rafmacalaba/data-use-mentions-extended huggingface dataset:
vulnerable populations are reached, especially given the higher poverty risk for families with young children (World Bank, 2012).

Second, there is a need to better understand constraints and effective mechanisms to increase access to and quality of ECE programs. Research on age of entry, program type and duration, and intensity of attendance is needed to understand which ECE programs are working and for whom. Program quality data are almost entirely lacking. Reliable administrative data on elements of quality, including teacher-child interactions, student-teacher ratios, mixed-aged classrooms, physical environments, availability of materials, curriculum, teacher training, incentive structures, and parent engagement, among other inputs, would be invaluable. Notably, it would be worth exploring the frequency and effectiveness of psychosocial stimulation in the classroom. Data on both supply- and demand-side barriers are needed, with analysis of the impacts of sociocultural norms and income inequalities, to allow for more targeted access to ECE and entry points for coordinated service provision.

Third, further research is needed to explore the role of gender-related norms, knowledge, and skills relevant to ECD. While this study provides an initial analysis of parental stimulation and ECD, further research is needed on differential patterns of early stimulation between women and men caregivers, how those might impact child development, and what approaches to parenting education programs are effective for women and men respectively.

# Limitations

Several constraints in data collection limit the conclusions we could draw. First, the respondents were predominantly male, which meant that most respondents were not the child’s primary caregiver. Second, an issue with cell-phone towers in Sindh resulted in under-sampling the province, however we were able to account for any selection bias with survey weights. Third, approximately 6 percent of Pakistani households that do not own a phone were excluded from our study. As such, our study results are only representative of the phone-owning households in Pakistan. Fourth, given the nature of the phone survey, all responses were caregiver-reported. Fifth, since the survey was cross-sectional, we could not
that TFP increases between 4% and 5% for each additional year a plant has exported, after accounting for the effect of current exports on TFP. A particularly important issue in our empirical specification is to take into account the persistence of TFP. In our data we cannot reject the hypothesis that TFP has a unit root. Therefore, using differences or the within transformation produces consistent estimates of the effect of export experience on plant TFP. Third, our results on export experience are robust to the use of different subsamples of our main dataset, such as the subsample of plants that export in at least one year (exporters) and the subsample of plants that start exporting from their first year (born exporters).

Fourth, using a larger dataset that includes also old, established plants, we compare the effect of entry into export markets and export experience on TFP for young and old plants. We find that the gap in annual average rates of TFP growth between entrants to the export markets and nonexporters is 3.5% for young plants compared to 1.8% for old plants. We also find that each additional year of export experience increases TFP by 6.3% in young plants, compared to 2% in old plants. Fifth, we include export experience directly in the estimation of the production functions of the five largest Colombian manufacturing industries, the so-called one-step approach. The results confirm that export experience variables have a positive and generally significant effect on young plants’ TFP. The estimates of the effect of an additional year of export experience on TFP range between 3.4% and 5.4% on average, which are consistent with those obtained using the two-step approach. These regressions uncover important differences in the magnitude of the learning-byexporting effect across industries. To explain these differences, we augment the dataset with Colombian export data by industry and country of destination. Our results, using the two-step 34
%|-0.33%|-0.54%|0.38%|0.39%|0.40%|-1.10%|-0.26%|-0.25%|-0.25%|-1.70%|
|**9**|-0.33%|-0.33%|-0.33%|-0.47%|0.22%|0.22%|0.23%|-0.94%|-0.39%|-0.38%|-0.38%|-1.40%|
|**10**|-0.30%|-0.30%|-0.30%|-0.31%|-0.04%|-0.03%|-0.03%|-0.58%|-0.50%|-0.49%|-0.49%|-0.89%|

Source: Estimates based on POF 2017/2018. Lower-bound and Upper-bound are estimated using a 95% confidence level. Estimates assume a price shock of 20%.

Figure 3 demonstrates that the impact of changes in product expenditure after taxes for processed and ultra-processed foods together is not simply an average of the two components. Although both food groups exhibit a similar pattern with a gradual decrease in their effects, the level of impact is noticeably higher for ultra-processed foods compared to processed ones.

