rafmacalaba/datause-encoder-data huggingface dataset:
subsistence agriculture in small-scale farms, ranging from 0.5 to 1.5 hectares. The overwhelming majority of farmers lack formal land titles. The movement of labor out of agriculture has been slow, and is largely attributable to low rural-urban migration.

The pace of sustained and high economic growth has not translated into fast poverty reduction. After the end of the civil war in 1993, Mozambique ranked third among the poorest countries in the world. Since then, the country has seen a moderate decline in poverty rates. However, poverty remains unevenly distributed and concentrated in rural areas. Along the same lines, Mozambique continues to perform poorly across a wide range of human development indicators. In 2014, Mozambique was ranked 178 out of 198 in the Human Development Index. The coverage and quality of public service provision remain low, especially in rural areas.

# _Climate risk profile_

Globally and regionally, Mozambique is among the countries with the largest exposure to multiple natural hazards as it is extensively and increasingly subject to cyclones, floods and droughts and secondary hazards arising from these events (EM-DAT, INFORM, 2016). Long droughts are recurrent and stem from a combination of low levels of precipitation and the overgrazing and overuse of agricultural lands. They are experienced in 7 out of 10 years in the Southern regions, and in 4 out of 10 years in the Central regions. Drought risk is projected to increase over the coming years, both in terms of higher frequency and longer duration of droughts.

Mozambique is also particularly exposed to cyclones. The Mozambican coastline borders one of the most active basins of tropical cyclones, the Southwest Indian Ocean, which alone produces about 10% of all cyclones worldwide. Each year, on average, Mozambique is hit by one tropical storm or cyclone, and by three or four additional tropical disturbances (UN-Habitat, 2015). The cyclone season goes from October to April, with the most intense storms usually taking place between February and April. Tropical cyclones have produced devastating effects in the country, with five tropical cyclones (of category 1 to 4) making landfall between 2000 and 2008. The coastal region, home to over 60 percent of Mozambicans, is the most heavily affected and often experiences widespread destruction of infrastructure and population displacement because of cyclones (GFDRR, 2012).

5
even banked persons request loans from informal lenders, as personal loans may be harder to obtain from formal lending institutions than business loans or subsidized housing loans. However, as discussed in Chapter 1, consumer lending in Colombia has grown significantly over the past two years.

**Table 3. 4  Reasons for requesting a loan**

|**Income percentile**|**0-20%**|**21-40%**|**41-60%**|**61-80%**|**81-00%**|
|---|---|---|---|---|---|
|Personal Expenses|34.5%|32.2%|23.4%|17.5%|21.9%|
|Education or Health|8.3%|7.7%|17.4%|20.2%|17.9%|
|Vehicle|7.4%|11.1%|2.1%|6.7%|16%|
|Housing, including equipment and furniture|25.6%|31.4%|30.3%|28.5%|13.5%|
|Business, investment, equipment and repairs|14.0%|9.2%|12.3%|10.6%|20.3%|
|Payment of debts|8.0%|6.5%|10.6%|13.2%|9.1%|
|Other|2.3%|2%|4%|3.3%|1.4%|
|_Totals_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|

_Source: DANE survey, 2003_ 25. **More people seek loans in the informal than in the formal sector.** The Bogotá survey reveals that the banked population is more likely to seek loans than the unbanked - by a factor of six to one (see Figure 3.12). Among the population surveyed 13.6% of the total population successfully sought loans with the informal sector in the past thirty six months while another 8.6% successfully sought loans from the formal sector. This proportion is higher than that reported in other studies. Only 10% of those surveyed in Mexico City reported requesting a loan in the past 36 months from either formal or informal sources.

