rafmacalaba/datause-encoder-data huggingface dataset:
appears that initial out-migration is offset by significant in-migration and, on average, involves afflicted migrants moving to counties that are no less at risk of future storms than the sending county.

However, a lack of migration after individual storms does not necessarily indicate the absence of adaptive migration. It is possible that the average trends in migration result in movement away from at-risk areas and towards lower-risk areas. To examine whether this is occurring, we consider how exposure to storms has changed at the population level over our full sample period.

We find that with respect to both all storms and the set of the most damaging storms, overall population exposure to hurricanes has increased over our sample period (Figure 2a). We calculate the average exposure to each type of storm – high and low damage – across all the counties in our sample in each year of the sample, weighting each year individually based on the population living in the county in that year. This holds exposure to storms constant at a county level and reallocates risk across years based on changes in county populations. Migration is not the only determinant of county population, but if there is systematic migration to places that reduce population level risk – adaptive migration – the overall population risk would be declining, holding storms constant. We observe the opposite. The blue and dashed orange line in Figure 2a are both increasing relative to the solid gray and dashed gray lines, which represent the population-level risk had the distribution of population remained the same across counties.

We also find that the total number of storms a county experiences over our 25-year sample and the total net migration it experiences are positively correlated. In Figure 2b, we show that the z-score of total storms and the z-score of total net migration is positively correlated. That is, counties that experience more storms in our sample have on average more migrants moving in than moving out. This relationship is somewhat weak, but is statistically significant. It is also inconsistent with migration patterns where individuals systematically move away from locations that experience storms.

# **Moving to opportunity versus moving to adaptation**

Migration is often driven by economic opportunity, with individuals choosing to relocate to locations with better economic prospects. Even migration that occurs after weather shocks may be motivated by economic opportunity.<sup>34</sup> Using data on county level GDP as a proxy for economic opportunity, we observe that higher county GDP is strongly positively correlated with total net migration over our sample period (Figure 3a). Using the same data, we examine the extent to which there is a trade-off between moving to economic opportunity and moving to places that face lower storm risk.

We find that places with greater economic opportunity, proxied by higher county level GDP 9
characterized by increasing returns to scale and knowledge externalities, so that _yi_ is increasing in _si_ . Alternatively, too much congestion in a sector may lower the sectoral productivity, so that _yi_ is decreasing in _si_ (Fujita and Thisse, 2002). As a descriptive starting point, this could be examined by allowing the rate of poverty reduction to depend only on the share weighted change rates in the share of the middle and the metropoles, i.e. by estimating a reduced version of equation (2): ^ ^ In this equation, β _U_ and β _N_ capture both the direct effects of the sectoral transformation on poverty through changes in the income distribution and the indirect effects through changes in the income levels, each time controlling for the relative size of the sector. By comparing the share weighted change rate of the metropolitan share of the population and the share weighted change rate of the share of those living in the intermediate space with and without controlling for income growth, it can be explored whether and how the patterns of spatial and occupational transformation matter for poverty reduction.

# **4 Poverty, occupational, and spatial transformation in 1980-2004 – The information base**

The World Bank’s POVCAL data are used to construct the poverty spells and the rate of poverty reduction.<sup>20</sup> The $1-day and $2-day poverty headcount ratios are taken as measure of poverty, _P_ . Real GDP per worker (in thousand PPP US dollars) is taken from WDI. The annual change rate of each variable _x_ , _dx_ / _x_ , is given by (ln _xt_ − ln _xt_ −t )/ t , where _t-τ_ and _t_ are the initial and the final year of the period, respectively.

The metropolitan share of the population, _sU_ , is represented by the share (in %) of the

> 20 <u>http://iresearch.worldbank.org/PovcalNet/ April, 2008 (i.e. before the latest revisions of the poverty numbers</u> using the 2005 poverty purchasing power corrections).

