rafmacalaba/datause-encoder-data huggingface dataset:
|Question:**Pessimisticscenario/alternative**|Scenarios|Variables|
|---|---|---|
|In a more pessimistic (worse) scenario, do you expect that your sales<br>for the next 6 months will increase, decrease, or remain the same,<br>compared to the same period last year?|**cov3a_3**|1= Increase<br>2=Decrease<br>3=Remain the same|
|Increase by how much?<br>CONDITION:Ask onlyif**cov3a_3**=1|**cov3a_3a**|change %|
|Decrease by how much?<br>CONDITION:Ask onlyif**cov3a_3**=2|**cov3a_3b**|change %|
|In the same pessimistic scenario, do you expect that the number of full-<br>time workers in this establishment will increase, decrease, or remain the<br>same, compared to the same period last year?|**cov3b_3**|1= Increase<br>2=Decrease<br>3=Remain the same|
|Increase by how much?<br>CONDITION: Ask only if**cov3b_3**=1|**cov3b_3a **|change %|
|Decrease by how much?<br>CONDITION: Ask only if**cov3b_3**=2|**cov3b_3b **|change %|
|In a scale from 0 to 100, what is the chance (probability) you believe<br>this will happen?|**cov3a_3L**|probability %<br>(between 0 and 100)|
|Is it expected that this establishment will fall in s in any of its outstanding<br>liabilities in the next 6 months? [OPTIONAL]|<br>1 = Yes<br>2 = Ye|**cov3d**<br>. It is already in arrears.<br>s. It will fall in arrears.<br>2 = No|

# **COV3M. Expectations and uncertainty (FOR MICRO FIRMS ONLY)**

**CONDITION: This section applies if the establishment has less than 5 workers cov0c1 < 5**

|Looking ahead to the next**3 months**do you expect<br>that your sales will increase, decrease, or remain the<br>same, compared to the same period last year?<br>**ALTERNATIVE FORMULATION IF FIRM HAS BEEN**<br>**IN EXISTENCE FOR LESS THAN 12 MONTHS**<br>**(covm3b)**<br>_Looking ahead to the next_**_3 months_**_do you expect_<br>_that your sales will increase, decrease, or remain_<br>_the same, compared to the last 3 months?_|**covm3a**|1= Increase<br>2=Decrease<br>3=Remain the same<br>-9=Don’t know|
|---|---|---|
|Looking ahead to the next**3 months,**what is the<br>expected change in sales that you anticipate for this<br>establishment compared to the same period last<br>year?|**covm3c**|% change|
73 Table 36. Progression of U.S. emission standards for automobiles (in **grams** per mile)

|Model Year|Initial cost increase<br>HC/CO/NOx<br>(in 1981 US$)|
|---|---|
|1968-69|5.9/50.8/N.R.'<br>30|
|1970-71|3.9/33.3/N.R.<br>50|
|1972|3.0/28.8/N.R.<br>70|
|1973-74|3.0/28.0/3.1<br>100|
|1975-76|1.5/15.0/3.1<br>150|
|1977-79|1.5/15.0/2.0<br>175|
|1980|0.41/7.0/2.0<br>225|
|1981|0.41/3.4/1.0<br>350|
|1990 (proposed|0.25/3.4/0.4 (by 1995/96) n.a.|
|legislation)|0.125/3.4/0.2 (by 2003)<br>n.a.|

N. R. = not required.

**n.** a. = not available.

Source: U.S. EPA [1988]; OECD [1988a]

# **Technological Advances on the Horizon**

The technology to reduce vehicle emissions continues to evolve and develop. Lower trace lead levels in unleaded gasoline and more advanced emission control components, particularly more durable catalysts, better air fuel management systems, and electronics will be key elements of future control. California (USA), still plagued by severe smog conditions in Los Angeles, continues its worldwide leadership in extending the pollution control requirements. While it already has the most stringent NOx requirements in the world, 0.4 grams per mile, it has indicated its intention also to adopt more stringent hydrocarbon levels as well as extending the mileage over which the standards apply.

More stringent control of passenger cars and light trucks is feasible and is actually being achieved by conventional vehicles, at least at the design stage. This is illustrated by an analysis of 1987 U.S. certification data which shows that the average gasoline-fueled vehicle emitted about 0.2 gpm HC, 1.91 CO, and 0.37 NOx, compared to standards of 0.41, 3.4, and 1.0, respectively.

