rafmacalaba/datause-encoder-data huggingface dataset:
Table 2.2: TURKEY, Percentage of observations lost due to missing values by industry and size

||Size|S|mall|Me|dium|La|rge|T|otal|
|---|---|---|---|---|---|---|---|---|---|
|Industry||#Obs|%Lost<sup>(d)</sup>|#Obs|%Lost|#Obs|%Lost|#Obs|%Lost|
|Food and<br>|Sampling frame<sup>(a)</sup>|192||170||202||564||
|Beverages|Complete case<sup>(b)</sup>|56|70.8|57|66.5|82|59.4|195|65.4|
||With replacement<sup>(c)</sup>|134|30.2|116|31.8|150|25.7|400|29.1|
|Textiles and<br>|Sampling frame|110||230||398||738||
|Apparel|Complete case|14|87.3|47|79.6|115|71.1|176|76.2|
||With replacement|48|56.4|130|43.5|257|35.4|435|41.1|
|Chemicals|Sampling frame|118||98||136||352||
||Complete case|24|79.7|29|70.4|51|62.5|104|70.5|
||With replacement|60|49.2|67|31.6|87|36.0|214|39.2|
|Non-metallic<br>|Samplingframe|54||66||46||166||
|mineral products|Complete case|15|72.2|20|69.7|19|58.7|54|67.5|
||With replacement|46|14.8|51|22.7|30|34.8|127|23.5|
|Metal products<br>|Sampling frame|94||98||92||284||
|(ex. M&E)|Complete case|30|68.1|43|56.1|34|63.0|107|62.3|
||With replacement|68|27.7|82|16.3|59|35.9|209|26.4|
|Machinery and<br>|Sampling frame|98||78||80||256||
|Equipment|Complete case|37|62.2|31|60.3|38|52.5|106|58.6|
||With replacement|79|19.4|52|33.3|63|21.3|194|24.2|
|Electrical<br>|Sampling frame|58||40||36||134||
|machinery|Complete case|19|67.2|19|52.5|15|58.3|53|60.4|
||With replacement|42|27.6|34|15.0|24|33.3|100|25.4|
|Transport|Sampling frame|64||30||58||152||
|equipment|Complete case|31|51.6|18|40.0|15|74.1|64|57.9|
||With replacement|54|15.6|25|16.7|46|20.7|125|17.8|
|Total|Sampling frame|788||810||1048||2646||
||Complete case|226|71.3|264|67.4|369|64.8|859|67.5|
||With replacement|531|32.6|557|31.2|716|31.7|1804|31.8|

Notes: (a) “Sampling frame” refers to the total number of observations (firms surveyed multiplied by the number of years of information).

(b) “Complete case” refers to the complete case of production function variables (sales, materials, capital and labor), missing values in other IC variables—other than production function— are not considered.

(c) “With replacement” refers to the sample after imputing IC variables according to the ICA Method; missing values in other IC variables—other than production function—are not considered. Notice that only observations with information available on at least one of sales, labor, labor cost, materials or capital, are imputed (d) “Perc. lost” refers to the percentage of observations lost with respect to the sampling frame. Source: Authors calculations with IC data.

11
Fourth, management issues<sup>are critical; systematic institutional</sup> analyois and early emphasis<sup>on building local and central capacity for managing</sup> educational development<sup>are essential to all implementation strategies. The</sup> principal weakness of<sup>Bank supported primary education projects is the absence</sup> of a systematic measurement<sup>of student learning and evaluation of effectiveness</sup> of various interventions.

Fifth, successful programs<sup>aim at comprehensive change, but adopt a</sup> phased implementation strategy with considerable experimentation and testing in the early phases. Curriculum change is combined with the provision of materials, in-service teacher training,<sup>and administrative</sup> and management training.<sup>The</sup> end result is a multi-faceted design with several objectives supported by a mix of interventions.

