rafmacalaba/datause-encoder-data huggingface dataset:
reporting the school’s progress to the Mexican Secretariat of Public Education. The community advisor receives training from the Mexican Secretariat of Public Education before the information intervention and they receive an allowance of approximately USD 40 at the end of the school year. In the vast majority of schools (98%), the school principal serves as the community advisor. Overall, the information component of AGE is extremely low-cost with a per-student cost of approximately USD 0.98.<sup>3</sup> The information component consists of five sessions, each lasting approximately one hour. Each session was facilitated by the community advisor and focused on group discussion among parents. The first session is an introduction to AGE, highlighting the importance of parental involvement in schools. The second session covers the role of parents in their children’s education and ways in which parents can become involved in school activities and decision-making processes. The third session informs parents about education and health resources in their communities. The fourth session covers the key developmental milestones of children and adolescents, and introduces ageappropriate activities for parents to support to their children’s learning. The fifth session encourages parents to develop an action plan on how they will play a more active role in their children’s schooling and learning. Appendix A1 provides additional details of the information intervention.<sup>4</sup> As the Government of Mexico gradually expanded AGE, two randomized controlled trials were conducted in four states (Chiapas, Guerrero, Puebla and Yucatan). As shown in Figure 1, these four states are home to a large indigenous population, which have faced a long history of discrimination in Mexico (Hall & Patrinos, 2004). The expansion of parental involvement programs in these states holds great promise for improving education but also presents unique challenges for engaging with families that have been historically marginalized.

Given the gradual roll out of AGE, some schools in these states were already participating in AGE while others were not. Thus, the government designed two randomized controlled trials to tease out the effect of the financial component from the information intervention of the parental involvement program. The design of the experiments is outlined in Figure 2.

The first experiment consists of 250 public schools that were already participating in AGE.

> 3Cost calculations are based on administrative data from CONAFE during the expansion of information interventions in 2006.

> 4To encourage the proper dissemination of funds and information to parents, the Mexican Secretariat of Public Education conducts an audit for a random sample of schools each year.

6
may be more related to real economic shocks as a result of the global economic crisis rather than domestic banking crises.

The LiTS survey provides qualitative information on how households responded to economic difficulties during the crisis. Households were asked whether they took any of 17 different measures as a response to the crisis. The survey elicits reduced consumption of _Staple foods_ or _Other consumption_ goods (alcohol, tobacco, luxury goods, use of a car, or vacation). With respect to _Health care_ , households were asked whether they skipped a visit to the doctor; reduced medication or cancelled health insurance. With respect to _Education_ households indicated whether they postponed or withdrew from university or a training course.<sup>11</sup> Table 3 provides an overview of household responses to the crisis by country. The table displays the share of households which reduced the consumption of staple foods, reduced other consumption, reduced education or reduced health care. Overall, the table displays a picture similar to that painted in the studies reviewed in section 2.4: Households cut back strongly on non-essential consumption while the reduction in spending on staple foods is less pronounced. Reflecting the differences in income shocks and income levels the reduction in staple food consumption is much stronger in middle-income crisis countries (43%) and non-crisis countries (35%) compared to the high-income crisis countries (11%).

Households in middle-income crisis countries also experienced a much stronger cut-back in health expenditures (19%) than high-income crisis countries (6%). By contrast very few households reduced their educational activities in either sub-sample. Hereby it is important to note that the survey elicited only changes in higher education, i.e. university education and training courses. Previous evidence suggests that in economic crises households are less likely to reduce basic education than higher education (see e.g. Smith et al. 2002). Thus it is reasonable to assume that primary and secondary school enrollment in the ECA region was hardly affected by the recent banking crises. The fact that education was less affected by the crises than the use of health care suggests that the public-sector channel may have played a role in shaping the impact on households in Eastern Europe and Central Asia. A recent study by the World Bank (2011) suggests that governments in the region did cut back their spending on health more than their spending on education.

> 11 Households were further asked whether they took one of seven other measures including delayed utility payments; delayed payments on loans, had utilities cut, cut TV, internet, and phone services, reduced help to friends; sold off assets or move to another premises.

