rafmacalaba/datause-encoder-data huggingface dataset:
- Kunreuther, H. (2000), Strategies for Dealing with Large-Scale and Environmental Risks, _in_ H. Folmer, L. Gabel, S. Gerking and A. Rose, eds, ‘Frontiers in Environmental Economics’, Edward Elgar, pp. 293–318.

- Kunreuther, H. and Pauly, M. (2006), ‘Rules rather than discretion: Lessons from Hurricane Katrina’, _Journal of Risk and Uncertainty_ **33** , 101–116.

- Kydland, F. and Prescott, E. C. (1977), ‘Rules Rather Than Discretion: The Inconsistency of Optimal Plans’, _Journal of Political Economy_ **85** (3), 473–491.

- Marshall, M. G. and Jaggers, K. (2005), ‘Polity IV Project. Political Regime Characteristics and Transition, 1800-2004’, Version 2004.

- Nichols, A. L. and Zeckhauser, R. J. (1982), ‘Targeting Transfers through Restrictions on Recipients’, _American Economic Review_ **72** (2), 491–502.

- Raschky, P. A. and Weck-Hannemann, H. (2007), ‘Charity Hazard - A Real Hazard to Natural Disaster Insurance?’, _Environmental Hazards_ **7** (4), 321–329.

- Schmidtchen, D. (1999), ‘Two Help or not to Help: The Samaritan’s Dilemma Revisited’, CSLE Discussion Paper 9909.

- Skidmore, M. and Toya, H. (2002), ‘Do Natural Disasters Promote long-run Growth?’, _Economic Inquiry_ **40** (4), 664–687.

- Strömberg, D. (2007), ‘Natural Disasters, Economic Development, and Humanitarian Aid’, _Journal of Economic Perspectives_ **21** (3), 199–222.

- Tavares, J. (2004), ‘The open Society assesses its Enemies: Shocks, Disasters and Terrorist Attacks’, _Journal of Monetary Economics_ **51** , 1039–1070.

- Voeten, E. & Merdzanovic, A. (2009), United Nations General Assembly Voting Data, hdl:1902.1/12379 unf:3:hpf6qokddzzvxf9m66yltg= =, Georgetown University.

- Vuong, Q. H. (1989), ‘Likelihood Ratio Tests for Model Selection and Nonnested Hypotheses’, _Econometrica_ **57** (2), 307–333.

19
||De<br>|**Table 5: P**<br>**Regress**<br>pendent variable:<br>|**roduction Functio**<br>**ions on separate s**<br>Logof annual value<br>|**ns – OLS**<br>**amples**<br>-added in 1000 Bi<br>|rr<br>||
|---|---|---|---|---|---|---|
|Sample<br>Sector<br>Specification|**Large**<br>**Manufactu**<br>**ring**|**Small urban**<br>**Manufacturing**<br>**Baseline**|**Rural**<br>**Manufacturing**<br>**Baseline**|**Large urban**<br>**Manufacturing**<br>**Baseline + IC**|**Small Urban**<br>**Manufacturing**<br>**Baseline + IC**|**Rural**<br>**Manufacturing**<br>**Baseline + IC**|
||**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|
|**_Factors_**<br>lnK|0.149***|0.096**|0.213***|0.156***|0.087*|0.223***|
||(0.047)|(0.048)|(0.054)|(0.047)|(0.051)|(0.053)|
|LnL|0.761***|0.850***|0.854***|0.749***|0.795***|0.857***|
|**_Activity_**|(0.087)|(0.173)|(0.177)|(0.086)|(0.196)|(0.172)|
|<br>Food and beverages|0.026||-1.038*|0.056||-1.034*|
||(0.295)||(0.553)|(0.304)||(0.532)|
|Garments and|-0.486|0.026|-1.006*|-0.481|0.047|-1.012*|
||(0.310)|(0.253)|(0.542)|(0.312)|(0.271)|(0.522)|
|Leather|<br>0.021<br>||<br>0.980<br>|<br>0.002<br>||<br>1.117<br>|
||(0.414)||(0.859)|(0.425)||(0.856)|
|Wood, furniture &|<br>-0.274||<br>-2.144***|<br>-0.281||<br>-2.144***|
||(0.283)||(0.667)|(0.293)||(0.639)|
|**_Management_**|||||||
|<br>Female management|-0.008|0.017|-0.588**|0.017|-0.004|-0.466*|
||(0.179)|(0.308)|(0.265)|(0.180)|(0.331)|(0.270)|
|Manager’s<br>hlin|-0.089|0.565***|-0.146*|-0.103|0.458*|-0.125|
|scoog|(0.098)|(0.217)|(0.084)|(0.097)|(0.249)|(0.082)|
|Manager’s<br>schooling<sup>2</sup>|0.006|-0.032**|0.013|0.006|-0.025*|0.011|
||(0.005)|(0.013)|(0.010)|(0.005)|(0.015)|(0.009)|
|**_Major Constraints_**|||||||
|Credit||||-1.161**|-0.494|-1.253***|
|||||(0.571)|(0.944)|(0.449)|
|Transport||||0.397|2.525|0.349|
|||||(1.090)|(2.123)|(0.480)|
|Utilities||||<br>-1.112|<br>0.388|<br>-0.261|
|||||(0.912)|(2.113)|(0.380)|
|**_Geography_**<br>Rural town|||0.544***<br>|||0.459**<br>|
||||(0.211)|||(0.213)|
|Constant|3.166***|-0.527|1.758***|3.836***|-0.350|2.459***|
||(0.627)|(0.763)|(0.573)|(0.744)|(0.888)|(0.586)|
|N|301|53|294|301|53|294|
|R2|0.732|<br>0.458|<br>0.261|<br>0.743|<br>0.479|<br>0.291|
|Adjusted R2|0.724|<br>0.388|<br>0.235|<br>0.732|<br>0.370|0.259|
|<br>**_Median Solow shares_**||<br>|<br>|<br>|<br>||
|lnK|0.10|0.24|na|0.10|0.24|na|
|lnL|0.90|0.76|na|0.90|0.76|na|
|**_Mean Solow shares_**|||||||
|lnK|0.15|0.31|na|0.15|0.31|na|
|lnL|0.85|0.69|na|0.85|0.69|na|

