rafmacalaba/datause-encoder-data huggingface dataset:
have a steady wage and salary job that “employment” in an OECD country usually connotes. They may only be able to work part of the year owing to lack of rainfall or water management, or lack of demand for the goods and services that they are selling in their primary economic activity. Often the proportion of time in which the individual actually worked in an economic activity is used as a measure of underemployment. Failure to consider secondary activities could lead to an overestimation of the extent of underemployment. Not only would this result in the wrong diagnosis, it could also lead to the wrong policy prescription.

Failure to capture second jobs even affects aggregate statistics on livelihoods. Analysis of household livelihoods is very important when household production is the main income source for 70 percent or more of the population, and the type of activity the household is engaged in may vary over the year. Livelihood analysis measures the _portfolio_ of income sources in the household. Given that the primary employment status is an incomplete representation of all the economic activities undertaken by an individual, it follows that failure to account for secondary employment would mis-measure the structure of livelihoods in the country (see Fox and Pimhidzai, 2011). Figure 2 confirms this. By missing some secondary jobs, the 7 day recall understates people’s livelihood diversification because _even at an aggregate level_ it fails to pick up the income diversification of about 8 percent of households who have more than one source of income, but are identified as having a single labor income source when the 7 day recall period is used.

**Figure 2: Number of household labor income sources by recall period, UNPS 2009/10**

<!-- Start of picture text --> 60.0<br>51.9<br>50.0 43.8 43.6<br>39.3<br>40.0<br>30.0<br>7 day recall<br>20.0<br>9.9 12 month recall<br>10.0 6.3<br>0.4 0.7<br>0.0<br>1 2 3 4<br>Number of labor income sources<br>Source: Authors calculations based on the UNPS 2009/10<br>Proprtion of Households<br><!-- End of picture text --> The underreporting of both activity types and their number using a 7 day recall period means that indicators based on these data are plagued with measurement errors that would affect any micro level analysis. The measurement of earnings is one such example. Individual earnings will be understated in the 7 day recall period by virtue of missing out other income sources of the individual over the year. This measurement error is unlikely to be random. As Figure 1 shows, certain types of activities including temporary private agriculture jobs and casual non-agriculture jobs tend to be missed the most, and these are mainly performed by the poor. Furthermore, the shift in economic activities over the year by individuals and households will make it difficult to extrapolate 7 day recall earnings to annual earnings or to analyze variations within groups (Schaffner, 20
services value added among ECA countries. Estonia and Hungary show well aboveaverage shares of services value added in real estate, renting, and business activities, while Slovenia shows a particularly high share of employment in that sub-sector during 1997-2004. Finally, note that real estate, renting, and business activities is the only subsector for which there is a clear divide between EU-8 countries (with the exception of Lithuania) – where it is substantially more important for value added and employment – and South Eastern European countries and Ukraine.

The patterns identified above do not inform on the importance of disaggregate sub-sectors such as software activities, which are part of the more aggregate sub-sector real estate, renting, and business activities. The KLEMS database provides information for 2-digit NACE sub-sectors which we use to document the importance of disaggregate services activities across ECA countries between 1997 and 2004. Given the large number of 2-digit NACE sub-sectors, we show in Figure 4 only the share of value added of subsectors that (i) represent more than 1% of total value added on average in EU-8 countries or (ii) have a high technological or skill content. Within wholesale, retail, and repair of motor vehicles, the most important sector is wholesale trade, whose average share in value added in 2001-2004 ranges from 4.8% in Hungary to more than 10% in Latvia. Retail trade is also important, particularly in Poland where it contributes more than 8% of value added. Within transport, storage, and communications, the most important subsectors in value added terms - representing 3% to 4% on average across EU-8 countries - are inland transport and post and telecommunications. Financial intermediation (excluding insurance and pension funding and activities auxiliary to financial intermediation) contributes more than 2% to value added in all EU-8 countries except Lithuania. Within real estate, renting, and business activities, real estate activities contribute the highest share to total value added (7.5% on average). Legal, technical, and advertising activities are the next most important sub-sector by their contribution to value added which increases in all EU-8 countries (except Estonia) between 1997 and 2004, averaging 3.7% in 2001-2004. Computer and related activities represent a small but growing share of value added in all EU-8 countries, reaching the highest average share in Latvia in 2001-2004 (1.8%). The share of wholesale trade, retail trade, inland transport, and post and telecommunications in value added in EU-15 countries, also shown in 9
5

