rafmacalaba/datause-encoder-data huggingface dataset:
13 Table 2: Growth accounting regressions, Instrunental variables estimates.

|||Barro-L|ee data|Nehru|-Swanson-Dube|y data|
|---|---|---|---|---|---|---|
||OLS|IV<br>(w/Nehru)|IV<br>(w/ other<br>country|OLS|IV<br>(w/Barro)|IV<br>(w/other<br>country)|
|Growth of capital<br>per worker|.458<br>(10.19)|.460<br>(10.18)|.527<br>(12.42)|_.455_<br>(10.08)|_.455_<br>(10.01)|.501<br>(9.35)|
|Growth<br>educational capital|-.091<br>(1.61)|-.120<br>(1.42)|-.088<br>(.593)|-.076<br>(1.41)|-.13<br>(1.59)|-.104<br>(2.30)|
|Number of<br>countries|70|70|77|70|70|79|
|R-Squared|.611|--||.607|--|--|

Notes: Absolute values of t-statistics in parenthesis.

Another possible instrurment is to use the growth rate of educational capital in a similar country as an instrument. To implement this I sinply maiched each couutry with another country that I felt (without actually examining the data) was similar, usually using the geographically closest neighbor. One can expect the correlation of the educational capital growth rates in similar countries to be positively correlated (and the actual correlation is .316 for the B-L data and .619 for the N-S-D data) while the pure measurement error of country reported enrollment rates is plausibly uncorrelated across countries. The results from this instrument are in columns 3 and 6 of table 2. Again, the coefficients estimated with IV are the same or larger more negative than OLS estimates.

Taken together. the correlations of the two measures of educational capital growth and the IV regressions suggest that there is in fact substantial pure measurement error. The signal to signal plus noise
The application of the “emerging” theories to housing and particularly to tenure issues belongs to a work program of the future, and is beyond the scope of this study. However, there are two relevant bodies of literature dealing with rental housing supply within a rudimentary equilibrium theory approach: (i) on the theory of rent control; and (ii) on the theory of residential filtering process.

Rent control and its distorting effects, including politics, populism and implications for housing market equilibria, have been extensively researched. The literature survey (Annex 1) gives an overview of these issues. The economists’ verdict against first-generation “hard” rent controls and ceilings is universal; however, more indirect, softer forms of second-generation rent controls that cap only usurious (short-term monopolistic) practices have been found to exercise a demand-stabilizing effect. Again, the theories are not sufficiently rich in structure to explain partly diverse empirical outcomes.

The theory of residential filtering (trickle-down) seems more useful, so far. Consider several submarkets of the housing sector that compete for consumers. Consumers gain in housing consumption capacity over their life cycle and face financial and transaction cost hurdles when trying to realize their perpetual desire to upgrade their housing status.<sup>12</sup> Filtering assumes not only that households change submarkets over their life cycles, but also that supply changes too, as older units filter down in the rent hierarchy, until they are modernized or exit the market through dilapidation and demolition. No rigorous specification of this model with modern economic theory tools is known to us; however, its rich structure has the potential to integrate both complex supply and tenure choice elements in a consistent equilibrium theory. As discussed later (Annex 1) in more detail, numerous conclusions can be drawn for tenure policies and need to be researched further. The most important one is that price controls or subsidies in a specific sub-market may impede the entire filtering chain, leading to a decline in overall housing investment.

Summarizing the small body of applicable supply-side literature, one may predict that households tending to be renters will:

- Decrease as land prices decline, allowing for lower urban densities and less complex building structures

- Increase with economic agglomeration effects

- Increase with inter-urban and inter-regional changes in economic dynamics and city hierarchy, which warrant higher labor mobility

- Increase with complexity of the housing production and management structures, which raise the costs of individual homeownership

- Increase with efficiency of the servicing of rental contracts, which in turn depends on local conditions for optimum firm size for landlords

- 12  For example, from living with parents to subletting, from subletting to renting, from renting to owning a small house, from owning a small house to owning a large house.