17
**Table IV.13 Share of Cocoa Exports (Average 1996/7-2001/2) by Major Exporters**

|**Commercial Firms**|**1996/97**|**1997/98**|**1998/99**|**1999/2000**|**2000/01**|**2001/02**|
|---|---|---|---|---|---|---|
|Nazario Rizek|34%|37%|36%|27%|37%|27%|
|Comercial Roig|29%|27%|28%|26%|29%|21%|
|Munne & Co.|13%|11%|13%|15%|9%|9%|
|Paiwonsky|8%|7%|6%|5%|6%|4%|
|Garcia & Mejia|3%|4%|5%|3%|2%|3%|
|Cacaoinsa|7%|7%|2%|7%|1%|0%|
|Others|1%|>1%|>1%|1%|2%|1%|
|**Sub-Total**|**95%**|**93%**|**90%**|**84%**|**86%**|**66%**|
|**Cooperatives**|||||||
|Conacado|5%|6%|10%|16%|11%|30%|
|Aprocaci|>1%|1%|>1%|>1%|2%|2%|
|Yacao|>1%|>1%|>1%|>1%|1%|2%|
|**Sub-Total**|**5%**|**7%**|**10%**|**16%**|**14%**|**34%**|

Source: Secretaria de Estado de Agricultura, Departamento de Cacao “Informe Annual de las Estadsticas del Cacao 2001/2” 2002.

CONOCADO dominates the market for _Hispaniola_ and certified organic _Hispaniola_ , but Nazario Rizek has also entered into this export market (see table IV.14). Other producer associations established in the 1990s (e.g., Aprocaci and Yacao) are experiencing growth in exports and market share and are trying to produce and export _Hispaniola_ and organic cocoa.

**Table IV.14  Share of** **_Hispaniola_ Cocoa Exports (Average 1997/8-2001/2) by Major Exporters**

||**1997/98**|**1998/99**|**1999/2000**|**2000/01**|**2001/02**|
|---|---|---|---|---|---|
|**Nazario Rizek**|5%|41%|19%|16%|27%|
|**Comercial Roig**|1%|0%|0%|0%|0%|
|**Conacado**|92%|59%|73%|64%|63%|
|**Aprocaci**|1%|0%|0%|5%|0%|
|**Yacao**|0%|0%|0%|9%|7%|
|**Total**|99%|100%|92%|94%|97%|

Source: Secretaria de Estado de Agricultura, Departamento de Cacao “Informe Annual de las Estadsticas del Cacao 2001/2” 2002.

# _Nazario Rizek C. Por A._

The Nazario Rizek company has long dominated the cocoa market in the Dominican Republic, and is also an exporter of coffee. Rizek has mobile purchasing agents who go to farms and villages to purchase cocoa beans. Rizek also provides substantial amounts of short-term credit for working capital. Loans are typically 40-50% of the expected FOB value for pre-harvest finance. Rizek also allows farmers to fix a price up to 10 months in advance on 10% of total expected production. During harvest Rizek sets the prices twice daily, by subtracting a standard marketing margin from the international price, and communicates that price to his purchasing agents. Producers who sell their cocoa to Rizek may take the spot price but also can deliver their 65
# Table III-5

# MER Activities in Completed Population Projects

Manpower Studies Jamaica, Trinidad and Tobago Indonesia, Kenya Facilities Scheduling or Jamaica, Indonesia, Egypt Maintenance Studies Management, Service Delivery India, Malaysia, Egypt & or Program Effectiveness Studies Dominican Republic Family Size Motivation and Kenya, Egypt Acceptor Studies Review Existing or Design New Trinidad and Tobago, Evaluation Systems Malaysia, Dominican Republic Develop or Improve Management Trinidad and Tobago, Information System Tunisia, India, Indonesia, Dominican Republic External Reviews Jamaica, Trinidad and Tobago, Tunisia, Malaysia Establish Demonstration Project Areas

-------------------------------------------------------

Malaysia, Indonesia __--.____Source: Staff Appraisal Reports, PHN, as summarized in Lauren A. Chester (April, 1985), p. 37.

# 2. Nature of Planned Aproach to Organizational Structure

Projects are defined not only by expectations concerning the nature of activities to be carried out under the mandate of the project but also by the organizational approach to their implementation. In this section we discuss aspects of organizational structure.

a. Location of organizational responsibility in the bureaucracy Most Bank projects with population content are located in ministries of health. This location tends to be confining in a number of respects. Ministries of health tend to be relatively weak in political and administrative terms, and they often have a 35
# 1. **Introduction**

The development and efficiency of fmancial intermediaries are important determinants of economic growth. Countries with higher levels of financial development experience higher productivity and GDP per capita growth rates.<sup>2</sup> Brazil exhibits both a low level and low efficiency of financial intermediation, as evidenced by the indicators in Table 1. This underdevelopment applies to bank intermediated funds, private bond markets, and stock markets. Liquid Liabilities to GDP are lower than the Latin American and the upper middle income group average. Private credit as share of GDP is at the average Latin American level, but lower than the average for upper middle income countries. Furthermore, half of this credit is provided by state-owned institutions. The ex post net interest margins and the overhead costs of banks are significantly higher than the averages for both Latin American and upper middle income group countries.

