rafmacalaba/data-use-mentions-extended huggingface dataset:
IBGE) in 2010 and data provided by _Instituto de Pesquisa Econômica Aplicada_ (IPEA), to capture characteristics of the municipalities (IDHM), population, etc.<sup>38</sup> We have information only from the demographic census in 2010, and so we used population estimates for the other analyzed years (2007–11), calculated by IBGE.

We measure educational outcomes using three different data sets. First, we measure student academic achievement in Portuguese and mathematics with a national standardized test, _Prova Brasil_ . _Prova Brasil_ is a census evaluation of public primary school students from the fifth and ninth grades. Implemented in 2005, the evaluation takes place every two years. We thus will exploit the data for 2005, 2007, 2009, and 2011. In our main sample, we exclude schools in rural areas,<sup>39</sup> because it is not possible to gather information about their student scores in _Prova Brasil_ over the analyzed period (2005– 11).<sup>40</sup> Second, to measure the quality of education in a broader way, we use the Basic

> _36_ Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP).

> 37 Fundo Nacional de Desenvolvimento da Educação (FNDE).

> 38 UNDP Atlas (2013) aggregates census data by municipality, and this dataset will be used in the analysis.

> 39 In the short-term analysis, when estimating the average impacts for the sample of schools that joined the program in 2010, rural schools are included.

> 40 Although in 2005 the sample of schools evaluated in _Prova Brasil_ included only urban schools, with at least 30 students enrolled in each of the grades assessed, from 2007 on some rural public schools started participating in the evaluation, but only students enrolled in the fifth grade. In 2009 _Prova Brasil_ began to

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Subsidies and contributions in the payment of utilities were determined using the value reported by households in the ENPH and the percentage of subsidy or contribution available in the administrative data. For those departments for which administrative information was not available, aggregate information was taken nationally. In addition to these subsidies, in Colombia, there are gasoline and diesel subsidies. However, due to the lack of a historical series of reference prices for these fuels, it was not possible to model them.

In-kind transfers in education were determined by calculating how much each student costs the national government and local administrations. For the calculation of this per capita value, the value allocated by the national government per student at the beginning of each year was taken, and an adjustment factor was calculated to account for the additional resources provided by local administrations to finance primary and secondary education. Subsequently, this value was imputed to the students of public schools, differentiating them according to the level of education, the territorial entity in which they live, and whether it is an urban or rural area.<sup>39</sup> The exercise concerning higher education is still under construction because of the many obstacles present in carrying it out. The first problem is that even if the household survey allows students who are attending higher education to be identified, it does not allow their differentiation between technical, technological, and university levels. Added to the preceding is that it is not possible to identify to which educational institution they belong: one of the 32 public universities, or any technical or technological institution, or the SENA (National Apprenticeship Service). In the case of public universities, the national government contributes to their financing, but so do the local administrations, often through stamp taxes that are not recorded in the FUT (Local Fiscal Account). Likewise, the budget of technical and technological institutions can fall heavily on the contributions of departments and municipalities, making identification of the budget very difficult. Due to these difficulties, and as a temporary alternative,
If attention is restricted to national stock markets, several indices are generally available. Often the choice is between an 'all-share' index and one that is only made up of the largest or most frequently traded companies. In an analysis of the interval problem (see Section 6.2 and Appendices A1.3 and A3), the beta values of large companies (as the utilities tend to be) are found to have a smaller, upwards bias when calculated against an index consisting only of frequently traded shares. As is clear from Table 6.1 for AT&T, the large-security indices (Dow Jones Composite 65 and FTSE 100) produce lower beta estimates than their respective all-share indices. Economic theory suggests that largesecurity indices would produce more accurate beta estimates as they are less prone to price adjustment lags, and so, where possible, beta estimates in this paper have been derived from large-security national stock market indices.

