rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A6: The effects of temperature on poverty – Subnational GDP analysis**

||Poverty|rate $1.90<br>Lon|Poverty|rate $3.20<br>Lon|Poverty|rate $5.50<br>Lon|
|---|---|---|---|---|---|---|
||Panel FE|g<br>differences|Panel FE|g<br>differences|Panel FE|g<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|0.148**|0.057***|0.206**|0.120**|0.224**|0.105*|
||(0.064)|(0.021)|(0.084)|(0.057)|(0.095)|(0.060)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|No|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|Yes|No|
|Number of countries|74|61|74|61|74|61|
|Number of regions|3,394|1,306|3,394|1,306|3,394|1,306|
|Observations|138,060|1,306|138,060|1,306|138,060|1,306|
|Adjusted R-squared|0.334|0.204|0.350|0.369|0.385|0.254|
|Mean headcount<br>poverty rate (percent)|16.847|16.847|30.152|30.152|45.559|45.559|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Poverty rate is calculated using subnational GDP from Kalkuhl and Wenz (2020) and the poverty lines of $1.90, $3.20, and $5.50. Poverty rates and weather variables in the long-differences model are measured by the difference between averages of the earliest 10-year period (1979–1988) and averages of the latest 10-year period (2009–2018). The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 54
7 excludes informal payments made by patients to providers. In practice, there is a strong bias in reimbursements towards higher-level facilities and inpatient care: in 2004, 40% of the HCFP budget went to provincial hospitals, 34% to district hospitals, and 20% to commune health facilities; and nearly 60% of the budget in both 2004 and 2005 was spent covering the costs of inpatient care. These figures mask variations across provinces, which may reflect choice of modality, as well as different ways of implementation. For example, some provinces operating the direct reimbursement modality (Lao Cai is an example) do not actually reimburse providers for delivering care, but rather divide up the budget and allocate specific amounts to facilities, with facilities themselves being expected to meet any excess over their HCFP allocation out of other revenues.

The identification of HCFP beneficiaries has built upon processes that had already been in place for some time. The communes that are covered by Decision 135 are well known. In fact, the problem has not been identifying them but rather ensuring that provinces are aware of their obligation to include residents of Decision 135 communes in the HCFP program. A list of ethnic minority households already existed for use in another government program. The toughest group to identify has been the poor. But even here, local governments have been able to build on lists of officially poor households produced for other government programs. Further household surveys are conducted by commune officials (in part to collect information on household members), and the proposed list of HCFP beneficiaries is then discussed and voted upon at a public meeting presided over by the village or commune leader. Officials from the district government’s labor and social affairs offices then check the list, which could be revised before it is sent to the provincial department of labor and social affairs for final approval.

# III. **METHODS**

The impacts of HCFP are estimated by comparing out-of-pocket payments and utilization between those covered by HCFP and comparable individuals not covered. Comparability is assessed by means of propensity score matching, the propensity score measuring the closeness (in terms of a vector of observable characteristics) of ‘treated’ and ‘untreated’ individuals. The score is simply the predicted probability of an individual
enough for a bilateral barter among developing countries by a request-and-offer approach to be rewarding as it had been in the early days of the GATT. Thus, it is no surprise that this approach was discarded by negotiators. As to high income countries (HIC), average tariffs were 2.2 %, so their expected gains from participation in the negotiations would be from reduction in tariffs by developing countries (which explains why developing countries were generally opposed to a list approach, which would have resulted in larger tariff reductions for them under a proportional reduction formula). Taking into account that many countries are members of Preferential Trade Agreements (PTAs) that are mostly Free Trade Areas, actual applied tariffs are even lower than the MFN-based estimates reported here.

