rafmacalaba/datause-encoder-data huggingface dataset:
17

||Generalized Va|riance Decompo|sition of**Industria**|**l Output- Vene**|**zuela**|
|---|---|---|---|---|---|
|||<br>Percentage of th<br>|<br>e Variance Associ<br>|<br>ated with Shock<br>|<br>to:<br>|
|Horizon<br>(in<br>months)|Terms of<br>Trade|Spreads|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|10|7|2|7|88|
|4|19|_5_|19|11|65|
|8|29|8|20|20|50|
|12|27|14|16|17|54|
|16|21|14|13|13|62|
|20|18|13|15|11|62|
|24|17|13|16|11|60|
||<br>Generalized V|<br>ariance Decompo|<br>sition of**Industri**|**al Output- Ecu**|**ador**|
|||Percentage of th<br>|e Variance Associ<br>|ated with Shock<br>|to:<br>|
|Horizon<br>(in<br>months)|Terms of<br>Trade|Spreads|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|11|3|9|3|99|
|4|35|3|7|6|82|
|8|36|13|6|12|62|
|12|30|17|17|15|55|
|16|34|17|17|13|44|
|20|28|14|14|13|35|
|24|24|14|26|17|34|

factors as being the main sources of output variability<sup>1 8</sup> . This last classification does not seem unreasonable, given that we omitted nominal variables and fiscal policy variables which might have been important determinants of short run output fluctuations<sup>19</sup> These results are in line with those reported by Hausman-Gavin (1995), Hoffmaister- Roldos (1997), and Ahmed (1999) according to which domestic factors were the main sources of output variability in Latin America, even though our sample period and data sets are different.

As expected, growth rates react differently to similar shocks across countries. For example, shocks to the external capital markets play a major role explaining the variance of output growth in Brazil, Mexico and Argentina, while in Venezuela they are the least important factor with an almost negligible effect: in Brazil, Argentina and Mexico these innovations explain about fifty percent (50%) of output variance, while in Venezuela they account for less than 15%. This is a reasonable result given Venezuela's low external financing requirements. Colombia, and Chile are intermediate cases where there are other dominant factors that account for output variance.

1 The approach of associating "own" output shocks with domestic factors, common in the literature, has been criticized by Kamin and Babson (1999).

'9 The reason for not having included nominal variables are a combination of a) Results of previous papers, such as the Hausman-Gavin (HG) and Hoffinaister-Roldos (HR) in which nominal shocks are absent (HG) or their effects are on output variability is small (HR). b) Data availability imposed degrees of freedom restrictions, limiting the option of including additional variables.
the information system of _Familias en Acción_ . The first research design employs matching techniques to compare the school completion rates and test scores of different cohorts of children from treatment and control areas that could have finished high school during the program implementation period of 2003-2009 and were interviewed prior to the initiation of the program. The second design exploits variation in assignment to treatment arising from the sharp discontinuity that emerges at the eligibility threshold defined to participate in the program. Household are assigned a poverty index score from a census of poor people, which determines their eligibility into different social programs (including FA) with different thresholds.

We show that the program helps participant children to increase their school attainment by making them more likely to complete high school. Results from the first empirical approach (matching analysis) indicate that program effects vary between 4 and 8 percentage points. The RDD (Regression Discontinuity Design) <spell out RD> approach yields estimates of program effects on school completion that are similar in direction and magnitude. Overall, focusing on the preferred specifications, we estimate that beneficiary children that belong to households near the threshold of eligibility are between 3 and 6.5 percentage points more likely to graduate from high school. There is also evidence of heterogeneity in program impacts, with effects on school completion being larger for girls and beneficiaries in rural areas. Regarding long-term impacts on tests scores, the analysis shows that program recipients who graduate from high school perform at the same level as equally poor non-recipient graduates in Mathematics, Spanish, or the overall test. This result still holds after correcting for possible selection bias when low-performing students enter school in the treatment group. Finally, there is no indication of indirect program effects on the school completion of ineligible older children residing in the same households as participant children. The results are robust to a variety of controls for observable differences between participants and non-participants, the possibility of sorting around the threshold of eligibility or manipulation of the proxymeans test used to allocate the program, misspecification bias, and differences in the accuracy of data that could lead to spurious differences in test registration between treated and control children.

