rafmacalaba/datause-encoder-data huggingface dataset:
# **5.2 The impact on poverty, inequality and shared prosperity**

Figures 13a and 13b present the results of the micro-simulations on poverty and inequality. Figure 13a shows the levels of moderate poverty at the individual and household level and the poverty gap (Foster-Greer-Thorbecke - FGT1), while Figure 13b shows the Gini and Theil coefficients.

As expected, the level and depth of poverty is higher in the crisis scenario. In the benchmark scenario, poverty declines from 13.7 in 2011 to 11.7 percent in year 2. On the other hand, if a crisis were to strike poverty is predicted to increase to levels of 15.4 and 15.2 in year 1 and year 2, respectively. This increase represents an average a poverty rate that is 3.5 percentage points higher than it would have been in the absence of the simulated macroeconomic shocks and 1.5 percent higher than the poverty levels in 2011. An important results is that under the crisis scenarios not only more people live under the poverty line, but the depth of poverty, measured by the FGT1, increases as well, implying that more resources would be needed in order to assist the poor population.

<!-- Start of picture text --> Figure 13. Impact of the crisis on poverty and inequality<br>Poverty Inequality<br>0.45<br>20%<br>15% 13.7% 15.4% 15.2% 0.40 0.382 0.388 0.392 0.382 0.391<br>13.5% 12.5% 0.383<br>11.7% 0.35<br>10% 9.5% 9.4% 10.9% 10.8%<br>8.1%<br>8.6%<br>0.30<br>5% 3.7% 3.6% 4.2% 4.2% 3.1% 0.261 0.284 0.280 0.262 0.277<br>3.3% 0.25 0.266<br>0%<br>2011 baseline year year 1 year 2<br>0.20<br>(2012)<br>2011 2012 2013 2014<br>Poverty (ind) No crisis Poverty (ind) Crisis<br>Poverty (hh) No crisis Poverty (hh) Crisis gini No crisis gini Crisis<br>Poverty gap No crisis Poverty gap Crisis Theil No crisis Theil Crisis<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011 The impact on poverty, however, would be less severe than during the 2001/02 Argentine crisis. At this stage, it is worth putting the results of the predictions in context. While any increase in the proportion of poor people should warrant attention, it is equally important to contrast the 3.5 percentage point expected increase with the 20 points spike observed after the 2001/02 Argentine crisis. There are certainly various factors that might be explaining these results, notably the increasing role of social transfers and other non-labor incomes in family’s incomes relative to the beginning of the decade.

In the past years, the non-contributory components of the Uruguayan social protection system have been expanded considerably. The implementation of the _“Plan de Atención a la Emergencia Social_ ” (2005), later replaced by _“Asignaciones Familiares-Plan de Equidad”_ (2008), and the _“Tarjeta Uruguay_ 13
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# _Hydrology data_

Data for several runoff and flow terms were obtained from University of Colorado and the IWMI. Each hydrological term was tested in the models. Flow sum gave the best results and was therefore used in revenue calculations.

# _Technology data_

Technology data was also obtained from the survey. It represents the quantity of heavy machinery that the farmers used on their farms.

# **_4.2 Statistical analysis_**

OLS estimation procedures using the Stata Statistical and Econometric software were used to fit the abovementioned models. To overcome the problem of heteroscedasticity, a robust estimation of the standard errors was undertaken, and to overcome the problem of multicollinearity, identified correlated variables were dropped from the model.

Several runs were done with different explanatory variables. Most of these variables were dropped from the model because of their low significance level and their low contribution in improving the overall significance of the estimated models. The marginal impact of seasonal temperature was estimated for each of the four proposed models. Furthermore, two climate change scenarios were used to assess the effect of heat stress on farm revenue. These two scenarios were MAGICC/SCENGEN results, which propose a 1.5 _°_ C rise in temperature and GCM results, which propose a 3.6 _°_ C rise. The expected reduction in farm revenue as a result of these two scenarios was then calculated.