10
# **Table 4: Firm Investment and Mobile Money Use**

|**Dependent variable: Firm purchased fixed assets Y:1**<br>**N:0**|||**Probit**|||
|---|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Mobile money for any financial transactions Y:1 N:0|0.376***|0.471***||0.517***|0.506***|
||(0.116)|(0.131)||(0.139)|(0.158)|
|Establishment uses cell phone for operations Y:1 N:0|||-0.189|-0.364||
||||(0.258)|(0.250)||
|Firms has ISO certification ownership Y/N|||||0.404*|
||||||(0.232)|
|Senior management time spent in dealing government<br>regulations|||||0.005<br>|
||||||(0.009)|
|Log of size||0.254***|0.213**|0.250***|0.223**|
|||(0.083)|(0.088)|(0.085)|(0.099)|
|Top manager experience in sector (years)||-0.004|-0.002|-0.004|-0.005|
|||(0.007)|(0.007)|(0.007)|(0.009)|
|Log of sales per worker (outliers removed)||-0.016|-0.015|-0.012|-0.033|
|||(0.040)|(0.041)|(0.040)|(0.043)|
|Log of age of firm||-0.098|-0.128|-0.094|-0.115|
|||(0.110)|(0.116)|(0.110)|(0.122)|
|Firm is part of a larger firm Y:1 N:0||-0.033|-0.110|-0.049|-0.100|
|||(0.236)|(0.227)|(0.220)|(0.259)|
|Exports 10% or more of sales Y:1 N:0||-0.119|-0.059|-0.115|-0.287|
|||(0.227)|(0.233)|(0.218)|(0.238)|
|Foreign ownership Y:1 N:0||0.307|0.263|0.304|0.295|
|||(0.276)|(0.270)|(0.275)|(0.296)|
|Firm with unfavorable credit record Y:1 N:0||0.133|0.162|0.095|0.284|
|||(0.162)|(0.169)|(0.167)|(0.190)|
|Establishment has a line of credit or loan at this time Y:1<br>N:0||0.224|0.256|0.254|0.191|
|||(0.165)|(0.166)|(0.166)|(0.180)|
|Firms use email to interact with clients/suppliers Y:1 N:0||0.135|0.183|0.177|0.122|
|||(0.184)|(0.188)|(0.187)|(0.211)|
|Establishment has checking or savings account at this time<br>Y:1 N:0||0.042|0.025|0.041|-0.162|
|||(0.255)|(0.268)|(0.258)|(0.308)|
|Log of mean distance - bank (km)||-0.834***|-0.775***|-0.796***|-0.760***|
|||(0.215)|(0.224)|(0.222)|(0.225)|

22
Table 2 (Cont’d). Descriptive Statistics – Cox’s Bazar Panel Survey

||Average|Std. Deviation|Observations|
|---|---|---|---|
|_E. Health-related Outcomes_||||
|Suffered from diarrhea last month [Yes=1]|0.071|0.257|1,842|
|Suffered from fever last month [Yes=1]|0.493|0.500|3,374|
|Suffered from cough last month [Yes=1]|0.224|0.417|2,206|
|Health provider: UN|0.006|0.076|7,173|
|Health provider: NGO|0.017|0.130|7,173|
|Health provider: Privately run|0.788|0.409|7,173|
|Health provider: Religious group|0.005|0.071|7,173|
|After 2017: Improved overall health care [Yes=1]|0.784|0.411|4,344|
|After 2017: Improved treatment in health care [Yes=1]|0.683|0.465|2,954|
|After 2017: Improved health care safety [Yes=1]|0.608|0.488|2,388|
|After 2017: Improved health care accessibility[Yes=1]|0.698|0.459|3,102|
|_F. Education-related Outcomes_||||
|Student [=1]|0.577|0.494|7,950|
|Primary Education [=1]|0.768|0.422|1,749|
|Junior Secondary Education [=1]|0.292|0.455|1,156|
|Secondary Education [=1]|0.196|0.397|725|
|Higher Secondary Education [=1]|0.037|0.188|765|
|Post-graduate Education [=1]|0.037|0.189|2,679|
|_G. Safety-related Outcomes: Didyou experience or witness..._||||
|Serious injury [Yes=1]|0.133|0.339|17,407|
|Combat situation [Yes=1]|0.115|0.319|17,407|
|Rape or sexual abuse [Yes=1]|0.019|0.137|17,407|
|Being close to death [Yes=1]|0.066|0.249|17,407|
|Witnessed murder of family or friend [Yes=1]|0.021|0.143|17,407|
|Witnessed murder of stranger or strangers [Yes=1]|0.040|0.197|17,407|
|_H. Host’s Attitudes Toward Rohingya Displaced Population_||||
|My family benefted from Rohingya presence|0.117|0.322|3,595|
|Rohingya presence negatively impacts neighborhood safety|0.739|0.439|4,031|
|Rohingya should live exclusively in camps|0.973|0.163|4,031|
|Rohingya families are poorer than local vulnerable families|0.427|0.495|4,031|
|Rohingya basic needs shouldjust be covered byexternal entities|0.868|0.339|4,031|