**Table 6.1: Daily asset beta estimates for AT&T against alternative indices**

|**NYSE All-share**|**Dow Jones Composite**65|**World**($)|**World**(£)|**FT All-share**|**FTSE 100**|
|---|---|---|---|---|---|
|0.85|0.72|0.42|0.35|0.20|**0.17**|

In the case of AT&T, the value of 0.72, calculated using the Dow Jones Composite 65 index, has been chosen as the best estimate of the true beta value.

# **6.2 Choice of interval length**

Another important issue is the frequency of the data used in calculating beta. Returns calculated over a daily, weekly, monthly or even longer period may be used in the calculation, but there
3|1.0|
|Vietnam*|76.5|81.0|72.3|74.5|79.0|70.4|2.6|2.5|2.7|

_Sources: Laborsta (ILO) – Labor Force Survey data. *Cambodia (2004) 10+; Vietnam (2009, VET2)_

# _Low unemployment rates_

The evolution described above translates into low unemployment rates, and little difference in unemployment rates between men and women (Table 1). A low unemployment rate in a lower-middle-income economy cannot be interpreted in the same way as in developed economies. In the latter, comprehensive social security systems, and in particular unemployment benefits, mean that workers who are out of a job have the opportunity and time to look for another suitable job. The poorest people are therefore found among the unemployed. In middle-income countries that offer no or limited financial help to those looking for work, workers who are newly unemployed often cannot afford to stay out of a job very long and therefore take up new employment more quickly, even if the job is not suitable in terms of pay, required qualification, etc. Often, such jobs are found in the informal sector, which has much more flexible labor practices than the formal sector, but which also afford lower employment security. The unemployed are not therefore especially the poorest people; they are on the contrary those who can afford to remain unemployed. The structure and quality of employment therefore matter even more than unemployment in these countries.

There was a modest increase in the unemployment rate between 2007 and 2009. The introduction of unemployment insurance just before the financial crisis could have been associated with a large increase in the proportion of people registering as unemployed, 6
school the day of the application with regard to PROSPERA receipt. This seems a reasonable assumption.

The construction of the panel allows us to study educational trajectories of youth between sixth grade and twelfth grade beginning with sixth graders in 2007-2008. We choose this panel as it allows following students for the longest time period possible with the ENLACE. We merge ENLACE exams from seventh grade (first grade of secondary school) in 2009-2010, ninth graders in 2011-2012 and twelfth graders in 2014-2015. Ideally, we would have had ENLACE scores for tenth graders in 2012-2013 so as to better analyze the transition from secondary to high school, but ENLACE tests were only administered in high schools to twelfth graders.

The ENLACE data also contain information on the type of school attended. Thus, we are also able to study program effects on enrollment in different types of schools in addition to achievement test results. There are a number of pathways youth may take, for instance, sixth graders may dropout or repeat sixth grade, or they may enter a tele-secondary, technical, general or private school. At the secondary level we study enrollment in technical, tele-secondary or general schools (less than 1 percent enroll in private secondary schools). At the high school level, we distinguish between general high schools, technical high schools, CONALEP high schools and private high schools.

> 5 Data available for this version of the paper did not include students who continued to be enrolled but repeated a grade. Thus, these students are excluded in this analysis. Failure rates are, however, relatively low in sixth grade and beyond (Behrman, Sengupta and Todd, 2005).

10
we have at least our cutoff of five or more establishments per cluster).<sup>9</sup>

> 6 Due to confidentiality restrictions, establishment-level data were not able to be procured through the NSO. 7 The Census of Industry surveyed all plants, although those with 1-4 workers were not included in the dataset. The analysis excludes utilities, mining and quarrying, and a few other industries which, due to the methodology of data collection were available but are commonly thought of as outside the manufacturing sector. To protect the confidentiality of survey respondents, the Department of Census and Statistics have top-coded employment size categories for certain sectors with few very large firms (into an “other” category) to prevent them from being identified in the cell data. This necessitated aggregation of cells in the 100-499 and 500+ size categories into one single 100+ size category. We focus on seven provinces since Northern and Eastern provinces have been omitted due to civil unrest causing questions of data reliability and consistency. Specifically, conflict in these areas precluded the collection of surveys in 1993 and 1998, so we excluded them from all years to maintain a consistent sample.