# **5. Results and discussion**

# **_5.1 Farm net revenue determination_**

Farm net revenue is defined in the model as household crop gross revenue less fertilizers and pesticide costs per hectare of total cropped area. Four models were developed to assess the impact of increased temperature on farm net revenue and to explore different adaptation options to reduce the harmful effect of these temperatures. ARTES temperature data were used in these four models.

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The main strength of the DLM database is that it distinguishes between three levels of education for migrants. High-skilled migrants are those with postsecondary/tertiary education. Medium-skilled migrants are those with uppersecondary education completed. Low-skilled migrants are those with less than upper-secondary education, including those with lower-secondary and primary education or those who did not go to school. The main characteristics of the diaspora that we consider in this paper are the following:

- The bilateral migration flow for each skill group from origin country o to destination OECD country d is proxied by the change between 1990 and 2000 in the stock of migrants from o to d.

- The bilateral indicator of positive selection is proxied by the log-ratio of the number of high-skilled to low-skilled new migrants from o to d (we disregard medium-skilled migrants for this specification with no impact on the results)

- The bilateral indicator of relative concentration is the ’high-skill minus low-skill’ difference in the squared proportions of migrants from a given origin country o to the 30 possible destinations, following equation (4).

- The size of the existing diaspora is measured as the immigrant population born in country o and living in the OECD country d (= o) in 1990.

# 3.1 Individual vs aggregate data

The use of aggregate macro data has many advantages but also introduces certain complications. In this section, we discuss the pros and cons of using this dataset, and the main econometric issues triggered by our approach. An important distinction in the empirical analysis of migration data concerns the use of a individual (micro) data as opposed to aggregate (macro) one. Micro data are collected at the household and/or individual level to study the impact of networks on the propensity to migrate and the educational composition of the migrants (Massey, 1986; Munshi, 2003, McKenzie and Rapoport, 2010). These datasets include different measures of individual economic, demographic and social characteristics, such as age, income, occupation and education. This contrasts with more approaches the employ aggregate international migration data. Both approaches have their advantages and drawbacks and should be seen as complementary strategies to address the key issues at stake here.

By focusing on individuals or households, micro data directly accounts for the role of individual characteristics of the migrants. For example, McKenzie and Rapoport (2010) confirm that the networks are more important for uneducated migrants than for educated ones in the case of Mexican migration to the United States. Another appealing feature of the micro datasets is that they can distinguish between different types of networks such as networks defined at the community (city or region) level or at the household (family) level. If such data were available, then we can identify what kind of assistance (such as financial 7
lower-quality schools.<sup>4</sup> The LBC phenomenon has also attracted much attention from the media, which highlight the psychological costs of family separation that can potentially lead to suicides of left-behind children (see, for example, Xinhua news agency (2015) and The Economist (2015a, 2015b, 2016)). Against this background, the Government of China has set a priority to make urbanization "more inclusive" for migrant workers and their families (World Bank and DRC, 2014).

We provide a framework of analysis that closely integrates theory and empirics. Both our theoretical and empirical evidence suggests that increases in school fees decrease the chance that migrant households bring their children to the city, the number of children they bring, as well as the likelihood that they bring a daughter given preferences for sons. These results especially hold for more vulnerable migrant workers and for those who place a lower value on non-schooling outcomes. Furthermore, the negative impacts of higher school fees may be amplified during an economic crisis. Our findings suggest that a 10 percent increase in median school fees results in a reduction of 2 percentage points (or 5 percent) in the probability that the migrant worker brings his children along, and 0.02 fewer children being brought along.<sup>5</sup> This paper is organized as follows: we start by providing an overview of the country background in the next section before presenting our theoretical model in Section 3 and the empirical model and the data in Section 4. We then discuss estimation results, various robustness checks, and the heterogeneity analysis in Section 5. We offer further analysis of related outcomes in Section 6 and conclude in Section 7.

> 4 These findings contrast with Chen et al. (2009) who reported evidence of the positive impacts of parental migration and Mu and de Brauw (2015) who found that parental migration has no significant effect on the height of children, but has positive effects on their weight. 5 These empirical results are obtained using the latest data available from a household survey specially designed for the study of internal migration in China (i.e., the 2008/09 RUMIC data), which we discuss in more detail in the next section.