Figure 2: A Summay Cht of the<sup>World Banks Primay Education</sup> nvesment Objetives, Strategies and LemonS

|Objecties||Stategies|Main Lessons|
|---|---|---|---|
||School Constuctn<br>-|* P anoting<br>loed design,<br>laborand materials<br>Pfabicating<br>componts<br>Mobilitng voluntary<br>community labor|Problems included<br>tation;<br>manaeent;<br>t<br>of wokers<br>s<br>i<br>sIte paron<br>with component<br>delvy;<br>and maintaining minimum standards<br>Bettcr use ezsting facilities and<sup>introduce</sup><br>doubleshift and multi-gade teaching<sup>to avoid</sup><br>constrction of new buildings.|
|Expanding EducaTonal<br>Opportunities|<br>Pservicng<br>Tewher Training|*Expanding and improving<br>cxisting programs<br>Modifyng reuitment criteria<br>Limiting programs to professional<br>and practical training|Sbifted away from the provision of hardware<br>for preservice trining to support<sup>for</sup><br>educatinal inputs and in-service training.<br>Important to consider alternative<br>strategies andanalyzethe trade-offs<sup>between</sup><br>eeonomic efficency and educational<br>effectvens in desning teacher training<br>Pwm|
|||Pviding free uiforms||
||Demand Mobilization<br>|- Recruiting female teachern<br>from loeal communities<br>- Housing for female<sup>teaches</sup><br>- Locating schools closer<br>to community<br>Building sanitay facilities<br>for girl|Imporant to pay attention to cultural,<br>relgous and financial barriers<br>in designing internentions.|

23
DTS has only been implemented for one year, it is difficult to fully compare the impact between PPTP and DTS.

Theoretically, case-based payment systems should lead to greater efficiency in hospital services as hospitals are no longer able to earn additional revenue by conducting more diagnostic tests, performing more procedures, or extending patient stay for a particular case. Hospitals should have incentives to treat a case in the most efficient and cost-effective manner, and a substantial amount of research has found that length of stay and costs declined with the introduction of case-based payment systems (Serra and Wagstaff 2010, Shmueli et al. 2002, Cutler 1990, Kahn et al. 1990).

At the same time, since hospitals are able to earn revenue for each additional case, it is possible that the number of hospital admissions (and possible readmissions) may increase. Empirical research suggests that volume of activity does not increase, although there are some mixed results (Serra and Wagstaff 2010, Dafny 2005, Shmueli et al. 2002, Kroneman and Nagy 2001, Cutler 1990).

From a quality perspective, theoretically there may be negative effects on patient health outcomes if hospitals use less costly and less effective tests or procedures. This potential negative effect should be balanced, however, by the incentive to fully treat a patient so that additional costs are not incurred under the same ―case‖. Empirically, most evidence suggests that introduction of case-based payment systems have had no effect (and even positive in some cases) on health outcomes (Serra and Wagstaff 2010, Cutler 1995, Feinglass and Holloway, 1991; Kahn et al., 1990).

An initial comparison of the first year of DTS implementation shows that detailed groupings appear to have lowered average length of stay (ALOS) and lowered bed occupancy. Both Figures 3 and 4 show data from 2008 and 2009 across all acute services by different types of hospitals in Croatia. There is a consistent decline in ALOS across all hospital types, but especially for clinics (0.9 days). Bed occupancy declined in the majority of hospitals, except for in clinical hospital centers. While it may be counterintuitive to think that increasing efficiency has led to decreasing bed occupancy rates, this result makes sense as hospitals now have incentives to discharge patients earlier, on average resulting in lower bed occupancy. This simple comparison of before and after cannot account for pre-existing trends, however. For example, ALOS or bed occupancy may have already been declining over time due to changes in clinical practices or technology. The rest of the paper conducts a more robust analysis to examine whether these changes can be attributed to the introduction of PPTP and/or DTS.

4
# **VIII. Heterogeneity in Academic Ability**

The final aspect of heterogeneity in the labor market effects of different school types that we consider pertains to academic ability. Do higher entering test scores mitigate or magnify the labor market effects of school types?<sup>21</sup> Since test scores are only available for the youngest cohort, the relevant benchmarks are given in Table 6, which shows that recent male private general and public vocational graduates experience a substantial wage penalty.

