rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary Statistics for Regression Sample

||Observations|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|---|
|_All expenditure on primary education_||||||
|Before FPE|7459|33.09|139.78|0|6101|
|After FPE|9428|38.01|224.75|0|10726|
|_All expenditure on secondary education_||||||
|Before FPE|7459|47.02|180.54|0|7612|
|After FPE|9428|42.09|233.96|0|10212|
|_Fees for primary education_||||||
|Before FPE|7459|14.66|122.12|0|5945|
|After FPE|9428|18.95|184.86|0|10638|
|_Fees for secondary education_||||||
|Before FPE|7459|34.39|154.34|0|7340|
|After FPE|9428|22.24|182.58|0|7978|
|_Kids in public primary school per household_||||||
|Before FPE|7459|1.76|1.54|0|8|
|After FPE|9428|1.54|1.46|0|11|
|_Kids in public secondary school per household_||||||
|Before FPE|7459|0.16|0.47|0|6|
|After FPE|9428|0.19|0.50|0|4|
|_Kids in private primary school per household_||||||
|Before FPE|7459|0.08|0.42|0|6|
|After FPE|9428|0.16|0.53|0|6|
|_Kids in private secondary school per household_||||||
|Before FPE|7459|0.02|0.16|0|3|
|After FPE|9428|0.04|0.24|0|5|
|_Years of schooling of household head_||||||
|Before FPE|7459|5.74|4.51|0|15|
|After FPE|9428|6.10|4.68|0|15|
|_Log food expenditure of household_||||||
|Before FPE|7459|6.97|0.62|6|10|
|After FPE|9428|7.86|0.76|1|11|

Source: Authors’ calculations based on 1997 Welfare Monitoring Survey and 2006 Kenya Integrated Household Budget Survey. The table reports unweighted sample statistics for the observations and variables used in the regression analysis of education expenditure and enrollment choices. Note that all statistics are at the household level, including expenditure and fees, which are reported in the survey as the aggregate for all children in the household. All monetary values are reported in 1997 U.S. dollars.

8
<!-- Start of picture text --> 120 Climate change<br>impacts<br>100 Low<br>High<br>80<br>60<br>40 39.7 35.7<br>20<br>14.2 13.9<br>7.5 5.8<br>3.3 2.4<br>0<br>SSA SAS EAP LAC<br> 2030)<br>(million,<br> people living in extreme poverty<br>due to climate change<br>Additional<br><!-- End of picture text --> Figure 3: Regional breakdown of people pushed to extreme poverty in 2030 poverty is by and large eradicated by 2030 in most scenarios, so even a relatively small absolute increase in poverty due to climate change has a major relative effect. On the other hand, Sub-Saharan Africa experiences an increase in average poverty headcount of “only” around 17.5%. This is simply due to the larger denominator (i.e., number of people in extreme poverty in the baseline scenarios) in the poorest regions of the world.

Table 2: Summary of poverty headcount based on different scenarios. Numbers in climate change scenarios <u>are additional headcount from the baseline scenario</u>

||Bli|High climate|Low climate|High climate|Low climate|
|---|---|---|---|---|---|
||asene<br>(illil)|change (million|change (million|change (increase|change (increase|
||mon peope|people)|people)|from baseline)|from baseline)|
|EAP|11.8|7.5|3.3|63.6%|28%|
|LAC|1.9|5.8|2.4|305%|126.3%|
|SAS|30.6|35.7|13.9|116.7%|45.5%|
|SSA|224.4|39.7|14.2|17.7%|6.3%|

# **3.3 Impacts of individual channels**

We now explore the effect of individual climate impact channels to extreme poverty (see Figure 4). On average, the health channel leads to the largest increase in poverty headcount. This channel pushes more than 44 million people into extreme poverty in the high climate impact scenarios. Even with a less stringent climate impact assumptions, 25 million people are still pushed into poverty due to impacts on health.