Note:

-  .01 - ***; .05 - **; .1 - *;

- Robust standard errors in parentheses

- Solow shares could not be computed for rural firms since most rural firms do not hire workers (relying on household members instead) and because the rural data do not contain information on rental expenditure.

-38-
**Table 8. Energy requirement by demographic group and share in the Brazilian population**

|Category|Age (years) or<br>condition|Per day<br>required<br>calorie intake|% Brazil<br>population|% bottom 40%<br>population|
|---|---|---|---|---|
|Infants|0-1|750|1.2%|1.7%|
|Children|1–3|1,300|3.8%|5.3%|
||4–6|1,800|4.1%|5.7%|
||7–10|2,000|5.4%|7.6%|
|Males|11–14|2,500|3.2%|4.4%|
||15–18|3,000|3.4%|4.5%|
||19–24|2,900|4.7%|4.9%|
||25–50|2,900|17.9%|16.5%|
||51+|2,300|11.8%|8.3%|
|Females|11–14|2,200|2.9%|4.0%|
||15–18|2,200|3.2%|4.1%|
||19–24|2,200|4.5%|5.0%|
||25–50|2,200|19.7%|18.6%|
||51+|1,900|14.3%|9.6%|
|_Population_|_-Weighted Average R_<br>_Intake_|_equired Calorie_|2,286|2,268|

_Source:_ Own compilation using data from US National Research Council and POF 2017/18. _Note:_ Bottom 40% refers to the population in the poorest two quintiles of the consumption-based per capita welfare distribution.

**Table 9. Poverty lines and headcounts using 2,250 kcal/day requirement**

|**Methodology**|**Food**<br>**Poverty**|**Lower Total**<br>**Poverty**|
|---|---|---|
|Parametric|R$276|R$487|
|Nonparametric|R$276|R$484|

_Source:_ Own calculations using POF 2017/18.

# Food away from home

In Brazil, 30 percent of households’ expenditures on food take place where it will be consumed – that is, away from home. Table 10 shows the distribution of the share of expenditures on food consumed away from home in total food expenditures. Food consumed away from home accounts for a sizeable proportion in terms of food expenditures, ranging from 19 percent in the bottom decile of the income distribution to 43 percent in the top decile, and continually increasing across the welfare distribution.

