rafmacalaba/datause-encoder-data huggingface dataset:
the eight personality trait domains, latent personality trait scores were calculated based on raw item scores.<sup>7</sup> One of the main advantages of using the Rasch scores, as opposed to raw scores, across items in a single domain is that it allows for estimating latent traits that cannot be captured by raw scores. For example, if certain test questions are more difficult than others, the Rasch model automatically weights the harder questions differently than the easier ones. In the context of personality traits, if certain characteristics are commonly observed in the sample, while other characteristics are rare, Rasch scores are weighted to account for those differences.

Another advantage of the Rasch model is that it objectively justifies the aggregation of different items within a domain based purely on the collected data (Wright and Douglas, 1986). When using raw scores, one can never be fully certain if different questions claiming to measure a certain personality trait are actually doing so until the estimate is justified through a statistical procedure. The Rasch model allows researchers to verify the soundness of grouping multiple questions to calculate one aggregate trait score. While other models, such as factor analysis (Ajwad _et al_ ., 2014a, 2014b, and 2014c; Cunnigham _et al_ ., 2016), allow for this as well, the Rasch model was selected for this analysis because of its other advantages and because of the relatively low number of items in each domain.<sup>8</sup> A comparison of standardized distributions between the Rasch model and the raw score model is presented in Figure 1.

# _d) Employment, Wages, and Other Variables_

The ESS survey includes a number of questions on employment and wage-related outcomes. As noted above, entry-level and current wages are our outcome variables, which we convert to log scales to facilitate interpretation. All regressions control for a host of individual characteristics,

> using PCM but not RSM. Since all 24 personality trait questions in our data had four uniform responses, namely 1 “Almost Always”, 2 “Most of the Time”, 3 “Sometimes”, and 4 “Almost Never”, we use RSM instead of PCM.

> 7 We use the RSM package in R, which is a part of the “eRM” library developed by Mair _et al_ . (2015) in R. Observations with missing item scores were omitted. The statistical fit of the item in a given domain was checked using the Infit MSQ and Outfit MSQ measures in R. According to the rule of thumb in Rasch literature, Infit MSQ and Outfit MSQ of 0.6-1.4 validate the grouping of items within a single domain (Bradley _et al_ ., 2006). In our data, three of the 24 items had an Infit/Outfit MSQ < 0.6 (0.58, 0.59). Since these numbers were close enough to the rule of thumb, we decided to keep the items in the domain. Finally, estimated latent scores for each value of the raw score (across items) were calculated using the _person.parameter_ function in R.

> 8 Factor analysis requires a minimum of three items per domain.

8
**Table 10. Determinants of the value of Vaso de Leche transfers (Tobit) received by households,** ENNIV **1997**

||Summary St<br>|atistics|Mod|el1|Mod|el2|Mod|el3|
|---|---|---|---|---|---|---|---|---|
|"|Mean<br>|S.D.<br>|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|
|PerCapitaVLTransferValue<br>|49.39<br>|76.99<br>|||||||
|No.ofkids0-6<br>|1.07|1.05|54.71<br>|9.18**<br>|54.62|9.15**|46.21|6.64**|
|_No.ofkids0-6squared_<br>|||-7.79|-4.98**|-8.49|-5.39**|-7.82|-4.89*|
|No.ofkids7-15<br>|1.37|1.28|25.49<br>|5.03**|25.50|5.03*|17.95|3.02+|
|_No.ofkids7-15squared_<br>|||-2.53|-2.16*|-3.04|-2.58**|-2.30|-1.91+|
|No.ofwomen16-25<br>|0.61|0.77|8.41|2.68**|9.46|3.03**|6.03|1.79+|
|No.ofwomen26-65<br>|1.12<br>|0.67<br>|3.07<br>|0.77|3.96|1.00|2.06|0.52|
|No.ofmen16-25<br>|0.58|0.85|-3.55|-1.21|-4.14|-1.41|-8.15|-2.52*|
|No.ofmen26-65<br>|1.05<br>|0.65|-0.870|-0.20|0.025|0.01|-1.542|-0.36|
|Spanish speakinghead<br>|0.772<br>|0.42|-17.54|-2.63**|-15.66|-2.33*|-8.22|-1.11|
|Malehead<br>|0.871<br>|0.33<br>|-15.39<br>|-2.11*|-13.97|-1.92+|-14.31|-1.96*|
|Ageofhead<br>|48|14|-0.570|_-0.50_|-0.610|-0.53|-0.160|-0.14|
|Ageofheadsquared<br>|||0.0021|0.19|0.0022|0.20|-0.0025|-0.22|
|Logpercapitaconsumption<br>|7.72|0.66|||-48.52|-9.60**|||
|Logpercapitaconsumption(IV)<br>|||||||-68.64|_-5.50_**|
|2ndpercapitaconsumptionquintile<br>|0.20<br>|0.40|-3.10|-0.48|||||
|3rdpercapitaconsumptionquintile<br>|0.20<br>|0.40<br>|-8.67<br>|-1.23<br>|||||
|4thpercapitaconsumptionquintile<br>|0.20<br>|0.40|-29.16|-3.62**|||||
|5thpercapitaconsumptionquintile<br>|0.20|0.40|-94.45|-8.97**|||||
|<Departmentdummiesommitted><br>Constant|||-116.4|-3.29**|223.7|4.47**|379.2|3.88**|
|No.ofobservations<br>|3,752||3,752||3,752||3,752||
|Waldchi2<br>|||804.39||790.75||723.18||
|DF|||45||42||42||
|PseudoR2|||0.051||0.050||0.459||