> 8 The sample size for the 2004 WBES in Table 3 reflects the estimation sample used in the firm-level TFP analysis presented in Section VI. The cluster level indicators, however, are based on the full sample of 452 surveyed establishments.

> 9 A similar approach has been used, among others, by Aterido et al. (2011). While in principle there could be up to 490 possible location-industry-size clusters (14 2-digit industries x 7 provinces x 5 size classes), in practice given available data we are able to construct 322 location-industry-size clusters.

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that shortterm elasticities of substitution for firm inputs may be low in Tanzania. Previous literature has found that low substitution elasticities translate into costly supply chain multiplication effects, thus propagating shocks (Barrot and Sauvagnat 2016; Boehm et al. 2019). Colon et al. (2019) first presented evidence of this effect for Tanzania. The firm data presented here sheds further light on the size of the effect.

The data collected through this survey suggest that the primary reasons for general supply and delivery delays in Tanzanian firms are upstream supply chain issues. About 53 percent of firms reported that their supplies were delayed as a result of problems with the supplier’s supplier (figure C.11, appendix C), and 32 percent indicated that issues with their own suppliers were the primary reason for delayed deliveries to their clients (figure C.12, appendix C). This highlights that a significant share of delays can be explained by supply chain multiplication effects.

By correcting for network multiplication effects, we show that most residual supply chain delays can eventually be attributed to disruptions of transportation and power infrastructure. For instance, if a firm reports that the delayed delivery of supplies is partly due to transport disruptions and partly due to delayed dispatch by their supplier – then the delayed dispatch can in turn be partly explained by transport disruptions further up the supply chain. Hence, ultimately, any delays from suppliers must be attributable to infrastructure disruptions or on-site flood impacts that affected up-stream suppliers. By proportionally re-attributing the reasons for supply and delivery delays, we account for the supply chain multiplier and 13
**Table 8. Explaining Social Norms**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||PNB Index -<br>HH Roles|PNB Index -<br>Public Space|SNE Index -<br>HH Roles|SNE Index -<br>Public Space|Sanctions<br>Index|
|Respondent is female|-0.00|-0.03|-0.01|-0.07*|0.02|
||(0.03)|(0.04)|(0.02)|(0.04)|(0.11)|
|Respondent’s age in years,|0.00|-0.00|-0.00|-0.00**|-0.00|
|squared|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Years of education, derived|-0.01|-0.02*|-0.00|-0.02***|-0.08***|
|from education level|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|
|Indicator for respondent|-0.01|0.05|-0.04|-0.00|0.33|
|having child younger than 5<br>years|(0.02)|(0.04)|(0.03)|(0
> _Mexico: Alternative_ _<u>estimates of Household Inequality Gini 1992-1998</u>_ <u>.</u>

|_Author _|_Concept_|1992|1994|1996|1998|
|---|---|---|---|---|---|
|Luxembourg Income Study|Net Income|54.5|55.3|53.4|54.8|
|Szekely and Hilgert (1999)|Net Income|53.4|53.6|52.8||
|Lopez-Acevedo and Salinas (2000)|Total Income|53.1|53.4|51.9||
|Baldacci et al (2002)|Income|54.1|54.2|51.6||
|Baldacci et al (2002)|Consumption|52.7|51.6|50.2||
|Deininger and Squire (2004)|Gross income|60.7|56.4|54.9|55.4|
|WorldDevelopment Indicators/Povc<br>|al Expenditure|50.3|53.7<sup>a</sup>|51.9|53.1|
|a1995||||||

Source: WIDER World Income Inequality Database (WIID), Baldacci et al. (2002), Lopez-Acevedo and Salinas (2000), Povcal (accessed August 2004).

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description of our analysis on likelihood of being in the treatment group. We proceed with the shortterm impacts of the EF program on the full sample and on various demographic and socioeconomic subgroups.