Based in part on such data and the critical need for more NOx control, the California Air Resources Board (CARB) in 1986 determined that a 0.4 gpm NOx standard for automobiles was necessary, feasible, and cost effective. It adopted this standard, to be phased in starting in 1989 and estimated the costs to be about US$25 to 30 per vehicle. The incremental costs are modest
Although the level of the minimum wage relative to average wages is an important piece of information, one must keep in mind that minimum wages are designed to increase earnings of workers at the lowest end of the earnings distribution. These workers are typically those with lower levels of education. Thus, what ultimately matters is the ratio of minimum wages to average wages for low educated and low paid workers. Table 18 below shows that even workers with no diploma earn on average more than 3 times the minimum wage. This reflects the small earnings differentials by levels of education in Vietnam. As can be seen in table 18, workers with upper secondary education only earn 20% more than workers with no diploma.

**Table 18: the minimum wage is low even for workers with little education**

||Monthly<br>Average wage<br>(in Dongs)|Ratio of minimum to<br>average wage|
|---|---|---|
|No diploma|486,230|0.30|
|Primary diploma|509,430|0.28|
|Lower secondary diploma|497,050|0.29|
|Upper secondary diploma|567,620|0.25|
|College and university|747,840|0.19|

_Source:_ VLSS 2 (GSO, 1999> Given this paper's focus on Vietnam's export potential, the question as to whether the minimum wage is binding in the labor-intensive export-oriented sector is of particular interest. A visit to Haiphong's export-oriented garments, footwear and food processing enterprises shows that here again market wages are above minimum levels. In Haiphong, fulltime workers earn on average 300,000 to 550,000 VND per month, depending on productivity. This is two to four times more than the mandatory minimum. The lowest level of pay that was observed in Haiphong was 10,000 VND a day, which still yields a monthly income of more than 50% above the minimum wage. Thus, the minimum wage is unlikely to present any obstacle to the development of the export-oriented domestic private sector.

# _c) Evidence: foreign invested enterprises_

As pointed out above, the minimum wage for foreign companies is higher than for domestic enterprises and varies across regions. Table 19 below shows that it represents between 93% and 106% of the country's average wages, and between 78% and 89% of private sector wages. In both urban and rural areas the minimum wage for foreign companies is set a levels roughly equal to average wages. Such ratios of minimum to average wages are high. Table 20 shows that minimum wages are even higher than wages of well-educated workers, with upper secondary diplomas. What about labor-intensive export sectors? A case study in Haiphong (where the minimum wage is set at VND 556,000) shows that the mandatory minimum wage is about 25% above market wages for the type of workers employed in the garment and footwear sectors (Belser, 1999). As pointed out above, the lowest wage level observed in Haiphong's domestic export-oriented enterprises was 10,000 VND per day - which would yield less than 50% of the mandatory minimum.

20
Table 7: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Regulatory Index

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.323|-0.211|-0.052|-0.001|0.166|0.168|
||(0.08)|(0.084)|(0.05)|(0.051)|(0.073)|(0.072)|
|Log(Rev. per worker)|-0.169|-0.143|-0.009|0.004|0.119|0.123|
||(0.046)|(0.05)|(0.029)|(0.029)|(0.041)|(0.04)|
|Invest (binary)|-0.004|-0.003|-0.004|-0.001|-0.005|0|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.049|-0.214|0.033|-0.164|0.098|-0.124|
||(0.131)|(0.14)|(0.076)|(0.079)|(0.105)|(0.102)|
|Log(Avg. wage)|-0.098|-0.08|-0.022|-0.002|0.039|0.061|
||(0.039)|(0.039)|(0.025)|(0.024)|(0.036)|(0.034)|
|Log(Capacity Util)|0.03|-0.004|0.009|-0.002|-0.008|0|
||(0.019)|(0.02)|(0.012)|(0.012)|(0.017)|(0.017)|
|Log(Energy Intensity)|0.055|0.071|0.036|0.043|0.021|0.021|
||(0.056)|(0.057)|(0.039)|(0.038)|(0.057)|(0.056)|
|Innovation|-0.026|-0.018|-0.016|-0.008|-0.008|0.001|
||(0.013)|(0.014)|(0.008)|(0.008)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of regulation. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the regulatory index and columns labeled “high” evaluate the estimated model at the 90th percentile of the regulatory index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

42
that high levels of top inequality serve as a proxy for “social separatism” where the rich lobby for policies that benefit themselves but ultimately limit the growth opportunities of the poor.