Table 9 provides the estimated effects for men that scored above and below the median on their junior high exit exam. Those scoring below the median face a smaller wage penalty of 25 percent, compared with 33 percent for the full sample, which is no longer statistically significant. Interestingly **,** the results show that lowscoring public vocational graduates have significantly lower unemployment rates compared to graduates of the other three school types. However, the wage penalty for vocation education is highest – 41 percent – for men scoring above the median. There is also a high penalty for private vocational graduates that score high on exams. It is these high scoring men who stand the most to lose from investing in vocational education in an economy that increasingly values broadly educated and cognitively skilled workers.

Table 9. Estimated Effect of School Type on Employment and Job Quality, Men, by test score

|||**Low score**<br>|**s**<br>|||**High sc**<br>|**ores**<br>||
|---|---|---|---|---|---|---|---|---|
||LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|
|Public Vocational|0.028|-0.176*|0.089|-0.235|0.023|-0.047|0.026|-0.409***|
||(0.049)|(0.092)|(0.098)|(0.191)|(0.041)|(0.063)|(0.066)|(0.157)|
|Private general|0.031|-0.041|0.010|-0.163|0.014|-0.066|-0.018|-0.330**|
||(0.048)|(0.080)|(0.082)|(0.150)|(0.041)|(0.050)|(0.093)|(0.153)|
|Private vocational|0.030|-0.037|0.062|-0.127|0.002|0.021|0.067|-0.492***|
||(0.048)|(0.082)|(0.097)|(0.150)|(0.067)|(0.076)|(0.099)|(0.187)|
|Average among public<br>general graduates|0.924|0.208|0.427||0.944|0.133|0.539||
|R-squared overall|0.282|0.309|0.650|0.419|0.282|0.327|0.653|0.482|
|Observations|717|667|570|484|705|664|581|477|

Notes: *** 1% significance, ** 5% significance, * 10% significance; figures are marginal effects; standard errors in parentheses, they are robust to heteroskedasticity and clustered at subdistrict level. Low scores are below median.

> 21 The sample is rebalanced and has common support over the test score distribution, which allows for valid comparisons across school types despite large differences in average test scores.

29
# **Supplementary Information**

## **Supplementary Texts**

## **Arsenic concentrations in groundwater**

The first national-scale survey of groundwater in Bangladesh (National Hydrochemical Survey) was conducted jointly by the British Geological Survey (BGS) and the Department of Public Health Engineering (DPHE) of Bangladesh during a 1998–1999 campaign that sampled a total of 3,534 well points throughout Bangladesh except for some parts of Chittagong Hill districts and the Sundarbans.<sup>1</sup> According to the national hydrochemical survey, nearly 90% of these surveyed wells have an intake depth of <150 mbgl and are considered shallow wells. Approximately 25% of these shallow tubewells were drawing groundwater with arsenic (As) concentrations >50 μg/L (Bangladesh drinking water standard); nearly 42% of these wells were recorded to contain As concentrations >10 μg/L (WHO drinking water standard). The distribution of observed As concentrations in Bangladesh is highly (positively) skewed, with values ranging from <0.5 to 1660 μg/L, and the spatial distribution is also highly variable.<sup>2-5</sup> High As concentrations (>50 μg/L) are observed in most parts of southern Bangladesh at depths <100 mbgl<sup>1</sup> . There is no systematic, long-term monitoring of As concentrations in groundwater anywhere in Bangladesh. Patchy and short-term, time-series monitoring of groundwater As concentrations suggests that there is no discernible long-term trend in As in groundwater but seasonal variations in As concentrations are reported by some studies.<sup>6</sup> Our analysis applies interpolated As concentrations (Figure 2a) for developing groundwater risk maps at the national scale in Bangladesh. Arsenic concentrations at geographic points ( _n_ =3207) were interpolated using the Inverse Distance Weighting (IDW) algorithm and resampled at 2.5-km × 2.5-km grid resolution using the ‘raster’ package in the ‘R’ programming language.<sup>7</sup>

## **Groundwater salinity: Electrical conductivity**

Elevated groundwater salinity is common at shallow depth (<150 mbgl) in coastal aquifers of southern Bangladesh and is generally defined by total dissolved solids (TDS), or Electrical Conductivity (EC) or chemical constituent such as chloride (Cl).<sup>8</sup> The Electrical Conductivity (EC) of a solution (e.g., groundwater) is a measure of its ability to carry an electric current. In this study, groundwater salinity is expressed by EC, which has a unit of micro-Siemens per centimeter or μS/cm.