## **a. First-Stage Estimation of Propensity Scores**

At the heart of the propensity score matching method is pairing trainees with non-trainees who are similar in terms of their observable characteristics. This pairing between a trainee and comparable non-trainees is done via a propensity score, as described in section IV.

> 44 An individual is recorded in the EF monitoring database as a trainee only when the T&E provider submits the person’s name at the end of the training. Since the EF only reimburses T&E providers at the conclusion of the training, they do not record nor do we have any way to track how many people enrolled, but did not complete, the training in 2010 to 2012.

> 45 The monitoring records indicate that the control group individuals who received training did so in the original training course that they applied for. There is no evidence that they applied for and participated in a different EF training course sponsored by a different T&E provider, nor that they participated in EF training courses in a different year.

> 46 In practice, while we cannot be sure what happened in each case, the T&E providers probably dipped into the pool of lowerscoring applicants in an attempt to fill up the training slots as people dropped out.

> 47 The stable unit treatment value assumption (i.e., SUTVA) based on Rubin (1980) assumes that (1) the treatment status of any unit does not affect the potential outcomes of the other units (i.e., non-interference) and (2) the treatments for all units are comparable (i.e., no variation in treatment). Note that take-up of training among control group units is not a violation of the SUTVA, as take-up among non-beneficiaries was not directly a result of the take-up of training
two aspects deepen the inefficiency across sectors as some incur very high abatement costs to reduce carbon emissions while others do not abate even when their abatement costs are significantly low. On the contrary, with a uniform carbon price endogenously determined by the model, all profitmaximizing firms will reduce emissions up to the point where marginal abatement costs are equal to the carbon tax rate ensuring that the required level of emissions reduction is achieved at least cost. Finally, as shown in Bohringer et al. 2008 and 2016, overlapping regulation can determine efficiency losses as the use of multiple policy instruments to curb greenhouse gas emissions as it is in the presence of a comprehensive emission pricing and energy taxes can cause what is defined as “excess cost”. Thus, the cut of energy taxes could reduce this additional cost due to the overlapping regulation.

_Table 3: Macroeconomic results in 2030 (% change wrt Baseline)_

|**Scenarios**|**consumption**|**gov.**<br>**Expend.**|**investment**|**public**<br>**investment**|**export**|**import**|**gdp**|**Gini index**<br>_(Average_<br>_2022-2030)_|
|---|---|---|---|---|---|---|---|---|
|f55|-0.9|-0.5|0.4|-0.5|-1.0|-1.3|-0.5|0.009|
|f55 & wage tax|-0.6|-0.4|0.1|-0.4|-0.9|-1.2|-0.4|0.004|
|f55 & CIT|-
# **3. Some documented cases**

# _Indonesia_

Here an important and well-documented case is the Indonesian crisis of 1997-9. Detailed analysis of household surveys taken just before, during and after the crisis displays graphically how living standards fall across the board, but tells a more mixed story about relative effects (Frankenberg et al, 1998, Strauss et al. 2004 – see Annex 1). The indicators are that, while absolute poverty rates soared from perhaps 15 percent of the population to 33 percent, the (relative) distribution of income became less unequal.<sup>11</sup> Also, remarkably, three years after the crisis hit, absolute poverty rates had dropped back below their pre-crisis levels.

Is the Indonesian case representative? Because of the nature of the data collected, the channels of influence from financial firm failure to poverty are hidden. Huge changes in relative prices, and some deterioration in the quality (and increase in the relative price) of public health services are the proximate causes of changes in poverty and inequality as measured in the Indonesian household data. These relative price changes were closely linked with the very large exchange rate movements that characterized the Indonesian crisis. Food prices in particular adjusted more and faster than other prices. In addition, there were widespread business failures and civic disturbances. Other financial crises have not been associated with as much macroeconomic and political turmoil and with such sharp changes in relative prices. This is the first reason for skepticism about how readily one can generalize from the Indonesian experience.

