rafmacalaba/datause-encoder-data huggingface dataset:
collected by Rama and Artecona (2000). It includes 44 indicators on policies and outcomes related to labor markets in 121 countries since the 1950s. A more recent attempt of providing cross-country information on labor market policies is given by Djankov et al. (2003). These data set is quite comprehensive in terms of country and thematic coverage; however, it lacks time-series variation as its observations correspond to the early 2000s only.

**_3. Outcome Indicators._** We consider general and specific outcome indicators of labor market reform.  General indicators assess outcomes from the perspective of the overall functioning of labor markets, while specific indicators measure closely the immediate impact of particular policy changes.

Among general indicators, we should look for measures of improved adjustment flexibility of labor markets to aggregate and industry-specific shocks and for indications of reduced labor market segmentation. Two broad outcome indicators are the rates of unemployment and underemployment. These data are readily available from national or labor statistics; they are difficult to compare across countries but, with enough care, are useful for overtime comparisons. More precise measures of flexibility, although more difficult to obtain, are given by sectoral-, gender-, and age-specific data on labor force participation, worker turnover rates, average time between jobs, and labor force dropout rates (for an application to several Latin American countries, see Micco and Pagés 2002). Regarding labor market segmentation, a “quantity” approach would measure the share of the labor force in the informal sector, defined as the segment of the economy that does not comply with labor-market regulations (see Loayza 1996). A “price” approach would compare wages and total compensation between similarly qualified workers across different firms (in terms of size and age, for example) in comparable industries and sectors.

Specific indicators measure the immediate outcomes of policy changes. In fact, we can group these outcome indicators using the same taxonomy as the one used for policy indicators. On hiring and firing of workers, the best indicators are turnover rates, unemployment duration, and unemployment rates --cited above as general indicators. Various worker ratios --such as the ratio of temporary to permanent workers and the proportion of workers according to seniority-- are also useful but require an estimation of 23
Our index of regulations is composed of data on labor regulations and business entry regulations drawn from the “Doing Business” dataset of the World Bank. Data are available for a list of 133 countries benchmarked at January 2003. While some business and labor regulations may be necessary to protect consumers and workers, evidence implies that too much regulation is inefficient and harms the very groups it claims to protect. Djankov et al. (2002) find that countries with more restrictive entry procedures have higher corruption and larger unofficial economies, but not better quality public or private goods. Botero et. al. (2003) show that countries with more restrictive labor laws have a larger unofficial economy, lower labor force participation, and higher unemployment, especially of women and young adults.

These measures of entry and labor regulations also have been shown to have large effects on industrial structure and adjustment. Fisman and Sarria-Allende (2004) find that in countries with excessive regulations, industries respond to shocks to growth opportunities through the expansion of existing firms, while in countries with low barriers to entry, industries respond through the creation of new firms. Moreover, in countries with high entry barriers, industries characterized by large turnover tend to have only a few large firms while countries with low barriers have many smaller firms. Klapper, Laeven, and Rajan (2003) use data on firms in Western and Eastern Europe and find that entry regulations lead to less entry, especially in industries with naturally high entry. They also find less entry into labor-intensive industries in countries with excessive labor regulations.

The labor regulations index consists of the logarithm of an employment laws index, where the latter reflects how regulated the labor market is and is constructed through an examination of detailed provisions in the labor laws of each country. The employment laws index is an average of three indices covering flexibility of hiring, conditions of employment and 7
Figure 4.1: Proportion of Heads of Household with No Education by Youth undertaking Education or not in Haiti 2001.

<!-- Start of picture text --> 90.0<br>Not in school<br>80.0<br>In school<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30<br>Age<br>Percent<br><!-- End of picture text --> _Source:_ Own calculations based on ECVH 2001.

The older the youth is the more likely (but decreasingly so) he or she is to leave school. Graduating from primary or secondary school has a large negative impact on the probability of leaving the educational system compared to not having completed the primary level, i.e., completing a level of education makes the youth less likely to drop out _ceteris paribus_ . Household size is negatively correlated with school dropout. That is, the larger the household size the lower is the likelihood of youth not attending school. This is consistent with the fact that some families have domestic labor in order to enroll their own children in school. Yet the result is surprising because on average poor families tend to be larger than nonpoor families. But it does confirm our field work which clearly showed that poor households value education very highly and spend all their savings on educating their children. However, youth in a newly started family must provide for themselves by working, and when the family grows the oldest children must support the household. Having their basic needs taken care of by older siblings may allow one or more of the siblings to attend school as a family investment. Moreover, it is common to send children and youth to other households for the purpose of education. The household of origin may send money for schooling, hoping that the new household will send the child to school in return for work as a domestic servant or perhaps a combination of the two (Sommerfelt 2003).