**Figure 3. 12  Loan request from formal institutions**

<!-- Start of picture text --> 97%<br>100%<br>91%<br>90% 82%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>18%<br>20%<br>9%<br>10% 3%<br>0%<br>Banked Unbanked Total population<br>Requested a formal loan in the past 3 years<br>Did not request a formal loan in the past 3 years<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 26. **Higher loan approval rates help explain the relatively higher importance of credit obtained outside the banking system.** Table 3.5 shows the approval rate, according to the would-be borrowers surveyed, 56
6 This question of whether countries belong in the same sample is, at least implicitly, recognized by many researchers when they exclude major oilexporting countries, or isolate 'developing" countries, or sort countries by continent. In excluding major oil-exporting countries, Barro (1989) argues that 'These countries tend to have high values of -eal GDP per capita, but ... act more like countries with lower values of income. This behavior can pLobably be explained by thinking of these countries as receiving large amounts of income from natural resources, but otherwise not being advanced in terms of technology, human capital, and so on." (p.18) Alternatively, one may argue that it is not the large percentage of income received from natural resources that motivatas the exclusion of major oil-exporting countries from the sample but the fact that these countries experienced large terms of trade changes.' Wlile plausible, these arguments imply that countries receiving a "large" amount of income from natural resources or countries experiencing "large" terms of trade shocks are not part of the sample of countries we have in mind in considering a particular set of hypotheses. Should we then exclude all countries that :eceive some specified amount of income from exporting natural resources (not just oil producers) and all countries that experienced sufficiently large terms of trade shocks?

Another example of our difficulty in deciding which countries belong in the same regression is the frequent separation of developing from industrialized countries. Table 1 gives a list of cross-coantry studies of 5 An important statistical problem with major oil exporting countries is that many data sets use 1980 prices. Consequently, when initial income is computed in 1950 or 1960 measured initial income for major oil exporting countries is huge and does not appropriately reflect relative income levels in these earlier years.
8

# **II. Women Workers: The Main Issues**

Before an analysis can be undertaken, one must understand the characteristics and aspirations of the respondent group. Furthermore, it is important to understand the issue of labor mobility. Finally the concept of empowerment and how it is important for ensuring worker rights needs to be discussed.

# _11.1 Characteristics of Women Workers_

Social characteristics as well as individual characteristics influence the level of participation of women in the work force. Individual characteristics include level of education, age, and marital status (Bukit and Bakir, 1994). The above factors not only determine the participation of women in the sectors relevant to this research but also in the informal sectors. White (1991) explains that the purpose of such information is to determine who works in these sectors, where they are from, why they work in a factory, and why they are recruited as good employees.

The combination of age, education and marital status, according to a study on gender marginalization and rural industries done by Grijns (1992), determines the stage of their life cycle that women are in, which in turn affects their labor participation and social mobility. The different and distinct phases of women's lives are: young and single women; married, divorced or widowed with young children; women with adult children (married, divorced or widowed); and older women without husband and children. Women in the first and the two last categories are more likely to join the work force since their domestic condition allows them to work outside the home without taking too much time away from their housework. Nevertheless, a divorced or widowed woman even with small children will have to work to support the family. This can only be done if the woman receives some help from other members of the family to take care of the children.

The women workers surveyed in textile, garment, and footwear industries, in previous studies have the following characteristics: 75-85 per cent of the women workers are between the ages of 15-43 years old and are single (Lok 1993, Indraswari 1995, Singarimbun 1995). Women in this age group are typically in good physical condition and will therefore have higher productivity. In terms of compensation, an umnarried woman will incur less cost to the company
||**Table A**|**12: Interactio**|**n term results**|**forgender spe**|**cific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(9)**|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.196**<br>(0.088)|0.190**<br>(0.085)|0.169**<br>(0.085)|0.204**<br>(0.092)|0.200**<br>(0.091)|0.235***<br>(0.088)|0.212**<br>(0.093)|0.218**<br>(0.092)|
|Exports (%, cell average)*Women|0.127|0.158|0.159|0.161|0.209*|0.222**|0.227**|0.234**|
|<br>can work in the same industries as<br>men Y:1 N:0|(0.104)|(0.102)|(0.104)|(0.104)|(0.109)|(0.102)|(0.102)|(0.099)|
|Exports (%, cell average)|-0.069|-0.066|-0.062|-0.087|-0.084|-0.159*|-0.726|-0.754|
||(0.080)|(0.078)|(0.078)|(0.084)|(0.083)|(0.081)|(0.487)|(0.506)|
|Women can work the same night|<br>-8.333***|<br>-7.915***|<br>-7.468***|<br>-5.639**|<br>-5.771**|<br>1.469|<br>0.831|<br>-0.390|
|<br>hours as men Y:1 N:0|(2.594)|(2.633)|(2.627)|(2.768)|(2.794)|(3.045)|(3.976)|(4.011)|
|Women can work in the same|-3.485|-3.124|-3.290|-2.507|-2.610|-4.647*|-2.901|-3.433|
|industries as men Y:1 N:0|(2.240)|(2.156)|(2.158)|(2.105)|(2.379)|(2.417)|(3.128)|(3.094)|
|Exports (%, cell average)*GDP per<br>capita (logs)|||||||0.065<br>(0.056)|0.083<br>(0.072)|
|Exports (%, cell average)*WBL<br>Index||||||||-0.206<br>(0.367)|
|No. of Workers (logs)*WBL Index||||||||-3.269*<br>(1.737)|
|No. of Workers (logs)*Women can|||||||0.256|0.577|
|<br>work the same night hours as men<br>Y:1 N:0|||||||(0.709)|(0.720)|
|No. of Workers (logs)*Women can|||||||-0.464|-0.326|
|work in the same industries as men|||||||(0.534)|(0.524)|