**Table 1.  The Effect of PARE Intervention on Spanish Test Score**

||**Befo**<br>|**re (1992)**<br>|**Aft**<br>|**er (1994)**<br>|**Dif**<br>|**ference**<br>|
|---|---|---|---|---|---|---|
||**Students**|**Average test**<br>**Score**|**Students**|**Average test**<br>**score**|**Total**|**Percentage**|
|**Native**|||||||
|Experimental|564|14.6|356|29.1|13.9|95.3|
|Control|205|23.2|125|26.8|4.1|17.7|
|Total – t/test|769|16.9|481|28.5|11.4|67.3|
|**Rural**|||||||
|Experimental|645|20.7|421|32.9|11.6|56.0|
|Control|208|20.1|128|29.7|8.2|40.6|
|Total – t/test|853|20.5|549|32.1|10.8|52.5|
|**Urban**|||||||
|Experimental|337|26.9|238|39.7|12.0|44.5|
|Control|361|26.9|221|44.3|15.9|59.3|
|Total – t/test|698|26.9|459|41.9|13.9|51.6|
|Source: PARE S|urvey||||||
**Table 1. Countries with Interest Rate Caps, by Region**

|**SSA**|**EAP**|**ECA**|**LAC**|**MENA**|**SA**|**Western Europe**|**Others**|
|---|---|---|---|---|---|---|---|
|Eritrea|China|Armenia|Argentina|Algeria|Bangladesh|Belgium|Australia|
|Ethiopia|Japan|Estonia|Bolivia|Egypt,<br>Arab Rep.|India|France|Bahamas|
|Ghana|Lao PDR|Kyrgyz<br>Republic|Brazil|Libya|Pakistan|Germany|Canada|
|Guinea|Myanmar|Poland|Chile|Malta||Greece|United<br>States|
|Mauritania|Philippines|Slovak<br>Republic|Colombia|Syrian<br>Arab<br>Republic||Ireland||
|Namibia|Thailand|Slovenia|Ecuador|Tunisia||Italy||
|Nigeria|Vietnam|Turkey|Guatemala|||Netherlands||
|South<br>Africa|||Honduras|||Portugal||
|Sudan|||Nicaragua|||Spain||
|Zambia|||Paraguay|||Switzerland||
|WAEMU|||Republic<br>Dominican|||United Kingdom||
|CEMAC|||Uruguay<br>Venezuela,<br>RB|||||

_Sources_ : Helms and Reille 2004; Mbengue 2013; Castellanos 2012; Porteous, Collins, and Abrams 2010; iff/ZEW 2010; EIU 2012, 2013; CGAP-MIX 2011; Steiner and Agudelo 2012.

_Notes_ : The West African Economic and Monetary Union (WAEMU) includes Benin, Burkina Faso, Cote d'Ivoire, GuineaBissau, Mali, Niger, Senegal, and Togo. The Economic and Monetary Community of Central Africa (CEMAC) includes Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon.

SSA = Sub-Saharan Africa; EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; WE = Western Europe.

Romania established interest rate ceilings by law in 1938 but does not currently apply caps on bank loans or credit lenders (iff/ZEW 2010). Peru allows interest rates to be determined by the market, but in exceptional circumstances the central bank has the power to set caps. In Mexico, the central bank by law can regulate interest rates but has not done so. Panama, which used to have caps based on its usury law, eliminated the law in 2008 (Castellanos 2012). Morocco has the option to impose ceilings according to its Law on Microcredit Associations but has not exercised it (Khaled 2011). Neither Austria nor Denmark has maximum interest rates. Denmark debated the issue in early 2009 and 2010 but did not follow up. Finally, Cyprus, which used to have interest rate ceilings, ended the practice in 1999 to enhance its competitiveness in the banking sector (iff/ZEW 2010).

