rafmacalaba/datause-encoder-data huggingface dataset:
Two other recent, related studies are worth mentioning. One finds that moving from complete financial autarky to complete financial openness would augment consumption by about 1 percent per year or 10-20 percent of initial GDP depending upon the discount rate; the actual benefit would be smaller as most countries are not financially autarkic to start with.<sup>14</sup> On the other hand, if a developing country were able to achieve 50 percent of the total factor productivity level of the USA, the effect would be of the order of 200-300 percent of initial GDP. The mechanism through which financial liberalization enables this is not obvious, unless financial globalization results in countries becoming irrelevant for asset prices, portfolios and firm financial policies, the so-called “country irrelevance” proposition. This would presumably speed up convergence of TFP levels and not just growth rates. The other study argues that such financial globalization is limited by agency problems that arise because rulers of sovereign states and corporate insiders pursue their own objectives at the cost of outside investors.<sup>15</sup>

**<u>Table 2. Major recipients of net international resource flows</u>**

|Country (Year of Crisis)*|Total net resou<br>19<br>US$ billion|rce flows 1990-<br>96<br>Share<br>of 1996 GDP<br>%|Share<br>Debt|of total fl<br>FDI|ows, %<br>Equity<br>portfolio|
|---|---|---|---|---|---|
|China|234|29|29|67|3|
|México (1994/1995)|118|36|35|39|26|
|Brazil (1999)|90|12|45|27|28|
|Argentina (2001/2002)|71|26|45|39|16|
|Korea (1997)|68|13|59|12|29|
|Malaysia (1997)|63|63|18|47|35|
|Indonesia (1997)|55|24|50|35|13|
|Thailand (1997)|52|29|58|27|13|
|India|43|11|46|16|30|
|Russia (1998)|32|8|57|16|7|
|Poland|26|20|6|49|4|
|Chile|25|37|29|56|13|
|Turkey (2000/2001)|22|12|49|23|11|
|Hungary|20|45|25|68|3|

* Year(s) of macroeconomic crisis are indicated in brackets. Source: World Bank (2005).

Lastly, there is a high correlation among the major country recipients of capital flows during the 1990s and the occurrence of crises, as seen in Table 2.

_Crisis Literature: Three Generations and Counting_ There is a close link between the actual unfolding of macroeconomic crises with their growing complexity and the theoretical literature, leading so far to three generations of crisis models. These are described in Box 3.

> 14 Gourinchas and Jeanne (2004) develop a simulation model to calculate the benefits from financial liberalization to a developing country in a neoclassical setting. 15 Stulz (2005).

9
three studies that provide variable targeting costs for PMT and CBT methods: Alatas et al. (2012) provide variable costs for CBT and PMT-based targeting of a single program in Indonesia; Karlan & Thuysbaert (2019) provide variable costs for CBT and PMT-based targeting of an ultra-poor program in Honduras and one in Peru; and Schnitzer & Stoeffler (2021) provide variable costs for three CBT-based programs and four PMT-based programs in seven countries in Sub-Saharan Africa.<sup>15</sup> Table S10 summarizes the cost estimates from each of these papers; we use the median per-household targeting cost for each method in our analysis ($2.20 per household for CBT and $4.00 per household for PMT). While using these global estimates to inform our model of targeting costs in Afghanistan is not ideal, since no data on targeting costs from the TUP program or other anti-poverty programs is available for the country, these values are the best available estimate on which to base our cost analysis.

We are unable to find any papers that document the targeting cost associated with consumption-based targeting, as consumption data is rarely used as a real-world targeting strategy. We therefore consider the costs of consumption to be strictly greater than the costs of targeting on a PMT, since consumption modules take longer to collect than PMT data in household surveys. In practice, we expect that the cost of targeting on consumption would be substantially greater than the cost of targeting on a PMT.

For phone-based targeting, we associate no cost with the collection and analysis of phone data. While in some cases phone data may require purchase from the operator, partnerships between mobile network operators and governments for social protection and public health applications have not, to date, involved payment (Milusheva et al., 2021). The fixed cost of mobile data analysis is non-negligible but its contribution to marginal cost is close to zero as the number of screened households increases. A phone-based targeting method that collects informed consent from program applicants to analyze phone data would have nonzero marginal costs, though the cost of consent would depend on the modality of consent collection. If consent was collected in person, these costs would likely be only slightly lower than those of a PMT, as every household would need to be surveyed in person. If consent was collected over the phone via SMS or voice, these costs would likely be significantly lower.