However, as explained in previous sections, we were not able to calculate cost per calorie for food to be consumed away from home because of data limitations. While we cannot tackle this caveat directly, we run the following alternative specification. We assume that calories are typically more expensive when food is purchased away from home than when purchased to be consumed at home (for instance, from a grocery store). In particular, we apply a cost per calorie to FAFH that is 1.5 times the household-specific average cost per calorie for food purchased to be consumed at home. The new weighted average cost per 18
7 to be estimated, and e is an error term.<sup>2</sup> Different reasons can justify the inclusion of individual (X), and household and market (Z) factors as explanatory variables in labor market participation equations (1)-(2). An individual characteristic, such as the level of education can be treated as an explanatory variable that may indicate the poten.ial productivity of an individual at home and in market production. Holding market wages constant, an increase in the level of an individual's education can increase his or her probability of labor market participation if it increases the opportunity costs of staying at home. The household's constraints include such household asset variables as landholding, which may act as a proxy for productive household assets. The productive assets exert a price effect and an income effect on an individual's labor market participation. The price effect would raise the marginal product or "shadow price" of an individual's labor, while the income effect would encourage an individual to cons'±me more cf his or her leisure -- eve at its given opportun_ty cost. The household's unearned income -- anocher household characteristic -- can influence labor market participation via a pure income effect. Such market factors as market wages exert an income and a substitution effect on an individual's time allocation. Market factors may 2 y equ.al to zero includes individuals who are either selfemployed in family business and farming or exclusively engaged in nonmarket home producti,n. The results may be biased if selfemployment and nonmarket home production are not highly substitutable. This requires analyzing the degree of substitutability between selfemployment and nonmarket production (Khandker 1987). No analysis has been done with the PLSS data. We assume for simplicity that home production and selfemployment are highly substitutable so that we can lump these two categories into one category. Our interest here is to find out what influences labor market participation, not so much what influences women's labor force participation (which consists of both self-employment and labor market work for cash). We wish to study the factors that influence women's participation and productivity in the formal sector.
“high” or “low”; averaging over these, the food and cash transfer are of equivalent value. But in terms of food consumption, for risk averse beneficiaries, the expected utility of the certainty of food transfer is higher than the expected utility of the cash transfer. There may also be behavioral or psychological factors at work. Beneficiaries might wish to receive payments in cash if in‐kind transfers are perceived to be stigmatizing (Moffitt, 1983). Receiving in‐kind payments can be seen as a commitment device. As part of the debate regarding replacing India’s food transfer system (the Public Distribution System, PDS) with cash payments, Jean Dreze has noted PDS participants “...are worried that cash can be more easily misused than food, because food can only be consumed in small quantities over time while cash is easily spent in one go”.<sup>3</sup> Lastly, if questions regarding beneficiaries’ views about payment modalities are asked in the context of an ongoing program, responses might reflect a concern that giving the “wrong” response might lead to the transfer program being terminated (Berg, Mattinen and Pattugalan, 2013).

Few studies provide systematic empirical evidence on why households might rather receive in‐kind rather than cash transfers.<sup>4</sup> This lacuna motivates this study that uses unique

> 3 <u>http://www.ideasforindia.in/topics/miscellany/ashok-kotwal-speaks-with-jean-dreze.html. Accessed July 24, 2018.</u> 4 An exception is Khera (2013). Looking at a range of household characteristics, she finds a strong relationship between literacy and food preferences; illiterate persons are more likely to prefer food. She finds no relationship between preferences and household demographic characteristics, nor are there associations between cash or food preferences and distances to markets. A related exception is work by Ghatak, Kumar and Mitra (2016) who look at associations between beneficiary characteristics and cash payments versus an in‐kind payment – a bicycle that will assist adolescent girls in attending school. They find that wealthier and larger households are more likely to prefer

5
**Table B.2: Percent Agreement, Exact + Adjacent (%)**

||**Guangdong**|**S-MTP**|**MET**|**UTQ-A**|
|---|---|---|---|---|
|**Positive Climate**|78.4|79.3|74.6|69.3|
|**Teacher Sensitivity**|58.4|73.8|72.6|65.0|
|**Regard for Student Perspectives**|64.8|78.6|67.9|70.2|
|**Behavior Management**|80.4|89.2|85.6|92.8|
|**Productivity**|91.5|84.3|82.4|87.2|
|**Negative Climate**|95.6|95.1|95.1|97.6|
|**Instructional Learning Formats**|72.0|80.3|78.2|73.9|
|**Content Understanding**|70.7|73.4|75.5|76.6|
|**Analysis and Inquiry**|66.4|73.7|71.5|84.3|
|**Quality of Feedback**|78.1|72.5|71.9|64.1|
|**Instructional Dialogue**|64.8|—|74.8|—|
|**Student Engagement**|83.1|81.2|78.4|82.0|

Source: Guangdong observations; Teachstone.

Note: — indicates that the dimension was not scored for that study.

