rafmacalaba/datause-encoder-data huggingface dataset:
8 if they were **traded in** hArd currency **with** the Soviet Unior.. By objective data we mean that for homogeneoue products, such as oil and raw materials, a world market price was known from international commodity marktt quotations. For manufactured goods however, this approach was not possible because of the great variation in product quality. Therefore, for heterogeneous products, expert opinion was sought. In these cases, individcuals from foreign trade orgi.nizations and manufacturing firms who were most likely to have the best information were requested to give an estimate. In all cases where expert opinion was sought an in depth interview was performed. Based on their knowledge of the prices of similar products on the world ma-.ket on the one hand, and on conditions in the Soviet market on the other, they were asked to estimate the pl_,es thuir exports could fetch or what they would have to pay for imports, if they were priced and paid for in US dollars in trade with the Soviet Union. The survey was conducted for the year 1988.

As we discuss below, given the importance of the terms-of-trade costs of the switch-over to the Eastern European economies, a number of researchers have attempted to estimate the terms-of-trade costs of the switch-over on the basis of somewhat aggregate unit value data. Without a detailed microeconomic investigation, however, these estimates, while useful, can only be viewed as illustrative. The serious alternative approach to ours is to compare unit values -- at a somewhat disaggregated level, compiled from foreign trade statistics the approach adopted by Gacs (1989) and Marrese-Wittemberg (1990). These researchers must either choose representAive markets as Marrese-Wittenberg, or choose a quality adjustment coefficient. Relying on a small team of researchers to make adjustments for product quality for literally hundreds of products about which their expertise is somewhat limited presents problems that are at least
Table 16: Trade, Legal Origins <u>and Property Rights, Rich Countries vs. Poor</u> Dep. Var.: log of average trade flow

|||All|Rich|Partner|Poor|Partner|
|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|
|Different Legal Origin|-0.25|-0.26|-0.27|-0.16|-0.28|-0.31|
||[5.96]|[5.76]|[5.55]|[2.90]|[6.34]|[6.23]|
|Protection of Property<br>|0.31|0.36|0.3|-0.1|0.32|0.41|
|Rights|[21.43]|[6.35]|[15.83]|[1.52]|[20.33]|[6.05]|
|log distance|-1.2|-1.2|-0.99|-0.96|-1.25|-1.27|
||[48.76]|[48.48]|[35.11]|[31.77]|[45.62]|[43.92]|
|log product of GDP|0.98|0.97|1.01|1.11|0.99|0.97|
||[105.61]|[49.37]|[88.91]|[58.67]|[98.69]|[44.96]|
|log product of land area|-0.1|-0.09|-0.1|-0.15|-0.1|-0.08|
||[12.20]|[6.45]|[10.83]|[11.96]|[11.34]|[5.06]|
|no. of landlocked|-0.54|-0.55|-0.61|-0.54|-0.53|-0.55|
||[15.70]|[15.35]|[15.54]|[13.04]|[14.84]|[14.54]|
|common border|-0.18|-0.2|-0.2|-0.07|-0.27|-0.31|
||[4.15]|[3.98]|[4.23]|[1.18]|[5.69]|[5.55]|
|common border|1.04|1.05|-0.03|0.19|1.22|1.23|
||[8.38]|[8.37]|[0.15]|[0.96]|[9.44]|[9.46]|
|common language|0.41|0.38|0.41|0.62|0.4|0.35|
||[7.72]|[6.26]|[6.82]|[8.48]|[7.00]|[5.12]|
|currency union|1.31|1.32|0.31|0.6|1.21|1.23|
||[5.46]|[5.48]|[0.68]|[2.33]|[5.05]|[5.07]|
|Observations|29772|29772|14160|14160|26948|26948|
|R^2|0.72|0.72|0.81|0.79|0.69|0.69|
|First stage R^2||0.42||0.30||0.38|
|% increase in trade due to<br>1s.d. institution|58%|70%|42%|-11%|57%|79%|

Notes: t-values in Brackets. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for Protection of Property Rights. The Rich Partner subsample includes all country pairs where at least one is "rich". The Poor Partner subsample includes all country pairs where at least one is "poor". "Rich" countries are defined as those with PPP GDP per capita larger than 10,000 dollars in 2000 and the rest are "poor". The percent increase in trade due to 1s.d. institution is calculated as exp(coef*sd)-1 and takes into account different standard deviations in each subsample. Summary statistics for the institutional quality variable in each subsample are reported in Table 16.

