rafmacalaba/datause-encoder-data huggingface dataset:
The results show that a significant proportion of households changed the structure of employment within the household. However, there is only weak evidence that these shifts were associated with better poverty outcomes. About a quarter of households modified the types of employment that their members did (Table 24). The households that became non-poor are the most likely to have changed the types of employment of household members. They are statistically significantly more likely than non-poor households to have done so. While one-third of the former households changed employment type, only a quarter of non-poor households changed employment type.

**Table 24: Type of employment by household poverty** **<u>(2006-2008)</u>**

|Change in type of employment|Non-poor in<br>both 2006 &<br>2008|Poor in<br>both 2006<br>& 2008|Non-poor in<br>2008 and poor<br>in 2006|Poor in 2008<br>and non-poor<br>in 2006|total|
|---|---|---|---|---|---|
|1. No change|**74.3**|**72.0**|**64.8**|**69.4**|**73.4**|
|2. FROM Wage+self-employment TO<br>self-employment only|**8.2**|10.6|12.1|**13.0**|**8.8**|
|3. FROM wage work only TO self-<br>employment only|0.2|0.7|1.1|0.0|0.3|
|4. FROM wage work only TO|1.7|0.9|0.8|0.7|1.5|
|Wage+self-employment||||||
|5. FROM self-employment only TO|0.6|0.3|0.0|0.0|0.6|
|wage work only||||||
|6. FROM self-employment only TO|8.4|**12.1**|**11.7**|11.1|9.0|
|Wage+self-employment||||||
|7. FROM Wage+self-employment TO|3.9|1.4|6.2|3.9|3.8|
|wage work only||||||
|8. FROM no economic activity TO work|0.9|0.0|0.3|0.0|0.8|
|9. FROM work TO no economic activity|1.7|1.9|2.8|2.0|1.8|

_Source: VHLSS 2006 and 2008._ The most common changes that households do are to shift between having members working in wage work and in self-employment, to having workers working in selfemployment only and vice versa. This is true for poor and non-poor households. Nonpoor households are the least likely to go from a situation where members work both in wage work and self-employment to a situation where they only work in self-employment. Households that became poor are the most likely to have had this strategy, while households that were poor in both years are most likely to have had the opposite strategy (although these differences are not statistically significant).

38
To be consistent with the Philippines impact evaluation sample of poor households, we restricted our analysis to rural households with total expenditure in the national bottom quartile. We also restrict to households that do not have individuals attending other school grades, or individuals over 18 attending school, to limit the probability that we are picking up other household education expenditures in the regression. The regression does not include a constant, as we assume that the cost of education is zero if no one in the household attends school. The estimated monthly household expenditure on education increases by US$3 for every child in primary school and US$5 for every child in secondary school. The direct cost of education was thus substantially lower than even the minimum value of the _Prospera_ education grants.

48
The Better You Are the Stronger It Makes You: Evidence on the Asymmetric Impact of Liberalization<sup>∗</sup> Leonardo Iacovone<sup>†</sup> First Version: April 2007 Current Version: May 13, 2009

> ∗The author is thankful to Gerardo Leyva and Abigail Dur ́an for granting access to INEGI data at the offices of INEGI in Aguascalientes under the commitment of complying with the confidentiality requirements set by the Mexican Laws. I also wish to thank each one of the INEGI’s employees that helped me during the work at Aguascalientes, in particular Gabriel Romero and Alejandro Cano. I am grateful to Alan Winters and Gustavo Crespi for their support and guidance. Special thanks also to Gerardo Esquivel, Caroline Freund, Rafael De Hoyos, Beata Javorcik, Hiau Looi Kee, Eric Verhoogen, Carolina Villegas, the participants at the conference on “Welfare Effects of Trade Liberalization in Less Developed Countries” at An ́ahuac University, ELSNIT 2007 conference in Barcelona, World Bank Research Group Trade seminar, and the participants to the Conference on “Organization and Performance: Understanding the Diversity of Firms” in Tokyo for their useful comments. Finally I gratefully acknowledge the financial support from ESRC and LENTISCO. The views expressed in this paper are those of the author and should not be attributed to the World Bank, its Executive Directors or the countries they represent.

