rafmacalaba/datause-encoder-data huggingface dataset:
Table 41

|Dependent variable: Fis|cal adjustm|ent when n|eeded base|d on debt/|revenue|
|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|
|Rigid expenditure|0.003<br>|-0.008|0.001|0.005|-0.008|
|(% of GDP)|(0.003)|(0.011)|(0.010)|(0.010)|(0.014)|
|**Economic factors**||||||
|GDP growth (t-1)|3.803|4.782|3.811|4.094|3.957|
||(2.999)|(3.212)|(2.997)|(3.187)|(2.911)|
|Inflation (t-1)|-0.332**|-0.385*|-0.335**|-0.349*|-0.333**|
||(0.168)|(0.200)|(0.170)|(0.188)|(0.165)|
|U.S. Interest rate|-0.018|-0.011|-0.018|-0.018|-0.018|
||(0.012)|(0.016)|(0.013)|(0.016)|(0.012)|
|**Political factors**||||||
|Gini|-0.003|-0.005|-0.004|-0.004|-0.004|
||(0.004)|(0.005)|(0.008)|(0.005)|(0.004)|
|Rule of law|-0.008|-0.032|-0.008|0.005|-0.006|
||(0.030)|(0.036)|(0.029)|(0.044)|(0.026)|
|Margin of Majority|-0.059|-0.045|-0.067|-0.062|-0.357|
||(0.110)|(0.108)|(0.105)|(0.104)|(0.312)|
|**Interactions**||||||
|emes||-0.461||||
|||(0.379)||||
|EMEs * Rigidity||0.014<br>(0.014)||||
|Gini * Rigidity|||0.000|||
||||(0.000)|||
|Rule of law * Rigidity||||-0.001<br>(0.003)||
|Majority * Rigidity|||||0.016<br>(0.019)|
|lambda|-1.202|-1.540|-1.207|-1.312|-1.257|
||(0.892)|(0.992)|(0.899)|(0.973)|(0.874)|
|Constant|1.559|2.378|1.601|1.658*|1.834|
||(1.042)|(1.452)|(1.172)|(0.938)|(1.235)|
|Observations|755|755|755|755|755|
|R-squared|0.074|0.081|0.075|0.082|0.075|
|CountryFE|No|No|No|No|No|
|Standard errors in pare|ntheses|||||
|*** p<0.01, ** p<0.05,|* p<0.1|||||

- 70 -
Overall, the returns to schooling increased over time (see Figure 2 and Table 3). There was a significant increase from 1993 to 1994, and again from 1999 to 2000, with some stability in the last three years.

<!-- Start of picture text --> Figure 2: Returns to Schooling over Time (percent)<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Also there were significant changes in the returns to schooling by level. At the start of the period, primary education exhibited the highest returns. Five years later, the returns to primary decreased, only to recover again in the latter years. The returns to secondary incomplete have decreased, while the returns to secondary have remained remarkably stable. The returns to higher education, both university complete and incomplete have increased substantially, with university now exhibiting the highest returns (see Figure 3).

12
**<mark>Short case studies (vignettes)</mark>**

# _1)_ _<u>Sea level rise in the coastal United States</u>_

In a simulation of disadvantaged populations at risk of future sea level rise, Martinich and colleagues (2013) assess not only disproportionate impacts of sea level rise stemming from exposure of coastal communities, but also differential merit in protecting some areas from inundation while leaving others at risk of abandonment. In the Gulf region of the United States, the model results suggest that over 99% of the most socially vulnerable populations live in areas unlikely to benefit from protection compared to only 8% of the least socially vulnerable groups (those with highest resilience) who live in the same areas. The study focuses on four regions (North Atlantic, South Atlantic, Gulf, and Pacific) and combines the Social Vulnerability Index (SoVI), introduced by Cutter et al. (2003) and applied to demographic variables available for coastal census tracts in these four regions, with the national coastal property model (NCPM), developed by EPA and collaborators. Three distinct sea level rise scenarios throughout the 21<sup>st</sup> century are used (low = 28.5cm rise by 2100 compared to 1990 levels; medium = 66.9cm rise; and high = 126.3cm rise) to identify areas at risk and adaptation responses considered economically efficient based on property value, elevation, and protection costs. The adaptation responses are protection (e.g. sea wall construction and maintenance), beach nourishment through replacement of sand, and abandonment.