51
Table 3: Policy levers that affect the know-can relationship

||(1)<br>Can|
|---|---|
|Know|0.921<sup>_~~∗∗∗~~_</sup><br>(0.114)|
|Average of necessary equipment working|-0.0485<br>(0.106)|
|Know_×_Average of necessary equipment working|0.103<br>(0.199)|
|Constant|0.0119<br>(0.0583)|
|Observations|831|
|Adjusted_R_<br>2|0.937|

Clustered standard errors in parentheses. _∗ p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01

**Self-satisfaction** : Eight questions related to the worker’s perception of herself and not directly related to the facility or her role in it. Most of them measure her feelings of selfworth. For example, “I feel that I have a number of good qualities.”

**Feeling valued by the facility** : Nine questions related to the worker’s perception of whether she is a valued and useful employee in the facility. For example, “In this organization, I am valuable.”

**Job satisfaction** : Six questions related to the worker’s satisfaction with the facility and feelings of fulfillment in her job or profession. For example, “I would recommend to my children that they choose the health profession” and “I am satisfied with the opportunity to use my abilities in my job.”

**Positive facility characteristics** : Nineteen questions related to the facility’s processes, resources, and coworkers. For example, “This facility has a fair system for rewarding staff”

21
_Connecting Lagging and Leading Regions: The role of labor mobility_ We find strong evidence that individuals’ migration decisions depend on more than just returns in the labor market. Ignoring these non-pecuniary determinants may cause us to overstate the role of wages in driving migration decisions. This can be seen in Tables 3 and 4, which describe the results of the procedure described in Section 2.1 for those with less (i.e., 0-6 years) and more (i.e., 7-12 years) education.

Columns 2-5 of the lower panel of each table describe the results of cross-sectional procedures applied to each census year individually and ignoring moving costs. The likely effects of omitted variable bias are evident in the estimates of the utility parameters on access to piped water and number of hospitals. It is likely that each of these variables (particularly the number of hospitals in an AMC) is correlated with other desirable urban amenities. This has the effect of biasing upward the coefficient on each of these variables for both education groups. Access to electricity has a counterintuitive sign or is insignificant. While access to sewage shows the expected sign for those in the lower education group, it exhibits the counterintuitive sign (although it is insignificant) for the higher education group in 1991. In all, these results appear to be unstable over time and likely reflect omitted variable biases caused by unobserved urban amenities.

Columns 6-7 report the results of a differencing procedure that ignores the costs of migration. While controlling non-parametrically for time-invariant unobservable local attributes, this specification ignores the fact that it may be difficult, for example, for someone born in the Northeast to migrate to locations in the Southeast or South of Brazil. The signs of most of the coefficients correspond to expectations; very few of the parameter estimates are, however, statistically significant (only access to electricity and the number of hospitals for those in the lower education group, and the number of hospitals for those in the higher education group are significant). For both groups, an increasing cost of transporting commodities to São Paulo (a measure of national market connectedness) enters negatively into utility, while the cost of transporting commodities to the nearest state capital (a measure of local market connectedness) enters positively. This latter result is counterintuitive.

14
Policy Research Working Paper 5564

# **Abstract**

Natural disasters could constitute a major shock to public finances and debt sustainability because of their impact on output and the need for reconstruction and relief expenses. This paper uses a panel vector autoregressive model to systematically estimate the impact of geological, climatic, and other types of natural disasters on government expenditures and revenues using annual data for high and middle-income countries over 1975–2008. The authors find that, on average budget, deficits increase only after climatic disasters, but for lower-middle-income countries, the increase in deficits is widespread across all events. Disasters do not lead to larger deficit increases or larger output declines in countries with higher initial government debt. Countries with higher financial development suffer smaller real consequences from disasters, but deficits expand further in these countries. Disasters in countries with high insurance penetration also have smaller real consequences but do not result in deficit expansions. From an ex-post perspective, the availability of insurance offers the best mitigation approach against real and fiscal consequences of disasters.