In Haiti, religious practice is correlated with dropping out of school. Compared to Baptists and other religiously affiliated persons, Catholics and Voodooists are more likely to not attend school. One explanation may be that the number of Protestant missions has increased rapidly in recent years in Haiti. The Baptists<sup>19</sup> have a network of private schools around the country and tend to strongly emphasize education; as findings indicate, the efforts seem to be paying off in terms of reduced dropout rates relative to other religiously affiliated youth.

> 19 Baptism is the main Protestant affiliation in Haiti.

13
## **Figure 8**

## **China as a standalone engine for growth (b)**

This figure presents R-squared from individual regressions of growth vs PC1 or PC2 from principal component analysis of G8 (G7+China) growth. Growth is proxied by real GDP annual log growth (q/q) and industrial production annual log growth (m/m). Source: IMF IFS Statistics, GEM World Bank.

# **Real GDP Growth**

<!-- Start of picture text --> 19%<br><!-- End of picture text --> <!-- Start of picture text --> 87%<br>79%<br>1%<br><!-- End of picture text --> <!-- Start of picture text --> 1% 0%<br><!-- End of picture text --> <!-- Start of picture text --> 2%<br><!-- End of picture text -->

# **Industrial Production Growth**

<!-- Start of picture text --> 13%<br><!-- End of picture text -->
|**Country**|**Shadow**<br>**as % of**|**Actual**<br>**Tax/GDP**|**Tax**<br>**Revenue/**|**Pote**|**ntial**|**Tax**|**Effort**|**Tax**<br>**Effort**|
|---|---|---|---|---|---|---|---|---|
|Slovenia|26.10|37.73|34.74|31.70|37.12|1.19|1.02|0.94|
|Korea, Rep.|26.60|24.43|22.46|30.51|27.19|0.80|0.90|0.83|
|Spain|22.43|34.37|32.00|29.32|33.49|1.17|1.03|0.96|
|Sweden|18.65|47.92|45.15|29.67|44.88|1.62|1.07|1.01|
|Switzerland|8.51|28.32|27.55|32.22|26.62|0.88|1.06|1.03|
|UK|12.46|35.38|33.98|29.26|33.24|1.21|1.06|1.02|
|**LAC**|||||||||
|Argentina|25.31|26.48|24.44|19.23|30.60|1.38|0.87|0.80|
|Brazil|38.83|32.24|28.58|21.58|26.95|1.49|1.20|1.06|
|Chile|19.20|19.87|18.69|25.32|27.78|0.78|0.72|0.67|
|Colombia|37.08|16.89|15.05|18.79|23.07|0.90|0.73|0.65|
|El Salvador|44.94|13.89|12.10|13.60|11.28|1.02|1.23|1.07|
|Mexico|29.91|17.83|16.23|20.53|22.16|0.87|0.80|0.73|
|Peru|57.79|15.76|13.24|17.17|21.61|0.92|0.73|0.61|
|Uruguay|50.66|21.82|18.70|20.96|31.43|1.04|0.69|0.59|
|Venezuela|33.85|13.73|12.35|17.16|17.10|0.80|0.80|0.72|
|**Others**|||||||||
|Indonesia|18.85|17.27|16.26|15.88|13.47|1.09|1.28|1.21|
|Malaysia|30.76|21.31|19.34|24.44|21.31|0.87|1.00|0.91|
|Thailand|50.25|19.86|17.03|25.04|21.42|0.79|0.93|0.80|
|Tunisia|37.04|27.27|24.30|23.73|28.99|1.15|0.94|0.84|
|South Africa|27.18|28.76|26.39|21.28|25.01|1.35|1.15|1.06|

Higher tax efforts are associated with higher VAT productivity, both for the whole sample as well as the ECA region ( _Chart_ 7).

