rafmacalaba/datause-encoder-data huggingface dataset:
# 3.1 Determinants of the PCI

We want now to isolate the effects of environmental policies and factor endowments on the PCI. The PH effect is captured by differences in the lead content of gasoline which is our proxy for environmental policy.<sup>9</sup> The capital-labor ratio serves as proxy for endowment differences, i.e. for the FE effect. Table 1 summarizes the sample characteristics of these variables for each group of countries along with the average PCI values for each group of countries. The figures confirm intuition. The average lead content is higher in the South and hence low income countries have lower environmental stringency. Likewise, the capital-labor ratio is on average higher in the North. Finally, PCIN > PCIS for each one of the three pollutants.

## Table 1 here: Summary Statistics by Income Group

The empirical specification is given by equation (3) below. It is inspired by the gravity framework, previously used in cross-country studies of trade in pollution-intensive activities.<sup>10</sup> To avoid erroneously attributing variations in bilateral PCIs to the two effects of interest, it is important to have as complete as possible a set of control variables in the vector of “deep” determinants. The α and β coefficients control for “deep” determinants of bilateral trade, the former relating to gravity-type equations, and the latter including other controls. The gravity-type controls include log distance (DISTij), the extent of the potential market (MKTij = ln(GDPi ∗ GDPj)), proxied by the product of GDPs (since these cannot be entered individually in the presence of fixed effects), common religion (CRij), common language (CLij) and landlockedness (LLij).

(3) Other controls include importer (DMj) and exporter (DEi) fixed effects. These dummy variables control for country-specific omitted determinants. As further control variables, we include proxies for differences in natural resource

> 9 This index is one of the few available proxies for environmental stringency for a large number of countries that covers the 1980s. It is a market-share weighted sum of the maximum lead content of different gasolines (see Grether and Mathys (2003) and appendix 1 for further details). Hilton and Levinson (1998), Damania et al. (2003), Fredriksson et al (2005) and Cole et al. (2006) also use this proxy for environmental policy.

> 10 An advantage of the gravity model over the earlier standard factor endowment-based studies (e.g. Tobey (1990), is that it exploits the large amount of information contained in bilateral trade flows. It was first applied by van Beers and van den Bergh (1997) on a cross section of OECD countries. This initial approach has been extended in a number of directions, including the panel dimension (Harris et al (2001)), developing countries (Cagatay and Mihci (2003) and Grether and de Melo (2004)) the role of product differentiation (Jug and Mirza (2005)) or of regional free trade agreements (Kahn and Yoshino (2004)), while the endogeneity of environmental policy has been examined in Mantovani and Vancauteren (2005).

7
if subject _i_ chooses to invest amount _bj_ . The log-likelihood function for treatment _t_ can be written as: The log-likelihoods can then be summed across treatments. Following the procedures outlined in Train (2003), we simulate the log-likelihood by taking one thousand random draws from the standard normal distribution, and maximize the simulated log-likelihood numerically.<sup>38</sup>

# **5.1 Heterogeneity in Individual Risk Preferences**

We express the CRRA utility function as where _ηi_ = 900<sup>1</sup><sup>_−ρi_</sup> _−_ 10<sup>1</sup><sup>_−ρi_</sup> , the difference between the utility of the highest possible payout in the experiment and the utility of the lowest strictly positive payout.<sup>39</sup> Since VNM expected utility functions represent preference orderings over lotteries and are robust to positive, affine transformations, this utility function represents the same preferences as the standard CRRA formulation, However, in a mixed logit framework, the probability of choosing investment option _bj_ depends on the magnitude of the difference between _EVij_ and the utilities associated with other options, and not just the position of _bj_ in the preference ordering. Hence, the scale of _EVij_ is directly related to the likelihood of choosing an investment option, _bk_ , that is less-preferred in the sense that _EVik < EVij_ .<sup>40</sup> The standard normalization of the CRRA utility function leads to very different

> 38We implement this using the MATLAB command `fminunc` using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) updating procedure. Standard errors are calculated using the outer product of the gradient.

> 39See Goeree, Holt, and Palfrey (2003) for a similar approach. Using the lowest possible payout, zero, is not feasible because _u_ ( _x_ ) _→−∞_ as _x →_ 0 for subjects with _ρ ≥_ 1. Results are almost identical when 10<sup>1</sup><sup>_−ρ_</sup> is replaced with 1<sup>1</sup><sup>_−ρ_</sup> or 0 _._ 01<sup>1</sup><sup>_−ρ_</sup> .