Table 1: Financial intermediation in Brazil relative to other countries

||Liquid<br>Liabilities|Private Credit|Net Interest Margin<br>. .|Overhead Costs<br>_|
|---|---|---|---|---|
|Brazil|28.04|29.70|7.76|7.30|
|Sample mean|54.00|50.77|3.99|4.03|
|Latin America|34.90|29.49|5.54|5.81|
|of which: Argentina|<br>21.67|18.29|4.20|6.74|
|Chile|42.03|68.17|3.89|2.95|
|Mexico|26.56|15.79|3.64|6.57|
|Upper middle<br>income countries|57.56|49.65|4.09<br>I_I|3.87|
|OECD countries|76.28|88.51|2.68|2.73|

Notes: Liquid Liabilities = currency plus demand and interest-bearing liabilities of financial intermediaries as share of GDP; Private Credit = total clains on private nonfinancial sector by financial institutions as share of GDP; Net Interest Margin = accounting value of a bank's net interest revenue as share of total assets; Overhead costs = accounting value of a bank's overhead cost as share of total asset; Net interest margin and overhead cost numbers include both private and public and both domestic and foreign banks. All numbers are for 1997. Source: Beck, Demirgfii -Kunt and Levine (1999).

In addition to the low level of private credit, the average maturity of loans is low in Brazil. The ratio of long- to short-term debt for the median Brazilian company is 1.23, compared to an average of 2.20 for OECD countries.3 Anecdotal evidence suggests that most small and medium size companies in Brazil only obtain short-term bank credit with maturities of at most 90 days. Sources of long-term finance are very limited and mainly from state-owned development banks, such as BNDES.

> 2 See Beck, Levine and Loayza (2000) and Levine, Loayza and Beck (2000).

3These numbers are for 1997 and from a firm-level database for 46 countries. The database only includes listed firms. Any debt that is due in six months or less is considered short-term. The average for Latin American countries in the sample is 1.30.

2
simple model of parental investment with local spillovers to formalize the idea that parents have an active role in promoting educational opportunities, and that educational investment at the community level are a socially determined outcome (List et al., 2020). We show that the extent to which an educational program preserves or loses impact at scale depends upon its ability to promote parental coordination and engagement in the local community. We empirically corroborate these predictions by leveraging the changes in community-level parental engagement induced by the experiment. Using this variation, we document that parents prevent schools from closing, and as a consequence promote the effectiveness of the mentoring intervention during the government implementation. We find that an increase of half standard deviations in parental engagement decreases the probability of school closures by 11 percentage points over the subsequent two years. Our qualitative data from in-depth surveys of mentors and local instructors further corroborate the role of parents in guaranteeing the continuation of educational activities in the communities, in a context with poor school infrastructure and where schooling activities are often disrupted.

Finally, we study the educational impacts of the policy reform across the overall population of schools in the state of Chiapas. The assignment of the mentoring program under the _Plus_ modality at scale was done through a rotating scheme with a priority-based mechanism. We exploit the quasi-experimental variation in the program rollout once we condition on the set of eligibility criteria officially used by the government. After providing evidence that this variation appears conditionally “as-good-as random” via various placebo tests, we show that the program was successful in the schools that were previously part of the evaluation sample as well as in the rest of the schools in Chiapas. Within the evaluation schools, the marginal effect of the _Plus_ modality after one year of government implementation on the probability of enrolling in seventh grade is +5.4 percentage points. The cumulative effect of continuous exposure to the program for three years (two years under the experiment and one year under the government) implies that the enrollment rates in these disadvantaged and rural areas achieve the secondary school enrollment rates in urban Mexico (95 percent). For the much larger sample of schools that did not participate in the experimental evaluation, the results show a positive effect on secondary school enrollment, with an average program impact of 4.5 percentage points under the _Plus_ modality at scale. We further document positive effects of the program on child literacy, which imply a reduction of illiteracy rates by 20 percent with respect to the sample mean, as well as a decrease in school closures that is remarkably similar to the corresponding impact of the program under the experimental assignment. Taken together, these findings corroborate the effectiveness of the intervention in increasing 3
For each of these service sectors, authors in the Findlay and Warren volume evaluated the regulatory environment across many countries. The price of services is then regressed against the regulatory barriers to determine the impact of any of the regulatory barriers on the price of services. Kimura et al. then assumed that the international regression applies to Russia. Applying that regression and their assessments of the regulatory environment in Russia from the questionnaires and other information sources, they estimated the ad valorem impact of a reduction in barriers to foreign direct investment in these services sectors.<sup>16</sup> The results of the estimates are listed in table 12a.<sup>17</sup> In the case of maritime and air transportation services, we assume that the barrier will only be cut by 15 percentage points, since WTO commitments in these sectors are weak.