_Chart 7._ **Tax effort and VAT Productivity** <!-- Start of picture text --> Tax Effort (Legal) and VAT Productivity : All Countries<br>ZAF BLR Tx Effort (Legal) and VAT Productivity : ECA BLR<br>HRV CAN MNE HRV MNE<br>CHLIRLVEN KORTUNBGRESTJPNMYSDEUNLDCZEHNORVNRUSN DNKSWECHEFINBRAAUT IDN BIH SRB BGR CZERUS BIH SRB<br>AZEGEO SVKMDAPRTLVALTUKSV MKDBELESPUKR FRA AZEGEO SVKMDA KSV MKD UKR<br>KAZ GBR KAZ<br>URY ARGPOLGRC ROM ITA POL ROM<br>PERCOL ALB SLV ALB<br>TURKGZ TURKGZ<br>ARM ARM<br>MEX<br>TJK TJK<br>.6 .8 1 1.2 1.4 .6 .8 1 1.2 1.4<br>taxeffort2sA taxeffort2sA<br>Fitted values vatprodmean Fitted values vatprodmean<br>1.2 1.2<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br><!-- End of picture text -->

**_f. Tax effort and tax collections_**

19
<!-- Start of picture text --> 48.0 2.0<br>46.0 - | 1.0<br>340 - ~* -5.0<br>32.0 - -6.0<br>30.0 -7.0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>M@@iBalance (RHS) Revenues and grants —Expenditures<br><!-- End of picture text -->
|**Impact variable**|**U**<br>**P**|**R**|**C**|**T**|**UP**|**UR**|**UC**<br>**RP**|**RC**|**PC**|**Index**|**Number**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Electricity generationp.c.|Y:↑|||Y:↑|||||||1|
||N<br>N|N|N||||||||20|
||Y:↑|Y:↑|N||||||||2|
|||||||||||N|17|
||N|N|N||||||||25|
||N|N|N||||N|N|N||26|
|Generation capacity p.c.|N|N|N||||||||2|
||N<br>N|N|N||Y:↑|Y:↑||N|||16|
||N|N|N||||||||25|
||N|N|N||||Y:↑||Y:↑||25|
||Y:↑o|Y:↑o|||||||||3|
||Y:↑|Y:↑o|||||||||5|
|||||||||||N|17|
|||||||||||Y:↑|23|
|Private investment in sector $|||||||N||||10|
|CAPEX per worker|N|||N|||||||12|
|Access|Y:↑|||Y:↑|||||||1|
||N|Y:↑ o|N||||||||2|
||Y:↑ o|Y:↑ o|||||||||3|
||Y:↑|||Y: ↑|||||||1|
||N<br>Y:↑ o|Y:↑|Y:↑ o||||||||24|
|Duration of interruptions|Y:↓|||Y:↓|||||||1|
|Frequency of interruptions|Y: ↓|||Y: ↓|||||||1|
|Emissions CO2/kWh|||||||Y:↓||||10|
||N<br>N|Y: ↓|Y:↓ o||||||||24|
|Renewables capacity p.c.||||||||||N|17|
|GDPp.c.|||||||||Y:↑||17|
||N<br>N|N|N||||N||||21|
|HDI|N<br>N|N|N||||N||||22|

35
The Gallup International data contain fewer individual-level variables.

Demographic variables record a respondent’s AGE, whether the respondent is FEMALE, whether the respondent is EMPLOYED, and an individual’s level of EDUCATION in three categories. POLITICAL INTEREST in the GI survey is measured with a question asking whether the respondent had contacted any public officials to provide an opinion on a public issue (1) or not (0).

More questions about individual trust are available in the GI survey. TRUST is measured with the same question as in the WVS survey -- “generally, speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” But the GI survey also asks whether individuals feel “this country is run by a few big interests looking out for themselves, or that it is run for the benefit of all the people” (RUN FOR BIG INTERESTS). Further, GI asks individuals about their trust in a variety of specific institutions: TRUST IN GOVERNMENT, TRUST IN THE UNITED NATIONS AND THE WORLD BANK, and TRUST IN NGOS (non-governmental organizations such as environmental or social advocacy groups). The trust in institutions variables are coded along a four category scale, from “a lot of trust” to “no trust at all.”

# _Contextual-Level Independent Variables_

Analysis is restricted to the 22 donor countries that are members of the Development Assistance Committee (DAC). Of these, 19 appear either in our analysis of WVS data (2), Gallup data (10), or both (7). Belgium, France, and Greece are missing from both surveys.