It is worth noting that our benchmark in this paper is the hybrid model with a CBT plus verification component, but due to limited estimates in the literature we leave this strategy out of our cost analysis. We consider the CBT a lower-bound estimate for the hybrid strategy, and therefore our results would be qualitatively unchanged if the hybrid

> 15To our knowledge, no studies incorporate fixed targeting costs, as these are typically indistinguishable from fixed costs of other components of program set-up.

32
annual forest loss in another signatory country. As we do not estimate post-enactment average annual estimates (but rather leads and lags around enactment) we present estimates of three-year cumulative net forest loss after RTA enactment by presence or absence of provisions as an analogue to our triple difference approach at the RTA-level. We also present our full event study graph in the text with coefficients tabled in the Appendix.

Although the multiple event study identification strategy is not explicitly designed to address the endogeneity of environmental provisions like our matched sample approach above, there are several features of the model that account for a great deal of the underlying endogeneity. First, and perhaps most importantly, countries enter into multiple RTAs over our sample period—some with provisions and some without. Our use of country fixed effects creates a within country interpretation to our coefficient estimates that yields arguably the most relevant observable counterfactual to provision inclusion. Second, as argued in Abman and Lundberg (2020), the _timing_ of entry into force of RTAs—both those that include provisions and those that do not—is plausibly exogenous due to the multilateral ratification process. This exogeneity in treatment timing creates credible identification of the dynamics around entry into force of both RTAs that do not include the provisions as well as those that do that is largely uncomplicated by potential endogeneity of provision inclusion.

# **5.2 Country-level Results**

We report country-level event study coefficients from Equation (9) in Figure 6 with the full set of coefficients and standard errors reported in Appendix Table A3. The coefficients that correspond to ‘+/- 4’ in event time are long-run leads/lags— _δLR_ + and _δLR−_ in Equation (9)—and do not hold the same interpretation as the other event study coefficients. Consistent with Abman and Lundberg (2020), we find that RTAs lead to net increases in log forest loss upon enactment and the subsequent three years (black lines) relative to the year prior to enactment. We see no significant leading coefficients which provides evidence against effects being driven by diverging trends in forest loss prior to RTA enactment. This increase is offset by the inclusion of the forest and biodiversity provisions. The red lines correspond to the event study coefficients for RTAs with environmnetal provisions. These provisions dampen the observed increases the year of and year 24
union's utility function (equation (30)), and the leadership effect of public sector wages in the union's target wage (equation (31)). These parameters are obviously important in assessing the labor market effects of a variety of policy shocks. Although we limit ourselves below to presenting a small set of experiments, assessing the sensitivity of the results to alternative values of these parameters, as well as various others for which guesses need to be made, is essential to assess the robustness of the policy conclusions that one can draw from the model.

# 4 **Policy** Experiments

As noted in the foregoing discussion, the constraints and challenges that Morocco faces in reforming its labor market are the result of a variety of structural factors, including the demographic structure of the population, the lack of sustained rates of economic growth (which translate into weak growth in labor demand), the high degree of vulnerability of the economy to exogenous shocks (such as weather-related shocks, and the business cycle in industrial countries), and to the distortions introduced by existing labor market regulations. In a context of high and persistent unemployment, we argued that labor market reforms should be at the forefront of the policy agenda in Morocco.

Many economists have argued that reducing hiring and firing regulations, restricting the scope of minimum wages (particularly among the young and unskilled), reducing nonwage labor costs and payroll taxes, and moderating the influence of trade unions in the collective bargaining process would improve flexibility and have a positive effect on growth and the demand for labor, thereby reducing unemployment.'<sup>3</sup> The quantitative framework described earlier can indeed be used to provide quantitative estimates of the effects of these various labor market policies on resource allocation, the rate of economic growth, and unemployment in Morocco. Specifically, we focus here on the impact of a reduction in the minimum wage (which is binding only for 1<sup>3</sup> For instance, a study of 19 Latin American countries by Rama (1995) showed that economies with more flexible labor regimes enjoy higher productivity and faster growth, compared to those with a rigid labor code. Others have argued that the fairly limited impact of trade unions and other distortions on the labor market was an essential contributing factor to the sustained growth rates achieved in East Asia prior to the1997-98 crisis.