This paper is a product of the Private & Financial Sectors Development Sector Unit, Europe and Central Asia Region. 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 mmelecky@worldbank.org. and craddatz@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
in Brazil as a whole (14.7 percent). This indicates that a possible catch-up effect in the quality of life is at play in Brazil.

**<u>Figure 4.7: Growth rate of the Quality of Life, 1991-2000</u>**

<!-- Start of picture text --> 4 0 .0 %<br>3 5 .0 %<br>3 0 .0 %<br>2 5 .0 %<br>2 0 .0 %<br>1 5 .0 %<br>1 0 .0 %<br>5 .0 %<br>0 .0 %<br>Source: Calculations based on IPLANCE data.<br>36.9%<br>35.2%<br>33.7%<br>31.3%<br>30.1%<br>29.2% 29.0%<br>27.7% 27.6% 27.0%<br>21.5%<br>19.6%<br>17.4% 16.8% 16.3% 16.3%<br>15.0% 14.7% 14.7% 14.6%<br>13.5% 13.3% 13.1% 13.0% 12.5% 12.4%<br>10.6%<br>9.1%<br>8.4% 7.7%<br>6.9%<br>6.0%<br>4.7%<br>Alagoas NordesteTocantins PernambucoBahia RG Norte SergipeRoraimaMinas Gerais Mato GrossoBrasil Norte Santa CatarinaSul AcreC Oeste RG do SulMS AmazonasRio de Janeiro SudesteDistrito Federal<br><!-- End of picture text -->

# **5. Poverty Profile**

After counting the poor we need to know who they are, where they live, and what they do. Comparing average levels of poverty for different categories is useful for learning about which population groups are falling behind or catching up in terms of poverty. This is useful for the design of policies: we would like to know not only whether, for example, more- or less-educated people are more likely to be poor in Bahia, but how the relative odds of being poor have evolved for these groups. This section traces the evolution of the P0 for various population groups during 19812001. The poverty profile constructed is based on data from the Brazilian household surveys (PNAD). The main questions addressed are: (1) who are the poor, (2) what are the characteristics of poor households, (3) where do they live, and (4) where do they work.

The structure of poverty is clear in Bahia: (a) blacks and mulattos are poorer than whites, (b) young households/household heads are poorer than older households/household heads, (c) the poor tend to work more in the informal sector, and (d) a greater share of those engaged in agriculture are poor as compared to industry or services. Furthermore, the deepest poverty is in rural areas, and among the poorly educated, and young household heads with children. In fact, since 1995, the P0 is actually rising for these groups. Without interventions to improve their opportunities and assets, their plight is likely to worsen. The social protection measures described in Section 6 are particularly relevant for them.