Interestingly, when considering the effects of joint taxation on both groups, there is a significantly greater impact on households at the lower end of the consumption distribution. Furthermore, the decrease in the effect is much more pronounced, resulting in a final product expenditure level that is lower for the top consumption decile compared to the separate components.

_Figure 3_ - Change in product expenditure <!-- Start of picture text -
02 03 04 05 06 07 08 09 10 11<br>Russia South Africa<br>percent percent<br>20 3<br>18<br>16 2.5<br>14 2<br>12<br>10 1.5<br>8<br>6 1<br>4 0.5<br>2<br>0 0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Turkey<br>7<br>6 Exports to all economies subject to TTBs in effect<br>5<br>4 Exports to G20 High Income economies subject to TTBs in effect<br>3 Exports to G20 Emerging economies subject to TTBs in effect<br>2<br>1 Exports to all other economies subject to TTBs in effect<br>0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> Notes: Shares of nonoil exports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

23
earned more than 10 times the minimum wage, whereas the proportion was only 2% in the labor force survey. The authors then adjust the survey weights so that the proportion of formal sector workers in each earnings category in the surveys matches the proportion in the administrative data.

Campos-Vazquez and Lustig (2019) note that the item non-response rate for the earnings questions in the Mexican labor force survey rose from 5% in 2002 to 30% by 2017. To solve this issue one of the sets of corrections implemented by the authors is to first undertake hot deck imputation for the item non-response and then the reweighting explained above. So this set of corrections actually involves replacement and then reweighting. As well as mechanically increasing the share of individuals in the 17
has a significant negative impact on profitability and productivity for otherwise comparable firms, and this finding is robust to different measures of informality and profitability. The paper also provides some evidence on the determinants of this relationship, such as the ability to avoid fines, issue receipts, and obtain improved access to credit for more formal firms.

The remainder of the paper is structured as follows: Section 2 presents a profile of the surveyed firms, focusing on those characteristics which are particularly relevant to the paper’s analysis; Section 3 develops a simple model of profitability and formality and discusses estimation issues; Section 4 presents the estimation results; Section 5 discusses the channels through which formality affects profitability; and Section 6 offers some concluding remarks.

# **2. What are the main characteristics of surveyed firms and their owners?**

## **2.1 Data**

The data used in this paper come from a new survey of firms with 1-50 employees designed specifically for this study. The survey, the Ecuador Micro-Enterprise Survey (EMES), focused on the eight most important sectors of urban economic activity: textiles, apparel, shoes, and leather manufacturing; other manufacturing; grocery retailing; street food vendors; hotels and restaurants; ground transport; auto repair; and construction. The survey respondents were the "individual ultimately responsible for the operations of the company or business" and the participating firms were chosen through random geographic sampling by census tract in Quito, Guayaquil, Machala, and Tulcán.<sup>1</sup> Firm size was defined

> 1 The universe of firms that the EMES survey represents—namely, firms with 50 employees or less operating in manufacturing and service sectors in Quito, Guayaquil, Machala, and Tulcán—represent 28 percent of the total number of economic establishments in Ecuador according to the Economic Census (INEC, 2010).

2
# **1. Introduction**

The world continues to struggle with high levels of pollution caused by human activities such as fossil fuel consumption, industrial production, and depletion of forests. These produce excessive greenhouse gasses heating the planet and have devastating effects on the environment and humanity especially in developing countries. In fact, the developing countries are responsible for 63 percent of current carbon emissions globally (Center for Global Development 2015).<sup>6</sup> The urgency of addressing the threat of global warming has led countries to combine their efforts to combat climate change and adopt a legally binding international treaty, the Paris Agreement.<sup>7</sup> This became effective in 2020 and aims at limiting global warming to well below 2 (preferably 1.5) degrees Celsius compared to pre-industrial levels. In this regard, environmental regulations and their enforcements have become a critical mechanism for reducing emissions and their detrimental effects on nature and humans as widely discussed in the literature. For example, Danish et al. (2019) find that the current environmental regulations are successful in achieving pollution reduction targets in Brazil, the Russian Federation, India, China, and South Africa and are also responsible for an inverted U-shaped relationship between income and pollution. Similarly, using yearly data for 17 European Union countries over the last two decades, Neves et al. (2020) show that environmental regulations are effective in reducing CO2 emissions in the long-run, while policies supporting renewable energy sources tend to decrease CO2 emissions in both the short- and long-term.