The health channel includes the increasing prevalence of child stunting, malaria, and diarrhea, three diseases which commonly affect low income households and trap them in poverty (Andres, Brice ̃no, Chase, & Echenique, 2017; Lloyd et al., 2011; Organization, 2014). This result emphasizes the need for better access to health care, including better health care supply (with trained staff and appropriate equipment) and universal healthcare coverage. Health policy is therefore one of the most important adaptation policies to reduce the vulnerability of poor and near-poor households. More context-specific public health interventions such as subsidizing bed nets and antimalarial drugs along with context-based 6
-29-

values of the coefficients are not very different from those of ordinary least squares, and the variations of the estimated values are in the direction that can be expected from the simultaneity bias.

TABLE 3 SMPL 1972 - 1989 18 Observations LS // Dependent Variable is DRER

|VARIABLE<br>COEFFICIENT<br>|STD. ERROR|T-STAT.|2-TAIL SIG.|
|---|---|---|---|
|DRER(-l)<br>0.2408597<br> <br>|0.1180427<br>|2.0404453<br>|0.061|
|DTBY<br>-1.0123158<br> <br>|0.2336479<br>|-4.3326559|0.001|
|DLPXM<br>0.2537120<br> <br>|0.0612287<br>|4.1436799|0.001|
|DGY<br>0.2954056|0.2216652|1.3326658|0.204|
|R-squared<br>0.797<br> <br>|402<br>Mean of<br>|dependent var|-0.002754|
|Adjusted R-squared<br>0.753<br> <br>|989<br>S.D. of<br>|dependent var|<br>0.070566|
|S.E. of ragression<br>0.035<br> <br>|000<br>Sum of s<br> <br>|quared resid<br>|<br>0.017150|
|Durbin-Watson stat<br>1.498<br> <br>|803<br>F-stati<br>|stic|18.36750|
|Log likelihood<br>37.06|402|||

|||Res|idual Plot|obs|RESIDUAL|ACTUAL FITTED|
|---|---|---|---|---|---|---|
||||:*<br>|1972|0.00326|-0.07960 -0.08286|
|||:|*<br>:||1973<br>|0.00145|0.04774 0.04629|
||*|:|:<br> <br>|I<br>|1974|-0.06313|-0.11441 -0.05128|
|||:|I<br>*<br>:||1975|0.01386|-0.00626 -0.02011|
|||:<br>.|*<br>:<br>||1976|0.01174|-0.01981 -0.03155|
|||**.**:||<br>*<br>:<br> <br>||1977<br>|0.01432<br>|0.15135 0.13703<br>|
|I||:|I<br>*<br>*|I1978|0.02395|0.06577 0.04182|
|||:|1<br>:<br>|g1979|0.00582|0.02817 0.02234|
|I||:|*<br>:||1980|-0.00047|0.00796 0.00843|
|||||1981|-0.03689|-0.12903 -0.09214|
|i||:|*<br>:<br>|§1982<br>|-0.00056|-0.08718 -0.08663|
|||:|*I|I<br>|1983<br>|-0.00976<br>|-0.02452 -0.01476<br>|
|||:<br>.|:*<br>|I1984<br>|0.04952<br>|-0.03610 -0.08562<br>|
||||I:|:|*I1985<br>|0.07241<br>|0.01279 -0.05962<br>|
|||||I1986<br>|-0.00630<br>|0.05501 0.06131<br>|
|||||I1987<br>|0.03863<br>|0.04779 0.00916<br>|
|I||:|I<br>*:|I1988<br>|0.02655<br>|0.05046 0.02391<br>|
|||:<br>**.**|*|I1989|0.02492|-0.01970 -0.04462|
The other agent in the model are the trophy hunting concessionaires who are granted an allocation Ω by the government.<sup>7</sup> The harvest of wildebeest allocated to trophy hunting is: Locals in the model engage in farming and hunting for bushmeat.<sup>8</sup> Numerous empirical studies confirm that bushmeat remains an important source of protein for the (mainly) poor households that live in the Serengeti ecosystem. In some parts of the ecosystem bushmeat hunting is legal, though subject to controls. Let _N_ be the legal allocation of bushmeat, the model subsumes the case where all hunting is illegal ( _N_ =0) and allows for poaching and noncompliance in subsequent sections. Farming in this context could represent either livestock rearing (the traditional Maasai activity), or crop production (dominant among other groups). An important feature of the model is that there is competition for land used either for farming or wildlife. Let _L_ = _Lw_ + _Lg_ be the total amount of land allocated to wildlife and agriculture respectively and further assume that _Lw_ = _Lp_ + _Lwnp_ , where, _Lp_ denotes land in the protected national park, _Lwnp_ is land outside the national park used by wildlife. Finally let _Lnp = Lwnp + Lg_ be land outside the protected areas. Utility to locals from hunting and farming is given by: _V_ ( Π ) = [( ρ − _c_ )( _WN_ ) + ( _P_ − _k_ )(( _L_ − _Lw_ )] θ = [ π _NWN_ + π _L_ ( _L_ − _Lnp_ − _Lp_ )] θ ; θ _< 1_ (1c) where _ρ_ and _c_ define the benefits and costs respectively from the harvest of wildebeest<sup>9</sup> and π _N_ = ( ρ− _c_ ) while π _L_ = ( _P_ − _k_ ) are unit profits from land used in agriculture, _Lg_ = _L_ − _Lw_ .

