20|0.78|10|10|
|PCI|8|0.31|6|2|
|PAIDEC|13|0.51|9|4|
|Fondo Pyme|27|1.05|13|14|
|PROMODE|9|0.35|3|6|
|PROSEC|113|4.39|88|25|
|Mixed or Sectoral Funds|36|1.40|23|13|
|Productive Chains|47|1.83|33|14|
|Financing|39|1.52|23|16|
|Crediexporta|47|1.83|28|19|
|PAT|44|1.71|31|13|
|PNAA|247|9.60|189|58|
|Fiscal Support and<br>Technological|||||
|<br>Innovation|187|7.27|124|63|
|State Government|||||
|Support|67|2.60|40|27|

18
agriculture, we also include a seasonal dummy (1 for lean season, and 0 otherwise) by assuming that there is a common non-crop seasonality that also matters for consumption variations across seasons.<sup>24</sup> Table 6 presents the results with two-model specifications (one for total per capita consumption and the other for per capita food consumption). The results are reported with cross-sectional data for 2000 and 2005. First, we estimate the first stage of income equations of (3.2) and (3.3) (reported in appendix table A1) and use the predicted values in equation (3.1) to estimate the influence of predicted income and its seasonal crop shares on seasonal consumption per capita. We test the endogeneity of income and its seasonal share to justify the two-stage IV model.<sup>25</sup> The Hausman endogeneity test results (table 6) show that the two-stage technique is a valid strategy to estimate the consumption model.

Next, we consider the role of seasonal income shares in consumption, conditional on overall yearly income, for the cross-sectional estimates of 2000 and 2005. We do find that the coefficient of seasonal income shares is statistically significant and different from zero, at least in 2005. That is, our results reject the model of perfect consumption smoothing. In other words, seasonal crop income tracks seasonal consumption (both food and total consumption) significantly – a clear case of lack of consumption smoothing. Although per capita consumption is determined by overall income, it can also depend on its seasonal shares. For example, in 2005, a 10 percent increase in total income raises per capita food consumption by 2.1 percentage points while a similar percentage increase in seasonal crop income share raises food consumption by 17.0 percentage points. Note that this is

> 24 This two-way classification of the country or the year is an attempt to highlight the differences between monga and non-monga periods or between Rangpur and the rest of the country.  Note that Rangpur belongs to Rajshahi division, which is one of 6 divisions of Bangladesh. Similarly, there are 4 distinct seasons but we are collapsing 4 seasons into only 2 seasons.

> 25Appendix Table A shows complete first stage estimates of income equations.

20
8 force participant.9 However, in the case of reported female-headed<sup>households, the alternate</sup> head is most like;y to be a son, suggesting that age is an important<sup>factor used by households</sup> to define their head. Given that women tend to outlive men, we suspect<sup>that a number of the</sup> reported female-headed households are actually extended-family<sup>households, with an</sup> important income contribution from an adult male relative, usually<sup>a son. To verify this</sup> assumption, we looked at the number of households in which<sup>at least one member is older</sup> than the reported head and is not the head's spouse. In 94<sup>percent of reported male-headed</sup> households and 92 percent of repGrted female-headed households,<sup>the head is the oldest</sup> member.

Overall, in over 90 percent of households, the household<sup>survey data designate as</sup> head of the household an adult who is the same gender<sup>as at least one of the adults most</sup> responsible for the family's economic welfare. For that<sup>reason, we continued to use the</sup> reported head in our analysis. If a net gender bias exists<sup>in the headship designation, it</sup> favors female headship, as female-headed households were<sup>more likely to be reclassified than</sup> male-headed households. This bias toward female headship<sup>designation appears to be a result</sup> of an age bias, as women live longer and therefore are<sup>more likely to be named head.</sup> Nonetheless, this analysis suggests that some caution<sup>is warranted in using the headship</sup> concept too narrowly in a situation like that found in Brazil,<sup>where most households depend</sup> on more than one income-earning member.