Many developing countries have sizable informal sectors operating outside government control: Schneider et al. (2010) show that from 1999 to 2006 the informal sector GDP accounted for more than 50 percent of total in Bolivia, Georgia, Guatemala, Panama, Peru, Tanzania, and Ukraine. According to the World Bank (2021), in emerging markets and developing economies the informal sector contributes about a third of GDP and more than 70 percent of employment of which self-employment
## 3.2.3 Starving the beast

In a paper closely-related to Talvi and Vegh (2005), Alesina et al. (2008) endogenize the political distortion that is taken as given by Talvi-Vegh by providing a compelling political-economy story. Specifically, in good times voters seek to “starve the beast” to reduce political rents appropriated by corrupt politicians. Voters observe the state of the economy but not the rents appropriated by corrupt politicians. Therefore, when voters observe a boom, they optimally demand more public goods and/or lower taxes, which leads to procyclical fiscal policy. The more corrupt the government and close political allies, the higher the rents that they will appropriate, and the higher the public demand for public goods and/or lower taxes. From an empirical point of view, the key variable to capture Alesina et al.’s story is some measure of corruption. We will use the variable “corruption”from the International Country Risk Guide (ICRG).<sup>24</sup>

# 3.3 Regressions

Table 1 reports the results of cross-country regressions for commodity exporters. The dependent variable is the country correlation between the cyclical components of real government spending and real GDP measured over the entire time interval. The explanatory variables are the four independent variables discussed above that capture each of the four explanations for the existence of procyclical fiscal policy: the financial openness index; the political constraint index; the standard deviation of income (SD(y)); and control of corruption.

Columns (1)-(4) report the regressions results with each variable individually. We can see that each variable is significant at least at the five percent level. In light of the discussion above, three of the four coeffi cients have the expected signs: higher financial openness reduces procyclicality (Column (1)); more control of corruption decreases procyclicality (Column (2)); and larger volatility of output increases procyclicality (Column (4)). In contrast,
Although reduced form econometrics provides useful insights on the distributional effects of trade shocks, structural estimation is the only known method to address certain important issues, such as adjustment dynamics, general equilibrium effects, and counterfactual policy simulations, despite the challenges we mentioned earlier.

In this paper, we study the impact of trade liberalization along the life cycle of workers from different skill and experience groups without imposing any strong restrictions on workers’ expectations in the estimation stage. Therefore our estimation strategy, which is described fully in Artuc (2012), is applicable to environments that are subject to aggregate uncertainties. The econometrician does not need to know the distribution of aggregate shocks due to changes in trade policies, labor market policies, financial crises, technological progress, etc. We investigate how age interacted with education and experience affects the mobility of workers, and report the increase in mobility costs as workers get older. Then, we show that workers’ mobility determines loss and gain from trade shocks, and provide a general picture of welfare changes across different worker subgroups.

To illustrate the connection between mobility and diffusion of gains from trade, imagine that all workers were perfectly mobile across sectors. Then, all workers would be unanimously better off or worse off after a policy shock thanks to factor price equalization. If workers were immobile and attached to their original sectors, then there would be distinct winners and losers from free trade. In that case, workers’ sectors would determine their gain and loss. In reality, mobility costs probably lie between these two extremes and vary across groups. A major source of variation in mobility has to do with the age of affected workers, causing differences in their position towards free trade. For example, the Pew Global Attitudes survey, conducted in 2002, shows that young people are more enthusiastic about free trade compared to older people.