Social welfare is simply the aggregate utility of the three agents and takes a Cobb-Douglas specification, defined as:

> 7 Trophy hunting in Tanzania is largely outsourced to commercial organizations who market the hunting experience as an elite and high-end activity often with “guaranteed” kills (Kideghsesho 2006). The aim here is not to examine the bioeconomics of trophy hunting but to explore the interactions of multiple uses, so we abstract from more detailed industrial organization concerns in what follows.

> 8 In an extension we explicitly model labor supply decisions.  This adds realism but does not alter the qualitative conclusions, so is ignored in what follows.

> <sup>9</sup> Note that ρ and c can be derived from the primitives of a Cobb Douglas utility function.  We avoid this step in order to economize on space.

6
plant. Multi-establishment firms respond to the shock by increasing their production at other establishments. Related recent research based on micro data from India shows that flood events disrupt local supply chains and firms respond by diversifying across locations which has significant distributional consequences (Castro-Vincenzi et al., 2024).

Production networks are formed through a complex web of contracts between firms and these firms use public infrastructure to trade goods. During times of extreme weather, public goods such as logistics infrastructure that facilitate production networks and trade in goods faces disruptions due to the “Tragedy of the Commons” problem. In addition to direct effects, firms in unaffected locations are indirectly impacted through production networks. For example, suppliers and buyers suffer from reduced revenues as a result of suppliers’ exposure to extreme heat and flooding incidents. In Tanzania, floods destroyed the fragile supply chain infrastructure and led to a disruption in the access of necessary inputs for firms even in unflooded areas. This caused 30-50% of all supply and delivery delays in the region and a drop in sales. Such disruptions in production networks transcends national borders as evidenced in a storm that affected not only Chinese plants’ performance but also reduced their foreign transactions (Elliott et al., 2019). Japanese affiliates in Thailand experienced a drop in sales following 2011 floods, while imports from China and Japan increased (Hayakawa et al., 2015). Hurricanes-induced job losses occurring within multiplant firms were propagated across undisturbed distant regions in the US (Seetharam, 2018). For every job loss of the firm in an affected county, 0.19-0.25 jobs are also lost across other sites in unaffected regions.