25
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# **Table S1.6: Robustness - Using an ordinal probit regression model**

||DP<br>(1)|FDC<br>(2)|NRM<br>(3)|UPC<br>(4)|
|---|---|---|---|---|
|Enumerator gender (male)|-1.211***|-0.850***|-0.786***|-0.966***|
||(0.344)|(0.316)|(0.187)|(0.226)|
|Enumerator age|0.011|0.025|0.005|-0.043|
||(0.039)|(0.036)|(0.050)|(0.043)|
|Enumerator education|0.078|0.328|-0.916*|0.022|
||(0.297)|(0.385)|(0.503)|(0.317)|
|Enumerator from urban locality|0.985**|1.153***|-0.199|0.904***|
||(0.423)|(0.415)|(0.256)|(0.306)|
|Observations|6,245|6,333|6,553|6,320|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents regression results for equation (1) obtained using ordinal probit. For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). Work experience as enumerator is the number of months previously worked as an enumerator. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

39
|**Table 5: Interaction**|**term results fo**|**r growth rate**|**of labor prod**|**uctivity**||
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
||**Panel A: Regr**|**ession results**||||
|Ethnic Fractionalization*SME Y:1 N:0|-9.553**|-8.605*|-9.347*|-9.916**|-10.653**|
||(4.713)|(4.840)|(4.739)|(4.653)|(4.706)|
|Ethnic Fractionalization|-10.205**|-6.991|-6.121|-5.215|-3.844|
||(4.758)|(4.775)|(4.820)|(4.929)|(4.693)|
|SME Y:1 N:0|-1.372|-2.024|-0.097|0.510|0.754|
||(1.788)|(1.822)|(1.976)|(1.840)|(1.877)|
|Initial Labor Productivity (logs)|-5.586***|-5.864***|-5.963***|-6.026***|-5.974***|
||(0.562)|(0.544)|(0.529)|(0.547)|(0.570)|
|GDP per capita (logs)||2.979**|2.915**|2.861**|3.662***|
|||(1.216)|(1.249)|(1.214)|(1.363)|
|Age of firm (logs)|||0.260|0.363|0.468|
||||(0.955)|(0.960)|(0.951)|
|Manager experience (years, logs)|||0.894|0.762|0.878|
||||(1.028)|(0.985)|(0.964)|
|Exports (proportion of sales)|||4.427**|4.199**|4.168**|
||||(1.742)|(1.721)|(1.722)|
|Foreign Ownership Y:1 N:0|||2.584*|2.404|2.547*|
||||(1.447)|(1.468)|(1.502)|
|Firm is Financially Constrained Y:1 N:0|||-0.015|-0.014|0.066|
||||(1.790)|(1.715)|(1.689)|
|Female Top Manager Y:1 N:0||||0.173|0.101|
|||||(1.212)|(1.273)|
|Firm Provides Training Y:1 N:0||||0.010|0.009|
|||||(0.012)|(0.012)|
|Courts Obstacle Y:1 N:0||||-0.874|-0.936|
|||||(1.227)|(1.232)|
|Total hours of power outages||||-0.005|-0.003|
|||||(0.009)|(0.007)|
|Firm Purchased Fixed Assets Y:1 N:0||||1.305|1.180|
|||||(1.049)|(1.046)|
|Time Tax||||-0.058|-0.059|
|||||(0.058)|(0.058)|
|Population (logs)|||||-0.277|
||||||(0.869)|
|Primary Education|||||0.047|
||||||(0.086)|
|Freedom from Business Regulations|||||<br>-0225|
||||||.<br>(0.966)|
|Merchandize Trade to GDP ratio|||||0.004|
||||||(0.028)|
|Growth rate of GDP per capita (%,|||||-0.178|

52
household income in the neighborhood of the poverty line, then this wage rise will contribute 7 percent (0.70 * 10 percent) to the stratum’s income change at the poverty line.