<!-- Start of picture text --> (those with highest resilience) who live in the same areas. The study focuses on four regions<br>(North Atlantic, South Atlantic, Gulf, and Pacific) and combines the Social Vulnerability Index<br>(SoVI), introduced by Cutter et al. (2003) and applied to demographic variables available for<br>coastal census tracts in these four regions, with the national coastal property model (NCPM),<br>developed by EPA and collaborators. Three distinct sea level rise scenarios throughout the 21 st<br>century are used (low = 28.5cm rise by 2100 compared to 1990 levels; medium = 66.9cm rise;<br>and high = 126.3cm rise) to identify areas at risk and adaptation responses considered<br><!-- End of picture text --> Table 2: Dominant Social Vulnerability Index components for four regions in the United States and their percent variance explained by regional principal component analyses (Source: Martinich et al. 2013) 10
in rural Bangladesh, Asaduzzaman et al. (2010) find that cost of modern fuel and lack of supply contributed limited adoption of ICS. Based on life cycle analysis, Afrane and Ntiamoah (2012) find that fuelwood used in Ghanaian households for cooking has an annual environmental damage cost of US$36497 per household. Through a financial analysis in rural areas in India, Gupta and Ravindranath (1997) show that the ICS using fuelwood is the least cost option and biogas, which is the only quality fuel for rural areas, is the most expensive option. Although unrealistic, EAC (2006) reports that biomass collection time for rural households are as high as 4.5 hours in Kenya, 6 hours in Tanzania and Uganda. Based on Indian household energy survey in 1996, ESMAP (2004) finds that women spent on average 40 minutes for collecting fuels and almost three hours for cooking every day. The study finds that the opportunity costs of poor access to domestic energy have profound effects for all members of the family, particularly women who are the main managers of household biomass energy. On average, women worked for 12 hours of which only 2 hours are spent pursuing paid work indicating high opportunity costs of cooking activities. In rural Ethiopia, households, on average, spent between 11 and 12 hours per week collecting biomass (fuelwood and dung) fuels for cooking (Gwavuya et al., 2012). Female household members between the ages of 18 and 59 are mostly responsible for collecting these fuels. Using the opportunity cost of labor which is estimated through the marginal productivity of own labor in farm activities, the study estimates that on average households lose US$0.06 for each hour spent on collecting fuelwood. This is equivalent to daily rate of US$ 0.47, which is slightly lower than a government’s minimum daily wage rate of about US$0.62. Based on the economic evaluation of the ICS program in Uganda during 2005 and 2006, Habermehl (2007) estimates the opportunity cost (shadow wage) of fuelwood collection Euro0.01 per kg. The study assumed that 50% of the time saved by the households used for productive activities with average household income of Euro 0.1 per hour. Heltberg (2005) finds that cooking labor scarcity (i.e., household size) translates into high opportunity costs of fuelwood collection in Guatemala; high share of females in the households is more likely to use multiple fuels, and higher level education increases the opportunity cost of collection time. In a recent study in the same Bushbuckridge region, Matsika et al. (2013) find that 68% of electrified households still use fuelwood as the primary source of energy even as the resource becomes more expensive to use in terms of opportunity costs in collecting and/or purchasing. In Himachal Pradesh, India, Parikh (2011) finds that there is a substantial physical and economic burden in 26
Table 5: Spatial Disparity, Access to Infrastructure and Sorting of household Attributes