_Note:_ Instruments in IV models are education of head, employment status of head, dummy for own business/farm, head holds two jobs, frequency of payments.

a. Mean value conditional on receiving transfers.

> ** indicates signficance at 99% level of confidence, * at 95% level of confidence, and + at 90% level of confidence.

_22_
terms, such as disaster, hazard, unscheduled event, catastrophic event, among others, have been used interchangeably in the literature; however, not all disasters or hazards lead to catastrophic consequences, and not all hazards or disasters unscheduled events. In this context, the two terms, “ _disaster_ ” and “ _hazard_ ”, include a wider range of events than the others. The distinction between disaster and hazard can be found in Okuyama and Chang (2004b, p. 2); “ _hazard_ is the occurrence of the physical event _per se_ , and _disaster_ is its consequence.” Ariyabandu (2001) put this more specifically suggesting that a disaster is an outcome of a hazard impacting on the _vulnerability_ of a society. Furthermore, this paper focuses on natural hazards that can be classified into the following categories: hydro-meteorological origin, such as windstorms, floods, and drought; and geological origin, such as earthquakes, volcanic eruptions, and landslides.

According to the United Nations’ International Strategy for Disaster Reduction (UNISDR), the frequency of disasters caused by natural hazard has been increasing.<sup>4</sup> Figure 2-1 indicates the trends of disaster frequency by type between 1900 and 2005. All types of disaster are increasing, and especially hydro-meteorological ones have occurred much more frequent than the other two have. On average, 78 disasters per year had occurred during the 1970s. This number grew to 351 per year during 2000 and 2006. Meanwhile, the average number of people killed in any single disaster has been declining, making the total number of casualties per year from disasters fairly constant (UN, 2008).

> 4 http://www.unisdr.org/disaster-statistics/occurrence-trends-century.htm

4
(0.15) is associated with higher employment growth by 0.7 percentage points in non-big cities, and by 0.2 percentage points in big cities. The stand-alone Big City effect is also pronounced: its premium for employment growth is 1.7 percentage points.

To see if there is sorting of firms in big cities, we use the affiliation of high-tech industries by firms as a proxy of firm quality or technology. We want to see if firms located in big cities are more likely to be in high-tech industries. High-tech industries are those in metals and machinery, electronics, chemicals and pharmaceuticals, non-metallic and plastic materials, auto and auto components. The rest of the manufacturing firms are then classified as low-tech manufacturing, such as those in textiles, leather, garments, food, wood and furniture, and other manufacturing.<sup>12</sup> After controlling for country dummies, the coefficient that relates the high-tech dummy with the In Big City dummy clearly shows that there is no statistically significant correlation between the high-tech status and In Big City (Table 7).

**Table 7. A Check on Sorting of Firms and Cities**

||**High**<br>**coef**|**-Tech dum**|**my**<br>**se**|**High**<br>**coef**|**-Tech dum**|**my**<br>**se**|
|---|---|---|---|---|---|---|
|In Big City|0.040||0.033|0.004||0.011|
|Country Dummies|no||no|yes||yes|
|N||45,839|||45,839||
|r2_a||0.002|||0.155||

Standard errors are clustered at the city level.