No. of Workers (logs)*Women can work in the same industries as men Y:1 N:0 79
**Table 2 Povert** **<mark>y</mark> Headcount Ratio and Gini Coefficient in Honduras** **<mark>,</mark> 1991–2006 (percent)**

|_Welfare measure_|_1991_|_1995_|_2001_|_2006_|
|---|---|---|---|---|
|_Extreme poverty_|||||
|Rural|68.0|65.1|69.2|65.1|
|Urban|52.2|41.9|32.7|27.9|
|National|61.5|55.1|51.2|47.5|
|_Moderate poverty_|||||
|Rural|80.3|75.6|78.1|73.6|
|Urban|79.4|74.4|63.5|59.6|
|National|79.9|75.1|70.9|67.0|
|_Gini coefficient_|||||
|National|52.4|55.5|56.6|58.6|

_Source_ : Authors’ computation based on data from EPHPM.

How much of the significant reduction in poverty can be explained by the _maquila_ boom? The answer depends on the proportion of households in the neighborhood of the poverty line whose incomes depend on the _maquila_ sector, the change in real wages of _maquila_ workers living in households near the poverty line, and the possibility for people near the poverty line to get jobs in the _maquila_ sector. The share of Honduras’ workforce working in the _maquila_ sector increased from 1.3 percent in 1991 to 4.4 percent in 2006; during this time the prevalence of poverty among _maquila_ workers fell 22 percentage points (from 54.6 percent to 32.9 percent or 40.0 percent), an achievement well above the national level (table 3). This is an indicator that the overall poverty reduction documented in table 2 was at least partly caused by fast-growing labor participation and earnings in the _maquila_ sector. The potential contribution of the _maquila_ sector to poverty alleviation in Honduras is significant: in 1991, only 3.3 percent of the total households above the poverty line had a member working in _maquila_ , in 2006 this proportion increased to 10.8 percent. This is hardly surprising given the high number of employment created in the _maquila_ industry which had a direct positive income effect for those workers that found a job in this sector and an indirect effect via the general equilibrium wages in the economy (table 3).

6
**Table 1. Household Vulnerability Before and After Economic Shocks**

||No sh|ocks||Combine|d shock||35% de|valuation|plus other|shocks|
|---|---|---|---|---|---|---|---|---|---|---|
||Arbitrary<br>threshold|Financial<br>margin|thres<br>Arbit|hold<br>rary|thre<br>Financi|shold<br>al margin|thres<br>Arbi|hold<br>trary|thre<br>Financi|shold<br>al margin|
|||threshold|Level|Change|Level|Change|Level|Change|Level|Change|
|% of risk households|||||||||||
|relative to all HHs|4.6|7.6|6.7|2.2|9.4|1.8|8.2|3.6|10.3|2.7|
|tia HBS and authors’ calc<br>% of risk households<br>relative to indebted HHs|ulations.<br>13.5|22.4|19.8|6.4|27.7|5.4|24.0|10.6|30.3|7.9|

Source: Croatia HBS and authors’ calculations.

# <u>A 35 Percent Devaluation in Addition to Interest and Unemployment Shocks</u>

Perhaps the drawback of these calculations is that no meaningful devaluation takes place given Croatia’s exchange rate regime choice and the more limited role of loans in Swiss francs as opposed to the case of other emerging European countries (e.g., Hungary). To address this drawback, an arbitrary nominal devaluation of 35 percent is imposed on top of the described shocks through increases in interest rates and the unemployment rate. The focus is only on first round effects; i.e., pass-through effects, including the effects on incomes, are not considered.