# Characteristics of Female-Headed Households

Female-headed households in urban Brazil are a heterogeneous<sup>group (Tables 4</sup> and 5). Given the analysis above, it is not surprising that<sup>female heads are on average about</sup> seven years older than male heads, and a substantial<sup>portion are widows.'0Household type</sup> differs by region. Female-headed households are most likely<sup>to be found in the northeast,</sup>

> 9 This was confirmed in a special tabulation, not shown.

> **10** Note that marital status information is available only<sup>for women under the age of 56.</sup>
Policy Research Working Paper 8990

# **Abstract**

Policy toward fiscal rules is an important issue in the countries of the Western Balkans (Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia). According to a rough estimate, the countries with rules (all but North Macedonia) have complied with their debt and overall-deficit rules a little more than half the time. An online survey, conducted for this paper, suggests that public understanding of the rules is limited, which may reduce the political pressure for compliance. To get debt down to prudent levels, Albania and Montenegro will need a strong commitment to complying with their fiscal rules and will often have to do more than their deficit rules require. The following principles should guide future policy toward fiscal rules: more emphasis should be given to ensuring that fiscal rules are widely understood and enjoy the support of a broad range of stakeholders; policy toward the rules should be consistent with accession to the European Union, but the rules should be simpler than the European Union’s and the debt limits lower; limits in rules should not be mistaken for targets; and public financial management should be improved to support the implementation of rules.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ekikoni@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
other comparable household surveys. The last column of Table 1 reports the HIV prevalence rate in each country, estimated for 2005 in UNAIDS (2006) which can be compared to levels and changes in orphan rates.<sup>15</sup> HIV prevalence rates closely follow the typology, but not always. Rates are low in the countries with a low and unchanging orphan rate (Group A), somewhat higher in those with a high and unchanging orphan rate (Group B), and highest in the countries where the orphan rate has surged (Group C). In this latter group, the prevalence of HIV ranges from 6.0 percent in Kenya to 20.1 percent in Zimbabwe—with an average of 15.4 percent across the five countries. This congruence between changes in orphan rates and HIV prevalence among adults signals that the changes in orphan rates are indeed strongly related to HIV/AIDS in these countries. There are exceptions to the overall patterns: Rwanda, as discussed above, but also Mozambique where the orphan rate has remained relatively stable despite a very high estimated HIV rate for 2005 (16 percent); or Senegal where 8 percent of all children are single or double orphans despite having the lowest HIV prevalence among these 21 countries.

There are several reasons why HIV prevalence (measured in 2005) might not map closely to orphanhood levels and trends in every country. These include the lag between HIV prevalence and AIDS, as well as fertility patterns. More recently, introduction of ARV treatment will be expected to lower this correlation. Côte d’Ivoire, Kenya, and Tanzania, all with national HIV prevalence estimates between 6 and 7 percent, provide an illustration of the contrast. In Kenya, the orphan rate increased by 5 percentage points, whereas in Tanzania it increased by just below 2 percentage points, and in Côte d’Ivoire by less than 1 percentage point.<sup>16</sup> Clearly HIV prevalence and trends in orphan rates are strongly related—but other factors still contribute in determining the level and change in the share of children who are orphans.

_Orphan rates disaggregated by maternal, paternal, and double orphans_ The top left panel of Figure 2 illustrates changes in orphan rates by plotting the percentage of children who are orphans in the latest survey against the percent in the earliest

> 15 While it would be desirable to have prevalence rates corresponding to the same years as the surveys in this study, these are typically not available. Rates based on sentinel surveys, which are the only types of surveys available for the earliest years, have proven to produce misleading estimates. The data are restricted to the more robust estimates for 2005 which are primarily—but not exclusively—based on representative sample surveys. This means that this HIV indicator is a very crude proxy for recent adult mortality patterns, and its strength as such depends on stable incidence rate (the flow of persons with respect to contracting HIV).

> 16 As mentioned above, the relationship between change in HIV prevalence and change in orphan rates cannot be explored because of a lack of comparable estimates of national HIV prevalence for the early 1990s.