# 4. Pollution Effects on Firm Productivity

We now move our focus to the second question: is firm performance affected by air pollution? Following a similar strategy, we estimate the effect of PM 2.5 on employment growth and labor productivity growth. As mentioned earlier, because location data are missing for some of the firms, we restrict our sample to only those firms for which we have location and PM 2.5 concentration values. Additionally, data on PM 2.5 from van Donkelaar have better coverage than PM 2.5 obtained from NASA directly, so we opt to use the first one.

We explore different model specifications and present the results of the model that better fits the data. Globally, pollution seems to be associated with employment growth positively and

> 12 This exercise is only do-able for manufacturing firms, and we thus only use the manufacturing firms.

17
while the richest quartile gained over 150%, income gaps widened in 1989-2004. Though unevenly, everybody has been sharing the fruits of economic growth with a mean growth rate of 165% in 15 years (6.7% per annum) or a median growth rate of 100 % (4.7% per annum).

**Figure 5 – Growth incidence curves** (All provinces, 1989-2004)

<!-- Start of picture text --> Growth in mean<br>Growth in median<br>0 25 50 75 100<br>Percentile of the population (ranked by real per capita household income)<br>250<br>200<br>150<br>100<br>50<br>Real per capita household income increase (%)<br>0<br><!-- End of picture text --> Figure 6 shows that the distribution of growth has been uneven in the spatial dimension. The biggest gains have been registered by the already rich in leading coastal and urban areas. Among coastal provinces, a U-shaped growth incidence curve indicates those at the poorest segment in rural areas has experienced phenomenal growth and to some extent has been catching up; while among inland provinces, a positively-sloped curve suggests the initially richer rural households have always enjoyed a higher growth. The difference in growth distribution in urban areas has been less significant across regions – income have increased faster among the richer households for both the coastal and inland regions; and inequality have risen more rapidly in the coast due to the sharper income increase of the richest end. In both urban and rural areas, growth rate has been higher in coastal provinces. Growth in the urban lower-end, both in coastal and inland provinces, has been less satisfactory due mainly to the SOE retrenchments; growth in the rural lower-end, whose initial income level was lower, has been high and has largely contributed to poverty reduction.

**Figure 6 – Growth incidence curve: changes in per capita household income, percentile-to-percentile, eight Chinese provinces (1989-2004)**

<!-- Start of picture text --> Coastal provinces Inland provinces<br>0 25 50 75 100 0 25 50 75 100<br>Percentile of the population (ranked by real per capita household income) Percentile of the population (ranked by real per capita household income)<br>Total Rural Total Rural<br>Urban Urban<br>300 300<br>200 200<br>100<br>100<br>0<br>0<br>Real per capita household income increase (%) Real per capita household income increase (%)<br><!-- End of picture text --> 9
tor equaling one if the household received food assistance as the dependent variable. There does not appear to be a uniform increase in food assistance for all displaced households. Column (1) estimates a specification comparing pre- and post-displacement outcomes, and column (2) estimates a month-to-month change in the likelihood. Although the sign of the estimate in column (1) suggests that the likelihood of receiving food assistance increased following displacement, the coefficient is not precisely estimated. Many of the post-displacement coefficients in column (2) suggest that it is possible that the likelihood of receiving food assistance increased, but one cannot reject the hypothesis that all postdisplacement coefficients jointly equal zero at conventional significance levels (p-value of 0.143).

One possible reason for the lack of a uniform increase in the likelihood of receiving food assistance was that assistance may have been targeted more towards displaced households with the worst food access. Figure 5 demonstrates that was indeed the case; the top panel reports estimates of the change in food assistance for households with adequate access to food between two and six months before displacement, and the bottom panel reports estimates for those with inadequate access. There is little evidence of any changes in the likelihood of receiving food assistance relative to the month before displacement for households with an adequate pre-displacement FCS. However, there was a strong and slightly delayed increase in the likelihood of receiving food assistance for those with an inadequate pre-displacement FCS. One cannot reject the hypothesis that there was no difference in the month of displacement or in the first month after displacement; but nearly every month after that, the increase in the likelihood of receiving food assistance was large and precisely estimated.<sup>35</sup> The increase in assistance only for displaced households with particularly poor food access prior to displacement is surprisingly well-targeted in an environment where millions of households are receiving food assistance (e.g., OCHA 2018). The results are consistent with displacement status being used as a categorical target and with house-

> 35Appendix 9c more formally estimates this difference between households with and without adequate pre-displacement food access. All results in the text are qualitatively identical to these more complete estimations.