4
the Regional Financial Information (SIKD) and are broken down across 12 different economic sectors such as health, education, agriculture and infrastructure. In addition to the sectoral breakdown, local government spending is also classified into four categories; namely, capital expenditure, goods and services, personnel and other, providing an alternative for analyzing any changes among these categories.

To answer how disaster relief is financed, one needs a proxy for local damages. In Skoufias _et al._ (2017), the authors have constructed damage indices for different disaster types, and this paper will see how well these can be used to identify potential district budget reallocations due to natural disasters. An added benefit of the indices is that they are constructed from freely available data, implying that they can be a cost efficient resource for identifying local economic activity. The natural disaster damage indices in Skoufias _et al._ (2017) are constructed by modeling the local strength of each disaster using its physical characteristics and taking account of local exposure to these aspects using nightlight intensity derived from satellite imagery. The disasters examined are floods, earthquakes, volcanic eruptions and the 2004 tsunami, all of which are modeled using different remote sensing data that are aggregated up to district level.

The remainder of this paper consists of four parts. First, there is a brief section on the construction of the damage indices. Then a part presenting the budget data, followed by the main part with the methodology and results, before finishing with a conclusion.

4
throughout the entirety of the bootstrap procedure, only back transforming it at the end, in step 9.<sup>14</sup>

# **2.8 Evaluating Performance**

We have access to unit-level census data for seven countries. This allows us to calculate true sampling distributions with the unit-level census data by simulating separate surveys and saving the results from each iteration. In all countries, we treat subareas as enumeration areas. For each country, we design a sampling scheme that mimics sampling of an actual household survey from that country. For example, we mimic the sampling scheme used in the fifth Integrated Household Survey (IHS5) for Malawi. In all countries, we design the sampling process in a two-stage manner, first stratifying, then drawing an “enumeration area” (which in our case is always the lowest level of aggregation available in the census), before finally drawing households.

We independently draw 100 separate surveys, predict our outcomes of interest with each method, and then evaluate the performance of the methods against the ground truth derived from the full unit-level census data. We calculate the following statistics, where _i_ indexes areas, _y_ ˆ refers to the predicted outcome for area _i_ , and _yi_<sup>_truth_</sup> : refers to the true value for area _i_ :

- Correlation: We calculate both the Pearson correlation coefficient, _r_ , and the Spearman (rank) correlation coefficient, _ρ_ . We present the means across all 100 independent samples:

- Absolute deviation: This is defined for each area as �� _y_ ˆ _si − ysitruth_ ��. We present the average across all areas and all simulations:

- ˆ 2

- • Squared deviation: This is defined for each area as � _ysi − ysi_<sup>_truth_</sup> � . Similarly, we present the average across all areas and simulations:

- Width of confidence interval: For the EBP estimates, this is derived from the estimated MSE _E_<sup>ˆ</sup> _si_ as

> 14We of course also back transform for the original point estimate.

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**TABLE 5: Effects of the improvements on the intensive margins (per kilometer) of travel by car**