4
# Table 7. Consumption of Major Food Products, 1989

(kg/capLta)

||Meat<br>converted<br>to<br>meat|Milk<br>and<br>dairy<br>products<br>converted<br>to<br>milk|Sugar<br>converted<br>to<br>white<br>sugar|Cereals<br>converted<br>to<br>flour|Vegetable<br>converted<br>to<br>fresh|Eggs<br>(pieces)|
|---|---|---|---|---|---|---|
|Bulgaria|79.3|275.0|35.0|146.0|136.0|136.0|
|Hungary|78.2|134.0|34.0|108.0|88.0|315.0|
|GDR|100.2|n/a|41.4|90.0|106.0|106.C|
|Poland|76.1|415.0|46.2|119.0|115.0|115.0|
|Romania|52.0|132.6'|20.3a|17 5 <sup>. </sup>0 <sup>b</sup>|113.0'|n/a|
|Czechoslovakia|88.0|253.0|40.4|113.0|81.0|345.0|
|USSR|67.0|363.0|42.5|129.0|9.5|268.0<br>t|

n/a - Not available.

a. 1985 data.

b. 1980 data.

> _Source: CUBA Yearbook,_ Moscow, 1990.
Table 3: Classroom and Teacher Effects

||(1)<br>**M**|(2)<br>**ath**|(3)<br>**En**|(4)<br>**glish**|(5)<br>**U**|(6)<br>**rdu**|(7)<br>**Av**|(8)<br>**erage**|
|---|---|---|---|---|---|---|---|---|
||Class|Teacher|Class|Teacher|Class|Teacher|Class|Teacher|
|Full Sample|0.321|0.258|0.300|0.190|0.312|0.184|0.311|0.211|
|Restricted Sample|0.308|0.252|0.277|0.285|0.275|0.270|0.287|0.269|
|Public Schools Only|0.356|0.199|0.337|0.134|0.351|0.152|0.348|0.162|

This table reports the effect of moving to a 1sd better classroom and the effect of moving to a 1sd better teacher on students’ test scores in math, English, and Urdu. Test scores have been standardized to have a mean of 0 and a standard deviation of 1. In the first row, the sample includes all students and teachers in public and private schools. In the next row, the sample only includes classrooms where at least 75 percent of students had not been taught by the same teacher in the previous year. In the final row, the sample is restricted to students and teachers in public schools.

36
Table 1: Balance on Pre-Treatment Covariates

|Variable|Non-PVP|PVP|Dif.|Norm. Dif|P-value|
|---|---|---|---|---|---|
|No. of HH|657.871|736.042|-78.171|-0.143|0.443|
|Percent SC|0.378|0.343|0.034|0.173|0.569|
|Percent ST|0.012|0.010|0.002|0.063|0.525|
|Female Literacy Rate|0.592|0.573|0.019|0.229|0.591|
|I(Primary School)|0.980|1.000|-0.020|-0.200|0.421|
|I(Secondary School)|0.360|0.200|0.160|0.359|0.640|
|I(Health Center)|0.240|0.280|-0.040|-0.090|0.464|
|I(Hospital)|0.040|0.040|0.000|0.000|0.500|
|I(Clinic)|0.040|0.060|-0.020|-0.091|0.464|
|I(Medical Shop)|0.220|0.220|0.000|0.000|0.500|
|I(Big Gov’t Hospital)|0.040|0.020|0.020|0.116|0.546|
|I(Bank)|0.900|0.960|-0.060|-0.169|0.433|

_Note:_ Te table presents differences in means on relevant pre-treatment covariates between PVP and Non-PVP Villages. Following Imbens and Wooldridge (2008), normalized differences and associated _p_ -values are presented.