20
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|Return on assets (ROA)*Treatment|||(0.000)<br>0.057<br>(0.045)|
|---|---|---|---|
|Constant|0.106***|0.056|0.003|
||(0.018)|(0.076)|(0.102)|
|R-squared|0.001|0.039|0.053|
|N|432|432|432|
|F-test p-value: joint significance of interaction terms|||0.850|
|Mean of dependent variable|0.100|0.101|0.101|

Note: All explanatory variables are measured at baseline. Binary control variables included for when covariate is missing, and then missing covariate coded as zero. Variables with *Treatment at the end are interacted with a treatment group dummy. Robust standard errors in parentheses. Significance levels: *10 percent, **5 percent, ***1 percent.

# Appendix Table 2: Analysis of Matching with IMSS Data

OLS

||Dep<br>Binary<br>match<br>|endent vari<br>=1 if enterpr<br>ed with IMS<br>|able:<br>ise was<br>S data<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Treatment|0.019|0.014|-0.011|
||(0.050)|(0.049)|(0.258)|
|Commerce sector dummy||-0.018|0.016|
|||(0.062)|(0.076)|
|Services sector dummy||0.042|0.087|
|||(0.055)|(0.069)|
|Full-time paid employees||0.000|0.002***|
|||(0.001)|(0.001)|
|Age of principal decision maker (years)||-0.001|-0.002|
|||(0.002)|(0.003)|
|Male principal decision maker dummy||0.102*|0.036|
|||(0.053)|(0.064)|
|Business age (years)||-0.000|-0.001|
|||(0.001)|(0.001)|
|Log (Avg. sales Jul, Aug and Sep 2007 in 1000s USD)||0.083***|0.084***|
|||(0.014)|(0.017)|
|Profits (Sep 2007 sales minus costs, 1000s USD)||0.000|0.000*|
|||(0.000)|(0.000)|
|Return on assets (ROA)||-0.004|-0.020|

32
> - 51 -

Table 9 Stuc,ture of Subnational Government Finance (recent year)

|_<br>|Hungary|Poland|Romania|Czech R.|_<br>Slovak R.|China*|Vietnam|Bulgaria|Russia|
|---|---|---|---|---|---|---|---|---|---|
|Own Resources<br>|18%|50%|25%_2I_|9%|71%|15%|N.I.|4.4%|-|
|Sliared Tax<br>|13%<br>|25%|0%|6%|4.7%|85%|N.J.|49.4%|95%|
|Total Local Resources<br>|31%<br>|_75%_|25%|15%|76%|100%|N.J.|53.8%|95%|
|Transfers from Centrl Government<br>|68.5%<br>|25%|75%|85%|24%|0%|N.J.|46.2%|5%|
|Souce Mic.IBRD documents. See biblio<br>N 1I 514%|-gf-apu<br>||-<br>|||||||
|otes: . as grants and 17.1% as Soc<br>from aggregate figures; deemed negligible.; N|al Security F<br>.J.: No infor|unds trans<br>mation.|fers.; 2/ Exp|ected to decl|ine to 20% in|1992.;'|For 1985.|Also transf|ers not separate|
consisted of 26 questions about characteristics that were highly correlated with the probability of school dropout, as indicated by an analysis of recent nationwide household surveys; the questions were also reasonably easy for students of this age to answer, and for peers and teachers to validate during the verification stage of the application process.<sup>4</sup> Once completed and validated at the classroom level, forms were collected by head-teachers, and sent to the capital, Phnom Penh. There, a firm contracted for this purpose “scored” them, using the responses and the set of weights that reflected how well each characteristic predicted the likelihood of school dropout in the nationwide household survey analysis. The formula used was the same for every school and, once calculated, the scores could not be revised.<sup>5</sup> Finally, within every CSP school, all applicants were ranked by the score, regardless of which feeder school they came from. In “large” CSP schools, with total enrollment above 200, 25 students with the lowest value of the score were then offered a scholarship of $60, and the next 25 students with the lowest score were offered a scholarship of $45; in “small” schools, with total enrollment below 200 students, 15 students were offered the $60 scholarship, and 15 were offered the $45 scholarship. In total, just over 3800 scholarships were offered.6 The list of students offered scholarships was then posted in each CSP school, as well as in the corresponding feeder schools. Once families had been offered a scholarship, those who accepted received the cash award three times a year at widely-publicized ceremonies held in their school.