Firms that buy key inputs can adapt to climate change risk by diversifying away from weather-stricken suppliers (Pankratz and Schiller, 2021). Firms may also adapt by establishing financial links to other regions; viewed as an effective risk sharing and consumption smoothing tool (Albert et al., 2021). Such adjustments also occur in an international context, as illustrated in a study where the exports of poor countries to the United States decreased by 2.0-5.7 percentage points for each<sup>_◦_</sup> C rise in temperature (Jones and Olken, 2010).<sup>2</sup> In sum, the volatility in domestic production translates to greater variation in trade, with the effects being larger for poorer countries (Dell et al., 2009).

As more firms seek out ”climate resilient” supply chains, areas seeking to attract footloose firms will have a greater incentive to invest in local resilience infrastructure because this becomes an economic development strategy. Urban and regional economic studies of local agglomeration have documented how by attracting a major factory, a location can then

> 2In China, Li et al. (2015) suggest a negative effect of rising temperatures on exports ranging from 8.8% to 12.6%.

9
where _X ij_ is a vector of household ( _i_ ) characteristics influencing the participation in a microenterprise activity; _V jt_ is a vector of village-level characteristics including village electrification, share of irrigated land, prices of consumer goods, program placement of microcredit and other credit programs, and availability of government development programs such Vulnerable Group Feeding and Food-for-Work;<sup>τ</sup> _t_<sup>represents the year (2000=1, 2005=2 and 2010=3);α,βandγare</sup> unknown parameters to be estimated; and ε _ijs_ is a zero-mean disturbance term representing the unmeasured determinants of _Mijt_ that vary across households. Note that household participation is also affected by unobserved household heterogeneity and _thana_ heterogeneity represented by the error terms μ _ij_ and η _j_ , respectively, as well as unobserved thana-and year-specific heterogeneity ( μ _sj_ ). 5 Here, _Pr_ measures the probability of microenterprise participation (with a value 1 when a household participates and 0 otherwise).

Table 6 provides estimates of some of the household and community characteristics determining the adoption of an enterprise activity. Note that the regression is a linear probability model of the adoption/participation rate among rural households over the three survey periods (2000, 2005, and 2010). A thana is the lowest common unit in which households were interviewed over the three periods so that a thana level fixed-effects method can be operationalized. This is done in two ways. First a simple thana FE is applied to a single year of survey data (e.g., 2010) to control for thana-level unobserved heterogeneity affecting all households. However, it does not control for unobserved heterogeneity at the thana level that varies within a year (that is, seasonspecific). Hence, the second method involves a FE where the interaction variable of thana and year

> 5 A thana is the lowest geographical administrative unit in rural Bangladesh, consisting of a number of villages.

9
<!-- Start of picture text --> 0.74 4.27<br>o c<br>0<br>o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~c<br>0.00 0.00<br>.12 2 0stance frm local market in hours 2 (log scale) b2stance fm locl makti1hus(og2aemakt in hours (logwale$<br>Size of operated land Livestock sales<br>2.96<br>0,<br>0.00<br>1i2 .5l1 lb<br>.t2 lrstance om local market In hours (log scale)<br>Fertilizer per hectare<br>Figure 5. Farming and distance from local market<br><!-- End of picture text -->
# **OA4. Estimated Relative and Absolute Mobility**