11
|Irrigation Sub-Sector Project|LarryD. Simpson|
|---|---|
|Irrigation Sub-Sector Project|LarryD. Simpson|
|Irrigation Sub-Sector Project|LarryD. Simpson|
|Irrigation Sub-Sector Project|LarryD. Simpson|
|Irrigation Sub-Sector Project|LarryD. Simpson|
|Agricultural Credit Project<br>||
|Agricultural Credit Project||
|Agricultural Credit Project||
|Agricultural Credit Project||
|Agricultural Credit Project||
|Agricultural Credit Project||
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Upper and Middle Sao Francisco Irrigation Project|LarryD. Simpson|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Innovations in Basic Education Project|Robin Scott Horn|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Northeast Basic Health Services Project|Jean J. De St Antoine|
|Urban Transport Project(04)|Jorge M. Rebelo|
|Urban Transport Project(04)|Jorge M. Rebelo|
|Urban Transport Project(04)|Jorge M. Rebelo|
|Urban Transport Project(04)|Jorge M. Rebelo|
|Urban Transport Project(04)|Jorge M. Rebelo|
|Salvador Metropolitan Development Project|Braz Menezes|
|Salvador Metropolitan Development Project|Braz Menezes|
problems: first, using panel data aids in avoiding possible omitted variables biases (JOHNSTON; DINARDO, 2001).

Moreover, cross section models tend to underestimate the trade volume between pairs of countries with high volume of trade, and to overestimate it for pairs of countries with low volume of trade. This “heterogeneity bias” can be overcome by removing the gravity model’s assumption of a sole intercept for all trade flows between pair of countries (CHENG; WALL, 2005). Both problems could be solved using a fixed effects (FE) model,<sup>7</sup> as the ones used in Sá Porto and Canuto (2004) and in Sá Porto and Azzoni (2007).

Another point is that, often in gravity models there are zero trade flows between some country pairs, which could introduce problems, because the log-linearized model is not defined for observations with zero trade. In order to remove the influence of trade pairs with zero trade flows, we use an approach similar to the one used by Westerlund and Wilhelmsson (2009). They estimated the gravity model directly from its non-linear form by using a fixed effects panel Poisson maximum likelihood (ML) estimator. Since this removes the need to linearize the model by taking logarithms, the problem with zero trade disappears. Finally, in all four equations we adopted corrections for heteroscedascity and autocorrelation.

> 7 Note also that, in the Fixed Effect (FE) model, variables that are invariant with time, such as distance and adjacency variables, are eliminated (Cheng and Wall, 2005).

10
# **Table 3. WLS Estimates of the Employment Equation =** **<u>(weight employment by industry in 2007)</u>**

|Explanatory Variables:|(1)<br>Depend|(2)<br>ent Variable:|(3)<br>Change in l|(4)<br>ogemploy|(5)<br>ment|(6)|
|---|---|---|---|---|---|---|
|Imports from the U.S.|0.020*<br>(0.011)|0.014<br>(0.009)|0.014<br>(0.009)|0.017*<br>(0.009)|0.019*<br>(0.010)|0.017*<br>(0.009)|
|Exports to the U.S.|0.021|0.034**|0.030*|||0.025|
||(0.015)|(0.016)|(0.017)|||(0.017)|
|One-period ahead: Imports from the U.S.||-0.020*<br>(0.011)|-0.020*<br>(0.011)||||
|One-period ahead: Exports to the U.S.||0.039***<br>(0.015)|0.035**<br>(0.014)|0.032**<br>(0.014)|0.027*<br>(0.014)||
|Related imports from the U.S.|||0.138**|0.126*|0.039|0.145*|
||||(0.070)|(0.070)|(0.044)|(0.080)|
|Related exports to the U.S.|||0.151|0.177*|0.015||
||||(0.119)|(0.103)|(0.107)||
|One-period ahead: Related imports from<br>the U.S.|||-0.144<br>(0.109)||||
|One-period ahead: Related exports to the<br>U.S.|||0.180<br>(0.153)|||0.172<br>(0.124)|
|Crisis dummy * Imports from the U.S.|||||-0.021<br>(0.036)||
|One-period ahead: Crisis dummy *<br>Exports to the U.S.|||||0.001<br>(0.016)||
|Crisis dummy * Related imports from<br>the U.S.|||||0.417<br>(0.413)||
|Crisis dummy * Related exports to the<br>U.S.|||||0.451***<br>(0.159)||
|Observations|1,132|1,027|1,025|1,027|1,027|1,027|
|R-squared|0.257|0.294|0.309|0.288|0.303|0.287|
|Robust standard errors in parentheses; error<br>*** p<0.01, ** p<0.05, * p<0.1|s are clustered|by industry.|||||