<u>Data: We analyze the impact of the program among the first cohort of eligible children. These</u> children filled out application forms in May 2005, and the list of scholarship recipients was posted in November 2005. Data are available at numerous points in time. First, we have access to the composite dropout-risk score, as well as the individual characteristics that make up the score for all 26,537

> 4 In practice, the form elicited information on household size and composition, parental education, the characteristics of the home (the material of roof and floors), availability of a toilet, running water, and electricity, and ownership of a number of household durables. Forms were filled out in school, on a single day. Students and parents were not told beforehand of the content of the forms, nor were they ever told the scoring formula—both decisions designed to minimize the possibility of strategic responses; for example, by a student seeking to maximize her chances of receiving the award.

> 5 The program did allow for a complaints mechanism if an applicant felt they had been wrongly denied a scholarship, but there were virtually no revisions made as a result of this process.

> 6 Occasionally, there were tied scores at the cut-off. In these cases, all applicants with the tied score at the cut-off were offered the scholarships.

5
decrease in the share of landless from 49% to 34%, suggesting some ‘upward’ movement. For dynasties that split since then (84% of the sample or 80,527 households), the number of landless, owner-cum tenants, and bargadars increased slightly (by 2,665, 1,013, and 777), pattadars more than doubled (2,584 to 6,817), and the number of pure owners dropped correspondingly. Compared to other post-reform settings such as Taiwan, China or Korea over a comparable period, movement in the 30 years post-land reform seems limited.

# **3.2 Plot-level data for the owner-cum tenant sample**

Detailed information on plot characteristics is available for the sample of owner-cum tenants as reported in table 3. Plots measure 0.4 ac. on average (0.36 _vs._ 0.46 ac. for owned and _barga_ , respectively) with _barga_ plots about 170 m more distant from the cultivator’s home. Permanent plot characteristics such as soil type, color, or condition are nearly indistinguishable between owned and _barga_ plots. Although magnitudes are small, _barga_ plots are more likely affected by salinity and drainage problems caused by inadequate management over a long time, possibly a result of investment disincentives: 46.6% (50.6%) of own compared to 45.2% (52%) for barga plots have no (moderate) salinity problems. Similarly, 35.4% (14.1%) of own plots are “easy (difficult) to drain” compared to 34% (15.2%) for _barga_ plots.

Productivity per acre on tenanted plots is significantly lower than on owned ones -by some 24% for gross and net revenue excluding family labor (Rs 16,693 _vs._ Rs. 22,062 and Rs 11,051 _vs._ Rs 14,565, respectively). Interestingly, and consistent with predictions, this difference in net revenues is not due to higher levels of input use on _barga_ plots; to the contrary, intensity of fertilizer, pesticide, seed, draught power, and family labor use are significantly lower on _barga_ plots, as one would expect.

Data on investment suggest that land-attached investment is much lower on _barga_ than on owned plots. Although access to irrigation is a coarse measure only, we find a 10 point difference in access to private irrigation, largely borewells, which exist on 54% of owned but 44% of _barga_ plots. By contrast, there is no difference between owned and _barga_ plots in access to public irrigation.<sup>10</sup> Flows of less observable investments to maintain or improve land quality point in the same direction: Over the last 8 years, such investment was undertaken on 39% of owned vs. 12% of _barga_ plots. Amounts of capital and family labor spent on such investment in 2007 are 3 and 4 times larger on the former (Rs. 203 _vs._ 51 and 6.8 _vs._ 1.8 days, respectively). Econometric analysis can help to more clearly assess the magnitude of tenancyinduced effects.

> 10 This can be consistent with the finding that tenancy reforms, institutional credit, and public support through distribution of minikits fostered irrigation investment and drops in water prices that benefited all farmers and drove the 1980s and 90s green revolution (Bardhan, Mookherjee, and Kumar 2009).

10