7
# **<u>Disaggregating the analysis by location (Rural Vs Urban)</u>**

## **Table A27: Second stage: Effects of refugee presence on household welfare disaggregated by location, with analysis at cluster level and the distance weighted refugee -** **<u>index at 50km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|||||||Panel A|(Rural)||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.06253|0.06228|0.08484*|0.07676*|||||||||
||(0.04168)|(0.04158)|(0.04754)|(0.04500)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.06816|0.06820|0.09680*|0.08946*|||||
||||||(0.04171)|(0.04169)|(0.05031)|(0.04732)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.07232<br>(0.04870)|0.07187<br>(0.04854)|0.08732*<br>(0.05182)|0.08061<br>(0.04972)|
|Observations|4,541|4,538|3,648|3,640|4,541|4,538|3,648|3,640|4,541|4,538|3,648|3,640|
|Kleibergen-Paap rk Wald F|17.14|17.14|13.38|13.06|15.73|15.72|12.36|12.12|14.56|14.56|11.89|11.61|
|Root MSE|0.581|0.580|0.531|0.363|0.581|0.581|0.531|0.364|0.581|0.581|0.531|0.364|
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|-0.29374|-0.30683|-0.02938|-0.01592||Panel B|(Urban)||||||
||(0.39856)|(0.40022)|(0.12435)|(0.12484)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||-0.25184|-0.26495|0.03603|0.05103|||||
||||||(0.36502)|(0.36532)|(0.14931)|(0.15503)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||-0.30729<br>(0.40744)|-0.32002<br>(0.40861)|-0.06991<br>(0.11831)|-0.05700<br>(0.11830)|
|Observations|902|902|450|450|902|902|450|450|902|902|450|450|
|Kleibergen-Paap rk Wald F|4.440|4.434|3.361|3.494|4.610|4.609|3.254|3.339|4.044|4.042|3.214|3.393|
|Root MSE|0.697|0.695|0.499|0.322|0.692|0.690|0.499|0.325|0.699|0.697|0.500|0.322|
|**_Included controls_**|||||||||||||

45
<mark>percentage of regressions in which childcare attendance is significant at the 1% significance level. We repeat the analysis for different gaps in children’s birth month: from 1-3 months, only 1 month, only 2 months, and only 3 months.</mark> <mark>Figure 6 shows the distribution of the p-value of the variable “born earlier” in these regressions. For the birth month gap from 1-3 months, only 3.3% of regressions show a significant effect of “being born earlier” on parental outcomes. For a gap of 1 month, 1.8% of regressions show a significant effect of “being born 1 month earlier” on parental outcomes. No regressions show that the effect of being born earlier is significant at the 1% level. Thus, there is no evidence of the effect of being born earlier (for children of the same age) on parental outcomes.</mark>

# _5.5. Spill-over effects_

<mark>Table 5 presents 2SLS regressions of several household outcomes on childcare attendance. Taking advantage of childcare has a positive effect on per capita income. Childcare attendance increases the labor market participation and wages of women and as a result, increases household income. This income effect carries over to a poverty-reducing effect. A household is defined as poor if its per capita income is below the poverty line. The probability of being poor is reduced by 0.22 when children are placed in childcare. This finding suggests the important role of childcare in Vietnam where there is a low rate of childcare and a low rate of participation by women in the labor market.</mark><sup>17</sup> <mark>It is common in Vietnam for grandparents who live with their children to take care of the grandchildren. In the 2016 VHLSS, in approximately 21% of households, grandparents live with their children. We test whether formal childcare in a center can substitute for informal childcare from grandparents by running a regression of a dummy variable “living with grandparents” on childcare attendance. The effect of childcare attendance is minimal and is not statistically significant. There are no significant effects on parental migration and household size, or the interaction terms between childcare and households having an urban</mark>

> 17 We further explore whether the poverty-reducing impacts of childcare are stronger for certain disadvantaged groups such as ethnic minorities or unemployed individuals by interacting these variables with childcare attendance. Estimation results (available upon request), however, are not statistically significant.