# **_4.3.3 Share of Expatriate Labor Employed by Multinational Service Providers_**

The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use Russian labor less intensively than their Russian competitors, if multinationals use mostly Russian labor their expansion is likely to increase the demand for Russian labor in these sectors.<sup>18</sup> We obtained estimates of the share of expatriate labor or specialized technology that is used by multinational service providers in Russia, but which is not available to Russian firms, from the Russian research institutes that specialize in these sectors. In general, we found that multinational service providers use mostly Russian primary factor inputs and only small amounts of expatriate labor or specialized technology. In particular, the estimated share of foreign inputs used by multinationals in Russia is: telecommunications, 10% plus or minus 2%; financial services, 3%, plus or minus 2%; maritime transportation, 3%, plus or minus 2%; and air transportation, 12.5%, plus or minus 2.5%.

# **_4.3.4 Tariff and Export Tax Data_**

Tariff rates by sector are taken from the paper by Shepotylo and Tarr (2012). They estimate the applied MFN tariff rates at the ten digit level in Russia for all years from 2001 to

> 16  Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts. The estimates by Kimura _et al_ . that we employ are for “discriminatory” barriers against foreign direct investment. Kimura et al. also estimate the impact of barriers on investment in services that are the sum of discriminatory and non-discriminatory barriers.

> 17  See Jensen, Rutherford and Tarr (2006) for an explanation of the estimate in telecommunications. 18 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement.

18
44

# TAILE Ll CONTINUED $160 BILLION REDUCTION IN THE STOCK OF DEVELOPING COUNTRIES OE8T

PERCENTAGE DEVIATtON FROM tHE BASELINE UNLESS OTHERWISE NOTED

||1989|1990|1991|1992|1993|1994|1996|1996|
|---|---|---|---|---|---|---|---|---|
|DEVELOPINC<br>COUNTRIES:|||||||||
|REAL CDP .......................|0.23|0.41|0.51|0.55|0.54|0.52|0.43|0.32|
|REALDOMESTICASSORPTION<br>...........|1L.12|<br>1.22|1.2|1.1|0.97|0.84|0.6|0.4|
|REALGROSSINVESTMENT<br>..............|3.52|2.27|1.13|0.16|-055|-1.|-1.33|-1.27|
|GNPDEFLATOR 1/ .<br>....................|.28|0.4|0.43|0.36|0.23|0.09|-0.14|-0.26|
|TL CON. EXP. INCL. PUBLIC .<br>......|...<br>.55|0.97|1.21|1.32|1.33|1.27|1.06|0.79|
|STOCK OF DEST ......................|-10.56|-8.01|-5.85|-4.07|-2.65|-1.55|-0.13|0.49|
|STOCKOFNET DEBT ..................|-12.02|-10.05|-8.2|-6.55|-5.11|-3.9|-2.1|-0.96|
|NET FOREIGN ASSETS 3/ ..............|145.|128.96|111.96|95.06|78.9|64.02|38.91|20.02|
|VOLUME<br>OF TOTAL IMPORTS<br>............|4.5|4.15|3.55|2.83|2.16|1.61|0.86|0.39|
|VOLUME<br>OF TOTAL EXPORTS<br>............|0.16|0.25|0.25|0.2|0.14|0.1|0.06|0.|
|TRADE8ALANCE. VALUE 3.5/ ..........|-26.37|-26.52|-26.05|-24.39|-22.15|-19.47|-14.08|-9.41|
|NET INTEREST PYMT 3.6/ .............|-11.36|-10.46|-905|-7.48|-5.99|-4.61|-2.34|-0.77|
|CURRENT ACCOUNT BALANCE3/.........|-I.|-16.04|-16.99|-16.9|-16.16|-14.88|-11.78|-8.68|
|PRINCIPALRATIOS:|||||||||
|NETINTEREST PYKT/EXPORTS:|||||||||
|BASELINE<br>.<br>........................|-6.07|-7.96|-7.56|-7.14|-6.74|-6.38|-5.83|-5.39|
|SCEXARIO<br>.<br>........................|-6.22|-6.35|-6.24|-6.09|-S.93|-5.77|-S.52|-5.27|
|DIFFERENCE<br>.<br>...........|1.85|1.61|1.32|1.05|0.81|0.61|0.31|0.12|
|TOTAL DEBT/EXPORTS:|||||||||
|BASELINE<br>.........................|199.39|195.81|122.27|188.77|185.32|181.93|175.27|168.82|
|SCLNARIO<br>.........................|177.31|178.27|178.5|178.06|177.11|175.72|172.04|167.63|
|DIFFERENCE<br>.......................|-22.09|-17.54|-13.77|-10.71|-8. .1|-6.21|-3.23|-1.19|
|TOTAL DEBT/GOP:|||||||||
|BASELINE<br>.........................|38.42|38.93|39.44|39.96|40.48|41.|42.06|43.13|
|SCENARIO<br>.........................|34.28|35.66|36.95|38.12|39.19|40.16|41.82|43.2|
|DIFFERENCE<br>.......................|-4.14|-3.26|-2.49|-1.84|-1.29|-0.84|-0.24|0.07|
|INTERNATIONAL<br>PRICES 7/:|||||||||
|PRICEOF OIL INDOLLARS<br>............|0.46|0.81|1.19|1.5|L.?|L.77|1.62|1.21|
|PRICE INDEX OF COM ................|1.74|1.91|1.67|1.39|1.2|1.13|0.99|0.62|