8
**<u>Table 5: ITT, cross-sectional sample: child care practices and intermediate outcomes</u>**

||ITT|_std.dev_|Mean of dep.<br>var in non-<br>program area<br>(PSweighted)|
|---|---|---|---|
|_Micronutrient supplementation:_||||
|Received vitamin A supplementation|0.080|_0.060_|0.759|
|Received message with vitamin A<br>supplement.|0.128**|_0.064_|0.571|
|Having health card (carnet de santé)|0.265***|_0.072_|0.800|
|_Traditional practices: breastfeeding and_<br>_weaning_||||
|EBF during the first 6 months|0.201***|_0.064_|0.339|
|initial breastfeeding (within 1 hour of<br>birth)|0.083|_0.072_|0.506|
|Feeding colostrum (pre-lacteal)|0.227***|_0.075_|0.754|
|mother is eating more while breastfeeding<br>|0.064|_0.064_|0.126|
|mother is drinking more while<br>breastfeeding|-0.063|_0.085_|0.554|
|Still breastfeeding|0.132*|_0.071_|0.327|
|No. times child breastfed previous night|0.208|_0.186_|5.022|
|No. times child breastfed previous day|0.060|_0.218_|7.826|
|_Non-dietary aspects of feeding:_||||
|child eats different meal|0.272***|_0.073_|0.161|
|child encouraged to eat|0.113|_0.079_|0.795|
|consistency meal: boiled/puree|0.219***|_0.076_|0.187|
|child eats alone|-0.094|_0.068_|0.614|
|received nutritional counseling|0.972***|_0.074_|0.326|
|_Pregnancy:_<br>||||
|Received tetanus injection during<br>pregnancy|0.100|_0.067_|0.155|
|Delivered with medical assistance|-0.023|_0.073_|0.529|
|Received vitamin A after delivery|0.340***|_0.061_|0.288|
|_Treatment of illness:_||||
|incidence diarrhea past 2 weeks|0.144**|_0.063_|0.076|
|received ORS/homemade liquid|-0.088|_0.147_|0.379|
|Drank more during diarrhea episode|0.058|_0.152_|0.400|
|Ate more during diarrhea episode|0.564***|_0.195_|0.039|
|_Hygiene practice:_||||
|<br>Garbage disposal: hole in the ground|0.207**|_0.087_|0.496|
|Any water purification method: tablets or<br>boiling|0.183**|_0.083_|0.356|
|Toilet: hole in the ground|0.216*|_0.111_|0.509|
|Handwashing|0.117|_0.085_|0.269|

Note: Standard deviations based on Huber-White robust standard errors clustered at the community level. Significantly different at * 90%, ** 95%, *** 99%. Regression/Probit are PS–weighted on the trimmed sample. Subsumed regressors are: individual (age and birth order dummies, gender, perceived birthweight), mother (log height, education, age, work status), household (size, access to safe water, housing and durable indicators), rural dummy and areas indicators. In addition, the probit regressions related to the treatment of illnesses includes an indicator for the occurrence of a weather shock in the village in the previous three months.

26
coefficient for UCA is also generally significant. Trade credit is an exception, in that it becomes generally insignificant. This suggests that the positive correlation between trade credit and investment intensity in our base results largely stems from variation across industries. Overall, the industry-specific investment equation estimations are reassuringly supportive of the base results in Table 4.

# **Using liquidity classifications to check the effects of financial constraints**

As acknowledged by Fazzari, Hubbard, and Petersen (1988) and Hoshi, Kashyap and Scharfstein (1991), the standard criticism of using the investment-cash-flow sensitivity analysis to examine the effects of liquidity constraints is that liquidity variables also reflect unobserved investment opportunities. Since it is generally difficult to find convincing instrumental variables for liquidity variables in the investment equation, they propose classifying firms into various subsets based on a priori beliefs about the relative severity of information and liquidity problems faced by firms, and then checking whether the cash flow sensitivity is indeed larger for firms classified as facing greater information/liquidity problems. As described above, we classify firms based on their degree of state ownership (institutionalized connections) and whether the CEO was appointed by the government (CEO personal connections). We hypothesize that firms with close connections to the government have easier access to external financing, weaker incentives to be profitable, and lower investment-cash-flow sensitivities.