_Notes:_ The table presents descriptive statistics for the sample of hosts living around the refugee camps in Cox’s Bazar. Detailed variable definitions can be found in Appendix B.

43
# **D Econometrics Issues**

## **D.1 Location-Specific Parameters: Definition and Identification**

We define location-specific parameters by striking a compromise between the location-specific features we wish to capture and our data availability. In particular, we wish to avoid using moments calculated from data cells containing with too few observations (such as singlemothers that work part-time in Location 4, a cell that only contains two observations.) Therefore, we aggregate some moments and use the aggregates to estimate fewer but crucial location-specific parameters. We also scale some calibrated parameters to take into account cost and wage differences between locations.

First, with respect to heterogeneity in household labor market opportunities, we allow for gender-specific location effects in wage offers. We restrict these effects to be the same across parental educational attainments. This allows us to average out wages over education levels by location and gender and use the corresponding differences to identify the parameters of interest. Formally, we modify equations (22) and (23) to allow for: where **_χ_** 0( _l_ )<sup>_′_</sup> _≡_ [ _χ_ 0 _f_ ( _l_ ) _, χ_ 0 _m_ ( _l_ )] and **_χ_** 1( _l_ )<sup>_′_</sup> _≡_ [ _χ_ 1 _f_ ( _l_ ) _, χ_ 1 _m_ ( _l_ )] are the scale effect vectors in location _l_ for fathers and mothers, and _⊙_ denotes element-by-element multiplication.

Second, we allow fixed costs and variable costs of center-based care to vary by location. Similar to wages, we allow location effects to impact cost distribution parameters but restrict the effects to be the same across qualities. By aggregating cost-related moments over household types and center qualities we identify and estimate these location effects. For variable costs, we modify the parameters of the corresponding lognormal distributions to allow for: where _ψ_ 0( _l_ ) _, ψ_ 1( _l_ ) are the scale effects in variable costs in location _l_ .

As for fixed costs, in the aggregate model they are calibrated outside the estimation procedure using industry cost reports (see footnote 23). We scale these calibrated values by location to take into account possible cost differences among locations. We use the _Census Data for Residential Rents in 2000_ , which reports residential median gross rent at the county level. This is a price measure with national coverage, suitable granularity, and related to a critical fixed cost item for center-based providers, namely rent. We assign counties to our four locations based on population and income as described in Section C.2.2. We then take the mean of the county median rents for each location. The ratios between these means and the aggregate mean are the scaling factors reported in the “Rent” column of Table D.1 and are the ones use to scale fixed costs _{F_<sup>_L_</sup> _, F_<sup>_H_</sup> _}_ .

Third, we scale the single-mother transfer to account for cost-of-living differences across locations. We build the scaling factor with the same procedure used for the fixed costs 68
Table 10: Intergenerational correlations: Full Sample (Parent‘s average education level)