||All|Urban<br>2000|Rural|All|Urban<br>2010|Rural|
|---|---|---|---|---|---|---|
|**Base Case**|||Panel A (b|ase case)|||
|Neighborhood Correlation (r)|0.363***|0.385***|0.218***|0.266***|0.298***|0.199***|
||(19.929)|(13.271)|(11.820)|(20.405)|(12.998)|(14.158)|
|||Panel B (|Sorting, acc|ess to infrast|ructure)||
|Neighborhood Correlation (r*)|0.115***|0.057***|0.127***|0.124***|0.081***|0.132***|
||(10.832)|(3.974)|(9.025)|(14.223)|(6.842)|(11.623)|
|Cont. of sorting & infrastructure (r-r*)|0.248|0.328|0.091|0.142|0.218|0.067|
|% Decrease[((r-r*)/r)*100]|68.3|85.3|41.7|53.3|72.9|33.4|
|||Panel C ( S|orting, infrast|ructure & ge|o-climate)||
|Neighborhood Correlation (r*)|0.106***|0.050***|0.112***|0.102***|0.074***|0.099***|
||(10.136)|(3.440)|(8.194)|(12.823)|(6.228)|(9.961)|
|Cont. of sorting, infra., geo-clim. (r-r*)|0.257|0.335|0.106|0.164|0.225|0.100|
|% Decrease[((r-r*)/r)*100]|70.9|87.0|48.6|61.7|75.3|50.1|
||Pan|el D ( Sorti|ng, infrastruc|ture, geo-cli|mate & price|s)|
|Neighborhood Correlation (r*)|0.092***|0.043***|0.090***|0.099***|0.073***|0.094***|
||(9.388)|(3.075)|(7.351)|(12.591)|(6.162)|(9.687)|
|Cont. of all plus prices (r-r*)|0.271|0.341|0.128|0.168|0.225|0.105|
|% Decrease [((r-r*)/r)*100]|74.6|88.7|58.7|63.0|75.5|52.6|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
random, idiosyncratic error term. β1 is the (reduced-form) parameter of interest.<sup>9</sup> In order to better fit the distribution of the precipitation generated by the disaster and improve the measurement of the shock, we also estimate models that take into account the varying strength of the event. To do so, affected households are classified into low-, mediumand high-intensity groups if the standardized precipitation anomaly in May, 2010 falls between 2 and 3, 3 and 5 and more than 5 standard deviations away from the long-term mean, respectively. For these models, however, the definition of the comparison group stays the same as that used in equation (1). The econometric specification for the treatment dose analysis is as follows: where Low _mt_ , Medium _mt_ , and High _mt_ are binary variables to capture the sub-treatment groups. In this case, the parameters of interest are β _2_ , β _3_ , and β _4_ . Results from estimating (1) and (2) are presented in the next section.

A threshold of two standard deviations may be an arbitrary cutoff to accurately capture excessive (damaging) rainfall. In addition to the level of precipitation, the occurrence, magnitude and duration of floods are also determined by geological, topological and hydrological characteristics of the area under analysis. Our empirical models control for municipality fixed effects and thus could partly account for these factors. Notwithstanding that, concerns of measurement error in the way that the precipitation anomalies are defined may remain. To explore this, we test the ability of the shock measure to predict the actual manifestation of floods in the aftermath of Agatha. Yet, the flood data available have two caveats. They include only those events reported by local authorities –possibly missing some floods in a nonrandom fashion– and do not say anything about the intensity of the floods<sup>10</sup> . We run models with the probability of a municipality reporting at least one flood as the dependent variable and the standardized rainfall recorded in May 2010 and surface area of the municipality as regressors. We observe a strong and statistically significant association between the continuous shock measure and the occurrence of at least one flood in a

> 9 In all regressions, the standard errors are clustered at the municipality level to allow for correlation across households within a municipality.

> 10 The information contains geo-referenced incidents recorded by the National Coordinator for Disaster Reduction (CONRED) and the Secretary of Planning (SEGEPLAN) for the period 2008-2011. It allows identifying the type of incident (e.g. flood) as well as whether the event was caused by Agatha.

10
productivity and earnings in middle age—when children themselves become parents. To the extent that health in childhood depend on the time that mothers allocate to their brood, health dependence gives a crucial role to mothers' time allocation in shaping the future of their children.

# **2.  Women’s Labor Supply and Occupational Constraints**

# **2.1 Evidence on Women’s Participation Rates**

While participation of women in the labor force has increased in recent decades in almost all regions of the world, there are still significant gender gaps in participation rates, occupational levels and wages. As noted in the introduction, in today's low- and middleincome countries the labor force participation rate for women (the percentage of the women population aged between 15 and 65 years who are in the labor force) is only 57 percent, compared to 85 percent for men. The share of women above the working age who are employed (the employment-to-population ratio) was about 50 percent in 2009 compared to a male employment-to-population ratio of about 74 percent (International Labour Office (2010)). In the Middle East and North Africa adult labor force participation rates are below 30 percent, and in South Asia below 35 percent (see Table 1).<sup>8</sup> Moreover, paid employment for women has expanded slowly and women continue to assume the largest share of unpaid work. Close to two-thirds of all employed women in developing countries work as contributing family workers or as workers on their own account, typically in forms of employment that are highly vulnerable and lack job security and benefits. In that sense, the relatively high labor force participation rates for women in East Asia and Sub-Saharan Africa documented in Table 1 do not tell the full story. Indeed, women's share of waged non-agricultural employment has increased in the last decade but only marginally; the share of women in the agricultural labor force exceeds 45 percent in SubSaharan Africa and South East Asia and the Pacific (Figure 1). In most regions female employment remains concentrated in either services or agriculture, with fewer women than men employed in industry (ranging from 7 to 23 percent, compared to 12 to 34 percent for men). The only region where men and women have similar patterns of employment by sector, both in relative composition and in trend, is East Asia and Pacific. There is also considerable variation in the gap between the proportions of males and females participating in the labor market in some regions, especially Asia and Sub-Saharan Africa (Figure 2).