19
The results from OLS and probit models for the probability of a child attending a private school versus a public school are presented below, for all children attending school in 1997-98 (Table 12) and for children attending school in 1997-98 and receiving subsidies (Table 13).

Table 12: Determinants of school choice, all children attending school in 1997-98 Dependent <u>variable: probability of attending private school</u>

|Variable|OLS<br>Coefficient ( t-value)|Probit<br>Marginal Prob*(z-value)|
|---|---|---|
|Total Household Expenditure|3.77e-08 (12.61)|2.12e-08 (9.45)|
|Christian (omitted: other)|0.014 (1.61)|0.017 (2.09)|
|Urban area (omitted: rural)|0.620 (7.00)|0.076 (8.39)|
|Head attended private school)|0.187 (9.84)|0.149 (7.65)|
|(omitted: head attended public<br>school)|||
|Education of head:|||
|Secondary|0.0154 (1.58)|0.025 (2.67)|
|Tertiary|0.083 (4.55)|0.067 (3.88)|
|(omitted: less than secondary)|||
|Constant|-0.013 (1.64)|-|
|N<br>|5,107|5,107|
|R-sq adjusted (or Pseudo R<sup>2</sup>)|0.108|0.132|
|Observed/Predicted prob.||observed: 0.108<br>predicted: 0.084|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages Note: dF/dX is for discrete change of dummy variable from 0 to 1 In the case of Côte d’Ivoire, instead of the potentially under-reported family income, total household expenditure was used. After experimentation, the model includes five explanatory variables, namely total family expenditure, urban/rural residence, head having attended private/public school, and head’s level education, one dummy for Christian affiliation and the differences in education cost between private and public schools. The most important determinants of private school choice are the income variable (in our case annual household 23
part, foreign banks would also likely find it unattractive to enter restrictive environments. Thus, we expect a negative relation between the _Banking Index_ and _Foreign Participation_ .

_Property Rights Index_ measures the level of protection of private property in a country. Like the _Banking Index_ **,** it ranges from 1 to 5 with lower values indicating better protection of property. In constructing this measure, the Heritage Foundation considers the extent to which the government protects and enforces laws to protect private property, the likelihood that the government will itself expropriate property, the existence of a commercial code, and the speed and efficacy of the judiciary in resolving contractual disputes. We expect foreign banks to be more prevalent in countries that better protect private property, and thus a negative relation between the index and foreign bank participation in the regressions that follow.<sup>10</sup> In some specifications, we include a measure of market size, namely the log of GDP averaged from 1995 to 2002. Controlling for the quality of the macroeconomic environment and banking sector, foreign banks might find larger markets more attractive, and thus we expect a positive relationship between the log of GDP and foreign bank participation. We include the share of banking sector assets held by foreign-owned banks in 1995-96, _Initial Foreign Participation,_ in some specifications to test whether foreign bank participation grew more rapidly in countries that had little foreign bank presence at the outset of the period. The intuition is that the process of foreign bank entry was already complete in countries with high foreign participation shares. We would therefore expect a negative relation between the level of foreign participation in 1995-96 and subsequent changes in foreign bank participation.

Finally, we include regional dummy variables in some specifications. As illustrated in the figures above, the level of foreign bank participation and its response to banking crises varied across regions, with relatively high levels in Africa, Latin America, and Eastern Europe and Central Asia as compared with East Asia and the Pacific, the Middle East and North Africa, and South Asia. Post-crisis increases in foreign bank participation also tended to be steeper in Africa, Latin America, and, especially, in Eastern Europe and Central Asia. We include the regional dummies to control for some of these differences in the evolution of foreign participation. We note, however, that these dummies are also likely to rob the banking crisis variable of some

> 10 Two advantages of the Heritage indexes are that the underlying methodology has been consistent over time and the country coverage is wide. We can therefore construct a balanced panel that captures the same concepts over time for a wide array of countries.