|||**Base**||**Road ex**<br>**peri**<br>(<br>22<br>_Z_<br>incre|**pansion in**<br>**phery**<br>ased 20%)|**Tr**<br>**improvem**<br>(<br>112<br>_G_<br>dec|**ansit**<br>**ent in core**<br>reased 20%)|**Road ex**<br>**periph**<br>**transit im**<br>**in**|**pansion in**<br>**ery and**<br>**provement**<br>**core**|
|---|---|---|---|---|---|---|---|---|---|
|||_j=1 _|_j=2 _|_j=1 _|_j=2 _|_j=1 _|_j=2 _|_j=1 _|_j=2 _|
|**_Speeds_**|_i=1_|_4.37_|_14.41_|_4.41_|_14.55_|_4.14_|_14.72_|_4.19_|_14.86_|
|_(km/hr)_|_i=2_|_15.90_|_18.15_|_16.18_|_19.52_|_16.09_|_18.33_|_16.30_|_19.71_|
|**_Trips by_**|_i=1_|149,270|147,460|149,400|147,900|118,870|148,540|119,900|148,900|
|**_car_**|_i=2_|122,450|842,820|122,700|1,008,700|122,730|842,720|123,000|1,000,730|
|**_Fuel_**|_i=1_|_0.2238_|_0.1351 _|_0.2233_|_0.1343_|_0.2267_|_0.1333_|_0.2261_|_0.1325_|
|**_(liters/km)_**|_i=2_|_0.1270_|_0.1165_|_0.1259_|_0.1111_|_0.1260_|_0.1158_|_0.1250_|_0.1104 _|
|**_CO2_**|_i=1_|888.12|481.51|885.41|478.24|903.30|474.13|889.98|470.98|
|**_(grams/km)_**|_i=2 _|448.26|405.83|443.79|383.73|444.42|402.69|440.03|380.83|
|**_Car_**|_i=1_|5.06|18.80|||||||
|**_km/trip_**|_i=2_|22.84|20.52|_Unchange _|_dfrom base_|_Unchange _|_dfrom base_|_Unchange _|_dfrom base_|

# **4.2 The effect of improving public transit trip times in the core**

In step 3, we simulate the effect of improving public transit travel times for those residing and working in the core. The results are shown in column 3 of Table 6. Note that doing so would induce commuters who reside and work in the core to switch to buses from driving but also from the walking and bicycling mode. As a result, although public transit travel times are improved exogenously, the travel times of car users can increase if sufficiently more commuters switch from walking and bicycling to buses than from driving to buses, because buses create congestion (see (8) and (9)). It is indeed the case, however, that auto travel times within the core do decrease because of many non-motorized commuters switching to public transit. In the other commuting arrangements auto speeds increase since some marginal commuters switch to residing and working in the core in order to take advantage of the lowered bus commute times there. Rents in the core increase and the aggregate floor space consumption in the periphery decreases by 3%. The direction of change in aggregate trips by auto, travel time, vehicle kilometers, fuel and CO2 are down, opposite of what was observed when roads in the periphery were expanded. The expected utility of each income quintile increases but generally not as much as in the case of the peripheral road improvement.

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This paper is evaluating the impact of a randomized conditional cash transfer intervention targeting young women in Malawi that provides incentives (in the form of school fees and cash transfers) to current schoolgirls and young women who have recently dropped out of school to stay in or return to school.<sup>10</sup> Between October 2007 and January 2008, baseline surveys were conducted with 3,805 girls in 176 Enumeration Areas (EAs) in Zomba district of Malawi. These EAs were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. The random sample of 176 EAs consists of 29 EAs in Zomba town, 8 trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

The 3,805 girls were selected based on information collected during a listing exercise, which involved going door to door to _all_ households in these 176 EAs. This listing exercise identified all never-married, 13-22 year-old females living in the area. This age group was chosen because it represents the period during which school dropout coincides with the onset of sexual activity, and therefore maximizes the power for a causal study of the relationship between these two phenomena. For the study, we sampled **all** dropouts and 75-100% of current school girls, where the exact percentage sampled depended on the age of the school-girl; regression results are re-weighted to make them representative of the entire eligible frame.<sup>11</sup> A baseline ‘dropout’ was any girl or young

> 10  We selected only females for the treatment because the expected HIV prevalence among males of the same age cohort is much smaller, making it unlikely that we could detect program impacts with any kind of precision.  The HIV prevalence for males aged 15-24 is 2.7%, compared with 8.4% for females of the same age (UNAIDS, 2008).

> 11 These percentages were lower for urban areas since average population in urban areas is much higher than that in rural areas.