||All India<br>Rural<br>Urban|Less Dev.|Developed||
|---|---|---|---|---|
|All Ch|ildren|Region|Region||
||1993<br>0.585<br>0.500<br>0.580|0.595|0.576||
||(116.49)***<br>(77.44)***<br>(58.47)***|(59.55)***|(98.87)***||
||2006<br>0.549<br>0.493<br>0.539|0.599|0.528||
||(114.86)***<br>(74.55)***<br>(71.91)***|(71.88)***|(91.05)***||
|Brothers|||||
||1993<br>0.540<br>0.465<br>0.559|0.542|0.534||
||(89.23)***<br>(60.15)***<br>(47.52)***|(44.09)***|(76.42)***||
||2006<br>0.528<br>0.458<br>0.547|0.563|0.511||
||(88.95)***<br>(55.69)***<br>(59.60)***|(53.49)***|(70.95)***||
|Sisters|||||
||1993<br>0.648<br>0.569<br>0.603|0.721|0.631||
||(90.82)***<br>(60.51)***<br>(42.57)***|(48.13)***|(77.03)***||
||2006<br>0.573<br>0.545<br>0.522|0.645|0.547||
||(84.18)***<br>(57.98)***<br>(50.13)***|(53.71)***|(66.74)***||
|Robust t st<br>* significa<br>Table 11|atistics in parentheses. Standard errors corrected for clus<br>nt at 10%; ** significant at 5%; *** significant at 1%<br>: Intergenerational Regression Coefficients(β<br>Rural|tering at famil<br>): ByAge c|y level<br>ohorts<br>urban||
||15-19yr<br>20-24yr<br>25-29yr|15-19yr|20-24yr|25-29yr|
|2006|||||
|Parent's e|ducation<sup>1</sup><br>0.272<br>0.409<br>0.465|<br>0.259|0.427|0.508|
||(39.73)***<br>(31.79)***<br>(20.10)***|<br>(37.65)***|(36.50)***|(23.91)***|
|No. of o|bservations<br>8963<br>4132<br>1632|<br>7583|4284|1522|
|1993|||||
|Parent's e|ducation<sup>1</sup><br>0.383<br>0.504<br>0.550|<br>0.327|0.486|0.553|
||(46.63)***<br>(37.22)***<br>(23.35)***|<br>(32.54)***|(30.88)***|(20.53)***|
|No. of o|bservations<br>9709<br>5485<br>2124|<br>4694|2889|1063|

Note: 1: Maximum of Father and Mother's schooling years. Regressions include control for caste and religion, children‘s age dummies and state fixed effects. Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 45
Figure 3 East Asia The Movement of MCY over time <!-- Start of picture text -->
1.4<br>1.2<br>0.8-<br>0.6-<br>0.4<br>NG~~~~0  1<br>/  I<br>0 .2  -<br>............  .......<br>.... ~~~~~~~~~~~~~~~~.....<br>1977  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  1990  1991<br>year<br>- Korea  Philippines  Taiwan  l<br><!-- End of picture text -->
in key sectors of the economy. It will be necessary to open new schools and strengthen and upgrade existing technical and vocational institutions" (Somalia Ministry of National Planning, 1982, p. 310; for further examples, see Psacharopoulos, 1987).

But this type of rhetoric merely reflects wishful thinking about how to find employment for an exploding population and calls for extreme skepticism. It is not based on manpower analysis, a practical knowledge of training needs, or an understanding of how employment is actually generated.

Most entry-level occupations need relatively little training. This automatically makes mass pre-employment schemes highly wasteful. Not surprisingly there is more information in developed than developing countries concerning the amount of training required by entry-level workers. Perhaps the most reliable estimates are those for the United States based on the National Longitudinal Survey of the High School Class of 1972, whiich showed that 87% of the graduates employed needed less than three months of training, and most of the remainder needed less than six months (Sherman, 1983). Similar findings were reported by an earlier study by the U.S. Department of Labor: only 30% of adult workers in the U.S. labor force reported that they were using an skill acquired in formal training or specialized education, and only 12% said that it had been more useful than other forms of training; even among craftsmen, foremen and kindred workers the corresponding figures were only 41% and 17% (U.S. Department of Labor, 1964, Table 11).

Obviously other figures would be found in other countries, but it is not reasonable to believe that in most the situation would be materially different. Indirect evidence of the limited capacity of labor markets for the graduates of vocational programmes is provided by their low placement rates, as revealed by tracer studies.