The rest of the paper is organized as follows. A brief overview of the literature is provided in Section 2. In Section 3 we discuss the source of data we use and explain how we “disaggregate” both inequality and growth. Section 4 offers a first look at the data. The empirical results, and a discussion of their significance, are presented in Section 5. In the same section we also present an inductively-derived tentative hypothesis linking inequality and growth. Section 6 provides some robustness checks, and Section 7 concludes.

# **2. Brief discussion of the existing literature**

Early thinking was that inequality would be positively associated with growth. The theoretical arguments originally put forward by Kaldor (1956) for example viewed income inequality as necessary in order to provide for savings (only the rich would save), and thus key for capital accumulation and economic growth. Another possible argument for a positive association is that more unequal societies provide stronger incentives which ultimately motivate individuals to work harder in order to succeed. These arguments found support in the empirical studies by Forbes (2000) and Li and Zou (1998).<sup>4</sup> Forbes (2000) findings, however, were rather nuanced as she found that, in short-time spans of about 5 years, inequality appears positively related to growth, but over longer horizons (10 years or more) negatively.<sup>5</sup> Other schools of thought have since argued for a negative relationship between initial inequality and subsequent growth. One strand of this literature appeals to the Meltzer-Richard’s median voter hypothesis. Studies believed to have uncovered empirical

> 4 The authors were criticized for exclusion of many developing countries and small number of observations (a problem particularly acute in a dynamic panel setting), econometric procedures, and inadequate inequality statistics (see e.g. Aghion, Carolli and Garcia-Peñalosa 1999; Malinen, 2007). The latter is something, of course, the authors could not help: it simply reflected the existing statistical basis at the time.

> 5 This ambivalence in the results (“sign-switching”) was common to most of the literature. Generally speaking, a positive relationship was more commonly found in country fixed-effects formulations (rather than crosssectional OLS) which were indeed the specifications run by both Forbes (2000) and Li and Zou (1998).

4
Table 8: OLS and IV Estimates of Mother’s Labor-Supply Models in Albania (2002 - 2012)

|Method||OLS|IV|IV|
|---|---|---|---|---|
|Instrument for Fertility:|||||
|_(Number of children ever born)_||—|_Same-sex_|_Two girls_|
|Survey Fixed-Efects||Yes|Yes|Yes|
|District Fixed-Efects||Yes|Yes|Yes|
|Controls||Yes|Yes|Yes|
|Dependent variable<br>Mean|Obs.|(1)|(2)|(3)|
|_Panel A: All data_|_bases (LS_|_MS and DHS)_|||
|_Worked in last_ 7 days<br>0.445|7404|-0.0063<br>(0.0073)|0.0474<br>(0.0474)|0.0360<br>(0.0340)|
|_Worked of-farm_<br>0.164|7404|-0.0291***<br>(0.0045)|0.0052<br>(0.0354)|0.0503**<br>(0.0258)|
|_Worked on-farm_<br>0.218|7404|0.0347***<br>(0.0066)|0.0125<br>(0.0396)|-0.0019<br>(0.0280)|
|_Self-employed_<br>0.067<br>_Panel B: On_|7404<br>_ly LSMS _|-0.0086**<br>(0.0037)<br> _databases_|0.0404<br>(0.0270)|-0.0107<br>(0.0182)|
|_Hours per week (total)_<br>16.69|4862|-0.203<br>(0.3867)|4.697*<br>(2.5509)|4.409**<br>(1.8866)|
|_Hours per week (of-farm)_<br>7.21|4862|-1.429***<br>(0.255)|-0.078<br>(1.902)|2.683*<br>(1.449)|
|_Hours per week (on-farm)_<br>7.56|4862|1.924***<br>(0.321)|2.303<br>(1.779)|1.570<br>(1.332)|
|_Hours per week (self)_<br>2.36|4862|-0.470**|2.841**|0.225|
|||(0.181)|(1.444)|(1.024)|
|_Second occupation_<br>0.014|4862|0.004<br>(0.002)|0.010<br>(0.014)|0.002<br>(0.011)|
|_Log(Monthly Labor Income)_<br>50498.2|4862|-0.3972***<br>(0.0778)|0.4172<br>(0.5740)|0.8679<br>(0.4384)|

_Notes:_ Standard errors clustered at the PSU level are in parentheses. All the models include the following covariates: _Age, Age Squared, Age at First birth_ and _Years of Education_ . IV Models in columns (2) and (3) also include indicators for _Boy 1st_ and _Boy 2nd_ . The sample includes mothers aged 20 to 49 years old with 2 or more children younger than 18 years old. Each model is estimated with survey and district fixed effects. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%.