But in addition, it is well established that large macroeconomic adjustments can be associated with relative price changes that actually benefit large segments of the poor population (Ferreira et al. 1999, Manuelyan-Atinc and Walton, 1998). The contrast between Latin American and African adjustments in the 1980s is often mentioned: the increase in producer prices for small African farmers resulting from real devaluations in several countries helped improve income inequality there, whereas Latin America experienced deteriorating income inequality.<sup>12
to us more plausible than inferring that Chinese firms are less competitive than Ghanaian ones.

Labor costs, and the supply of labor in general and specific skills in particular, are important issues for firm performance and especially for the link between performance and poverty reduction. One striking result that has emerged from the research on the firm data is how much larger are wages in large than in small firms. This is only partly explained by large firms employing more skilled workers than small ones. That is, there is a large proportion of the wage differential across firms of differing size that is not explained by a differential in skills. Söderbom, Teal and Wambugu (2005) show that, conditional on all observed and unobserved worker skills that are fixed over time, workers’ earnings in Ghana and Kenya vary positively with firm size. The estimated effect is economically large, compared to other regions. In the preferred specification for Ghana, the estimated elasticity of wages with respect to firm size is 0.15; in Kenya it is 0.08. These numbers imply that as a firm grows from having, say, 20 employees to 40, wages will increase by 11 percent in the case of Ghana and 6 percent in Kenya.<sup>8</sup> While this might seem like good news for the employees, increasing wages results in higher total labor costs which, everything else held constant, tend to erode firms’ profits. Improved performance in the form of higher productivity would help to countervail this effect. Söderbom and Teal (2004) investigate if there is any evidence of increasing returns to scale in Ghanaian manufacturing. Under increasing returns to scale, the proportional change in output is larger than an equiproportional increase in all inputs (i.e. a doubling of all inputs leads to more than a doubling of output, for instance), and so when firms grow higher average labor costs would be offset by higher input productivity. Söderbom and Teal find, however, only weak evidence for increasing returns and
migrants; and (iii) per student education expenditure<sup>varies widely according to the city of residence and the head</sup> of household's occupation. Cities with higher public<sup>school repetition rates have a higher incidence of private</sup> school enrollments.

_Source:_ Psacharopoulos, Ariera and Mattson 1997 Findings of a study jointly commissioned<sup>by the World Bank and UNICEF, and</sup> conducted by Bray (1996), revealed<sup>that poor households in nine East Asian countries</sup> (Cambodia, China, Indonesia, Lao People's<sup>Democratic Republic, Mongolia, Myanmar, the</sup> Philippines, Thailand and Vietnam) already<sup>contribute a significant share of the total cost of</sup> public education. In these countries,<sup>household expenditure on education forms a significant</sup> share of household income. Given high<sup>inequality and inequity in income distribution, the</sup> poorest households' burden is much greater<sup>than that borne by the wealthiest households.</sup> Findings of the 1996 World Bank-UNICEF study<sup>also revealed that in some of the South-East</sup> Asian countries mentioned above, household<sup>contributions to education are greater than</sup> government contributions. In China, for<sup>example, where over 70 percent of the population</sup> resides in rural areas and where wages are about<sup>half of those in urban areas, parents and village</sup> communities have financed primary education<sup>through cash and/or in-kind contributions for</sup> both teachers' salaries and construction of school<sup>buildings. In some countries, awareness of</sup> such demands on household income discourages<sup>parents from enrolling their children in school.</sup> Governments and donors have been accused
# Figure 4 Expenditure Shares Across the Income Distribution

<!-- Start of picture text --> 2 4 6 8 10 12<br>log per capita expenditure<br>Agriculture and Food Manufacturing<br>Services Other<br>Home consumption<br>.5<br>.4<br>.3<br>.2<br>expenditure share<br>.1<br>0<br><!-- End of picture text --> Notes: non-parametric kernel regressions of budget shares on the log of per capita household expenditure across the 54 countries included in the HIT database.

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