19
|(4)|(5)|(6)|
|---|---|---|
|Female<br>21.10"<br>(4.96)|Gd<br>(9.75)|B21<br>(7.96)|
|;<br>someTracking torMath Classes|7.3<br>(10.91)|fest!<br>8.87)|
|;<br>Female x Some Tracking|43.85***<br>(10.84)|30.05*°**<br>(10 49)|
|Other Controls<br>Na|Mo|Yes|
|Constant<br>Bais"<br>(4.22)|S8a.s1—*<br>(9.05)|4e4.94*<br>(31.65)|
|Nurniber of Observations<br>aba2|a524|2739|
|Average R-Square<br>0.0154|O.0272|0.165|
|;<br>Female +Some Tracking x Female|-5.59*<br>627)|-6.16<br>6.09)|
|;<br>;<br>someTracking +Some Tracking x Fernale<br>Note: Standard errors are reported in parentheses and significance at the 1<br>denoted ***,<br>| and *, respectively. Estimates were calculatetd using 5 plau<br>astimates ofachievement and Balanced Repreated Replication with Fays(0.<br>to estimate the standard errors.<br>Source data: PISA 2003 Korea, General School Strata, Grade 10 Students|31.56**<br>(14.91)<br>%, 5%, 10% <br>sible values <br>5) adjustme|af647<br>(1.97)<br> levels is<br> for<br>nt were used|
_For labor or for divorce?_ An institutional environment favorable to women (as illustrated by figure 1), such as nofault divorce laws, coupled with increased chances of success in marriage markets<sup>6</sup> or the possibility of accessing the labor market in the event of a divorce, would influence intrahousehold decision-making processes, even if a divorce does not ultimately occur (Duflo, 2012). Hence, divorce and marriage legislation can exert a substantial influence on these power dynamics and effectively boost women's participation in the labor force. This paper investigates the effects of divorce legislation reforms on women’s labor outcomes in three MENA region countries: Morocco, the Arab Republic of Egypt, and Jordan. At varying dates during the late 1990s and early 2000s, MENA region countries consecutively introduced different forms of unilateral divorce and child custody legislation reforms (Welchman, 2007; Appendix A). These reforms allowed women to easily access divorce without having to prove fault nor require consent of spouse. In past research, Hassani-Nezhad and Sjögren (2014) investigated a similar question using cross-country variation in the timing of introduction of unilateral divorce between 18 MENA countries using aggregate data and focusing on younger women’s labor force participation in comparison to women in relatively older age groups. They showed that the entry into force of unilateral divorce laws effectively increased labor force participation of younger women. Using a different empirical strategy and employing individual panel data, we investigate the effects of unilateral divorce and custody legislation reforms on women’s labor outcomes by exploring differences between women who are more or less affected by the reform. Instead of exploiting cross-country variations, we purposefully isolate estimates for one country at a time to assess how context-dependent the findings can be. In fact, a growing body of literature has shown that the relationship between intrahousehold distribution of bargaining power, social norms on gender roles, and labor supply is context-dependent, especially as far as developing countries are concerned (Duflo and Udry, 2004; Ashraf et al.,2009; 2014; 2016; Bau, 2016; Heath and Tan, 2020; Field et al., 2021). Moreover, by using micro data, we explore pathways between micro-level gender-based discrimination and inequalities and macro-level outcomes, adding to the growing micro-founded literature that relates

> 6 Marriage markets in countries of the Middle East and North Africa differ from other countries where most research on the canonical collective household labor supply is developed and can be characterized by underdevelopment and missing markets (Anukriti and Dasgupta, 2017). Marriage rates are substantially higher, mate selection often involves family arrangements, divorce legislation is limiting to women and social norms about gender roles tend to determine spousal relations.

3
level, interpretation of the survey data is strongly guided by theory. As section 2 indicates, theory gives us every reason to believe that institutions affect performance and that rule credibility, policy credibility, and resource adequacy and predictability are strongly implicated. However, theory provides no guidance on which of these institutional arrangements matter most and the findings at this disaggregated level are purely empirical. Finally, the model that has been developed is plausible but only one from very many that could have been constructed. In the country studies that offer detailed results, the model is only used to develop one set of interpretations where dialogue with government and World Bank operational staff suggest that this is useful.

The key stages in making an informed speculation on performance pay-offs are as follows:

# **Stage 1 - Develop an appropriate model**

# Model I

Performance = f (Institutional Environment).

This model has already been estimated in the previous section. It showed that predicted correlations are high when simple averages of the elements of institutional environment is regressed against aggregate performance as well as its three main components.

However, this information by itself is not enough for decision-makers in governments. They already know that performance is sometimes low because of weaknesses in the institutional environment; and if they could improve the whole institutional environment, the overall performance would improve. They need more precision. The logical next step is to move from Model I towards one in which the institutional environment is disaggregated into its components.