Te probability of being treated, which in our case is the probability of being a PVP village, lies between zero and one. Figure 2 shows that there is a good overlap in the propensity score distribution across project and non-project VPs. To impose common support, we limit the comparison to a sub-sample of observations where the propensity score is more than the minimum value in the treatment group and is less than the maximum value in the control group. For our data, the region of common support is given by (.074, .86). Te final village sample thus consists of 100 matched villages, 50 in control and 50 in treatment.

# _3.2 Data Collection_

From this matched sample, we collected two forms of data: (1) full audio recordings of the _gram sabha_ , and (2) a standardized questionnaire to collect information on the atendance of citizens and local officials, on the nature of issues raised by citizens, and demographic data on who raised these issues (gender and caste). Tis survey data also included a roster of state and local government officials in atendance, how information on the timing of the _gram sabha_ was communicated, the physical location of the assembly, and atendance at regular intervals. Te audio recordings 13
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|---|---|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Rural Electrification and Renewable EnergyDevelopment II(RERED|JoonkyungSeong|
|Support city-wide upgrading process in 20 small and medium cities|Andrea Zeman|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|E-Health Project|Dinesh M. Nair|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National CommunityEmpowerment Program In Urban Areas For 2|0George Soraya|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|National SafetyNet Program for Results|Yuliya Smolyar|
|Support to UNCCD NAP alignment and reporting process|Ulrich Apel|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
|Senegal River Basin Climate Change Resilience Development Projec|tMarie-Laure Lajaunie|
there is greater expected rainfall variability but that this response is attenuated as wealth increases. To assess how changes in weather variability affect profit variability via the portfolio response, and how this relationship changes with wealth level, we used the inherited-wealth coefficients in Table 5 to predict r for the range of values of the standard deviation in the monsoon onset date in the six ICRISAT villages, from 9.4 to 25.1 weeks, and for quintiles of the wealth distribution. These predicted rts were then used to compute profit variability induced by rainfall, measured by the CV of profits (expressed as a percentage). The CV of profits at each wealth level is based on the relationship (3) between the standard deviations in profits and weather. Dividing both sides of (3) by profits yields: (10) CVI = Rr(a,, where R is the inverse of the profit rate expressed in terms of total wealth _(11/W)._ We fixed the profit rate at the sample mean of 0.11.

Figure 2 displays the predicted relationships between the onset standard deviation and the profit CV for four farmer wealth classes. As can be seen, for farmers at wealth levels below the top 20 percent, increases in rainfall variability increase the variability of profits less than proportionally, with the least wealthy farmers attenuating the effects of weather risk to the greatest extent as weather risk increases. The top 20 percent of farm households, however, are evidently able or willing to completely absorb all rainfall-induced profit risk, as reflected in the linearity of the profit-rainfall variability plot for this wealth group. As a consequence of the differential response of portfolio riskiness by wealth, differences in profit variabilit, by wealth class widen as rainfall variability increases. While the rainfall-induced CV in profits is approximately the same for all wealth classes at the sample average of the onset standard deviation (13.6), at a rainfall standard deviation of 19, for 26
least partly driven by liquidity constraints: Households depleted their stocks of durable goods in order to free up resources for essential consumption.

Corbacho et al. (2003) use panel data constructed from urban household surveys to examine the impact of the **1999-2002 Argentinian crisis** , which included a freeze on bank deposits in 2001. Their analysis shows that households experienced a substantial decline in per capita income between 1999 and 2002 with a particularly strong decline in 2001/2002 (-28%). Contrary to the Mexican crisis, the poor were much stronger hit by this fall in income than the rich. The labor market channel seems to have played a key role, with higher unemployment among low-educated households, especially those which were active in the construction sector.

Smith et al. (2002) use data from income and employment surveys as well household panel data to examine the impact of the **1998 crisis in Indonesia** which was ignited by the collapse of the Rupiah in January 1998 **.** Their analysis shows that also in this crisis households were strongly impacted through the labor market channel. Aggregated employment saw only a small decline, albeit with a significant reallocation of labor from the wage-sector to the self-employment sector. Falling real wages due to spiraling inflation were the main source of income shocks for households. This fall in wages (by roughly 40% between 1997 and 1998) seems to have affected rural and urban households similarly as well as households across all income and education groups. By contrast the wages of self-employed males in the rural areas declined much less than in the urban areas, most probably due to the relative price increase of agricultural products (product market channel). Family-level incomes fell much less in rural areas than in urban areas, while incomes declined more for poor households as more of their family members transitioned from paid jobs to unpaid family labor.