41
# **1 Introduction**

An unfortunate consequence of industrialization in developing countries is the contamination of drinking water, as chemical impurities associated with untreated industrial waste and excess use of agricultural fertilizers and pesticides have become major water pollutants in more and more countries. About 1.5 million episodes of skin lesions and one million of skeletal fluorosis are related to the poor quality of drinking water (World Health Organization, 2004). There is no doubt that industrial contamination of drinking water has contributed to the drinking water crisis in the world, with still 884 million people relying on unsafe drinking water in the world as of 2008 (World Health Organization, 2011). Despite significant welfare consequences associated with chemical impurities in drinking water, studies on the effect of treated water remain limited. Previous research has found significant effect of treated water on health (Zhang, 2012). In this paper, instead of focusing on the health consequence, we examine its longer-term benefits on youth’s final educational attainment following the rollout of the rural drinking water program, the same program as in Zhang (2012). In light of recent literature that emphasizes differential impacts of health intervention on gender due to gender-specific impact of health on brawn (Pitt et al. 2012), and the critical importance of early childhood health and nutrition (Almond and Currie, 2011; Chnha et al., 2006; Heckman, 2008), we also explore how the effects of treated water differ by gender and by age of first exposure.

The key challenge for studying the educational impacts of health in general and water quality improvement in particular is to uncover the causal effects. Many studies rely on observational data to explore the correlations between health and education at the individual level without having access to exogenous variation based on the introduction of a specific health intervention (e.g., Dean, 1986; Glewwe et al., 1999; Alderman et al., 2001; Glewwe et al., 2001; Alderman et al., 2006). However, without an explicit (potentially exogenous) health intervention behind the variation in health, the educational effects based on the regressions of education on health are likely to be contaminated by biases from selection on unobservables such as personal and family traits that affect both health and education. Other studies employ randomization and experimental methods to create exogenous health variation, which helps to avoid omitted variable bias more convincingly (e.g. Pollitt et al., 1993; Martorell et al., 1995; Nokes et al., 2
- Household incomes taxes adjust to make sure that the exogenous fiscal deficit, defined as a share of GDP, is realized. Government expenditure refers only to current expenditure or government consumption, and the fiscal deficit excludes public capital spending. In this sense, a positive fiscal balance contributes to a pool of savings to finance total investment in the economy. Other tax rates are fixed so that other tax revenues will change to the extent that the tax bases to which they are applied change over time. For example, tariff revenue will change when import values change, given the exogenous tariff rates.

- The real exchange rate adjusts to realize the exogenous external balance (also a fixed GDP share). As assumed, this balance adjusts exogenously at a slow pace such that it would reach a value of zero after 100 years.

- In the factor markets, wages and rents adjust to bring about equilibrium, i.e. making sure that total endogenous demands match exogenous projections of labor or the accumulation of capital stock. The supplies of land and natural resources are fixed at the base level. The supplies of labor depend on demographic changes that define the working-age populations (UN 2013). The split between skilled and unskilled labor is based on the level of education as projected in World Bank (2015) and Ahmed et al. (2017). For capital, the supply growth is endogenous; given an initial stock, it is determined by investment values, depreciation, and the price per unit of new capital (which depends on endogenous input prices).<sup>10</sup>

# **IV. Defining the AMR Shocks in the Counterfactual Simulations**

Given that AMR presents a current and future threat, how much economic resources should the world invest in reducing this threat? The answer depends on the economic costs of the expected impacts of AMR. The simulations of impacts reported in this paper are based on a review of recent simulations by other research groups, information on actual impacts of AMR to date, and on expectations about its spread. As in the baseline, the projection period ends in 2050, well within the lifetimes of present-day children and young people.

This analysis uses expert assessments of key factors to form low-, middle-, and high-case scenarios to address the significant uncertainties about the future progression of AMR and to provide a range of the uncertain severity of its impact. The low case can be interpreted to correspond to an effective outcome of policy measures taken today and in the future; the high case for lack of policy effectiveness.<sup>11</sup> In the simulations, the changes in savings and region-wide effects are driven by the effects over time on labor supplies, investment, output, and relative prices. These factors in turn change trade flows between regions and the relative and global prices of

> 10 See McDonald et al (2013) for the equations used in the capital updating each period.

> 11 T he list of possible policy programs and actions against AMR are many and beyond the scope of the paper. For more information, see O’Neill (2016) and World Bank (2017).

8
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