After the empirical exercise we conduct using the Current Population Survey and the 1979 cohort of the National Longitudinal Survey of Youth (henceforth CPS and NLSY respectively), we calibrate production
-3-

specific goods. This model was based on the assumption that developing countries should have a comparative advantage in goods that employ relatively large labor inputs. 4/ Given the obvious importance of issues concerning changing comparative advantage, this paper attempts to extend the related empirical analyses in several directions. First, an attempt is made to evaluate the predictive power of comparative advantage indices for future changes in trade by drawing on the NBER study. This will be done by analyzing the 1965-1986 trade changes in products identified as labor intensive in the earlier year. Aside from the ability to identify industries where major changes are likely to occur in trade or production, an attempt will also be made to determine the time frame for which predictions based on comparative advantage indices are valid. 5/ Other issues that will be addressed include: the importance oi textiles and clothing in the initial stages of a labor intensive trade expansion; how the product composition of "maturing" developing country

- 4/ See Lary (1968) for details. According to the United Nations product classification scheme, manufactured products are those items which fall in: SITC 5 (Chemicals); SITC 6 (Manufactured Goods Classified by Material), less SITC 68 (Non ferrous Metals); SITC 7 (Machinery and Transport equipment); and SITC 8 (Miscellaneous Manufactured Articles). The National Bureau of Economic Research study adopted a somewhat broader definition of manufactures in that it includes some processed foods falling in SITC 0, cigars (in SITC 1), shaped wood (in SITC 2), and fish oil (in SITC 4). See Appendix 1 for a listing of the labor intensive manufactured products identified in the NBER study.

- 5/ There have been several attempts to develop "early warning" systems for disruptive imports with studies by Watkins and Karlick (1981) and Tuong and Yeats (1975) being representative of the earlier work. Indices of comparative advantage could play a major role in the development of such systems if it were established that these
The CWR was followed by an in-person survey to determine whether nominated households met a set of qualifying criteria, coordinated by the NGO and government representatives, and based on a measure of multiple deprivation.

For a household to be designated as _ultra-poor_ , and therefore eligible for program benefits, it had to be considered extreme-poor in the CWR (43% of households), and also meet at least three of six criteria:

1. Financially dependent on women’s domestic work or begging

2. Owns less than 800 square meters of land or living in a cave 3. Primary woman under 50 years old 4. No adult men income earners 5. School-age children working for pay 6. No productive assets Ultimately, 11% of the households classified as extreme-poor in the community wealth — — ranking step 6% of the total population in the study villages were classified as ultrapoor and were thus eligible for TUP benefits.

# **2.2 Household Surveys**

To facilitate Bedoya et al.’s (2019) impact evaluation of the TUP program, household surveys were conducted in 80 of the poorest villages of Balkh province. A total of 2,852 households were surveyed, with ultra-poor households ( _N_ =1,173) oversampled relative to non-ultrapoor households ( _N_ =1,679).<sup>3</sup> Surveys were conducted between February and April 2016, following the CWR and eligibility verification. This survey window was timed to occur in the late winter and early spring, a few months before the harvesting season for wheat in Balkh.

The household survey was a long-form in-person survey that took approximately 3 hours for each household to complete. The survey covered a wide range of topics, including several modules related to household poverty and deprivation that feature in our analysis.

3In our analysis, we restrict to the 2,814 households for which asset and consumption data are nonmissing.

