rafmacalaba/datause-encoder-data huggingface dataset:
List of Tables 3

|Figure9|Cost-beneft analysis of Samurdhi scale-up<br>. . . . . . . . . . . .|29|
|---|---|---|
|Figure10|Cost-beneft analysis of scale-up versus scale-out . . . . . . . . .|30|
|Figure11|Cost-beneft analysis of post-disaster support scale-out using||
||PMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|
|Figure12|Avoided wellbeing losses for various ASP programs . . . . . . .|33|
|Figure13|Household asset vulnerability (vh)<br>. . . . . . . . . . . . . . . . .|47|
|Figure14|Household asset losses and reconstruction . . . . . . . . . . . . .|48|
|Figure15|Household consumption losses during reconstruction . . . . . .|52|
|Figure16<br>list of t|Household reconstruction time (τh) . . . . . . . . . . . . . . . . .<br>ables|56|
|Table1|Asset risk from precipitation fooding, by district . . . . . . . . .|10|
|Table2|Risk to assets, socioeconomic resilience, and risk to wellbeing .|21|
|Table3|Risk to assets, socioeconomic resilience, and risk to wellbeing<br>for the poorest quintile . . . . . . . . . . . . . . . . . . . . . . . .|23|
|Table4|Per capita risk to assets and wellbeing for the poorest quintile .|24|
|Table5|Total household expenditures, by district, as reported by the<br>2016HIES.<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|42|
|Table6|Asset vulnerability categories . . . . . . . . . . . . . . . . . . . .|46|

# abstract

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses losses as the main mea- wellbeing sure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular
## **7.2  Effect of Combined Financial Access Variables**

In this section we look at the effect of all four financial access variables combined on employment growth, by firm size, age, sector, and region. We use the following model: _EG_ = _b_ 0 + _b_ 1Small + _b_ 2Medium + _b_ 3Large + _b_ 4Mature + _b_ 5Older + _b_ 6Multi + _b_ 7Manuf + _b_ 8Exporter + _b_ 9Foreign + _b_ 10Govt + _b_ 11Loan + _b_ 12Credit Constraint + _b_ 13Sales Credit + _b_ 14 External Finance + Country Fixed Effects + _e_ (7) where _EG_ refers to the employment growth of firm _i_ at time _t_ (the growth in the number of permanent employees between _t_ − 3 and _t_ ).

Table 11 shows the estimation results for equation 7. The effects of all the financial access variables are statistically significant and have the right signs. The results in column 1 indicate that firm growth slows both as a firm expands its labor force and as it ages, controlling for other firm characteristics. Columns 2–5 suggest that having a loan and having external finance are important for firms of all sizes, though the effects are largest for small firms. The effects for medium-size and large firms are similar in size. The effect of being credit constrained increases with firm size.

Columns 6–8 show that having a loan is very important for firms of all ages, with the largest effect on young firms. Being credit constrained has the largest effect on medium-size firms followed by large firms. The effects of having external finance are statistically significant for firms of all ages. In other words, trust and external finance matter to firms, regardless of their age.

Columns 9–11 show that most forms of financial access are important to firms, no matter their sector. Having a loan has the largest effect on employment growth for firms in the manufacturing sector. Being credit constrained has a negative effect on firms in all sectors, with the largest effect in the manufacturing and sales sectors. Having external finance matters in all sectors, and the effects are of similar magnitude.

Columns 12–15 present the estimation results across regions. Having a loan and external finance matters to firms in different regions, with the largest effects in Latin America. Being credit constrained has a significant effect on firm employment growth only in Europe and Central Asia and Latin America. From these results, together with the results in tables 7–10, we find that the interaction between financial access variables and firms’ size or age is significant in explaining firms’ employment growth.