Country-level variables are all taken from the World Bank’s World Development Indicators dataset. Data correspond to 2002 in analysis of the GI data, and to 1995 for the World Values Survey analysis. ODA is measured as Official Development Assistance (ODA) as a share of GNP, or alternatively on the basis of aid in dollars per capita, using aid data collected by the OECD-DAC (Development Assistance Commission). Income per capita is measured using the purchasing power parity adjustments. MILITARY SPENDING is measured as a share of GNP. In adding up (and logging) the number of former COLONIES, we consider only the most recent colonizing power (e.g. Philippines counts as an American, not Spanish colony).

10
national level, about 14 percent higher than the share of the region’s total generation in the country’s wind power generation (about 43 percent).

- Generation losses in Northwest China were not as significant as in North China. The total losses in 2010 amounted to 305 GWh, accounting for 11 percent of wind power losses at the national level. But because of the fast development of the wind base in Jiuquan, Gansu Province, generation losses are bound to become more and more serious in this region.

- At the provincial level, generation losses in Inner Mongolia amounted to about 2,100 GWh, accounting for 79 percent of the total generation losses at the national level (Inner Mongolia overlaps on both North China and Northeast Grid), about 44 percent higher than the share of the province’s total generation in the country’s wind power generation, which is about 35 percent.

23. The financial and environmental impact of the wind power generation losses were US$433 million in 2010, of which US$254 million were from environmental impacts and US$179 million from unrealized potential coal saving<sup>11</sup> .

24. The estimate of the lost environmental benefits per ton of coal equivalent (TCE) is based on the following formula: Environmental benefit losses = [wind generation loss (kWh)*index of coal consumption (TCE) per unit of electricity generation on average in the network (g/kWh)]/1,000,000 * [index of TSP per TCE (t/TCE) * credit per unit of TSP (US$/t) + index of SO2 per TCE(t/TCE) * credit per unit of SO2 (US$/t) + index of NOx per TCE(t/TCE) * credit per unit of NOx (US$/t) + index of CO2 per TCE(t/TCE) * credit per unit of CO2 (US$/t)] _Total environmental benefit losses were estimated by multiplying the “environmental benefit losses per TCE” by the total coal consumption that could have been avoided if all wind-based electricity could have been used._ The calculation is based on the following assumptions (see Table 2): Indexes of environmental pollution per TCE shown in Table 2 are derived from a SEPA (State Environment Protection Administration) study in collaboration with the World Bank completed in 2008 and the index of coal consumption (TCE) per unit of electricity generation on average in the network (g/kWh) is derived from the 2010 electricity yearbook.

> 11 The actual financial and environmental losses

8
geocoordinates, can introduce mismeasurement when integrating them with remote sensing weather data.

In a new study based on a pre-analysis plan, Michler et al. (2022) employ 90 linked weather-household data sets that vary by the spatial anonymization method and show that, as the spatial resolution of most weather data produce is too coarse, spatial anonymization techniques have an overall small effect on the estimates of the weather-agricultural productivity relationship and do not introduce substantial mismeasurement. Depending on the specific type of weather data, however, measurement error can become significant, especially for higher-resolution data products. Importantly, Michler et al. (2022) also find that estimates of weather’s impact on agricultural productivity vary substantially in sign, significance, and magnitude, across different weather data sets for the same spatial anonymization technique. For these reasons, caution is in order when integrating household surveys such as the LSMSISA with external weather data, and the first-best would be to have high-resolution weather data already embedded in the survey data set.

# **4. LSMS-ISA data assessment**

To draw concrete operational implications from the review above, we start by outlining what the implications would be for one of the international survey programs that has been at the forefront of the methodological debate on data collection in low- and middle-income countries in the past 15 years or so. The LSMS-ISA program was launched in 2009 with funding from the Bill and Melinda Gates Foundation and the explicit aim is to fill the gaps in agricultural data through close collaboration with the national statistical offices (NSOs) of partner countries. The program is based on the implementation of multitopic, nationally-representative household longitudinal surveys and, to date, has been carried out in eight Sub-Saharan African countries, namely Burkina Faso, Ethiopia, Mali, Malawi, Niger, Nigeria, Tanzania, and Uganda.

LSMS-ISA survey panels are administered approximately every 1 to 3 years. In the LSMS-ISA program, not only original households are revisited each wave, even if they relocate within the country, but also individual household members who split off from previously selected households are tracked and included in subsequent waves. This intertemporal aspect of LSMS-ISA surveys, therefore, unlocks the potential for the analysis of internal and rural–urban migration patterns, among other things (Carletto and Gourlay, 2019).