Goh et al. (2005) examine the impact of the **1997/1998 crisis in Korea** on household income and consumption.<sup>8</sup> They show that real household income decreased on average by 24% between 1997 and 1998, while household expenditure fell even more (-29%). Households reacted to the crisis by strongly cutting expenditure on non-essential goods (-69%). Spending on food, clothing and housing as well as on health and education was also cut, but by a much smaller degree. Rural households and poor households seem to have been more vulnerable to the crisis; reducing essential spending more than urban and high-income households. The authors further show that prior to the crisis Korean households used formal and informal credit to smooth long-term income patterns. During the crisis the access to formal credit seems to have dried up (credit channel), while informal credit helped households cushion income shocks.

> 8 See also Kang and Sawada (2008)

11
supply response (e.g. labor supply to market work is ...xed) but there is heterogeneity in the technology of the locally produced market good (food) between the households. Suppose that the labor de...cit households use mechanized technology for rice cultivation so that productivity shock has no impact on its labor demand. On the other hand, labor surplus household uses more labor intensive technology and a rise in agricultural productivity increases its labor demand. Such heterogeneity can lead to higher wages and lower hired labor as surplus households reallocate labor to own farming in response to agricultural productivity increase, and thus the supply of labor to the market goes down. It is however important to appreciate that if the labor market response is driven by reallocation within the market goods sector, then we should not observe any change in the total labor supply to market work. We present a formal test of this prediction later in the paper. We emphasize here that there are good reasons to suspect that such technological heterogeneity in food production cannot drive the behavior of wages and hired labor in the context of Bangladesh. Because the level of mechanization is low in Bangladesh and there is very little heterogeneity in the technology used in production of any particular variety of rice across the country.

We provide direct evidence below that a positive rainfall shock induces the households to increase total labor supply to market activities as measured by hours worked in column 4 of Table 3.<sup>22</sup> The dependent variable here is log of per capita hours spent on market work. The results indicate a statistically signi...cant and positive impact of rainfall on hours spent on market work. This provides strong support to the conclusion that households allocate

> 22As in most households surveys in developing countries, HIESs have no data on time spent on home production over the time period considered here (2000-2010). We, however, have data on hours worked on market activities (including own farming). If households indeed reallocated labor from home activities to market work, then one would expect a rise in hours devoted to market work in response to agricultural productivity shock. From the information on employment and hours spent on each activity provided by the HIESs, we compute the per capita hours spent on market work.