# **8. Firm Size Distribution**

Because a firm’s size plays a significant part in determining its employment growth, we further assess the relationship between firm size and financial constraints. The survey data allow the creation of a variable showing which firms are credit constrained, which we identify as those that applied for a 15
setting, where there is no market test of whether increasing employee salaries also increases productivity, and where unconditional pay raises are difficult to reverse.<sup>6</sup> The rest of this paper is structured as follows: Section II describes the Indonesian education context, the teacher certification policy, and the mechanisms by which the policy could have improved teacher effort; section III describes our experiment (design, validity, and data collection); section IV presents our main results on the impacts on teacher welfare and student learning outcomes; section V interprets our results and discusses policy implications; section VI concludes. Tables A.1–A.8 and other appendices are available in an Online Appendix.

# **II. CONTEXT, POLICY REFORM, AND RATIONALE**

Indonesia has one of the largest school education systems in the world, catering to a school-age population of over 50 million across 34 provinces and over 500 districts. The country consists of thousands of islands spanning over 3,000 miles from east to west (Figure I), making service delivery challenging. Promoting school education was historically a higher priority for Indonesia than for many other developing countries in South Asia and Africa, and primary school enrollment rates in Indonesia exceeded 90% by the early 1980s (World Bank EdStats Database). This priority on education was further formalized in 2000-02, when the new Indonesian constitution committed the government to spend at least 20% of its budget on education (a considerable increase from before).

Policy deliberations on the best way to spend these extra resources identified poor teacher quality and motivation as key limitations in the performance of the Indonesian education system. The ambitious education reforms of 2005 aimed to address this issue. The highlight of these reforms was the Teacher Law of 2005, which stipulated that teachers who met certain eligibility criteria (being a civil-service teacher, and holding either a four-year university degree, or a high rank in the civil service – typically obtained through a long tenure) and who successfully completed a

> 6 In contrast, Henry Ford's famous "five-dollar workday" led to a similar doubling in wages, but also led to sharp increases in worker productivity (Raff and Summers 1987). Indeed, it is unlikely that Ford would have continued paying high wages if productivity did not go up, whereas the Indonesian government spent billions of dollars on teacher salary increases and has continued doing so each year despite our results showing no impact on student learning outcomes. But, the government had no good way of knowing this ex ante, in the absence of evidence on the question.

7
sector including hospitals and pharmacies in their local health market, only 18% of patients choose to bypass informal providers (i.e. traditional) in their neighborhood to go to a modern health center.

Unsurprisingly, it is in areas where competition is the fiercest that bypassing is more intense. In the capital city, 54% of patients claim they bypassed at least one modern health provider to seek care elsewhere. This number drops down to 28% in urban areas, and 14% in rural areas probably because of the low level of care supply.

Once the informal sector is accounted for, the bypassing rates jump to 28% for the rural areas, 34% in other urban areas, and 61% for the capital city N’djaména. The inclusion of the informal sector therefore doubles the rural bypassing rate, which is probably due to the large number of informal care providers in those areas who fill in the void left by modern health centers.

Furthermore, patients who attend private clinics, both for- and not-for-profit are more likely to engage in bypassing behavior. Indeed, 57% of NGOs’ patients and 49% of private clinics’ patients have bypassed another modern provider, vs. 20% and 19% for patients in public and religious facilities respectively. In the capital where more than half of the patients bypassed a modern facility, 34% have bypassed a public facility vs. 21% a private facility. Overall, 14% of the patients say they bypassed a public provider and 6.8% a private provider. As for the informal sector, it has been bypassed by 18.2% of the patients.

Table 8: Bypassing of Health Centers (Percentage)

||||TYPE|||LOCATIO<br>Other|N||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|Total|
|Public Clinics|10.5|28.2|12.5|44.7|34.7|15.1|6.8|14.0|
|Private clinics|4.8|19.4|3.9|17.0|21.0|4.4|2.9|6.8|
|Non profit (religious/NGO)|1.8|6.2|1.3|6.4|7.0|0.9|1.4|2.4|
|Formal sector + hospitals and<br>pharmacies|20.1|49.3|19.0|57.4|54.3|27.8|13.9|24.6|
|Informal sector|15.6|28.6|19.3|25.5|26.0|8.9|18.5|18.2|
|Total(formal + informal)|30.3|60.8|32.8|70.2|60.5|33.7|28.1|35.6|

Source: Chad 2004 QSDS and authors’ calculations. **Note** : The numbers do not add up to the total because a patient may bypass more than one type of health care provider.