> 40We acknowledge the slight abuse of the term “less-preferred” in this context since, by construction, the chosen option is always the most-preferred once the unobserved preference shock has been taken into account.

29
Financial Regulation and Development and the Development Bank of Ethiopia, DBE) might have been the recipients, rather than private commercial banks. Secondly, if the argument given above is true, we should observe a special increase in credit and branches in those regions that were particularly targeted for long-term investment. The region that has mostly been attractive to long-term investment projects is Benishangul-Gumuz, which hosts the construction site of the Grand Ethiopian Renaissance Dam (GERD). In Figure 9, we can observe in the upper panel the branch installation and total employment by medium-scale enterprises compared to the national average. As is evident, it is difficult to argue that such a region has been the destination of most attention and, for this reason, we believe that our claim concerning the mechanism through enhanced bank safety cannot be dismissed.

Figure 10: Policy Change and Prices: Lending and Deposit Rates <!-- Start of picture text --> 2008 2009 2010 2011 2012 2013<br>year<br>Mean Nom. Deposit Rate Min Nom. Deposit Rate<br>Max Nom. Deposit Rate Mean Nom. Lending Rate<br>Min Nom. Lending Rate Max Nom. Lending Rate<br>20<br>15<br>% Points<br>10<br>5<br><!-- End of picture text --> _Note:_ This figure reports the monthly evolution of the average nominal deposit rate (blue) and lending rate (red), with their respective minimum and maximum rates. The sources are the 2012, 2013, and 2014 Annual Reports of the NBE. As described in the text, there is no detectable change in either rate in response to the policy change.

Another core element that has been omitted in the analysis is the price response of the policy. The theoretical model took prices as given and was silent on ways in which the lending and deposit rates could respond to a shock in _s_<sup>_R_</sup> . This might create alternative channels through which the policy shapes the economic problem. For example, if the lending rate in the good state, _RG_ , grew in response to the policy (or the deposit rate _RD_ correspondingly declined), then the branch expansion effect could be entirely due to an increased profitability of the banking system, with safe assets being a negligible component of the story. We decided to leave prices constant because of anecdotal evidence from Ethiopian bankers on the lack of a price response due to competitive pressure, which was then confirmed in our data collection exercise. In fact, Figure 10 presents the mean lending 31
# **<u>Table C.9 Descriptive statistics of estimation sample, Jordan</u>**

||**2010**|**2010-2013**<br>**2013**|**diff**|
|---|---|---|---|
|Lo of er caita consumtion|7.29|7.35|0.1***|
|g p p p|(0.01)|(0.01)|(0.0)|
|Hd`|41.00|42.72|1.7***|
|eas age|(0.18)|(0.14)|(0.2)|
|Hd i fl|0.09|0.09|0.0|
|ea s emae|(0.01)|(0.00)|(0.0)|
|Hd' hiht dti ll i i|0.51|0.55|0.0***|
|eas ges eucaon eve s prmary|(0.01)|(0.01)|(0.0)|
|Hd' hiht dti ll i d|0.16|0.15|-0.0|
|eas ges eucaon eve s seconary|(0.01)|(0.01)|(0.0)|
|Hd' hih di ll i i|0.23|0.19|-0.0***|
|eas gest eucaton eve s tertary|(0.01)|(0.01)|(0.0)|
||0.74|0.63|-0.1***|
|Urban area|(0.01)|(0.01)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

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interventions and decisions related to social insurance, particularly when not enough time has passed to observe actual decisions. Liebman and Luttmer (2015), for example, similarly examine how an information intervention affects future intentions over when to draw social security benefits in the US.<sup>13</sup> Concerns related to attrition are also relevant when considering use of an information experiment jointly with the RUMiC survey. Due to the young and mobile nature of the migrant population, attrition across RUMiC waves is a possible concern. From 2015 to 2016, the attrition rate was 28.3%, which is similar to the 28% reported in (Chetty and Saez, 2013). Nevertheless, as the information intervention was implemented in the 2015 sample while the outcome of interest (insurance participation) was recorded in the 2016 wave, one might worry that attrition could affect randomization. To check this concern, a balance test is conducted in the data subsection and robustness checks employ sample selection models and reweighting approaches to examine potential attrition bias.