**Table A.10: Relative Mobility and Absolute Mobility Across Sons vs. Daughters in Urban Area**

|||**CHINA**|||**INDIA**||**IN**|**DONESI**|**A**<br>|
|---|---|---|---|---|---|---|---|---|---|
||RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|
|||||**U**|**rban Son**|<br>**s**||||
|No Sch|0.373|7.200|8.152|0.733|4.847|6.456|0.552|6.568|7.640|
||(0.014)|(0.151)|(0.131)|(0.012)|(0.108)|(0.084)|(0.024)|(0.243)|(0.208)|
|Primary|0.352|9.369|10.023|0.630|8.211|9.078|0.503|9.710|10.298|
||(0.013)|(0.106)|(0.098)|(0.009)|(0.061)|(0.055)|(0.021)|(0.124)|(0.116)|
|Junior High|0.346|10.415|10.958|0.588|11.242|11.700|0.490|11.199|11.627|
||(0.013)|(0.104)|(0.098)|(0.009)|(0.050)|(0.048)|(0.021)|(0.100)|(0.095)|
|Senior High|0.340|11.444|11.894|0.578|12.408|12.749|0.481|12.654|12.956|
||(0.013)|(0.115)|(0.109)|(0.008)|(0.056)|(0.054)|(0.021)|(0.113)|(0.108)|
|College|0.335|12.795|13.142|0.567|14.124|14.322|0.472|14.559|14.728|
||(0.012)|(0.144)|(0.138)|(0.008)|(0.071)|(0.069)|(0.020)|(0.166)|(0.161)|
|IGRC|0.312|||0.524|||0.443|||
||(0.012)|||(0.008)|||(0.020)|||
|||||**Urb**|**an Daugh **|**ters**||||
|No Sch|0.527|5.607|6.956|0.944|2.191|4.463|0.628|5.633|6.884|
||(0.019)|(0.220)|(0.180)|(0.019)|(0.133)|(0.085)|(0.024)|(0.231)|(0.190)|
|Primary|0.471|8.574|9.371|0.722|6.216|7.449|0.557|9.150|9.816|
||(0.017)|(0.134)|(0.121)|(0.010)|(0.079)|(0.067)|(0.020)|(0.110)|(0.102)|
|Junior High|0.456|9.963|10.578|0.661|9.646|10.434|0.540|10.795|11.281|
||(0.016)|(0.118)|(0.110)|(0.009)|(0.077)|(0.070)|(0.020)|(0.088)|(0.083)|
|Senior High|0.446|11.315|11.785|0.648|10.954|11.628|0.529|12.398|12.747|
||(0.016)|(0.122)|(0.116)|(0.009)|(0.084)|(0.077)|(0.019)|(0.103)|(0.099)|
|College|0.438|12.639|12.993|0.635|12.878|13.419|0.519|14.493|14.701|
||(0.016)|(0.143)|(0.137)|(0.009)|(0.099)|(0.093)|(0.019)|(0.156)|(0.153)|
|IGRC|0.402|||0.597|||0.489|||
||(0.015)|||(0.008)|||(0.019)|||

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) The RIGRC refers to the Risk Adjusted Intergenerational Marginal Effect. The RES refers to the Risk Adjusted Expected Schooling. The Linear ES refers to the Expected Schooling from a linear CEF mobility model. And the IGRC is the Intergenerational Regressional Coefficient from a linear CEF mobility model.

46
# estimate of the annual in-service training costs needed for a district of 200,000 persons:

|20 TBAs|$3,000|
|---|---|
|20 Outreach coordinators|3,000|
|50 Outreach volunteers|3,000|
|20 Nurse-Midwives|3,000|
|Training for Caesarean|6,000|
|Training for sterilization|6,000|

Transportation is a vital issue for operation of the entire Safe Motherhood Initiative. The lowest annual estimates of transportation costs come from Herz and Measham. They have the following projected costs for a district of 200,000 persons with one hospital, 8 health centers, and 20 health posts. Transportation costs include repairs, maintenance and gas (none of the three studies appear to include driver costs in these estimates):

|Supervision|$ 8,000|
|---|---|
|For clinic/hospital|$ 7,000|
|Obstetrical emergencies|$10,000|

In the Columbia study, one possible program model includes an ambulance at each major health center. They estimate the annual costs of a four wheel dcive vehicle for each center to be $30,000. PAHO assumes that there should be one vehicle per hospital for the program. Extrapolating from the PAHO report, the cost estimates of these vehicles are found to be about $100,000, with annual overhead at about 20% of the initial cost, or about $20,000 per vehicle.