# Model II

# Performance = g (Rule Credibility, Policy Credibility, Resource Adequacy and Predictability).

Estimating Model II will also not give enough detailed information. Decision-makers in government will agree that improving policy credibility can improve performnance, but they will still ask what specifically they need to do to improve performance. Accordingly, a third model is required to capture the relationship between performance and particular elements of the institutional environment. This third model needs to be disaggregated enough to provide input for reform interventions. Improving the institutional environment as a method for improving performance is a practical strategy only if we can assess which particular elements of the institutional environment really matter. In other words, decision-makers are looking for reliable answers on the following questions: 27
Table 1 Summary evidence on openness and growth

|Openness measure<br>C|ountries|Period|Impact|Source|
|---|---|---|---|---|
|1 Measures Based on Trade Shar|es||||
|.|||Coefficient<br>on openness||
|Deviation from predicted<br>trade|45|1973-78|Significant,<br>>0|Salassa (1985)|
|Deviation from predicted<br>trade (Leawmr (¶988))||1982|Significant,<br>>0|Edwards (1989)|
|<br>Changes in trade shares<br>Trade shares|19<br>81 LDCs|1960-85<br>1960-85|Significant, >0<br>Weakly significant,<br>>0|Helliwell<br>and Chung (1990)<br>Quah and Rauch (1990)|
|II.<br>Price-based<br>and administrative|<br>measure|s|||
|8halla/Lau (1991), using<br>the relative price of<br>tradables to<br>international<br>prices|60|1960-87|Raises GDP growth|Bhalla and Lau (1991)|
|Relative domestic price<br>of investment goods<br>t ittil<br>ri|98|1960-65|Raises GDP growth<br>per capita|Barro (1990)|
|o nernaona<br>pces<br>Relative price of traded<br>goods|95|1960-85|Raises GDP growth<br>per capita|Dollar (1990)|
|Effective rate of<br>protection in manufacturi|47<br>ng|1950-80|Lower protection<br>raises GDP growth|Heitger (1987)|
|Trade Liberalization<br>index<br>from Choksi (1989)|20|1964-84|Weak evidence of<br>increased productivity|Philtips and<br>Havrylyshyn (1990)|
|Trade liberalization<br>index<br>from Halevi (1989)|35|1975-85|Export incentives<br>positively<br>affect GDP<br>per capita<br>growth,<br>insignificant<br>inpact of<br>import restrictiveness|Lopez (1990)|
|Trade liberalization<br>index<br>from HaLevi-Thomas (1990)||1978-88|Trade reform positively<br>affects<br>GDPgrowth.|Thomas/Nash (1991)|
|III.<br>Micro and Productivity<br>stud|ies||||
|Deviation from predicted<br>export share|108|1960-82|Positive|Syrquin and Chenery (1989)|
|<br>Export growth|4|1955-78|Positive<br>|Nishimizu and Robinson 1984<br>|
|Export growth<br>|17<br>|1950-80<br>|Positive<br>|Nishimizu and Page (1990)<br>Tb 1990|
|Export growth<br>|4<br>|1976-88<br>|Positive<br>|yout ()<br> P 1990|
|Import penetration|17|1950-73<br>1973-85|Ambiguous<br>Negative|Nishimizuand age ()|
|Import substitution<br>(IS)<br>(1 - Inport penetration)|4|1955-78|IS negatively<br>affects<br>TFP|Nishimizu and Robinson<br>(1984)|
|<br>Import substitution|4|1976-88|ISpositively<br>affects<br>TFP|Tybout (1990)|
|Effi<br>t f|||||
|ectve<br>raes o<br>protection<br>and domestic|||||
|<br>resource costs<br>Change in import shares|1<br>UK|1963-76<br>1976-79|Ambiguous<br>Ambiguous|Krueger and Tuncer (1982)<br>Ge,oski (1989)|
|IV.**Causality**tests|||||
|Methodology<br>Granger tests<br>Whi ifii|37|1950-81|Exports cause growth?<br>For only 4 countries|Jung and Marshall(1985)|
|te speccaton<br>test<br>Granger, Sims tests<br>(Asi|73<br>4<br>n NICs)|1960-77|Yes<br>Sometimes|**Ram**(1985)<br>tsio(1987)|
|<br>Granger tests|<br>Austria|<br>1965|No, but productivity<br>growth causes exports|Kunst and Marin (1989)|

26