# **Firm ownership**

We estimate Equation (1) for three ownership groups: state-owned enterprises (SOEs hereafter), non-state domestic firms, and foreign firms. Ex ante we expect non-state domestic firms to face more severe financial constraints due to their lack of institutionalized government 27
# **VII. How Did the Firms Rate the Interpersonal and Communication Skills of Locally Educated MBAs?**

**Figure 3**

<!-- Start of picture text --> MBA Survey Question 6B: How would you rate<br>the interpersonal and communication skills of<br>locally educated MBAs?<br>2%<br>10%<br>29%<br>38%<br>LACKING (%) INADEQUATE (%)<br>ADEQUATE (%) GOOD (%)<br><!-- End of picture text --> As with the discussion of MBA training in technical skills above, respondents found that locally trained MBAs were acceptable in their interpersonal and communication skills. As shown in Figure 3, 67% of respondents found the interpersonal and communication skills of locally educated MBAs to be either adequate or good, with 38% saying that skills were adequate and 29% saying that they were good. Only 12% found that the interpersonal and communication skills of local MBAs were lacking or inadequate.

An analysis across regions demonstrates that the only standout is the Middle Eastern region, in which 26% of the respondents were dissatisfied with the level of interpersonal and communications skills (with a rating of either Lacking or Inadequate), which is significantly higher than the results of the other regions. This may imply a need to focus on the issue of developing interpersonal and communication skills in MBA and similar programs in the Middle Eastern region. On the other hand, more than 60% of the firms in each of the other regions rated the interpersonal and communication skills of local MBA graduates as either adequate or good.

Across World Bank income categories, the firms display a uniform level of satisfaction with the interpersonal and communications skills of locally trained MBAs.<sup>15</sup> There is a strong indication that locally trained MBAs (in areas where they are in sufficient numbers) are being satisfactorily trained in the area of communication.

> 15 World Bank income categories are based on the per capita incomes of countries.  The range of the income categories is I-V, with I being the lowest and V being the highest:  Countries in Category I have a per capita income of $746 or less;   those in Category II,  between $746 and $1435;  Category III,  between $1436 and $2975;  Category IV,  between $2976 and $5185;  Category V, over $5185.

14
=» ~~(——)~~ =1-> (share )

|Year|Plants|SME|Young’|Bank ties|FDI|Foodind.|AddisAbaba|Aggl. center|HGF|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(4)|(5)|(6)|(7)|(8)|
|1999|725|503|247|262|53|208|393|469|36|
|||(.694)|(.341)|(361)<br>=|(.0731)|(.287)|(.542)|(.647)|(.0497)|
|2001|721|508<br>|210<br>|118<br><br>|59<br>|217<br>|390<br>|473<br>|46<br>|
|||(707)|(.292)|(164)<br>~—|(.0821)_~—|(302|(.542)|(.658)|(.0640)|
|2003|939|661|213|146|78|271|511|619|59|
|||(706)|(.228)|~=(.156)|(0833)|~—-(.290)|(.546)|(.661)|(.0630)|
|2005|761|439<br>|163<br>|165<br>|76<br>|210<br>|447<br>|521<br>|114<br>|
|||(578)|(.215)|= (.217)_~—s|(100)|(.277)|(.589)|(.686)|(.150)|
|2007|1339|952|431|255|91|345|648|814|132|
|||(.712)|(.322)|(.191) —|(.0680)|(.258)|(.484)|(.608)|(.0987)|
|2009|1945|1483|830|270|115|480|786|1032|105|
|||(.764)|(.428)|(.139) —|(.0593)|(.247)|(.405)|(.532)|(.0541)|
|Total|13975|9903|4354|2334|1001|3807|7151|8777|884|
|||(.710)|(.312)|(.167)|_(.0718)|(.273)|(.513)|(.629)|(.0742)|