At the health center level, several estimates of staff costs are found. To be meaningful, however, staff cost estimates must be presented with an indication of their intended output. Herz and Measham make the following assumptions:

# Health Center

|Clinical officer (30%)|$ 700|
|---|---|
|Nurse-Midwife (100%)|1,500|
|Asst. Nurse-Midwife (100%)|1,000|
|Aide|200|
|Outreach workers (2@100%)|1.6|
|TOTAL|_$5,000_|

# Health Post

|Asst Nurse-Midwife (100%)|$1,000|
|---|---|
|Outreach workers (2@50%)|1,600|
|Outreach coordinator (40%)|400|
|TOTAL|$3,000|

The PAHO study does not estimate staff costs, but the Columbia study estimates that health centers would require a staff of five midwives at a salary of $300 each per month at an 25
across varying specifications and months relative to the start of the registrant’s course. As above, we estimate separate specifications including month fixed effects, class fixed effects, and including education and pre-period employment rates as additional controls. The final specification, with a full set of controls for pre-course employment at one, three six, and nine months prior to the start of the course, has slightly larger estimates than the main IV – estimates above although they are sharply decreasing over the study period. Nonetheless, the employment effects are still sizable and, on average, of a comparable magnitude to the main estimates above, and we confirm that these magnitudes are largely driven by the UB registrant sample (Table 13).

# **6 Explaining program effectiveness: Matching skill training to growth in skill demand**

Why did the demand-driven program exhibit such substantially larger employment effects than the larger, non-demand-driven segment? Our primary hypothesis is that the input from firms contained meaningful information that allowed the provision of skill trainings to be better aligned with aggregate growth in skill demand. To assess whether this was the case, we compare the occupational and geographic distribution of trainings in the demanddriven and main program to two “reference” distributions: the cross-sectional distribution of aggregate employment, and growth in employment over the 2013 - 2015 period. We compute the sum of squared deviations (SSD) of the focal training program to the two reference distributions to understand which training program was better matched to the cross-section of employment and which was better matched to aggregate employment growth.

In Table 14 we present the values of the SSD for the main and demand-driven programs compared to the focal reference distributions for three categorical variables: three-digit occu23
performed on a country-by-country basis, as well as results from the Levin et al. (2002) panel unit root test. For most variables, the fraction of countries where the hypothesis of a unit root cannot be rejected is high, but in most cases the panel unit root test cannot reject the null hypothesis of a unit root for the series in levels. For the series in differences, the fraction of individual countries where the unit root hypothesis is rejected and the panel unit root tests strongly reject the hypothesis of a unit root. Thus, there are arguments for estimating the model in both forms. The bulk of the discussion below focuses on the model in levels because the confidence bands for this model were more precisely estimated because more information is preserved for the estimation when employing levels of variables rather than their differences. However, we will also discuss the results in differences for the baseline estimation and, while we will not report the model in differences for all exercises for reasons of space, the results are qualitatively and quantitatively similar to those obtained for the model in levels. Furthermore, even when estimating the model in levels, the interest rate will be included in first differences. The reason is that, although panel unit root tests reject the null of a unit root, when included in levels this series exhibits explosive non-stationary behavior in some specifications.<sup>4</sup> For the version of the model in differences we also tested for the possibility of cointegration using Pedroni (1999)'s test for cointegration in panels. Although the various statistics proposed by Pedroni (1999) yield somewhat ambiguous results, in most cases the null hypothesis of no cointegration cannot be rejected (see Table 3). Moreover, Pedroni (2004) shows that for the sample characteristics that are closer to those used in this paper (N larger than T) the panel-rho test, which systematically cannot reject the null of no cointegration, has the best size and power properties. Consistently, the model in first differences will be estimated without a cointegration relation.

Standard lag tests suggest estimating the model including two annual lags (Schwartz information criterion). Three annual lags are also considered for robustness.

The parameters of the two versions of the model, estimated in reduced form by SURE are used to recover the impulse-response functions (IRF) of per capita GDP, government

> 4 This occurs in a few estimations for groups of countries by region. In these cases, the impulse response functions do not converge back to zero within a 20 year window.

9