> 8Studies based on data pertaining to both developed and developing countries suggest the existence of a U-shape relationship between labor force participation and the level of income per capita, with participation being the highest in the poorest and richest countries (see Mammen and Paxson (2000)). Box 4 discusses a possible rationale for this pattern, based on Galor and Weil (1996).

9
Table 4: Baseline Calibration Parameters

|Parameter|Value|Justifcation|
|---|---|---|
|Budget shares _s_<sup>_i_</sup><br>_j_ <sup>and</sup> <sup>_si_</sup><br>_j_1|Varying|Observed in our data|
|Household income (scaled) _φ_<sup>_i_</sup>|Varying|Observed in our data|
|Income elasticities of goods _ηj_|Food: 0.65,<br>Non-food: 1.2|From our data, using _ηj_ =1+<br>_βj_<br>_sj_<br>1|
|Income elasticities of formal varieties _ηj_1|Food: 1.14,<br>Non-food: 1.31,<br>All goods: 1.25|From our data, using _ηj_1 =1+<br>_βj_1<br>_sj_1<br>1|
|Informal share of consumption _αj_|Varying|From our data|
|Cross-variety compensated elasticity ̃_ε_<sup>_C_</sup>|1.5|Faber and Fally(2017);Atkin et al.(2018b)<sup>2</sup>|
|Own-price compensated elasticity _ε_<sup>_C_</sup>|-0.7|Deaton et al.(1994)<sup>3</sup>|
|Government preferences _g_<sup>_i_</sup>|1-10|Uniform tax rates in the[0.10, 0.25] range<sup>4</sup>|

1 The parameter _βj_ ( _βj_ 1) refers to the estimated slope of the Engel curve for good _j_ (variety _j_ 1). 2 For the cross-variety price elasticity we use estimates of the elasticity of substitution _σ_ across store types in consumption obtained by Faber and Fally (2017); Atkin et al. (2018b) which are in the [2, 4] range. With a CES utility function _ε_ ̃<sup>_C_</sup> = _σs_ 0 where _s_ 0 is the aggregate budget share spent in the informal sector, equal to 0.5 on average in our sample.

3 _C_ Our choice of value for _ε_ together with our estimated income elasticities and observed budget shares yield uncompensated own-price elasticities for goods in the [ _−_ 2, _−_ 0.5] range, in line with estimates obtained by Deaton et al. (1994) in the developing country context.

> 4 We set _gi_ = 10 for the first decile, _gi_ = 9 for the second decile, _gi_ = 8 for the third decile, ..., _gi_ = 1 for the tenth decile. This, together with our other calibration choices, yields optimal uniform rates when we assume only formal varieties can be taxed in the 10-25% range, in line with observed consumption tax rates in developing countries.

49
**Table A.4: RIF decomposition of the Gender Differential in Agricultural Productivity with region fixed effects - Food crops only (2008)**