As shown in Figure 6 (and Table 1), a 35 percent devaluation (in combination with interest rate and unemployment shocks, and when using the arbitrary threshold to tag households as vulnerable) has a larger impact on household vulnerability. In fact, 3.6 percent of all households (an increase from 4.6 percent of all households before the shock to 8.2 percent after the shock) and 10.6 percent of households with debt (an increase from 13.5 to 24.0 percent among households with debt) become vulnerable as a result of such an economic shock. This is above the combined shock described in Figure 5, Panel D. And yet, even with such an arbitrary shock, the increase in vulnerability among households does not appear to be too large. [Using the financial margin threshold the corresponding increases are about 3 and 8 percent, respectively.<sup>14</sup> ]

**Figure 6. Household Vulnerability to a 35 Percent Devaluation**

<!-- Start of picture text --> Panel A. Arbitrary Threshold Panel B. FM Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>% of Indebted Households<br>% of Households at Risk, prior to the shock<br>% of Households at Risk, due to the shock<br>Debt Service, % of Income, prior to the shock<br>Debt Service, % of Income, due to the shock<br><!-- End of picture text --> Source: Croatia HBS and authors’ calculations.

> 14 That fewer households are at risk when using the financial margin threshold reflects that rich households have more room to service their debt obligations and that poor households were at risk even before imposing an economic shock.

12
between trade and wage inequality (Frías, Kaplan, and Verhoogen 2012; Verhoogen 2008), as well as a link between FDI and a rise in the share of skilled labor in total wages (Feenstra and Hanson 1997). Hanson (2007) explores the distributional impacts of NAFTA and finds that Mexican regions more highly exposed to the agreement witnessed a larger decline in poverty and inequality. Prina (2013, 2015) finds that the change in prices of agricultural exports associated with NAFTA benefited small farmers more than large farmers. She also finds that agricultural wages did not change, but employment grew in sectors that had experienced a price increase. None of these studies, however, identifies the causal impact of the overall increase in exports experienced by Mexico on local household incomes.

This paper contributes to the literature by exploiting variations in export growth across Mexican municipalities between 2004 and 2014 to identify the impacts on poverty, inequality, and household incomes across the distribution. It finds that the effects of exports on household incomes tend to be progressive because they tend to be positive only among the poorest deciles. Nonetheless, these results are not robust to alternative specifications. To understand what is behind such weak impacts of rising exports on incomes, the paper investigates labor market mechanisms. It finds that, while exports do raise the total amount of labor income at the municipal level, average labor incomes do not change. This is because municipalities experiencing growing exports also experience an increase in labor supply that is driven by rising labor force participation, inflows of returning migrants, and lower emigration outflows. These results are consistent with the hypothesis put forward by Robertson (2007), who argues that the lack of positive labor market impacts of trade integration may be partly explained by migration. In addition, the paper finds that remittances tend to decline more in municipalities experiencing higher export growth.

This paper contributes to the literature in several ways. It is the first study to focus on the role of exports at the local level in a developing country using unique panel data from poverty maps, combined with data on exports at the municipal level. Second, it uses an instrumental variable that allows the causal impact of exports to be disentangled. The instrument combines the initial export structure by sector at the municipal level with global trends in exports from developing to developed countries by sector. Municipal-level trends in this instrument are thus not affected by local-level shocks. Third, it provides new evidence on the channels that could help explain why increasing exports may not necessarily translate into higher income growth 4
# 2 The model: A of process optimization under uncertainty

RESPONSE is an Integrated Assessment Model (IAM), which couples a macroeconomic optimal growth model<sup>1</sup> following the tradition launched by the seminal DICE model by Nordhaus (Nordhaus, 1994), with a simple climatic model. Here we present its basic equations, derived from the model used in Ambrosi et al. (2003).

The program maximizes an intertemporal social welfare function under uncertainty. Uncertainty holds on both climate sensitivity (and on temperature increase θ<sup>s,janddamagedenotedDj.Toencompassthewholerange</sup> t,at<sup>)</sup> of beliefs on the true climate damage, the model considers five states of the nature s for climate sensitivity (θ2<sup>s</sup> x<sup>in table 2) and five states j(Zjin table 2)</sup> for the form of damage. As climate change is basically a non-reproducible event, a subjective distribution of probabilities (ps and qj) is given to each state of the world (presented in table 2). These probabilities can be interpreted as the level of confidence a stakeholder attaches to each existing climate scenarios and to each assessment of climate change impacts.