> †Development Research Group, World Bank, 1818 H Street, NW; Washington, DC, 20433, USA. Email: Liacovone@worldbank.org

1
# **3 Survey Description**

This section describes the survey and the population in detail, emphasizing the research design used to identify and label debt aversion.

We designed the survey to refine our understanding of the sources of debt aversion in students. In particular, our design allows us to disentangle debt aversion due to the description of the contract (i.e., framing effect) from labeling debt aversion, that is, aversion due to the labeling of the contract. Previous work has not disentangled these two effects, and therefore their results reflect the combined effect of framing and labeling effects.

We conducted the survey by contacting via email over 3,000 people in Chile, Colombia and Mexico. This population had applied to get education financing, and about 1,800 of them had received financing. Out of these people, 1422 of them responded to the survey. The students who responded the survey were automatically assigned to either the treatment or the control group through a random algorithm.

After filling a form with demographic and current status information, the survey followed with 15 questions. The order of the questions was the same for all participants. The key questions for the purpose of this survey were questions 1 through 7.<sup>5</sup> Out of the 1422 respondents, only 767 responded the eight relevant questions, and throughout the paper we focus on this sample.<sup>6</sup> The appendix contains the translated text of the survey’s most relevant questions. Respondents in each country answered slightly different surveys that reflected the relative costs of education in each country. The amounts were translated into local currencies, and the size of hypothetical loans and payments were adjusted to reflect realistic contracts given each country’s own reality. Whereas students in Colombia typically need US$3,000 to finance a year of education, a Chilean needs US$6,700 and a Mexican US$4,150. The surveys for each country reflected these differences. The parameters used for each country can be found in table 9. The survey was conducted in Spanish and the original text is available upon request.

## **3.1 Survey Questions and Identification Strategy**

### **3.1.1 Questions 1–3: Choices between Contracts**

To explore the presence of debt aversion due to the description of the contract, we presented respondents with financially equivalent contracts framed differently (i.e., question 1). The

> 5The order of the questions in the survey did not follow the order in which we present them here. The survey asked the questions that presumably required higher effort at the beginning.

> 6We provide evidence in the next section that this subsample is reasonably balanced with respect to observables across treatment status. This is expected, as the difference in the questions for treatment and control groups are not likely to imply any difference in the costs of responding to the survey.

6
the “flat” test score after 60 months could be due to the fact that the minimum normed score is held constant from 60 to 71 months, and an increasingly large fraction of children are censored. Figure 3 graphs out the age effects from the full-sample OLS, censored normal and CLAD models. Coefficients have been normalized to zero at the youngest age. The results suggest that the patterns we observe—declining test scores from 36 to 57 months, and slight increases thereafter—are not driven by censoring. The censored normal and CLAD estimates are similar, and show a slightly smaller decline with age from 36 to 57 months than the OLS estimates. However, these results indicate that, all else equal, the test scores of 5-year-olds are roughly 10 points lower than those of 3-year-olds.

A plausible explanation for this decline is that the effects of SES on child cognitive development are cumulative, so that poorer children fall increasingly behind their wealthier counterparts.<sup>16</sup> To investigate this hypothesis, we split the sample into younger children—those aged 36 to 53 months at the time of the survey—and older children—aged 54 to 71 months, and re-estimate the models in Table 3. The results in Table 4 indicate that the associations between several measures of SES and cognitive development become larger with age. The coefficients on wealth and maternal education are more than twice as large for the sample of older children, and tests of the equality of coefficients (not shown) indicate that this difference is significant at the 1 percent level. The coefficients on the rural dummy, maternal age, and paternal education also tend to be larger for the older children, although these changes are generally not significant. <u>Health, parenting and cognitive development : We next examine how child health and</u> parenting quality are related to test scores. We focus on how much of the associations between cognitive development and SES can be explained by the inclusion of health and parenting measures. It seems plausible that health and parenting are routes through which wealth and parental education influence children’s cognitive development. Wealth is correlated with both measures of child health and parenting. For example, the correlation coefficient between our wealth index and the child’s hemoglobin level is 0.24; correlations between wealth and other key measures are 0.13 (height-for-age); 0.08 (weight-for-age); -0.20 (HOME score); and 0.39 (whether the child is read to). Similar correlations exist between parental education and measures of child health and parenting.