10
22 Individual data have also been analyzed to estimate the determinants of age-at-marriage among women. More educated women marry later, even in cases where marriage is sufficiently delayed in the overall society to avoid overlapping with school, **as** in gast and much of South-East Asia (Montgomery and Sulak, 1988; Anderson and Hill, 1980; King at &I., 1986). The growing tendency of young educated women to take paying jobs before marriage, financially encourages both them and their parents to delay their entry into marriage. Few studies have yet examined how local market demands for female workers affect the timing and length of marriage for womn. Evidence from Thailand suggests that **the** fmily bargaining model may help to understand variation in the prevalence of marriage in low income countries. Demographic and anthropological studies of Thai society document that marriage was until recently nearly universal. About 95 percent of men and women reported themselves **as** having been married (once) by **age** 35 (in the 1960 Census cited by Knodel et al., 1987; Table 5.1). An informal process of divorce has also been traditionally common with frequent remarriage (Smith, 1981). In the 1981 Socioeconomic Survey of Thailand 75 and 85 percent of the women and men, respectively, between the ages of 25 and 54 are living in the same household with their spouse. To explain who is currently married, the specialization argument as well as the bargaining model would suggest that marital gains would decrease with an increase in women's predicted wages and increase with an increase in men's predicted **wages,** other things equal. This pattern is confirmed for women's **wages** but not men's (see **Table** A-4). The bargaining model of the family suggests that **the** same pattern holds for nonearned income. If Thai women have unearned income this has a negative impcct on their living with a husband. The ownership of nonearned income is associated among Thai
followed by the Indonesia crisis in the late 1990s. The Philippines crisis was lengthy and continued more than three years. In contrast, the peak degree of crisis was not so large compared to other crisis episodes (Fig. 3). The second to fourth ranked crisis episodes are observed during the Asian crisis in the late 1990s. The feature of these crisis episodes is that while the length of crisis was relatively short, peak degree of cris is and accordingly the number of different types of crises at the peak quarter was larger (Fig. 3, Fig. 4).

||||**Table 4: Sel**|**ected Crisis E**<br>Lenth|**pisodes**<br>Peak crisis||Total|
|---|---|---|---|---|---|---|---|
|Rank|Country|Start|End|g<br>(quarters)|<br>number|Peak degree|<br>degree|
|1|Philippines|Q3 1981|Q3 1984|13|3|-8.8|-68.3|
|2|Indonesia|Q3 1997|Q1 1999|7|6|-18.9|-66.7|
|3|Korea|Q4 1996|Q2 1998|7|5|-16.7|-52.2|
|4|Thailand|Q1 1997|Q2 1998|6|4|-14.4|-50.9|
|5|Mexico|Q1 1982|Q1 1983|5|4|-11.5|-44.0|
||Episode<br>average|-|-|3.5|2.5|-6.7|-19.2|

_Note: Length: total length of a crisis episode; Peak crisis number: Number of crisis types at a peak quarter (maximum seven), Peak degree: the sum of standardized scores exceeding the threshold at the time of peak; Total degree: the sum of standardized scores exceeding the threshold in a crisis episode. Source: Author_ <!-- Start of picture text --> Fig. 3: Longer crises in the 1980s and deeper crises in Fig. 4: Severity of crises are associated with larger<br>the 1990s number of crisis types<br>-20 0<br>-18 Indonesia (1997-) -2<br>-16 -4<br>-14 -6<br>-12 Philippines (1981-) -8<br>-10 -10<br>-8 -12<br>-6 -14<br>-4 -16<br>-2 Average -18<br>0 -20<br>1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7<br>Quarters from the beginning of crisis episode<br>Number of different types of crisis<br>Source: Author Source: Author<br>Peak degree of crisis episode<br><!-- End of picture text -->

# **2.5.2 Crisis**

Currency crises are the dominant crises in seven crisis types throughout the sample periods (Table 5), but the composition of crisis differs in each period. In the crisis period identification, there are 409 quarters experiencing currency crises or 50 percent of total crisis periods (827) followed by 106 growth rate crises or 13 percent of the total. In terms of the ratio in total samples, currency crises account for 29.8 percent, which means on average currency crises are observed in every three quarters. On the other hand, there are only 32 crisis periods with balance of payments crises. The average degree of each type of crises is similar. Financial crises are the deepest (-2.6), while liquidity type banking crises and debt crises are relatively mild (-2.4) compared to other types of crises.

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