Notes: Establishment count, with share of total establishments in parenthesis
# **-** **<u>Appendix table A2: Likelihood of completing the follow up survey</u>**

|<br>PSG beneficiary|<br>0.097***|0.102***|0.107***|
|---|---|---|---|
||(0.025)|(0.026)|(0.027)|
|Applied to other programs|0.178***|0.176***|0.178***|
||(0.027)|(0.027)|(0.028)|
|Owner respondent|-0.021|-0.020|-0.013|
||(0.063)|(0.064)|(0.066)|
|Manager respondent|-0.012|-0.008|0.003|
||(0.068)|(0.068)|(0.071)|
|Log holding size (ha)|-0.021**|-0.023**|-0.020*|
||(0.010)|(0.010)|(0.010)|
|FOP|0.128***|0.123***|0.127***|
||(0.032)|(0.032)|(0.032)|
|Individual|0.063|0.058|0.080*|
||(0.041)|(0.042)|(0.044)|
|Managing multiple farms|-0.070**|-0.071**|-0.077**|
||(0.031)|(0.031)|(0.032)|
|Considered applying for credit|0.071***|0.070**|0.066**|
||(0.027)|(0.027)|(0.028)|
|Personal situation on a scale of 1 to 10|0.007|0.008|0.008|
||(0.006)|(0.006)|(0.007)|
|Not directly affect by conflict|0.046|0.048|0.041|
||(0.039)|(0.041)|(0.041)|
|Close to frontline (within 50 km from frontline)|||-0.013<br>(0.027)|
|East||0.022|0.037|
|||(0.053)|(0.055)|
|North||<br>-0.019|<br>-0.011|
|||(0.035)|(0.037)|
|South||<br>-0.027|<br>-0.012|
|||(0.030)|(0.033)|
|West||-0.065*|-0.055|
|||(0.035)|(0.036)|
|No. ofobs (farms)|2,101|<br>2,101|<br>1,987|

_Note:_ Reported coefficients are marginal effects from a probit model: * p<0.10, ** p<0.05, *** p<0.010 27
Given this evidence, we conclude that working with the actual beneficiaries does not introduce selection bias coming from heterogeneous take up responses.

# **3. Investment**

In this section, we estimate the effect of OPORTUNIDADES on investments in agriculture and micro-enterprise activities. We show that beneficiary households increase agricultural assets and participation in micro-enterprise activities, suggesting that some of the cash transfer was used for investment in productive assets. Section 3.1 presents the empirical specification based on the randomized design of the OPORTUNIDADES evaluation sample, section 3.2 presents comparisons of means, and sections 3.3 and 3.4 discuss the main investment results.

# **3.1. Specification and Identification**

Our analysis exploits the exogenous variation introduced by random assignment of treatment to estimate the effect of OPORTUNIDADES on investment in agricultural production and micro-enterprise activity, the two most common domestic income generating activities in this population. We estimate two types of models: one exploiting the community randomization to obtain average treatment effects, and a second which exploits the variation in transfer amounts to estimate the effect of the size of the transfer on investments.

To estimate the first specification we use the exogenous variation generated by the randomized phasing in of the program. Beneficiary households in treatment communities received transfers for just over a year and a half longer than beneficiary households in control areas. Given the timing of the ENCEL surveys, we first observe treatment households in October 1998, approximately 6 months following the first transfer payment. Treatment households would have received three bimonthly transfer payments by the time of the first follow up survey. Since control communities did not begin receiving payments until November-December of 1999, there are three rounds of data (October 1998, May 1999 and November 1999) where a simple comparison of eligible households in treatment and control communities is possible.

7
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Dilley, M., R.S. Chen, U. Deichmann, A.L. Lerner‐Lam, M. Arnold, J. Agwe, P. Buys, O. Kjekstad, B. Lyon, and G. Yetman, 2005, Natural Disaster Hotspots: A Global Risk Analysis, _Disaster Risk Management Series_ **_,_** _No. 5_ . World Bank: Washington, D.C..

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GLOBE Task Team (Hastings, David A., Paula K. Dunbar, Gerald M. Elphingstone, Mark Bootz, Hiroshi Murakami, Hiroshi Maruyama, Hiroshi Masaharu, Peter Holland, John Payne, Nevin A. Bryant, Thomas L. Logan, J.‐P. Muller, Gunter Schreier, and John S. MacDonald), eds., 1999. _The Global Land One‐kilometer Base Elevation (GLOBE) Digital Elevation Model_ , Version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325 Broadway, Boulder, Colorado 80303, U.S.A. Available from http://www.ngdc.noaa.gov/mgg/topo/globe.html.