> 16 Similarly, Case, Lubotsky and Paxson (2002) use data from the US to show that associations between family income and child health increase as children age.

16
ˆ _Table 5: Elasticity estimates (_  ) _between HR and a disaster type, with respect to the average number of_ _<u>people affected per capita.</u>_

|Specific|Ea|rthqua|kes||Storm|s|D|rough|ts||Flood|s|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|-tion|ˆ||# of<br>Years|ˆ||# of<br>Years|ˆ||# of<br>Years|ˆ||# of<br>Years|
|1|0.005|**|2|--|||--|||--|||
|2|0.007|**|2|--|||--|||--|||
|3|0.004|**|2|--|||--|||--|||
|4|0.008|***|2|0.006|*|2|0.019|**|2|0.006|**|1|
|5|0.038|**|3|0.007|*|1|0.022|**|3|0.005|***|1|
|6|0.088|**|4|0.020|**|4|0.018|**|4||||
|7|0.092|***|5|0.031|**|5||||0.060|**|5|
|<br>_Average_<br>|<br>_elasticity_|<br>_given_|<br>_duration_|<br>_of statis_<br>|<br>_tically_<br>|_significant_|_impacts_|||<br>|<br>||
|1 year||||0.007||||||0.006|||
|2 years|0.006|||0.006|||0.019||||||
|3 years|0.038||||||0.022||||||
|4 years|0.088|||0.020|||0.018||||||
|5 years|0.092|||0.031||||||0.060|||

Notes for Table 4 and Table 5: Numbers are rounded off to the nearest thousandth. All reported elasticities are significantly different from zero at the one (*), five (**) or ten percent (***) level of confidence. . “# of Years” refers to the duration that the impacts of a disaster are felt. For example, 2 years represent periods _t_ and _t-1_ . Elasticities corresponding to more than a year of duration mean that the total impacts of periods _t_ and _t-n_ are statistically significant _._ “--" means elasticity estimate is statistically insignificant, hence it is not reported. The different model specifications are provided in Appendix 2.

# **_Elasticity estimates of impacts of natural disasters on land resource values_**

We also estimated the _percentage reduction_ in the value of land resources resulting from a one percent increase in the average number of people affected by a natural disaster. Table 6 and Table 7 present the elasticity estimates with respect to the average number of people affected, and as regards the average number of people affected per capita, respectively.

ˆ _Table 6: Elasticity estimates (_  ) _between land resource values and a disaster type, with respect to the_ _<u>average number of people affected.</u>_

|Model|Ear|thqua|kes|S|torms||Droughts||Floods||
|---|---|---|---|---|---|---|---|---|---|---|
||ˆ||# of<br>Years|ˆ||# of<br>Years|ˆ<br># of<br>Years|ˆ||# of<br>Years|
|1|0.110|*|5|0.110|***|5||0.372|***|5|
|2|0.110|*|5|0.112|***|5||0.377|***|5|
|3|0.093|*|2|0.067|***|2|||||
|4|0.098|*|2|0.073|**|2|||||
|5|0.093|*|2|0.057|***|2|||||
|_Average_|_elasticity_|_give_|_n duration_|_of statis_|_tically_|_significan_|_t impacts_||||
|2 years|0.095|||0.066|||||||
|5 years|0.110|||0.111||||0.375|||

11
**Table 6: Regression coefficients of all farms, dryland farms and irrigated farms with regional dummies**