Table B.2 shows the average percent agreement between raters across the 12 CLASS dimensions. The raters in Guangdong had similar measures of agreement as those in three other studies that took place in a U.S. context. Certain dimensions proved harder for the Chinese raters to assess similarly, such as Regard for Student Perspectives, Teacher Sensitivity, and Analysis and Inquiry, perhaps due to differing definitions of what comprises effective Teacher Sensitivity in Chinese classrooms. On other measurements, such as Productivity and Student Engagement, the Chinese raters were in agreement a higher percentage of the time than in the U.S. context of the other three studies.

For future observations, trainers might be better to focus on more examples of those areas where raters had lower levels of agreement. While raters seemed to have similar ideas of what comprised a Positive Climate or a productive classroom, they were less sure as to Teacher Sensitivity or Regard for Student Perspectives.

34
Being the first quantitative research, based on a large survey data, in this topic in Indonesia, this study contributes to a broader view and understanding of the potential gains and losses from migration for sending households. It also contributes to the limited existing research on the gender dimensions associated with these gains and losses.

Our results suggest important gender differences in the impacts of international migration on sending households. In Indonesia, migration reduces the working hours of remaining household members, but this effect is mainly driven by what happens in households with male migrants. This negative relationship was not observed for households with female migrants. The results about child outcomes are also divided along the gender line. Female migration and their remittances tend to reduce child labor outside the home but not necessarily boost schooling activities. Though not borne out statistically significant, the direction of the estimates suggest that migration may have a slightly positive impact on school enrollment among households with male migrants, but this impact disappears among those with female migrants. The lack of oversight associated with the mother’s absence is likely to make it difficult to ensure sufficient schooling activities for children at home.

The following section reviews the relevant literature. Section 3 describes the IFLS data, and Section 4 provides a descriptive examination of migrants and migrant-sending households in Indonesia. The empirical strategy and results are presented in the subsequent two sections. The last section concludes.

# **2. Literature Review**

While there has not been any quantitative analysis, before this paper, about the development impacts of migration with a gender focus in the Indonesia context, there is a general literature on different country experiences of impacts of migration<sup>3</sup> and some indicative evidence on the differential impact by gender. Most studies find that migration and remittances tend to reduce the labor supply and participation of non-migrating family

> home country include Hildebrandt and McKenzie (2005), Mansuri (2006b,c), Acosta (2006) and Beaudouin (2005).

> 3  See Adams (2010) and Hanson (2008) for an extensive review

4
|**Regional Divergence _________________________________________________ 40**|
|---|
|**Urbanization and Development________________________________________ 42**|
|**A Dominant Primate City ____________________________________________ 44**|
|**Inability to Attract Manufacturing_____________________________________ 46**|
|**Migration for Wage Jobs _____________________________________________ 48**|
|**_VI. Skills _____________________________________________________________ 50_**|
|**Overview __________________________________________________________ 50**|
|**Catching Up on Enrollment___________________________________________ 51**|
|**Quality Gap with the Best ____________________________________________ 53**|
|**Better Educated Labor Force _________________________________________ 55**|
|**Flat Returns________________________________________________________ 57**|
|**Sluggish Demand____________________________________________________ 59**|
|**Recovery without Wage Growth _______________________________________ 61**|
|**_VII. Conclusion _______________________________________________________ 62_**|
|**Taking Stock _______________________________________________________ 62**|
|**Policies ____________________________________________________________ 63**|
|**_References ___________________________________________________________ 64_**|