14
- Ibsen R., and N. Westergaard-Nielsen. 2011. “Job Creation by Firms in Denmark.” IZA Discussion Paper No. 5458. Bonn, Germany: The Institute for the Study of Labor (IZA).

- Klinger B., A. Khwaja, and C. del Carpio. 2013. _Enterprising Psychometrics and Poverty Reduction_ . New York, United States: Springer-Verlag.

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- <mark>Lecuona Valenzuela, R. 2009. “El financiamiento a las Pymes en México: La experiencia reciente.”</mark> _<mark>Economía</mark>_ <mark>, UNAM, 6 (017).</mark>

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- Love, I., and N. Mylenko. 2003. “Credit Reporting and Financing Constraints.” Policy Research Working Paper 3142. Washington, DC, United States: World Bank.

- Martínez Peria, M., and S. Singh. 2014. “The Impact of Credit Information Sharing Reforms on Firm Financing.” Policy Research Working Paper 7013. Washington, DC, United States: World Bank.

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28
# **3.2 Summary Characteristics of Sample by Gender**

Table 2 compares the baseline characteristics of our sample by gender. Forty percent of the sample is made up of female entrepreneurs. Half the sample is located in Lilongwe, and the other half in Blantyre. Over 70 percent of the firms in our sample were in the retail sector, including selling groceries (21 percent of total), selling agricultural produce (10 percent), selling animal produce (10 percent), and hardware shops (8 percent). The focus on retail was particularly pronounced for men, while women were more prevalent in services (35 percent for women versus 14 percent for men).

Most firms in our sample were owned by a single individual and had an average of two people working in the business<sup>11</sup> . The average business was started by the owner and had been in operation for 8 years. Male-owned enterprises were more likely to operate in a space owned by the entrepreneur, to regularly advertise, to have a written business plan, to provide receipts to customers, to have a larger network of contacts, to pay city council (market) fees, and to be able to identify the benefits of business registration. In sum, male-owned enterprises were larger and more “formal”. Indeed, sales, profits and investments were also larger for male-owned enterprises. Average monthly profits were $243 per month for male-owned firms, versus $169 per month for female-owned firms. In terms of harassment, while men were more likely to have been asked for a business-related bribe in the past 12 months (5.5 percent versus 3.4 percent for women), women were significantly more likely to have been sexually harassed while on the job (11 percent for women versus 3 percent for men).

Education levels are similar by gender, 92 percent of the sample is literate, 65 percent have completed primary school or higher, but only 29 percent have completed secondary school. Men had, on average, a higher score than women on an index of financial literacy questions<sup>12</sup> . Male entrepreneurs were also more likely to be married or to be living with someone (86 percent vs 71

> 11 In Malawi it is difficult to find businesses with more than 5 employees that are still informal, for two main reasons. First, there are very few firms at those levels: according to 2004-2005 Malawi Integrated Household Survey (IHS), 99% of non-farm enterprises in the country have no more than 5 employees. And second, associated with that, larger firms are usually visible and hence subject to enforcement. It was thus critical to have the right balance between sampling informal firms that are large enough to benefit from the intervention, and targeting a meaningful number of businesses operating in Malawi.

> 12 This index comprises nine questions, such as “Suppose you need to take a loan of Malawian Kwacha (MWK) 10,000 and you have two opportunities. One is to pay an interest rate of MWK 100 every month for twelve months, and the other is pay an interest rate of MWK 1,200 at the end of one year. Which one has a higher interest rate?”