|**Variable**|**All farms**|**Dryland**|**Irrigated**|
|---|---|---|---|
|Winter temperature|-173.6**|-106.7|-93.5|
|Winter temp squared|6.1**|3.9*|4.9|
|Spring temp|115.1|-82.8|58.7|
|Spring temp sq|-5.0**|-0.3|-4.1|
|Summer temp|173.9**|198.6**|827.5**|
|Summer temp sq|-1.9|-3.2*|-13.1*|
|Fall temp|-98.1|-92.4|-824.2*|
|Fall temp sq|1.1|1.5|15.3*|
|Winter precipitation|-2.9*|-1.9|5.8|
|Winter prec sq|0.0**|0.00|0.00|
|Spring prec|3.5*|3.6**|-10.6|
|Spring prec sq|-0.001|-0.011*|0.091*|
|Summer prec|3.4**|1.9*|21.4**|
|Summer prec sq|-0.012**|-0.005|-0.086**|
|Fall prec|-0.5|-0.6|-14.7**|
|Fall prec sq|0.0055*|0.0053*|0.0586***|
|Mean flow|9.4**|-5.4|8.8**|
|Farm area|-0.1**|-0.3**|-0.0**|
|Farm area sq|0.0*|0.0**|0.0*|
|Elevation|0.035|-0.0009|0.229|
|Log (household size)|22.9|10.1|62.4|
|Irrigate (1/0)|237.5**|||
|Household access to||||
|electricity (1/0)|66.6**|47.7**|233.2*|

31
to GDP for countries in the region. By contrast, Caribbean countries appear “under-urbanized.” However, this result is driven by two extreme outliers – Barbados and Trinidad and Tobago.<sup>39</sup> When we turn to the results using the AI based on the _LandScan 2012_ population data, however, the estimated coefficients on the Latin American and Caribbean dummies are much smaller in magnitude and statistically insignificant (see, in particular, column (5) in Tables 2 and 3). As a consequence, Latin American countries, as a group, no longer appear “over-urbanized.” Likewise, the Caribbean countries no longer appear “under-urbanized.” This can also be seen in Figure 3, which shows the scatterplots that correspond to the regressions in column (4) of Tables 2 and 3. In particular, comparing Figure 3 with Figure 1, one can see that the Latin American countries are no longer systematically positioned below the fitted line, but, rather, are more evenly distributed around it. Meanwhile, for the Caribbean countries, Barbados and Trinidad and Tobago, no longer have the appearance of outliers.

**<u>Table 2: Regression results for relationship between ln(GDP per capita) and urbanization</u>**

|||**WUP data**||**AI**|**–****_LandScan 2_**|**_012_**|
|---|---|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|
|Urban pop. share|<br>4.215***|<br>4.338***|<br>4.254***|4.050***|4.102***|4.077***|
||[14.70]|[15.21]|[14.64]|[12.36]|[12.20]|[12.27]|
|LA_dummy|<br>|-0.386**<br>[-2.090]|||-0.049<br>[-0.237]||
|C_dummy|<br>|0.595*<br>[1.934]|||-0.267<br>[-0.753]||
|LAC_dummy<br>|<br>||-0.143<br>[-0.853]|||-0.099<br>[-0.537]|
|Constant|6.742***|6.697***|6.744***|6.685***|6.670***|6.685***|
||[38.22]|[38.67]|[38.20]|[31.48]|[31.01]|[31.40]|
|n|<br>146|146|146|146|146|146|
|R<sup>2</sup>|0.600|0.623|0.602|0.515|0.517|0.516|
|�<sup>**�**</sup><br>���|0.597|0.616|0.597|0.511|0.507|0.509|

**Dependent variable:** ln(GDP per capita); **Notes:** t-statistics in brackets.  *** Significant at 1 % level; ** at 5 % level; * at 10 % level

> 39 This is confirmed when the Caribbean dummy in the column [2] regressions in Tables 3 and 4 is replaced by two separate dummies – one for Barbados and Trinidad and Tobago, and the other for the remaining Caribbean countries. In particular, for these regressions, the Barbados and Trinidad and Tobago dummy is statistically significant with the expected sign, while the dummy for the other Caribbean countries is insignificant.

14
In those more export oriented firms the percentage of adjustment is larger both in the creation and destruction sides for blue collar labor, implying lower adjustment costs. Firms more export oriented, when their desired blue collar employment level is below the actual one, they adjust a higher share of the gap than firms oriented to the domestic market. The causality may go in the opposite direction. In order to be able to export, firms need to be more efficient, and therefore need to have more ability to adjust factor inputs.

There does not seem to be a clear significant pattern between export oriented and domestic market oriented firms with respect to capital and white collar adjustment functions.