23
lives in rural areas and relies mostly on subsistence agriculture.<sup>14</sup> The Malawi Diusion and Ideational Change Project (MDICP) data have been collected since 1998 in rural areas of three districts in Malawi: Balaka in the south of the country, Mchinji in the center, and Rumphi in the north.<sup>15</sup> The dierent rounds of the longitudinal dataset contain extensive information on more than 4,000 men and women, at the individual, household and community levels.

A unique feature of the MDICP dataset is that it includes measures of subjective likelihoods respondents assign to being HIV-positive. The subjective expectations were collected using an elicitation methodology developed by Delavande and Kohler (2009) for a developing country context with low levels of literacy and numeracy.<sup>16</sup> Respondents were provided with ten beans and a plate. They were asked to allocate dierent number of beans on the plate to express the likelihood that dierent events will be realized. The respondents were instructed that zero beans reect certainty that an event will not happen, more beans reect higher likelihood that an event happens, and that ten beans imply certainty about the event happening. The likelihood assigned to being HIV-positive is measured by asking: Pick the number of beans that reects how likely you think it is that you are infected with HIV/AIDS now. Delavande and Kohler (2009) nd that reported subjective expectations follow basic properties of probabilities and that the assessments of HIV-infection vary meaningfully with observable characteristics associated with dierent levels of HIV prevalence.

Since 2004, HIV testing has been oered to all respondents during data collection. The take-up rates of the tests were high, above 90 percent in all waves. The HIV prevalence rate in the sample was 6.9 percent Obare et al. (2009). In 2004, test results were available ve to seven weeks after testing. The testing component of the survey was linked to an experiment that is described and analyzed by Thornton (2008). Respondents were assigned vouchers

> 14data.worldbank.org

> 15Detailed information on the Malawi Diusion and Ideational Change Project can be obtained at http://www.malawi.pop.upenn.edu/.

> 16Attanasio (2009) and Delavande et al. (2010) review existing subjective expectations data from developing countries.

17
enables estimation by conventional panel-data techniques, random- or fixed-effects regressions. These panel-data estimators are asymptotically normal as _T_ **_-+_ oo,** but in small samples, and especially when the number of groups exceeds the number of time periods, these estimators yield overly optimistic standard errors, and lead to overconfidence in the results. Our base regression is instead a pooled OLS using panelcorrected standard errors, as suggested by Beck and Katz (1995). This allows us to correct for errors that are both heteroskedastic (that is, they differ systematically across countries) and correlated over time within countries. While the parameter estimates are found by the conventional method ,B = _(X' X)'_ X' Y, the estimated variance matrix is given by _(X'X)-'XQX(X'X) ',_ where Q=EE _x, Ei; Exi_ . This is similar to the **_i_** _j_ Huber-White cluster (sandwich) error correction (Q2 = (I}x,j-EX,)), but while that method controls for differences in errors across groups, it does not allow for correlation within groups.

Note that the variance correction (weighting) matrix Q does not assume a specific time-series error structure. We conduct an _ad-hoc_ test for serial correlation, by estimating a common serial correlation coefficient r = _wr,_ ,where _r,_ is the estimate of the within-country serial correlation, and _wi_ is a weight derived from the reciprocals of the variances, which increases the efficiency of the estimates (Greene, 1993, p.457). The _ad-hoc_ nature of the test is that we consider the test significant if the serial correlation coefficient is close to or above 0.3, the rule-of-thumb for correction suggested by Grubb and Magee (1988). We find significant serial correlation only in the 6-period sample, for 21
about $14 and $52 at 2011 PPP international prices.

- Some groups of households, such as families with one or more retired members are over-represented in the middle class, and others, such as families with more children than adults or with no income from employment, are underrepresented.

- Consumption aggregates and income aggregates do not differ significantly. Shares of consumption groups (food, essentials, optional items, and luxuries) show different patterns between poor/vulnerable, middle and prosperous classes.

- The Kazakh middle class has increased massively in size and in income concentration. Between 2003 and 2010 the middle class expanded from 3.7 to 31.2 percent of the population. Thereafter it continued to grow substantially, reaching 43.5 percent by 2013 and stayed relatively stable into 2015. The increase in the size of the middle class is essentially due to a growth effect.

- Among factors beyond income we found to influence the self-perception of being middle class were access to a household gas supply, to communication, and to the Internet, living in safe areas, and being educated. Household size and regional residence also play a significant role. Nevertheless, income was by far the most important determinant of middle-class status, which supports the income-based approach.

24