5
**Table 6: Impacts on Owner Time Use**

||Owner's<br>hours|Time<br>concentration|Growth-focused<br>Activities|Time on external<br>and future activities|Delegation|Percent of time<br>sales & marketing|Percent of time<br> accounting & finance|
|---|---|---|---|---|---|---|---|
|**Panel A: Impacts in First Follo**|**w-up Survey**|**(One-year afte**|**r intervention star**|**ted)**||||
|Assigned to Insourcing|-1.414|0.012|0.223|4.074*|0.058|3.172**|-0.474|
||(2.977)|(0.019)|(0.154)|(2.453)|(0.092)|(1.435)|(0.931)|
|Assigned to Outsourcing|-1.061|0.006|0.142|3.622|0.044|3.403**|-1.113|
||(3.175)|(0.019)|(0.160)|(2.575)|(0.084)|(1.400)|(0.948)|
|Assigned to Training|-0.659|-0.006|0.047|1.526|-0.017|1.100|-1.630*|
||(3.398)|(0.018)|(0.157)|(2.615)|(0.092)|(1.370)|
tax rate on income, profits and capital gains, compared to other countries, even those at similar income levels (Figure 3a). However, Value Added Tax (VAT) rates, which range from 5 percent to 20 percent across the continent, are comparable to other countries with an average rate of 15 percent (Figure 3b).

Taxpayers in Africa face higher tax compliance costs, even compared to countries at similar income levels. Figure 4 presents how much time, measured in hours per year, firms spend preparing and paying three major types of taxes: the corporate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions. African countries spend on average 273 hours per year on taxes, a greater burden than the average of 220 hours in other regions (World Development Indicators, 2018).<sup>2</sup> Tax administration patterns in African countries feature lower reliance on technology and greater reliance on manual systems and in-person interactions between taxpayers and tax collectors. In African countries, 72 percent of firms report being required to meet with tax officials, and for those affected, 3.2 meetings are held on average each year (World Bank Enterprise Surveys, 2010-2020). In contrast, 37 percent of firms in Europe and Central Asia are required to meet with tax officials and of those affected, 2.1 meetings are held on average. The high frequency of meetings is also correlated with corruption indicators in taxation. On average, 17 percent of firms in Sub-Saharan Africa report that they are expected to give gifts in meetings with tax officials. However only 7 percent report the same in Europe and Central Asia. Similarly, 28 percent of firms in Sub-Saharan Africa cite tax administration as a major constraint, compared to 16 percent in Europe and Central Asia (World Bank Enterprise Surveys, 2010-2020).

Along the same lines, the use of information technology in tax administration, such as e-filing, increases with national income. For example, as
)_|_(0.000)_|_(0.000)_|
|_Trade Openness_|||||||
|Exports and Imports<br>|0.016<sup>**</sup><br>|0.013<sup>**</sup><br>|0.006<br>|0.015<sup>**</sup><br>|0.013<sup>**</sup><br>|0.007<br>|
|_(% of GDP, logs)_|_(0.000)_|_(-0.004)_|_(-0.156)_|_(0.000)_|_(-0.004)_|_(-0.084)_|
|_Trade Diversification_|||||||
|Market concentration<br>|0.015<sup>***</sup>|-0.002|0.021<sup>***</sup>|0.016<sup>***</sup>|-0.003|0.023<sup>***</sup>|
|_(Herfindahl index)_|_(0.000)_|_(-0.411)_|_(0.000)_|_(0.000)_|_(-0.269)_|_(0.000)_|
|Product concentration|-0.029<sup>***</sup>|-0.019<sup>***</sup>|-0.022<sup>***</sup>|-0.029<sup>***</sup>|-
in Haryana, 3ie Grantee Final Report. _International Initiative for Impact Evaluation (3ie)_ .

- Duflo, Esther, Dupas, Pascaline, & Kremer, Michael. (2009). Additional resources versus organizational changes in education: Experimental evidence from Kenya. _Unpublished manuscript. Abdul Latif Jameel Poverty Action Lab (JPAL), Cambridge._

- Duflo, Esther, Dupas, Pascaline, & Kremer, Michael. (2011). Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya. _American Economic Review, 101_ (5), 1739‐1774.

- Duflo, Esther, Dupas, Pascaline, & Kremer, Michael. (2015a). Education, HIV, and early fertility: Experimental evidence from Kenya. _American Economic Review, 105_ (9), 2757‐2797.

- Duflo, Esther, Dupas, Pascaline, & Kremer, Michael. (2015b). School governance, teacher incentives, and pupil–teacher ratios: Experimental evidence from Kenyan primary schools. _Journal of Public Economics, 123_ , 92‐110.

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