# **3.3 Estimation Strategy and the Model**

Assuming implementation protocols were followed and that a balance test is passed, the random assignment of the information intervention allows us to identify whether poor understanding of the urban health insurance and the pension programs and enrollment procedures reduces the likelihood that respondents participate in them. We also model the decision to participate in social insurance as a function of a city fixed-effect, exogenously provided information and initial period participation, firm and individual characteristics. The binary choice, _Y_ 1 _ij_ , of whether to participate in health insurance and pension programs, is modeled as: where the subscripts 0 and 1 indicate variables measured in the pre-treatment (2015) and post-treatment (2016) survey rounds, respectively. _Y_ 0 _ij_ is a lagged (pre-treatment) participation indicator; _Tij_ indicates that individual _i_ in city _j_ was assigned to receive the information treatment between the 2015 and 2016 survey rounds; **X** 0 _ij_ is a set of pre-treatment (2015) individual-level controls, which include age,<sup>14</sup> gender, education, marital status, number of children, whether an individual is self-employed, an interaction between the self-employed

> 13In a robustness check, we examine results using actual participation as the dependent variable to estimate the model.

> 14In addition to age in years, we also include two indicator variables: whether the respondent is a woman and over 40, and a man and over 45, respectively. These threshold indicators are included because pension rules mandate that participants cannot receive full benefits without paying the premium for 15 years, and the retirement age for migrant women and men in China is 55 and 60 years, respectively. As they cannot expect to receive full benefits, individuals already within 15 years of the retirement age threshold may have less motive to participate in a pension program.

12
# **References**

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- **Athey, Susan, and Guido W. Imbens.** 2017. “The econometrics of randomized experiments.” In _Handbook of Field Experiments_ . Vol. 1 of _Handbook of Economic Field Experiments_ , , ed. Abhijit Vinayak Banerjee and Esther Duflo, 73–140. North-Holland.

- **Atkinson, Maxine P., and Jacqueline Boles.** 1984. “WASP (Wives as Senior Partners).” _Journal of Marriage and Family_ , 46(4): 861–870.

- **Attanasio, Orazio, Ricardo Paes de Barros, Pedro Carneiro, K. David Evans, Lycia Lima, Pedro Olinto, and Norbert Schady.** 2022. “Public Childcare, Labor Market Outcomes of Caregivers, and Child Development: Experimental Evidence from Brazil.” unpublished.

- **Barr, Abigail, Marleen Dekker, Wendy Janssens, Bereket Kebede, and Berber Kramer.** 2019. “Cooperation in Polygynous Households.” _American Economic Journal: Applied Economics_ , 11(2): 266–83.

- **Bernal, Raquel, Orazio Attanasio, Ximena Pea, and Marcos Vera-Hernndez.** 2019. “The effects of the transition from home-based childcare to childcare centers on children’s health and development in Colombia.” _Early Childhood Research Quarterly_ , 47: 418–431.

- **Bjorvatn, Kjetil, Denise Ferris, Selim Gulesci, Arne Nasgowitz, Vincent Somville, and Lore Vandewalle.** 2022. “Childcare, labor supply, and business development: Experimental evidence from Uganda.” C.E.P.R. Discussion Papers CEPR Discussion Papers 17243.

- **Blimpo, Moussa Pouguinimpo, Pedro Manuel Amaro Da Costa Luz Carneiro, Pamela Jervis, and Todd Pugatch.** Forthcoming. “Improving access and quality in early childhood development programs: Experimental evidence from the Gambia.” _Economic Development and Cultural Change_ .

- **Bossuroy, Thomas, Markus Goldstein, Bassirou Karimou, Dean Karlan, Harounan Kazianga, William Parient’e, Patrick Premand, Catherine C. Thomas, Christopher Udry, Julia Vaillant, and Kelsey A. Wright.** 2022. “Tackling psychosocial and capital constraints to alleviate poverty.” _Nature_ , 605(7909): 291–297.

- **Cascio, Elizabeth U.** 2009. “Maternal Labor Supply and the Introduction of Kindergartens into American Public Schools.” _Journal of Human Resources_ , 44(1): 140–170.