As seen in this simple example, implementation of equation (2) requires mapping factor earnings in the general equilibrium model (e.g., agricultural unskilled wages) to income sources obtained from the household survey (imputed returns to self‐employed unskilled labor in agriculture). In the micro‐simulation analysis, self‐employed agricultural labor and capital receive the corresponding farm factor returns from the general equilibrium model, as do non‐ agricultural labor and capital. Wage labor for diversified households reported in the surveys presents a problem because information is lacking to assign it to a specific industry. Accordingly, we apply the composite wage for skilled or unskilled labor determined by the general equilibrium model in these respective labor markets. Finally, transfer payments are indexed by the growth rate in net national income.

Summing over the share‐weighted change in factor returns yields the total real income change for households in the neighborhood of the poverty line for a given stratum‐region combination. The real cost of living at the poverty line is obtained by solving the demand system for the level of income required to attain the poverty level of utility, given a vector of prices. By solving this for the initial consumer prices and then for the post‐exogenous shock prices, we can obtain the change in the cost of living at the poverty line, taking into account price‐induced changes in the mix of goods and services consumed.

The ensuing change in real income is, in turn, multiplied by the second class of parameters in (2): εrs . This is the estimated elasticity of the stratum‐specific poverty headcount ( Hrs ) with respect to income which is obtained by evaluating the density of the stratum population in the neighborhood of the poverty line. In order to turn these stratum changes into the estimated percentage change in national poverty headcount, they must be weighted by each stratum’s share in national poverty, the third class of parameters: Summing across strata, we thus obtain the percent change in national poverty headcount,<sup>ˆH</sup> r<sup>. By multiplyingˆH</sup> r<sup>with the national poverty rate we ultimately obtain the</sup> percentage point change in the national poverty rate due to changes in factor earnings as well as the cost of living at the poverty line, dh r : If this rises by one percentage point, then poverty has risen by one percent of the national population, equivalent to more than 344 thousand people. Such a change would indicate a very large poverty impact in Tanzania.

# 3.2 POVERTY IMPACTS

The assessment of poverty vulnerabilities to interannual climate variation over different time periods is complicated by the dynamics of the global and Tanzanian economies as we go 12
# **H Estimates of upward IGM using primary-to-secondary**

# **education**

**Table A10:** Country-Level Estimates of Upward IGM using primary-to-secondary education

|||(1)|(2)|
|---|---|---|---|
|mobility / N|census years|upward|N|
|age range||19-25|19-25|
|Trinidad and Tobago|1970,1980,1990,2000,2011|.466|8,506|
|Peru|1993,2007|.416|131,085|
|Saint Lucia|1980,1991|.388|1,452|
|Jamaica|1982,1991,2001|.315|4,304|
|Bolivia|1976,1992,2001,2012|.237|66,410|
|Chile|1970,1982,1992,2002|.19|97,017|
|Brazil|1960,1970,1980,1991,2000,2010|.187|6,142,101|
|Cuba|2002,2012|.187|4,037|
|Uruguay|1963,1975,1985,1996,2006,2011|.178|25,192|
|Argentina|1970,1980,1991,2001,2010|.177|226,100|
|Dominican Republic|1981,2002,2010|.161|64,387|
|Panama|1960,1970,1980,1990,2000,2010|.161|23,221|
|Venezuela|1971,1981,1990,2001|.148|185,993|
|Costa Rica|1973,1984,2000,2011|.133|28,829|
|Ecuador|1974,1982,1990,2001,2010|.128|121,410|
|Colombia|1973,1985,1993,2005|.121|354,007|
|Mexico|1970,1990,2000,2010|.107|1,008,707|
|El Salvador|1992,2007|.092|37,462|
|Paraguay|1962,1972,1982,1992,2002|.085|54,934|
|Haiti|1971,1982,2003|.073|62,660|
|Guatemala|1964,1973,1981,1994,2002|.042|125,087|
|Honduras|1974,1988,2001|.036|52,754|
|Nicaragua|1971,1995,2005|-.004|47,560|
|Suriname|2012|-.094|200|
|mean / total||.164|8,873,415|

Notes: Column (1) gives upward-IGM estimates. It reflects the likelihood that children, aged 19-25, whose parents have not completed primary schooling will manage to complete at least secondary education. Column (2) gives the number of observations used to estimate the country-specific IGM statistics (children whose parental education is reported in the censuses). Countries are sorted from the highest to the lowest level of upward IGM (column (1)). “mean” gives the unweighted average of the 24 country-estimates.

57