3
Table 5: Summary statistics by migration path

||Did not|move for|college|Mov|ed for co|llege|
|---|---|---|---|---|---|---|
||Stayed<br>(1)|Move<br>bigger<br>city<br>(2)|Move<br>smaller<br>city<br>(3)|Stayed<br>(4)|Move<br>bigger<br>city<br>(5)|Move<br>smaller<br>city<br>(6)|
|_Individual and parental characteristics_|||||||
|Standardized Saber 11|0.967|0.846|0.750|1.077|0.854|0.758|
|Female|0.587|0.554|0.552|0.556|0.508|0.514|
|Very high household income|0.226|0.160|0.139|0.233|0.128|0.199|
|<br>High household income|0.208|0.168|0.170|0.181|0.173|0.166|
|<br>Moderate household income|0.229|0.205|0.226|0.179|0.182|0.198|
|Low household income|0.279|0.331|0.333|0.264|0.321|0.289|
|Very low household income|0.058|0.136|0.131|0.143|0.196|0.147|
|<br>Mother, college degree|0.313|0.255|0.223|0.360|0.256|0.299|
|Mother, associates degree|0.221|0.195|0.200|0.180|0.188|0.181|
|Mother, high school degree|0.276|0.306|0.307|0.248|0.295|0.280|
|Mother, less than high school degree|0.190|0.243|0.270|0.211|0.261|0.241|
|Attended a public high school|0.362|0.558|0.532|0.538|0.666|0.570|
|_Lifetime city sizes_|||||||
|<br>In a big city during high school|0.691|0.111|0.407|0.041|0.003|0.027|
|<br>In a medium city during high school|0.187|0.415|0.251|0.179|0.097|0.149|
|<br>In a small city during high school|0.122|0.474|0.342|0.780|0.900|0.825|
|<br>In a big city during college|0.703|0.117|0.419|0.733|0.185|0.477|
|<br>In a medium city during college|0.190|0.424|0.255|0.171|0.449|0.294|
|<br>In a small city during college|0.107|0.459|0.326|0.096|0.366|0.229|
|<br>In a big city during work|0.707|0.803|0.048|0.733|0.833|0.044|
|In a medium city during work|0.188|0.114|0.118|0.170|0.115|0.171|
|<br>In a small city during work|0.105|0.083|0.834|0.097|0.052|0.785|
|_Higher education_|||||||
|Attended a public college|0.343|0.583|0.544|0.327|0.626|0.409|
|Tier 1 (highest quality)|0.411|0.345|0.381|0.469|0.419|0.389|
|Tier 2|0.222|0.238|0.214|0.196|0.166|0.196|
|Tier 3|0.171|0.143|0.146|0.156|0.130|0.171|
|Tier 4|0.124|0.141|0.131|0.110|0.166|0.122|
|Tier 5|0.071|0.134|0.128|0.070|0.120|0.122|
|Arts major|0.054|0.026|0.021|0.041|0.023|0.034|
|<br>Education major|0.118|0.135|0.250|0.072|0.130|0.130|
|<br>Social science major|0.185|0.150|0.168|0.200|0.149|0.207|
|<br>Economics & business major|0.313|0.273|0.178|0.292|0.181|0.217|
|<br>Engineering major|0.295|0.367|0.311|0.353|0.449|0.354|
|<br>Math & science major|0.024|0.031|0.043|0.030|0.045|0.032|
|<br># of public colleges within 50km|4.406|1.534|3.177|1.088|0.687|0.882|
|<br># of private colleges within 50km|15.494|3.514|10.364|2.080|0.632|1.361|
|Log wages|14.086|14.156|14.157|14.183|14.181|14.070|
|Observations|142,361|18,350|12,796|21,218|5,810|11,488|

12
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facts. At the same time the model is designed to be flexible enough to allow for alternative effects of disasters concerning the first group of stylized facts, trying to identify conditions that could be at the source of the empirical ambiguities and lack of robustness of existing empirical analyses.

We alternatively consider two types of economies: (A) A “ **poor”** economy where the capital market is not well developed and where only the state is allowed to invest in human capital. The main implication of the former assumption is that the household-producer and enterprise sectors are only connected via the labor market. Capital flows between the two sectors are not possible or are severely restricted. (B) A _“_ **middle-income”** economy which is identical to the poor one except that the capital market is assumed to be well developed and that the human capital sector is open to the for-profit private sector. It turns out that these two distinctions between poor and middle income are of great significance for the evaluation of the effects of shocks affecting the economy, including the effects of disasters<sup>6</sup> .

A main reason to allow for this dichotomy is based on recent empirical evidence which suggests that the impact of disasters is linked quite closely to the existence of capital markets. Micro studies of rural households by Carter et. al. (2007), Gitter and Barham (2007), and Jayachandran (2006), for example, have found that when capital markets are well developed the economic effects of disaster shocks tend to be smaller than when they are imperfect or non-existent.

The model considers the existence of three productive sectors in the economy, a _household-producer sector_ , an _enterprise sect_ or, and a _sector producing human capital._ The _household-producer sector_ has two characteristics: (i) it produces natural resource-intensive commodities using labor-intensive technologies; (ii) the household-producers are generally poor, many of them operating near a subsistence threshold caused by non-convexities associated with the existence of minimum subsistence consumption limits and/or with ecological irreversibility limits.

> 6 As can be seen in Table 11, there is a great diversity among developing countries on the participation of the private sector in the provision of education, from levels below 7% of the total school enrollment in countries such as Perú to over 33% in Korea.

12
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