14
# **3.2 Dealing with Attrition**

A key threat to our ability to measure firm death rates comes from survey attrition, in which owners of businesses could not be found or refused to be re-interviewed in the follow-up survey. The studies we draw on mitigate this threat in two main ways. The first has been to conduct multiple follow-up attempts and track individuals who move, thereby reducing overall levels of attrition. Second, the specialized firm surveys usually record whether the business is still in operation or not, even if the owner refuses to be re-interviewed or is away from the business when survey attempts are made. Table A.5. summarizes the attrition rates in our combined dataset by period of measurement. Attrition rates are relatively low for this combined sample, at 4.8 percent for one year, 10 percent for two years, and 9.5 percent over three years, and are around 20 percent in many periods beyond 5 years, with some exceptions of lower attrition rates.

We then form bounds for death rates to account for attrition. A lower bound for firm death is obtained by assuming that all enterprises which were operating at baseline, and then have missing values for firm death status at follow-up survived. An upper bound for firm death is obtained by assuming that all these firms died.

# **3.3 Summary Statistics on Firm Characteristics**

Table 2 provides baseline summary statistics of our sample of firms in order to provide a description of the types of firms for which we are able to examine firm death rates. The sample consists of small enterprises run by individuals with relatively low levels of education (only 6 percent have any tertiary schooling), and who have an average age of 43 years. 56 percent of the owners are female. The firms have been in business for a mean of 9 and median of 6 years, with just over half (52 percent) in retail, 31 percent in services, and 14 percent in manufacturing. Only 29 percent of firms have any paid workers, with a mean of 0.75 workers per firm. Conditional on having paid workers, the median firm has only 1 worker and only 10 percent have 5 or more workers. Mean (median) monthly profits are US$129 (US$63). The median firm is thus earning approximately $2.65 per day it operates.

# **4.  How Much Firm Death is There?**

The first question that we wish to use this data to answer is just what the rate of firm death is for small firms in the economy. That is, starting with a sample of existing firms today, how many of them will have closed a year from now, or five years from now? The answer to this 9
Raje, D.V., P.S. Dhobe, and A. W. Deshpande. 2002. “Consumer’s Willingness to Pay More for Municipal Supplied Water: A Case Study.” _Ecological Economics._ 42: 391–4000.

Rednet.cn. 2006. “Announcement of Wastewater Treatment Price and Tap Water Price in Changsha City.” Available online at: http://hn.rednet.cn/c/2006/12/06/1051157.htm Renzetti, S. 1991. “Evaluating the Welfare Effects of Reforming Municipal Water Prices.” _Journal of Environmental Economics and Management._ 22: 147–163.

Renzetti, S. 1992. “Estimating the Structure of Industrial Water Demands: The Case of Canadian Manufacturing.” _Land Economics._ 68: 396–404.

UNEP (United Nations Environment Program). 2000. _Global Environmental Outlook 2000_ . Nairobi. UNEP.

Wang, H. 1997. Treatment of “Don’t-Know” Responses in Contingent Valuation Surveys: A Random Valuation Model. _Journal of Environmental Economics and Management_ , 32(1997): 219-232.

Wang, H., and J. He. 2008. “Estimating Individual Valuation Distributions with Multiple Bounded Discrete Choice Data.” Washington, D.C. World Bank.

Wang, H., and D. Whittington. 2005. “Measuring Individuals’ Valuation Distributions Using a Stochastic Payment Card Approach.” _Ecological Economics._ 55(2005): 143–154 Welsh, M., and M. Bishop. 1993. “Multiple Bounded Discrete Choice Model.” In J. Bergstrom, ed. _Benefits & Costs Transfer in Natural Resource Planning_ . Western Regional Research.

Welsh, M., and G. Poe. 1998. “Elicitation Effects in Contingent Valuation: Comparisons to a Multiple Bounded Discrete Choice Approach.” _Journal of Environmental Economics and Management._ 36 (2): 170–85.

World Bank. 2007. _Water Supply Pricing in China: Economic Efficiency, Environment, and Social Affordability_ . Washington, D.C. World Bank.