||**Lo**|**g [Self-repor**|**ted Producti**|**vity (XOF/ha**|**)]-Food crop**|**s**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|**overall**||||||||||
|Male headed households|9.766***|10.493***|10.886***|11.180***|11.478***|11.726***|11.993***|12.288***|12.770***|
||(0.082)|(0.059)|(0.048)|(0.044)|(0.042)|(0.039)|(0.038)|(0.038)|(0.048)|
|Female Headed households|9.626***|10.161***|10.579***|10.872***|11.157***|11.414***|11.739***|12.030***|12.459***|
||(0.097)|(0.084)|(0.080)|(0.079)|(0.077)|(0.071)|(0.072)|(0.070)|(0.073)|
|difference|0.140|0.331***|0.308***|0.308***|0.321***|0.311***|0.254***|0.257***|0.311***|
||(0.112)|(0.088)|(0.079)|(0.076)|(0.074)|(0.070)|(0.071)|(0.073)|(0.081)|
|**% gap**|<br>**13%**|<br>**28%**|<br>**27%**|<br>**27%**|<br>**27%**|<br>**27%**|<br>**22%**|<br>**23%**|<br>**27%**|
|explained|-0.502***|-0.271***|-0.155**|-0.074|-0.074|-0.067|-0.045|-0.028|-0.188**|
||(0.134)|(0.099)|(0.078)|(0.076)|(0.074)|(0.070)|(0.072)|(0.070)|(0.095)|
|unexplained|0.643***|0.602***|0.463***|0.382***|0.395***|0.378***|0.299***|0.285***|0.499***|
||(0.143)|(0.109)|(0.090)|(0.090)|(0.085)|(0.081)|(0.080)|(0.089)|(0.115)|
|**explained**||||||||||
|Log [Total land area cultivated in ha]|-0.584***|-0.410***|-0.318***|-0.282***|-0.257***|-0.251***|-0.253***|-0.281***|-0.451***|
||(0.086)|(0.054)|(0.040)|(0.034)|(0.030)|(0.029)|(0.031)|(0.036)|(0.062)|
|Household head age (years)|-0.007|-0.009|-0.011|-0.019**|-0.018**|-0.006|-0.013|-0.012|-0.035***|
||(0.014)|(0.011)|(0.010)|(0.009)|(0.009)|(0.008)|(0.008)|(0.008)|(0.012)|
|Married|-0.031|0.006|-0.019|0.021|0.010|0.015|-0.019|-0.068|-0.141|
||(0.090)|(0.068)|(0.058)|(0.055)|(0.050)|(0.053)|(0.053)|(0.063)|(0.101)|
|Divorced/Separated|-0.011|-0.015|0.002|0.002|-0.003|-0.009|0.005|0.034*|0.044|
||(0.032)|(0.024)|(0.019)|(0.017)|(0.017)|(0.017)|(0.017)|(0.020)|(0.031)|
|Widowed|-0.007|0.007|0.032|0.042|0.018|-0.006|0.048|0.082|0.121|
||(0.088)|(0.068)|(0.059)|(0.058)|(0.056)|(0.055)|(0.055)|(0.057)|(0.085)|
|Household head has Primary education|-0.008|-0.005|0.002|-0.001|-0.004|0.004|0.013*|0.012|-0.003|
||(0.013)|(0.010)|(0.008)|(0.007)|(0.006)|(0.006)|(0.007)|(0.008)|(0.010)|
|Household head has Secondary|-0.011|-0.015|-0.006|-0.011|-0.008|-0.001|0.001|-0.000|-0.009|
|<br>education|(0.014)|(0.010)|(0.008)|(0.007)|(0.006)|(0.006)|(0.006)|(0.006)|(0.009)|
|Household head has Tertiary education|0.002|0.002|-0.001|-0.000|-0.001|-0.000|-0.002|-0.001|-0.001|
||(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|(0.002)|
|Household head disability|-0.001|-0.000|-0.000|0.000|0.001|0.001|0.005|0.006*|0.011*|
||(0.004)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.006)|
|Dependency ratio|0.001|-0.001|0.004|0.001|-0.006|-0.008|-0.008|-0.007|-0.019|

41
sources of finance in the post-disbursement period. (In 1995 we know who had received SWP loans so we can net this out of total loans received. Of course, in 2004/05 there were no new SWP loans.) By these calculations, we found no significant impacts on non-SWP loans in 2004/05. This does not suggest there was long-term displacement of other sources of finance.

While the displacement effect is presumably greater in the disbursement period, it cannot be ruled out post-disbursement. If there are in fact longer-term gains from the SWP and this is known locally then continuing positive displacement will be expected, making it harder to identify those gains. However, even the upper bound to the bias * derived above of _DD_ / _DD_ = 5.1 is well short of being sufficient to imply a significant long-term impact on mean income; assuming that the standard error is not biased by the spillover effect, one would need to quadruple the income gain in 2004 before it could be deemed statistically significant.

# **6. Conclusions**

The longer-term impacts of aid to poor areas depend crucially on why these areas are poor in the first place. If persistently poor areas arise from generalized capital-market failures then external aid can relieve the credit constraints and so enhance long-run growth. If instead the credit market failures are specific to certain (liquidity-constrained) subgroups of the population then the aid will need to be targeted to those groups. However, persistently poor areas can arise from other causes, such as governance failures or (possibly policy-induced) distortions in other markets (including labor, such as due to restrictions on migration). Heterogeneity in impacts can also interact with the beneficiary selection process in a way that attenuates the aggregate impact.

So the benefits from extra aid to poor areas may well be modest. Unfortunately, the absence of rigorous studies of the long-term impacts of aid to poor areas has left a gap in our knowledge about both the causes of geographically concentrated poverty and aid effectiveness.

To help fill this gap in knowledge, we have used a specially designed set of highquality surveys collected over a 10 year period to study the impacts of a World Bankfinanced poor-area development program in southwest China. We find a sizeable and 29