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highest for the 65+ group, at 29.6%, indicating that saving for old age peaks at a late age in ECA.

In OECD<sup>HI</sup> , the major rise in the proportion of the population that saves for old age happens at age 26-35. Specifically, in OECD<sup>HI</sup> countries, 21% of the population saves for old age, rising sharply to a 39.6% at age 26-35, and a further 48.2% at age 36-45. The figures remain at 45.6% and 48% at age groups 46-55 and 56-65, respectively. The figure for the 65+ age group is 40%. Notably, in comparison, the figures in the remaining four groups of countries − LAC, MENA, South Asia and Sub-Saharan Africa − are strikingly low and without any major rises occurring across the early age groups.

Departing from the age distribution of saving for old age, Figure 5 presents the figures by marital and family status, globally and for the regions of interest. 12.6% of the singles around the world save for old age, compared to 29.3% of the married and 25.9% of the widowed/divorced/separated. Concerning family status, 21.2% of the individuals with children younger than 15 save for old age, compared to 29.7% of individuals with no children younger than 15. The pattern of the married group being more likely to save for old age prevails in all regions.

Figure 6 presents the distribution of saving for old age by real (PPP-divided) household income per capita centile. The centiles presented in Figure 6 are for the global distribution of household-income-per-capita (not by country). Expectedly, the income distribution of saving shows considerable inequality in the degree of financial preparedness for old age. Globally, 9.7% of individuals in the bottom income centile save for old age, compared to 14.4% in the 2<sup>nd</sup> centile, 28.3% in the 3<sup>rd</sup> income centile, 35.3% in the 4<sup>th</sup> centile and 43.9% on the top income centile. In OECD<sup>HI</sup> countries, massive rises in saving for old age occur at the 4<sup>th</sup> and 5<sup>th</sup> income centile. The figures for EAP and ECA also show considerable rises in saving for old age after the 3rd income centile. However, income inequality appears to have the most significant impact on saving for old age in LAC and MENA, with low rates of saving for old age in the first four income centiles and massive rises occurring at the top income centile.

10
tend to be particularly affected during periods of economic distress and are, occasionally, forced to adopt potentially harmful coping strategies, such as reducing food and out-of-pocket health expenditure. The long-term consequence of these coping strategies could be damaging to the household’s human capital accumulation, which is particularly worrisome because poor and vulnerable people are more dependent on human capital over other types of capital for their long term welfare.

# 8. <u>Conclusions</u>

Financial and economic crises influence human development in various ways and can have a long term impact on household welfare. In this paper, we analyze the impact of the 2008-2010 global financial crisis on selected health and nutrition behavioral variables among Russian households.

Our analysis of the crisis in 2009 in Russia shows that there was no statistically significant impact on the average household on food consumption, out-of-pocket health expenditures, and doctors’ visits of crisis-affected households. However, we find robust evidence that poor (lowest quintile) crisis-affected households spent less on health services, compared to poor households not affected by an income shock.<sup>13</sup> Furthermore we find that vulnerable people affected by the crisis in 2009 altered their health and nutrition behavior. In particular, households with low educational attainment of household heads that suffered an income shock tended to decrease expenditures on both food and health services, while households that had a higher number of elderly people tended to curb the use of health services. These findings suggest a particular need to protect the health and nutritional needs of the poorest and most vulnerable in the population, but not necessarily the entire population.

The findings underscore the need to study crisis impacts for sub-groups, despite the finding that the overall health and nutritional behavioral responses may not vary between households that were and were not affected by income shocks. In addition, the study reveals the value of using longitudinal household survey data to analyze the impact of crises.

> 13 By the same token, when the analysis is conducted to a restricted sample of the two upper quintiles of the population (4<sup>th</sup> and 5<sup>th</sup> ), we see that households that are affected by the crisis but are more affluent tend to spend more on health services.

19