Zhou, Y., and R. Tol. 2005. “Water Use in China’s Domestic, Industrial and Agricultural Sectors: An Empirical Analysis.” Working Paper. Hamburg, Germany: Research Unit, Sustainability and Global Change, Hamburg University.
# **Table 11: Summary of targeting performance for simulated alternative social** **<u>policies</u>**

||**Error**<br>**inclusion**|**Error of ex**<br>**of**<br> <br>Target<br>population|**clusion**<br>Total<br>populatio|n<br>**Concentration**<br>**coefficient**|
|---|---|---|---|---|
|National social tariff scheme for natural gas<sup>*</sup>|52%|44%|69%|-0.07|
|National social tariff scheme for LPG cylinders<sup>*</sup>|24%|19%|67%|-0.41|
|National natural gas connection subsidy|24%|0%|19%|-0.40|
|Public transport status quo (supply-side subsidy in<br>Metropolitan Area)|the<br>59%|0%|64%|+0.16|
|Simulated alternative|||||
|Demand-side subsidy in the Metropolitan Area (free passes)|36%|0%|35%|–0.21|

*Subsidy targeting based on use of multi-dimensional poverty proxy limited to subsistence consumption threshold

_Source:_ OPSM, May 2002 46
other hand, there are a range of health service concerns that could be more easily addressed, for example, access and coverage issues, financing systems etc. Data related to these options could be included as part of the WHS; specific suggestions are made.

# 3.2 Cgsts

For both CBA and CEA, monetary estimates are required of the inputs into the health programme, project or service being evaluated. These inputs or costs are various and can be summarised as below (Drummond and Stoddart 1985):

1. Organising and operating costs within the health care sector (eg.health care professionals' time, supplies, equipment, power and capital costs).

2. Costs borne by patients and their families - direct costs of outof-pocket expenses (transport and service fees) and patient and family inputs into treatment; indirect costs of time lost from work and psychic costs.

3. Costs borne externally to the health care sector, patients and their families.

Measurement of these costs is not the easy task it may seem and lack of appropriate information about costs to patients and their families often leads to their exclusion from analyses. Identifying the resources used by one programme, when they are shared with other programmes, is also a difficulty. Other informational problems relate to the budget:ng practices of developing countries, which result in little or no routine information that is disaggregated appropriately for economic evaluation purposes (eg. by programme). They can currently only be alleviated by special surveys or by accepting that the data is approximate. The problems of valuing indirect costs in monetary terms is considered in greater detail in relation to consequences.

15
design features of IBLI’s impact evaluation, including randomized exposure to various information treatments and randomized distribution of premium discount coupons, allow us to address the selection bias associated with insurance uptake choices. We first estimate selection into IBLI using randomized encouragement treatments as instruments. We then estimate the effect of instrumented IBLI on SWB. This approach allows us to derive unbiased and consistent causal estimates of IBLI’s impact on SWB.

IBLI uptake by household _i_ in village _v_ , sales period _s_ , and survey round _t_ is estimated using the linear probability model (LPM)<sup>15</sup> as: The randomly assigned treatments include dummy variables for receiving a randomly assigned premium discount coupon ( _D_ ) in the first sales period (August-September 2012), the second sales period (January-February 2013), or both; dummy variables for receiving randomly assigned extension treatments in either audio tape ( _A_ ) or comic book ( _C_ ) form in the first, the second or both sales periods, and a _woreda_ specific continuous measure of the randomly discounted IBLI premium rate ( _P_ ) in the first and second sales periods. These are all randomly assigned to households and should have no direct effect on SWB, only an indirect effect through their impact on inducing IBLI uptake. The lone possible exception is _P_ , since price variation has a (very modest) real income effect conditional on someone purchasing IBLI and thus could plausibly have some direct effect on SWB. A series of covariates, _X_ , that may influence the uptake of IBLI are included as controls, including household herd size and income, expectation of livestock death, gender, age and educational attainment of household head and household composition. Household fixed effects (FE), _τ_ , which control for, among other things, time invariant optimism or pessimism of individual respondents and survey round fixed effects, _κ_ , are also included.

We use the randomized coupon distribution and information treatments to instrument for the purchase of IBLI coverage in the first stage estimation. When applied to R2 data, equation (1) predicts current uptake, _IBLI_<sup>�</sup> _iv_ 2, based on purchases in sales periods 1 and 2. There were no lapsed contracts in R2. When applied to R3 data, it predicts current uptake, � _IBLI iv_ 3, based on purchases in sales periods 3 and 4. We use the _IBLI_<sup>�</sup> _iv_ 2 predicted value

> 15To avoid the “forbidden regression” problem associated with non-linear models such as logit or probit, we use an LPM to predict an endogenous dichotomous variable in the first stage of an instrumental variables (IV) regression (Angrist & Pischke, 2008; Wooldridge, 2010).