11
Promoting Democracy in Fragile States of (formal) political participation broadly defined as “those activities by private citizens that are more or less directly aimed at influencing the selection of governmental personnel and/or the action they take” (Verba and Nie, 1987, p. 2). This definition encompasses a wide range of political acts and behaviors such as turning out to vote, participating in political campaigns, in which ordinary citizens engage in order express their policy preference and influence politicians’ behaviors. The quality of participation is also based on whether votes convey policy preferences. Here our outcome measurement strategy is based on the implications of our theory, which suggest that obstacles to expression will tend to bias electoral outcomes in favor of parochial candidates—that is, local candidates who operate on an ethnic or region-specific appeals as opposed to national-level appeals. Finally, voters’ ability to express their preferences are a function of their sense that they will face little harm in doing so. To test whether such a mechanism is operating, we also consider outcomes related to voters’ exposure to intimidation or undue influence on their vote choices.

The outcome data that we have available for testing our hypotheses include official polling-place level election outcome data, survey data, a set of behavioral measures, and qualitative reports by our field facilitators. Table 2 shows how our data are used to test the hypotheses and Section F of the supporting information provide details on how the data were gathered. The polling place data include official party vote shares for president, house, and senate races, as well as invalid vote counts. Each town included in the study was matched to its appropriate polling place. The survey data were collected during the weeks just after the October 11, 2011 first-round polls and prior to the second-round polls on November 8, 2011. The survey included modules on contact by politicians and parties, various forms of civic participation, security considerations, and vote choice. For vote choice, we asked both for actual vote choice and, to get around the endogeneity of candidate appeals being targeted to voters, we also asked respondents to comment on who was most appealing in a voting vignette that provided a slate of fictional candidates with 21
27 Mexico MFA A test for the log specification of equation (10) was also run, with the bound data grovwps 5 and 6. Tle tests reveal that the linear specification fits the data better. Nevertheless, the results of the regression in logs, presented in Table 8, are consistent with the previous results in Table 7. Coefficients on the fixed effect dummies and the constan; are not significantly different from zero, suggesting no fixed quality effect was operating. These results are supported by<sup>the hypotheses tests</sup> which reject the existence of fixed quality aifferences as the sole explanation for the price differences, at the one percent level. The coefficient on the U.S. price is significant at the one percent level, with an elasticity of price transmission, all other variables held constant, of 0.59. The Herfindahl index is also significant and positive at the ten percent level. The only difference is that the coefficients on the log of the quota level Vjt and the log of the utilization rate Ujt are also significant and negative. This is the sign we would expect for the quota level effect.
<!-- Start of picture text --> Figure 7b: Weighted data<br>1900<br>1800<br>1700<br>80<br>1600<br>40<br>1500<br>0<br>-40<br>-80<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text --> For most individual African economies GDP per capita registered only modest increases between 1975 and 2005, and many countries – such as the Democratic Republic of Congo, Cote D’Ivoire, and Zimbabwe – had declining per capita incomes over the period. The SD of income per capita was generally low, and the CV of many countries is close to zero. Most of the variation (69 percent) in Africa’s average income per person occurs between countries (table 2) rather than within them.

Figure 8 shows comparative kernel density plots of GDP per capita for at ten year intervals between 1975 and 2005. The kernel plot is helpful in identifying shifting patterns in the distribution of country level incomes per capita, such as the formation of identifiable groups or “clubs” (Quah 1993a, 1993b). The slight movement toward the right of the GDP per capita plot of 2005 compared with that of 1975 (figure 8a) reflects the region’s slow economic growth. Most of that movement took place between 1995 and 2005, when we observe a noticeable slide to the right, reflecting increasing incomes throughout Africa (figure 8d). Since the mid-1990s, the variance of income per capita also appears to have declined.

Since 1975 there has been a pronounced bimodality in the distribution of income among countries in Africa. The twin peaks observed in each of the panels of figure 8 define two groups of countries, rich and poor, which are relatively stable over the thirty year period. The most significant shift toward polarization of the country level distribution occurred between 1985 and 1995 (figure 8c), a period when many countries were devastated by conflicts. Polarization was reduced somewhat between 1995 and 2005.