II INFLATION DIFFERENCE FROM THE BASELINE.

**3/** DIFFERENCE FROM THE BASELINE.

S/ NEGATIVE NUMBERS ARE DEFICIT AND POSITIVE NUMBERS ARE SURPLUS. IN BILLIONS OP US DOLLARS.

6/ NEGATIVE NUMBERS ARE REDUCTION AND POSITIVE NUMBERS ARE INCREASE IN INTEREST PAYMENTS. IN BILLIONS OF US DOLLARS.

7/ OENOMINATED INDICES IN TERMS OF US DOLLARS. 1980-1.
Table 2 presents household income composition based on the SAM. It shows that factor income represents the largest source of income for all household categories. Unskilled labor income and non-agricultural capital income each represent 35 percent of total household income, while skilled labor income and agricultural capital income come next with shares in households’ income of 16.06 percent and 10.32 percent. Unskilled labor is the primary source of income for, in declining order of importance, landless, illiterate, marginal farmers, non-agriculture and small farmer households. Low, medium and high education households receive most of their income from non-agricultural capital, although the latter two categories also receive a significant share of skilled labor income, whereas low education households are heavily dependent on unskilled labor income. Large farmers have agricultural capital income as their principal source of income. Given these substantial differences in income sources, we may expect that trade liberalization will have very different income effects depending on how factor remunerations are affected.

# **2.3 The year 2000 household survey**

To examine the link between the macro effects and micro effects in terms of poverty we use the representative household approach. The results of the model at the aggregate level, for the nine household categories, are subsequently linked to the household survey assuming that each household in the latter has the same variation in its income (or consumption) as the group or category to which it belongs. The survey includes 7439 households of which almost 80 percent live in rural areas. The base run poverty profile will be presented in the next Section.

10
# 1. **Introduction**

Since the Stockholm Conference on Environment and Development in 1972, many countries have taken steps to mitigate environmental damage. General environmental legislation is already common, although detailed rules and regulations are still far from universal. In many developing countries, it is clear that enforcement of environmental laws has been hampered by inadequate staffing and funding. Anecdotes abound, but more systematic comparative analysis of countries' environmental performance would undoubtedly help clarify the major policy issues and options. Unfortunately, comparable data on regulatory measures are available only for developed countries, and even these data are frequently scanty.

At present, therefore, comparat:.ve analysis must begin with basic data construction. One promising source is the set of environmental reports presented to the United Nations Conference on Environment and Development (UNCED, 1992) by 145 countries. The reports are reasonably comparable because the UN imposed a standard reporting format.

Using a multidimensional survey of 31 national UNCED reports, we have developed a set of comparative indices for the status of environmental policy and performance. This paper describes our methodology, the indices, and some results from a statistical analysis of their relationship to other more 1