The uncertainty actually operates at first period, before a point in time denoted tinfo at which uncertainty is resolved about the genuine level of climate change damage and on the climate sensitivity. Some argue that the “climate proof” was already provided by the two last IPCC reports, the Stern Review and the long serie of climate catastrophes over the past decade. What is meant by resolution of uncertainty is that all forms of controversies are stopped, and that there is a wide consensus on the validity of information. In the forthcoming simulations the date tinfo is set at 2050. At the end of this learning and self-convincing process, people adapt their initial behavior to new information, they accelerate abatements in case of “bad news” and relax the effort in case of “good news”.

We note S = (5, 5) the number of states for s and j. The intertemporal social utility function to maximize between now and the ultimate period T (here T = 2200) is: with u(.) the utility function, Nt the population at t which is assumed to grow at an exogenous rate, and Ct<sup>s,j</sup> the consumption of a composite good at

> 1very like Ramsey-Cass-Koopmans’ models (Ramsey, 1928; Koopmans, 1965; Cass, 1966)

5
percent, to 17 percent in 2000. However, Pará is still among the 11 states with the highest illiteracy rate in Brazil. Efforts to lower illiteracy even further are hampered by the fact that many of the illiterates are adult—the result of years of educational neglect. Efforts to improve adult literacy have been undertaken, but with poor results, because it is more difficult to teach basic skills to adults than to children.

|**Table 4.3: Infa**|**nt Mortali**|**ty Rate in Pará, 1**|**990-2000**|
|---|---|---|---|
|**Micro regions**<br>|**1990**|**1994**|**2000***|
|**State of Pará**|**48.1**|**38.7**|**34.9**|
|Almeirim|30.1|24.3|22.6|
|Altamira|41.2|34.1|32.5|
|Arari|40.4|34.7|31.9|
|Belém|32.5|28.4|26.8|
|Bragantina|61.7|52.3|49.2|
|Cametá|40.8|35.2|31.7|
|Castañal|42.7|36.7|34.8|
|Conc.Araguaia|52.9|41.5|40.1|
|Furos de Breves|35.3|29.8|28.4|
|Guamá|61.6|54.7|50.6|
|Itaituba|72.1|60.4|54.5|
|Marabá<br>|69|60.3|59.2|
|Óbidos|47.5|39.6|35.4|
|Paragominas|68.8|59.5|56.9|
|Parauapebas|71.6|59.5|58.1|
|Portel|62.4|57.8|56|
|Redenção|52.3|45.5|44.6|
|S.Félix Xingu|47.6|39.5|38.2|
|Salgado|48.8|42.5|39.8|
|Santarém|58|47.1|43.4|
|Tomé-Açu|36.7|31.8|29.6|
|Tucuruí<br>Source: SEOF/DIEEST/<br>_e Dados_ _Básicos"._|49.8<br>BDE based on Mi|41.6<br>nistério da Saúde/DATASU|39.5<br>S and_" Indicadores_|

Source: SEOF/DIEEST/BDE based on Ministério da Saúde/DATASUS and _" Indicadores e Dados Básicos"._ <u>(*) Calculations by Health Ministry in the publication</u> _" Indicadores e Dados Básicos"._ Illiteracy has a spatial dimension and it is not uniform across regions **.** Marajó and Southwest Pará, the two most sparsely populated regions and that are highly rural, have the highest rates of illiteracy in the state, 32 percent and 22 percent, respectively. This compares to the low illiteracy rate of 6 percent in the Metropolitan region of Belém.

20
Table 14: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose sports activities field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|–0.126|–5.468***|–3.804**|
||(0.702)|(1.952)|(1.820)|
|_R_ <sup>2</sup>|0.732|0.825|0.836|
|Observations|1,476|1,476|1,476|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|0.120|–4.185*|–2.121|
||(1.401)|(2.204)|(2.000)|
|_R_ <sup>2</sup>|0.728|0.773|0.787|
|Observations|1,238|1,238|1,238|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined sports and activities field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.