12
people were exposed to concentrations above the environmental contingency Stage I level of 300 ,g/m3 at least once during the year.

The baseline scenario for 2010 assumes emissions of NO, and VOCs, precursors of ozone and PM1o, to be the same as at the end of the 1990s. Likewise, we assume air quality in 2010 with respect to ozone and PM1O to be the same as the levels observed at the end of the 1990s. This assumption, however crude, seemed to be the most appropriate one in the absence of an integrated model of emission projections for 2010 for fixed and mobile sources in Mexico City.

Four alternative air pollution reduction scenarios for 2010 are evaluated. We do not appraise the policies needed to achieve the concentration reductions. The four scenarios are (population weighted exposure reductions are presented in table E.2):

- a 10-percent reduction in PM1O and ozone;

- a 20-percent reduction in PM10 and ozone;

- improved air quality compliance at an air quality standard of 50 Ftg/m3 for PM1O and 0.11 ppm 1 -hour maximum for ozone in all ZMVM locations (AQS 1);

- an air quality standard superimposing the required decrease in concentrations in the most polluted areas (Xalostoc for PM10 and Pedregal for ozone) across the ZMVM (68 and 47 percent reduction in ozone and PMIO concentrations, respectively) (AQS2).

**Table E.2: Reduction in Population-Weighted Exposure for the Analyzed Scenarios**

|_Scenario_|_Populationweighted_<br>_exposuretoPM10_<br>_(ug/m _<sup>_3_</sup>_/person)_|_Population weighted_<br>_Exposuretoozone_<br>_(ppm/person)_|
|---|---|---|
|10**percentexposurereduction**|**6.41**|**0.0114**|
|**20percent exposurereduction**|**12.81**|**0.0227**|
|**AQScomplianceineacharea**- **AQSI**|**14.06**|**0.0702**|
|AQScomplianceinworstarea- AQS2|29.99|0.0778|

The health risks due to air pollution (specifically ozone and PM1O) are quantified by estimating the relationship between the incidence of adverse health effects and air quality. To this end, a number of quantitative estimates of exposure-response relations of known health effects from various cities have been pooled together (meta-analysis).

Health impacts include eye irritation, respiratory diseases, cardiovascular effects, and premature death. This paper, unlike studies such as Hernandez-Avila and others (1995), who focused only on hospital costs, assesses a wide range of health benefits of reducing air pollution: (i) reduced cost of illness (COI); (ii) reduced losses in productivity; (iii) willingness to pay (WTP) for reduced acute and chronic morbidity effects; and (iv) willingness to pay for mortality effects associated with acute and chronic exposure.