Time effects are included but not reported. Crisis dummy equals one during 2008q4 and 2009q1.

49
<!-- Start of picture text --> 4<br>Policy and Coordination Comnnittee<br>11<br>Standing Committee<br>Scientific and Advisory Group<br>(STAG)<br>11<br>|j Director of<br>the Programme<br>Conunittee on Resources for Research<br>Toxicology Group (CRR)<br>Scientific and Ethical Review Group Laboratory Meth,ds Group<br><!-- End of picture text -->

<!-- Start of picture text --> Steering Committees of Task Forces<br>Safety Behavioral and Long- Post- I I I<br>and Social acting ovulatory Male Natural<br>Efficacy Determinants Systematic Methods Vaccines Methods Methods Infertility<br>I _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ A g en ts __ _ _ _ _ __ _ _ _ _ _ _ _ _ _ [ _ _ _ _ _ _ _ _ _ _ _<br><!-- End of picture text --> Figure 1. Governing and advisory bodies of the Programme.

Source: WHO. 1990. Research in Human Reproduction: Biennial Report 1988-89. Special Programme of Research, Development and Research Training in Human Reproduction, Geneva.
of greater interest since it re fl ects the cumulative effects of nutritional and other health related shocks during the child’s early years. Epidemiological evidence also suggests that nutritional and health shocks at a very young age are likely to generate long term growth faltering with substantial and irreversible adverse consequences for children in a large range of developmental outcomes.

The main econometric challenge for the paper is the endogeneity of the migration decision. We resolve this in two ways. We instrument for selection into migration using data on village migrant networks and household demographic characteristics which are correlated with the migration decision but not the outcomes of interest. This gives us a measure of migration networks which varies within the village, allowing us to both instrument for selection in the migration decision and to control for unobserved village characteristics that could impact child growth. Next, we restrict the sample to migrant households and exploit information on the timing of fi rst migration to split children in each household into two groups, those born before the household’s fi rst migration episode and those born after. We then use this information to estimate the impact of the child’s migration related birth status on the child’s height for age. This allows us to compare outcomes for siblings differentiated by their attained age by the date of migration.

We fi nd that migration, appropriately instrumented, has a positive and extremely signi fi cant impact on height for age for girls. In contrast, the effects are much smaller for boys, suggesting that boys may get preference in terms of nutrition and health care when resources are stretched. Moreover, when we split the sample by age group we fi nd that the height advantage of young girls is sustained in the older age group. This result underscores the long term salutary bene fi ts of averting nutritional and other health shocks in early childhood. Estimation using child weight for age z-scores yield similar results. Our results also suggest that, as expected, selection into migration as well as community level unobservables are quite important.

A comparison of siblings further corroborates our results on child height. Girls born before the household’s fi rst migration episode do much worse than girls born after. Now, however, there is no discernible difference among boys born before and after the fi rst migration episode. One of the main disadvantages of using growth measures like height for age to examine changes in growth for a given child is that height for age is a stock variable which gives a cumulative picture of the child’s overall growth status. Short term shocks, are thus unlikely to show up immediately in height and the child’s ability to withstand such shocks is also likely to be determined by previous exposure. Our strategy allows us 16