- **Clark, Gracia.** 1999. “Mothering, Work, and Gender in Urban Asante Ideology and Practice.” _American Anthropologist_ , 101(4): 717–729.

- **Clark, Shelley, Caroline W. Kabiru, Sonia Laszlo, and Stella Muthuri.** 2019. “The impact of childcare on poor urban women’s economic empowerment in Africa.” _Demography_ , 56(4): 1247– 1272.

- **Connelly, Rachel.** 1992. “The Effect of Child Care Costs on Married Women’s Labor Force Participation.” _The Review of Economics and Statistics_ , 74(1): 83–90.

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Panel A: Relationship between probability of care readiness under the assumption of full availability of equipment and drugs versus actual conditions observed at the facility, country by country <!-- Start of picture text --> KEN<br>50<br>40 SLE<br>TZA<br>30<br>20 MOZ<br>10<br>NER<br>NGA<br>UGA<br>MDG<br>0 TGO<br>0 10 20 30 40 50<br>Pr. of min. quality standards (%)<br>versus actual conditions observed at the facility, country by country<br>60 KEN<br>50<br>40<br>SLE<br>30<br>TZA<br>20<br>MOZ<br>10<br>NER<br>NGA<br>UGAMDG<br>0 TGO<br>0 10 20 30 40 50 60<br>Pr. of min. quality standards (%)<br>Pr. of min. quality standards: Drugs available (%)<br>Pr. of min. quality standards: No absence (%)<br><!-- End of picture text --> Panel B: Relationship between probability of care readiness under the assumption of low provider absenteeism versus actual conditions observed at the facility, country by country

**Figure 3: Comparing estimates of minimum quality care**

Panel A plots the relationship between the estimated probability of care readiness that meets minimum quality standards for child, neonatal, and maternity care, vs. the estimated probability of care readiness that meets minimum quality standards for child, neonatal, and maternity care assuming essential drugs treating the conditions are available. Panel B plots the relationship between the estimated probability of care readiness that meets minimum quality standards for child, neonatal, and maternity care, vs. the estimated probability of care readiness that meets minimum quality standards for child, neonatal, and maternity care assuming no absenteeism. Child, neonatal, and maternity care includes diarrhea, pneumonia, post-partum hemorrhage and neonatal asphyxia. Essential drugs are oral rehydration salts for diarrhea, antibiotics for pneumonia, and oxytocin for postpartum hemorrhage. Data are from clinical vignettes, unannounced visits, and visual inspections from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013). ISO 3-digit alphabetic codes are: KEN (Kenya), MDG (Madagascar), MOZ (Mozambique), NER (Niger), NGA (Nigeria), SLE (Sierra Leone), TZA (Tanzania), TGO (Togo), UGA (Uganda).

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tariffs and the export tariffs.<sup>19</sup> Table 8 in the Appendix displays the results for different occupations. Job adverts for production workers (SOC 51) are negatively affected via the export tariff channel, while postings for in Installation, maintenance and repairs jobs (SOC 49) are negatively affected by both tariffs on exports and intermediate inputs. But the impact of these tariffs extends to other occupations as well. The intermediate input tariffs also negatively influenced adverts for farming, fishing and forestry, construction and extraction jobs as well as postings for managers, engineers, architects and lawyers. The negative impact of retaliatory tariffs additionally extended to adverts for jobs in cleaning, health, education, social services and the sciences.

# **5.4 Aggregate counterfactual effects**

In order to evaluate the plausible effects of these results for the full sample, we carry out a basic counterfactual exercise on the baseline results. For each commuting zone, we consider what the predicted values for monthly postings would have been if the tariff rates had stayed the same as January 2017 throughout the period of analysis. This approach follows closely that from Chodorow-Reich (2014).

Specifically, we take the fitted values of the specification in column (7) of Table 3 and then subtract the difference in exposure between any given month and that at the start of the period (January 2017), multiplied by the relevant estimated coefficient. Using the exponential function we return the fitted value and the estimated counterfactual to a count measure of postings and then calculate the estimated difference in postings for each month and each CZ.

First focusing only on the counterfactual of zero changes to tariffs on imported inputs, we estimate a total of 121,571 fewer postings occurred over the period than would have been the case in this counterfactual world. Figure 7 shows how these were spread out over time, with month 1 being January 2017. All of the changes are concentrated in the period after February 2018, getting more severe month by month.