# 7.2 Strengths and weaknesses of vocational education

Speed and flexibility of response to training needs If nonformal institutional training is less responsive to changes in perceived needs than enterprise-based training, school-based training is as a rule less responsive still.

# Decentralization of decision-making

The capacity of individual schools to respond to changes in training needs is critically affected by the degree to which decision-making generally is decentralized within the education system. In particular, it depends on the latitude given to schools for modifying their curricula and for embarking on initiatives involving outside entities, especially local employers.

As a general rule, the degree of decentralization depends in turn on the source of finance for the school. If the school system is financed out of central government revenue, as in many countries, decision-making tends to be concentrated in the Ministry of Education and responsiveness accordingly is weak. As a case in point, the World Bank (1986) survey of training in the

- 72 -
and/or can be addressed in the most effective way. More specifically, policy makers should try to understand if the access problem is mainly because their financial system is inside the possibilities frontier, and why, or mainly because the achievable frontier is too low compared to similar countries and why. The latter problem will generally call for _market-developing_ policies that expand the possibilities of outreach through structural reforms that improve institutions and other state variables, thus moving the frontier outwards over time. The former problem rather calls for _market-enabling policies_ that, depending on the underlying reason, would put emphasis on raising financial literacy to mitigate self-exclusion or providing incentives to financial service providers to operate more efficiently.<sup>18</sup> Consider first _market-enabling policies_ . While the IT and communication revolution allows financial institutions to improve cost management, the motivation to seize such opportunities typically comes from the pressures of competition. In effect, competitive pressure and the search for profits are key factors behind such examples of outreach as offering of services that are tailor-made for low-income clients (e.g., simple debit accounts at lower costs than regular checking accounts) or the use of mobile branches or cell phone banking to reach populations in remote areas at low costs.<sup>19</sup> Policy makers have an important role to play in providing financial institutions with the necessary incentives to expand outreach through different regulatory policies and actions.<sup>20</sup> If the lack of innovation and the corresponding increase in outreach is due to imperfect competition, policy responses should aim at enhancing market contestability, so that service providers—existing or new—are under competitive pressures to achieve cost minimization

> 18 Compare a similar classification in Porteous (2004). 19 For a discussion of examples from Sub-Saharan Africa, see World Bank (2006b).

> 20

> As example, see Porteous (2006) for a discussion on the regulatory framework for cell phone banking.

18
The share of spells that enter care-required status, and the share of consultations occurring when care is required, are a function of healthcare demand combined with the incidence of different types of spells (implying that the number of “ care never required” and “care ever required” pre-care spells in Panels 3-4 are endogenous). Spell level data masks underuse; a child could go without needed care even if care is eventually sought. We also cannot know which of the spells in the “care never required” category that received care would have eventually entered care-required status. Overall, it is not possible to define “overuse” and “underuse” of care at the spell level, and spell level data does not allow us to isolate the effect of the policy interventions on parents’ care-seeking behavior. To understand demand for care, we instead estimate the daily _hazard_ of formal care, that is, the daily probability that the parents seek care on pre-care spell day _t_ , conditional on not having seen a doctor yet.

Figure 2 shows the simple average hazard of seeking care on each spell day, by treatment group and “care required” classification, estimated by regressing care seeking on a full set of interacted indicators for day of the pre-care spell, care-required status, and treatment group.

Since this non-parametric approach does not show treatment effects in a concise way, we additionally estimate a Cox proportional hazard model, shown in Table 6. Formally, the hazard at _t_ conditional on _Xit_ is This assumes an unrestricted baseline hazard _λ_ 0( _t_ ), where the independent variables _Xit_ shift the probability of seeking care proportionally on every day _t_ of the illness spell. As we show in Appendix Figure C.1, the proportionality assumption delivers a good description of our data. The estimates control for baseline family and child characteristics and include CSCOM doctors lament that it is very hard to convince patients to come back after a first visit, so that they often prescribe treatments for a range of conditions at once. We also know that patients often do not buy everything prescribed at a doctor visit. With subsidized care, doctors may be able to ask the parents to return to try a different treatment approach if the child’s condition has not improved.

26