# **5. Conclusions**

This paper intends to use micro data to improve our understanding of the effects of policy measures on the adjustment of factors of production. On the one hand, the paper finds evidence supporting a number of regularities that the previous literature on adjustment functions has highlighted.

Our investigation confirms that aggregate investment and job creation might be seen as the result of lumpy and discontinuous microeconomic decisions. Individual adjustment constraints depart significantly from the constraints implicit in the quadratic adjustment cost model. There are several sources of irreversibilities (technological, market-induced, increasing returns in the adjustment technology). The evidence provided seems to confirm a pattern that has important nonlinear features, hence consistent with such constraints. This impacts the use of all factors of production, particularly employment.

The existence of adjustment costs implies that the desired levels of white and blue collar employment and capital often deviate from the observed ones. In our data these deviations imply that the yearly gaps might be above 10%. To have an idea of the importance of the adjustment costs, it is useful to consider that for the whole period the average output gap is of 2%.

On the other hand, the paper intended to assess the effects of protection and trade liberalization on firms’ adjustment process. The constraints arising from the adjustment cost functions may become an important part of the policy analysis. Our results point to a significant shift in the adjustment functions for all the production factors before and after 1990, corresponding with significant changes in the trade openness process. The shifts point towards larger fractions of the gaps closed in the creation side, and lower in the destruction side.

Trade policy as measured by tariffs levels and their reductions also proved to significantly shift the adjustment functions. Firms in less protected sectors have shown higher adjustment fractions in the creation side and lower in the destruction side, particularly for blue collar labor. Sectors facing larger tariff changes, adjust less in the creation side, particularly for blue collars, and more on the destruction side. In the context of tariff reductions of Mercosur, those sectors more highly protected were probably those that faced the largest tariff reductions.

16
the precision of these estimates is increased when the gaps in time that need to be bridged are reduced (i.e. when the frequency of NSS surveys is increased) -- as the assumptions underlying the imputation methods come under increasing pressure when the most recent household consumption survey becomes increasingly outdated.

# **References**

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- Beegle, Kathleen, De Weerdt, Joachim, Friedman, Jed and Gibson, John (2012). Methods of household consumption measurement through surveys: Experimental results from tanzania. _Journal of Development Economics_ , **98,** number 1, 3–18.

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64
cost minimization problem is subject to the following constraints for every time interval throughout the planning horizon:

- Total energy demand is equal to the sum of generation and non-served energy.

- Available capacity equals existing capacity plus new capacity minus retired capacity.

- Power generation does not exceed the maximum and minimum output limits of a unit.

- Power generation by individual units is constrained by their ramping rates or limits.

- Reserves are committed every hour to compensate for forecasting errors.

- Renewable generation is constrained by their resource profile (i.e., the hourly rate of availability).

- Cross-border power flows are constrained by transmission network topology and transmission line thermal limits.

We use the exogenous forecasts for the peak load and most recent (2018) data for hourly load profiles. If the electricity supply is lower than the demand at a given time slot, it is referred to cost of energy not served or value of the lost load. This situation arises when supply is consistently lower than demand, thereby creating a situation of chronic outages (or load shedding) or due to scheduled or unscheduled outages. There is no universally acceptable value of lost load. Typical values used in developed economies range between $4,000 and $40,000/MWh while in developing countries it ranges between $1,000 and $10,000/MWh. In practice, it is determined through contracts with special customer groups that are willing to reduce their demand during peak hours, or with grid elements that can supply electricity. Grid elements of this category are emergency generators located in critical infrastructures and public facilities such as hospitals and government buildings, etc., to supply back-up power in case of blackouts. Typically, back-up generators are fueled with expensive fuel, diesel, and the value of the variable cost of diesel generators (~$500/MWh) is often used as the value of lost load or cost of non-energy services.

To enhance the reliability of a power system, a provision of the operational reserve is the norm. An operational reserve has two parts: (a) spinning reserves and (b) non-spinning reserves. The first refers to generators running at no load and ready to serve additional demand when needed. The second is generating units that can be started quickly (e.g., gas turbine, hydro, diesel engine) when there occurs an emergency demand due to the failure of some generation units in the system.

7