27
# **Table A5: Water Shortages and Capital and Labor use**

|**Model**|**Probit**|**Probit**|**OLS**|**OLS**|
|---|---|---|---|---|
|**Dependent Variable**|**Firm uses machinery**<br>**in its current**<br>**operations Y/N**|**Firm uses vehicles**<br>**in its current**<br>**operations Y/N**|**Log of total**<br>**number of**<br>**employees in**<br>**regular month**|**Manager**<br>**experience (years)**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Total duration of water shortages last month<br>(in days)|-0.005|-0.010|-0.007|0.065|
||(0.019)|(0.036)|(0.006)|(0.129)|
|Log of total number of employees in regular<br>month|0.201|0.532***||1.544***|
||(0.160)|(0.107)||(0.542)|
|Largest owner has secondary education or<br>higher: Y/N|0.434***|0.013|0.006|-1.831**|
||(0.119)|(0.151)|(0.058)|(0.862)|
|Manager experience (years)|0.018***|-0.000|0.007***||
||(0.006)|(0.005)|(0.002)||
|Largest owner has a job in formal sector or<br>looked for one over last 2 years Y/N|-0.238**|-0.118|0.175***|-0.826|
||(0.099)|(0.149)|(0.053)|(0.513)|
|Largest owner started another business in the<br>last three years Y/N|-0.148|0.051|0.027|-2.248***|
||(0.106)|(0.154)|(0.032)|(0.630)|
|Firm uses electricity for activity Y/N|0.819***|0.217|0.023|-0.374|
||(0.157)|(0.207)|(0.059)|(0.773)|
|Manufacturing firm Y/N|0.557***|0.111|0.060**|0.657|
||(0.116)|(0.095)|(0.029)|(0.403)|
|Produces or sells under contract from another<br>business or person Y/N|0.106|-0.145|0.197***|0.954|
||(0.119)|(0.148)|(0.062)|(1.019)|
|Firm uses bank account to run business Y/N|0.146|0.479***|0.194***|-0.055|
||(0.192)|(0.153)|(0.068)|(0.826)|
|Firm has a loan at this time Y/N|-0.040|0.159|0.151|1.408|
||(0.125)|(0.190)|(0.097)|(0.938)|
|Firm pays informal payment to remain<br>unregistered Y/N|0.486***|0.314|-0.112|-0.253|
||(0.165)|(0.250)|(0.079)|(1.343)|
|Firm experienced crime last month Y/N|-0.554***|-0.055|0.103|1.004|
||(0.121)|(0.199)|(0.103)|(1.103)|
|Firm pays for security Y/N|-0.064|-0.097|0.229***|-0.701|
||(0.073)|(0.147)|(0.075)|(0.618)|
|Firm uses machinery in its current operations<br>Y/N|||0.077|2.122***|

39
transferring wealth (in the form of an inherited property asset) from heirs to their parents who will benefit from access to the equity in the property during their lifetime.

In Hong Kong SAR, China, the RM market has failed to take off. With high property prices, an aging society, and a sophisticated business-friendly legal and regulatory environment, Hong Kong SAR, China, features promising preconditions for reverse mortgages. Several private lenders offer this financial product, and the Hong Kong Mortgage Corporation (a public entity that is part of the HK Monetary Authority) provides insurance for the lenders. The statistics it collects show that the average age of mortgagors is 69, similar to other jurisdictions. The median property is valued at ca. 680,000 USD, half of clients choose a monthly salary, and the other half opts for a lump sum payment (HKMC, 2019). But there are currently only about 3,300 RM outstanding, a minuscule market share in a jurisdiction with 2.5 million households, half of which are homeowners.

The “house rich, cash poor” phenomenon is especially salient in Singapore. The hybrid pension system of the city-state allows citizens to withdraw a significant portion of their retirement savings to fund housing purchase. This has led many to make sub-optimal savings decisions which turn them into lowincome but asset-rich retirees (McCarthy, Mitchell, & Piggott, 2002; Sing & Stewart, 2018). RM would serve to address this imbalance in principle, providing Singaporeans with the option to recover their pension income from housing equity. However, none of the (few) studies consulted for this review suggests the introduction of RM.

India is one of the few developing countries where RM have been introduced – but market development has been slow. India exemplifies the kind of demographic challenges many developing economies are grappling with: urbanization goes hand in hand with the waning of traditional financial support networks in an aging society, and a significant portion of citizens are not covered by social security or health insurance. Only 10% of senior citizens, mostly government and public sector employees, enjoy a public pension. In this context, RM were introduced in India in 2007. House owners over 60 years of age are eligible, and regulations allow lenders to extend RM for a period of up to 15 years and up to 60% of the home value (Agadi & Mamdi, 2012). Unlike the United States, Indian RM do not appear to come due when the homeowner moves away, only upon death (Agadi & Mamdi), although other studies provide somewhat contradictory accounts (Gupta & Kumar, 2017, 2018; Ray & Choudhuri, 2013). Over the years, uptake of RM has been slow. Gupta and Kumar (2018) show that among eligible homeowners, the less educated, older, and people with none or a girl child are more likely to take out an RM. Interviews with Indian financial experts indicate that in addition to regulatory uncertainties and a lack of market 16