11
<!-- Start of picture text --> Arm 2: Peer adherence support only 600<br>$<br>se2 500<br>$g8 z<br>ve 8<br>= 400<br>° 8<br>0) 6 12 18 24 a<br>Months between ART & FEATS initiation @<br>3 300 ie<br>Arm 8 5 7<br>3: Peer adherence support plus food & 9 Cc<br>g 200 $s —— PAS Only<br>mo585 sg32s o 2 +—— 95%PAS +Confidence Food interval<br>g8 e< —— Control group<br>ir 2 1 00 ie) Byi}6 12 24 36<br>° Months since ART initiation<br>0 6 Months between ART12 & FEATS ainitiation 18 24 PredictionsEffects7 are random,estimatedstandardat the sampleerrors areaveragesconventionalof age and gender<br>500 500<br>5 400 5 400<br>fe} ++ fe} A<br>a 8 Vz 8a<br>3 300 3 300<br>&83] 3s &3Ss ws55<br>a 5 5 a S g<br>200 2 e ——— PAS Only 200 2s — PAS Only<br>2 = +1 95% Confidence interval 2 £ '—— 1 95% Confidence interval<br>100 on2 ~a <t —— ControlPAS + Foodgrogro u p 100 ze o nw —— ControlPAS + Foodgrogro u p<br>0 12 24 36 0 12 18 24 36<br>Months since ART initiation Months since ART initiation<br>Predictions estimated at the sample averages of age and gender Predictions estimated at the sample averages of age and gender<br>Effects are random, standard errors are conventional Effects are random, standard errors are conventional<br><!-- End of picture text -->
Table 2: Aggregate results for 1,865 municipalities in “Census” (FGT0) - Results from 500 samples

||1|2|3|4|
|---|---|---|---|---|
|Transformation|Box-Cox|Nat. log.|Log. Shift|Log. Shift|
|**Direct**|||||
|AAB (_×_100)|11.314|11.314|11.314|9.722|
|ARMSE (_×_100)|14.051|14.051|14.051|11.997|
|**_CEBac_**|||||
|AAB (_×_100)|6.277|8.642|6.273|5.393|
|ARMSE (_×_100)|6.580|9.169|6.574|5.964|
|**_CEBsa_**|||||
|AAB (_×_100)|6.382|8.953|6.380|5.740|
|ARMSE (_×_100)|6.695|9.414|6.687|5.997|
|**_CEBa_**|||||
|AAB (_×_100)|6.080|8.800|6.092|5.395|
|ARMSE (_×_100)|6.584|9.525|6.589|6.034|
|**_CEBc_**|||||
|AAB (_×_100)|6.253|8.690|6.277|6.054|
|ARMSE (_×_100)|6.363|8.847|6.384|6.181|
|**_ELLc_**|||||
|AAB (_×_100)|6.685|8.781|6.685|6.961|
|ARMSE (_×_100)|6.820|8.854|6.820|7.087|
|**_UC −CEBa_**|||||
|AAB (_×_100)|6.171|7.531|6.016|5.982|
|ARMSE (_×_100)|6.607|8.274|6.461|6.672|
|**_UC −CEBac_**|||||
|AAB (_×_100)|6.121|7.596|5.998|5.926|
|ARMSE (_×_100)|6.446|8.186|6.332|6.652|
|**_UC −ELLc_**|||||
|AAB (_×_100)|6.250|8.019|6.250|7.581|
|ARMSE (_×_100)|6.421|8.100|6.421|7.758|
|**_UC −CEBsa_**|||||
|AAB (_×_100)|6.002|7.539|5.875|5.937|
|ARMSE (_×_100)|6.414|8.284|6.294|6.570|

AAB: Average absolute bias; ARMSE: Average root MSE ELL under Box-Cox and log shift, model is still for Nat. log. but errors are drawn from the empirical distribution. Columns 1, 2, and 3 under two-stage samples (Section 4.1) Column 4 results are under 1% SRS by PSU, all PSUs are included 27