18
There are 773 households in rural areas in the BDH evaluation surveys that were visited in both the baseline and follow-up surveys. Attrition over the study period was low: 94.1 percent of households were re-interviewed. Among households who attrited, most had moved and could not be found (4.2 percent), with smaller numbers where the household was located but no qualified respondent was available despite repeated visits (1.0 percent), or the respondent refused to participate in the survey (0.5 percent). There is no relation between assignment to the study groups and attrition, and baseline differences between attrited and other households in per capita expenditures, food expenditures, assets, and the education of adult males and females are small and insignificant. Attrition is most likely to introduce biases in estimation when there are large differences between attrited and other households (Fitzgerald, Gottschalk and Moffitt 1998), or when attrition is correlated with treatment status (Angrist 1997; Angrist et al. 2002), and there is no evidence that this is the case in our data.

# **3. Results**

# A. <u>Nonparametric results</u>

We begin our analysis with non-parametric regressions of the food share on the log of per capita expenditures at baseline and follow-up for households assigned to the treatment group—lottery winners—and households assigned to the control group—lottery losers. These results are presented in Figure 1. Note that this analysis only makes use of the random assignment into treatment and control groups, and not of the likely endogenous program take-up.

The food Engel curves in Figure 1 generally present the typical downward-sloping pattern observed elsewhere. With the exception of the poorest households, households with higher overall expenditures generally have lower food shares—Engel’s Law (Engel 1857). The positive slope in the food Engel curve observed at the lowest expenditure levels is not uncommon (Thomas 1986), and has been documented before for Ecuador (Lanjouw and Lanjouw 1997). One explanation that is consistent with this pattern is measurement error in food expenditures: If households buy some food items infrequently, for example because they buy in bulk, some households will not have purchased these items during the recall period considered in the surveys (the last two weeks). These households will appear to have low overall expenditures and particularly low food expenditures (Lanjouw and Lanjouw 1997).

By virtue of random assignment, we would expect that the food Engel curves at baseline for lottery winners and losers would be very close to each other. The upper panel of Figure 1 shows that this is generally the case above the 25<sup>th</sup> percentile of the distribution of log per capita expenditures. However, despite random assignment, the food Engel curve for the poorest lottery winners is below 8
over the period of analysis can be adequately summarized by the set of indexes introduced in section 2.

Until very recently, the only institutions considered to affect unemployment were labor market institutions such as unemployment benefits or employment protection legislation. Those institutions are important, among other things, because they affect the expansion and contraction of established firms, which have an important impact on unemployment.

But apart from the expansion of established firms, there is another source of job creation, namely, _the birth of new firms_ . The few statistics that we have show that between one third and one fourth of new jobs are created by new, genuinely private, firms in the ECA countries. Hence new firm creation is a very important source of new jobs in the group of countries we consider.

The rate of creation of new firms, and therefore job creation, depends on those institutions that shape the business environment where firms operate, described in section 2 of this paper. We intend to explore the impact of each of those institutions on the labor market.

The list of institutions considered in the regression model is as follows:

- Administrative burdens on firm creation (start-up costs)

- Cost of going bankrupt (exit costs)

- Access to finance (cost and availability of finance)

- Quality of the legal system (efficiency and independence of courts)

- Market regulations (credit and labor market regulation)

- Corruption (perceived corruption level, from Transparency International)

- Tax burden (tax revenue as percentage of GDP)

For a detailed account on the construction of indexes able to capture those institutions, please refer to Appendix 2.

The ECA countries have been hit by a major shock over the last decades: the fall of the communism regime and transition to a market economy. We will use the EBRD transition index to size the shock across countries and to follow its evolution along time.

The EBRD transition index aims to reflect each country’s moment in the transition towards a market economy. Hence it deals, among other things, with issues such as the emergence of a private sector, the size of the government and so on, which are some of the indicators of the business environment we have considered in section 2. Therefore we could have some multicollinearity problems in the regressions, that is, relationship between explanatory variables which could distort the estimation results.<sup>12</sup>

> 12 However, even so, the regression estimates will still be unbiased and consistent.

14