_ii_
|P082466|2004|2010|5/22/2010|12 Closed|Colombia|
|---|---|---|---|---|---|
|P082466|2004|2011|2/27/2011|13 Closed|Colombia|
|P082466|2004|2012|8/2/2011|14 Closed|Colombia|
|P082466|2004|2012|3/29/2012|15 Closed|Colombia|
|P082466|2004|2013|11/11/2012|16 Closed|Colombia|
|P082466|2004|2021|7/17/2020|17 Closed|Colombia|
|P082472|2003|2003||0 Closed|Afghanistan|
|P082472|2003|2004|1/13/2004|2 Closed|Afghanistan|
|P082472|2003|2005|5/5/2005|4 Closed|Afghanistan|
|P082472|2003|2006|12/12/2005|5 Closed|Afghanistan|
|P082472|2003|2006|6/12/2006|6 Closed|Afghanistan|
|P082472|2003|2007|12/20/2006|7 Closed|Afghanistan|
|P082472|2003|2007|6/14/2007|8 Closed|Afghanistan|
|P082472|2003|2008|9/13/2007|9 Closed|Afghanistan|
|P082474|2006|2003||0 Dropped|Sri Lanka|
|P082474|2006|2004|6/28/2004|3 Dropped|Sri Lanka|
|P082474|2006|2005|6/22/2005|5 Dropped|Sri Lanka|
|P082492|2006|2006|11/9/2005|1 Closed|Tanzania|
|P082492|2006|2006|5/26/2006|2 Closed|Tanzania|
|P082492|2006|2007|12/6/2006|3 Closed|Tanzania|
|P082492|2006|2007|6/26/2007|4 Closed|Tanzania|
|P082492|2006|2008|12/12/2007|5 Closed|Tanzania|
|P082492|2006|2008|5/30/2008|6 Closed|Tanzania|
|P082492|2006|2009|12/19/2008|7 Closed|Tanzania|
|P082492|2006|2009|5/28/2009|8 Closed|Tanzania|
|P082492|2006|2010|12/24/2009|9 Closed|Tanzania|
|P082492|2006|2010|6/29/2010|10 Closed|Tanzania|
|P082492|2006|2011|3/28/2011|11 Closed|Tanzania|
|P082492|2006|2012|12/17/2011|12 Closed|Tanzania|
|P082492|2006|2013|7/7/2012|13 Closed|Tanzania|
|P082492|2006|2013|2/20/2013|14 Closed|Tanzania|
|P082498|2004|2004|6/16/2004|1 Closed|Yemen,Republic of|
|P082498|2004|2005|4/27/2005|3 Closed|Yemen,Republic of|
|P082498|2004|2006|7/29/2005|4 Closed|Yemen,Republic of|
|P082498|2004|2006|1/30/2006|5 Closed|Yemen,Republic of|
|P082498|2004|2006|6/22/2006|6 Closed|Yemen,Republic of|
|P082498|2004|2007|12/27/2006|7 Closed|Yemen,Republic of|
|P082498|2004|2007|6/26/2007|8 Closed|Yemen,Republic of|
|P082498|2004|2008|12/21/2007|9 Closed|Yemen,Republic of|
|P082498|2004|2008|3/28/2008|10 Closed|Yemen,Republic of|
|P082498|2004|2009|7/28/2008|11 Closed|Yemen,Republic of|
|P082498|2004|2009|3/4/2009|12 Closed|Yemen,Republic of|
|P082498|2004|2010|8/12/2009|13 Closed|Yemen,Republic of|
|P082502|2005|2008|4/18/2008|1 Closed|Africa|
|P082502|2005|2008|5/30/2008|2 Closed|Africa|
|P082502|2005|2009|10/7/2008|3 Closed|Africa|
|P082502|2005|2009|12/19/2008|4 Closed|Africa|
naire, but a limitation is that respondents only make up a relatively small share of all formal firms and some sectors are excluded (e.g. the mining sector). In contrast, tax administrative data captures all firms in all sectors that file VAT and PAYE returns. However, not all firms file returns each month, and they are only required to provide limited information about their activities when they do (e.g. total monthly sales and number of employees paid each month). By comparing the findings across these different data sources, it is possible to have far greater confidence in what actually took place regarding the recovery (or lack thereof) of formal firms.

We show, in both the survey and tax administrative data, that the sales of formal firms in Zambia recovered from the pandemic far more strongly than their employment levels. The order of magnitude of the initial decline and subsequent recovery of firms varied between the survey and tax administrative data, with the latter presenting a more positive picture. However, the overall trend was the same. In June 2020, on average, formal firms reported a large decline in sales and a moderate fall in employment. In contrast, by July 2021, most firms had experienced a complete recovery in their sales, while the share of firms reporting that they had decreased their number of employees doubled. Econometric analysis examining the factors associated with an initial decline and subsequent recovery illustrates structural factors (e.g., the type of business activity a firm was involved in) were more closely correlated with changes in sales than changes in employment, whereas the latter was more closely correlated with firm-specific factors (e.g. the level of experience of the top manager).

These results provide several insights about how formal firms have recovered from the COVID-19 crisis. First, there has been a ‘decoupling’ between the levels of sales and employment among some formal firms in Zambia. Formal firms appear to have adjusted their operations in a way that reduced their need for labor to achieve the same (or higher) level of sales. As such, one way firms were able to be resilient and rebound from the crisis was to reduce their labor inputs, which can often be challenging to lower in more stable settings. Second, if formal firms’ reduced reliance on labor persists, lower levels of formal employment in low- and middle-income countries may be a concerning consequence of the COVID-19 2