## A similar calculation can be made for export tariff exposure leading to an estimated total

19The agricultural subsidies only have a positive and significant impact for high skilled workers (see column (5)), perhaps contrary to expectations. This could be because the online job postings data captures high skilled agricultural jobs more effectively than the more informal lower skilled agricultural jobs that are less likely to be published online.

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Morocco where all these variables have been shown to be closely associated with welfare indicators of various types.<sup>5</sup> Most of these variables are also typically used in the literature on the construction of multi-dimensional poverty indexes (see Decancq and Lugo, 2010 for a review).

The MCA procedure is carried out separately for urban and rural areas. Morocco is a country of great divides between urban and rural areas and the sets of variables relevant for constructing the CWI are expected to be different so as to reflect the respective peculiarities of rural and urban areas. Following the first-step procedure, the number of variables is reduced to meet the consistency properties of the principal component. We will see that this variables reduction process leads to different results in urban and rural areas confirming the initial hypothesis that urban and rural areas are better represented by two different models rather than by one model.

[Table 1]

# **5. Multiple Correspondence Analysis**

The MCA analysis is carried out in two steps. In the first step we use all variables listed in Table 1 to provide a first indication of the association patterns with the principle component. In the second step we remove or adjust variables so as to meet the basic conditions of the MCA discussed in previous sections.

**Step 1** - Table 2 and 3 illustrate the results of the preliminary MCA for urban and rural areas. The table of eigenvalues highlights the distinction between the first factorial axis and the following axes (labeled as dimensions “Dim” in the tables). In urban areas (Table 2), this axis alone explains more than 52% of the total inertia of the cloud of variables while the explanatory power drops to 9% for the second axis and to 6% for the third axis. Most of the variance of the set of variables used for urban areas is captured by one axis. In rural areas (Table 3), the first axis is less powerful capturing 46.6% of the total variance. This is also followed by one axis that captures over 11.5% of the total variance and a few other non negligible axes.

In order to improve the model in a way that satisfies that basic properties discussed in previous sections, we now need to look at the inertia of each variable and modality on the first axis, which is our latent multidimensional measure of welfare. Table 4 provides details of the various poverty proxies for the first axis in urban and rural areas. The state of poverty is described by the indicators negatively correlated to the first axis, while well-being is captured by the indicators that are positively correlated. A review of the most negatively correlated variables to the first axis allows identifying poor households as those

> 5 See http://www.omdh.hcp.ma/ for a number of poverty studies on Morocco conducted by the High Commission for the Plan.

9
In Table III-4 we show the results for the same regression for non-MENA countries. As the results show all the coefficients are negative and statistically significant. These outcomes show that credit cycle peaks have a negative correlation with recessions in non-MENA countries. These findings are in line with corresponding literature.

# **Table III-2: Oil Exporters and Non-oil GDP Growth**

This table show the result for the regression where the main dependent variable is real non-oil GDP growth for the oil exporters and the main explanatory variable is the local peak of medium-term credit cycle growth. The definition of the local peak of credit cycle growth is when the credit cycle growth is in its maximum volume compared to past and next five years. In all the regressions we control for oil-prices. All regressions are estimated using OLS. Standard errors appear in parentheses, and ***, **, * correspond to one, five, and ten percent levels of significance, respectively.

||1|2|3|4|5<br>6|7|8|
|---|---|---|---|---|---|---|---|
|**VARIABLES**||||**Non-oi**|**l GDP Growth**|||
|Saudi Arabia|3.41***<br>(0.9)|||||||
|Bahrain||-3.1||||||
|||(3.5)||||||
|Algeria|||1.5|||||
||||(1.9)|||||
|Iran, Islamic<br>Rep.||||2.7<br>(2.2)||||
|Kuwait|||||-5.6**<br>(3.7)|||
|Oman|||||-2.6|||
||||||(2.5)|||
|United<br>Arab<br>Emirates||||||-2.9<br>(2.4)||
|Qatar|||||||0.83<br>(4.4)|
|Observations|18|18|18|18|18<br>18|18|18|
|R-squared|0.02|0.08|0.04|0.23|0.23<br>0.03|0.13|0.02|

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