45
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Jones, S., Schipper, Y., Ruto, S. and Rajani, R. (2014). Can your child read and count? Measuring learning outcomes in East Africa. _Journal of African Economies_ , 23(5):643– 672.

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Mazumder, B. (2008). Sibling similarities and economic inequality in the US. _Journal of Population Economics_ , 21(3):685–701.

- —— (2011). Family and community influences on health and socioeconomic status: sibling correlations over the life course. _The B.E. Journal of Economic Analysis & Policy_ , 11(3):Article 1.

Mittag, N. (2012). New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from germany. FDZ Methodenreport 201201, Institute for Employment Research, Nuremberg, Germany.

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Pop-Eleches, C. and Urquiola, M. (2013). Going to a better school: Effects and behavioral responses. _The American Economic Review_ , 103(4):1289–1324.

- Pritchett, L. and Viarengo, M. (2015). Does public sector control reduce variance in school quality? _Education Economics_ , 23(5):557–576.

- Raaum, O., Salvanes, K.G. and Sørensen, E.Ø. (2006). The neighbourhood is not what it used to be. _The Economic Journal_ , 116(508):200–222.

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- —— (2002). Equality of opportunity: A progress report. _Social Choice and Welfare_ , 19(2):455–471.

Sandefur, J. (2018). Internationally comparable mathematics scores for fourteen African 28
2004, Aickin, 2001)<sup>2</sup> . However, it is precisely in small samples that the choice of randomization method becomes important, since in large samples all methods will achieve balance. We simulate different randomization methods in four panel data sets. We then compare balance in outcome variables at baseline and at follow-up. The simulations show that when methods other than pure randomization are used, the degree of balance achieved on baseline variables is much greater than that achieved on the outcome variable (in the absence of treatment) in the follow-up period. The simulations show further that in samples of 300 observations or more, the choice of method is not very important for the degree of balance in many outcomes at follow-up. In small samples, and with very persistent outcomes, however, matching or stratification on relevant baseline variables achieves more balance in follow-up outcomes than does pure randomization.

We use our simulation results and theory to help answer many of the important practical questions facing researchers engaged in randomized experiments. The results allow us to provide guidance on how to conduct inference after stratification, matching or re-randomization. In practice it appears that many researchers ignore the method of randomization in inference. We show that this leads to hypothesis tests with incorrect size. On average, the standard errors are overly conservative when the method of randomization is not controlled for in the analysis, implying that researchers may not detect treatment effects that they would detect if the inference did take into account the randomization method. However, although this is the case on average, in a non-trivial proportion of draws, it will be the case that not controlling for the randomization method will be anti-conservative, potentially leading the researcher to find a significant effect that is no longer significant when stratum or pair dummies are included. Moreover, we show further that stratifying, matching, or re-randomizing and then analyzing the data without controlling for the method of randomization results in lower power than if a pure random draw was used to allocate treatments, except in cases where the variables that balance is sought for have no predictive power for the future outcome of interest (in which case there is no need to seek balance on them anyway).

> 2 One other arguments in favor of ex-ante balancing is that, if the treatment effect is heterogeneous and varies with observed covariates, ex-ante balancing increases the precision of subgroup analysis.

- 4 -
# **Transportation fuel use, technology and standards: The role of credibility and expectations**

Gunnar S. Eskeland, Torben K. Mideksa<sup>1</sup>

# **ACRONYM LIST**

CAFÉ Corporate Average Fuel Economy IEA International Energy Agency LRMC Long Run Marginal Cost Curve MPG Miles per Gallon R & D Research and Development SRMC Short Run Marginal Cost Curve 1 CICERO Center for Internatinoal Climate and Environmental Research, Oslo. Eskeland was a consultant to the World Bank at the time of preparing this paper. We thank the World Bank for financial support, and Ariel Dinar, Tobias Persson, Zmarak Shalizi, an anonymous referee and participants at the EAERE conference in Gothenburg, 2008 for valued comments and suggestions. Comments are welcome: eske@cicero.uio.no.
|**Ta**|**ble 2: Real G**|**DP growth**|**in Sub-Saha**|**ran Africa**|||**Since**|
|---|---|---|---|---|---|---|---|
|**Categories of countries**||**1980-2006**||**Rec**|**ent Periods**||**mid-90s**|
||1980~1989|1990~1999|2000~2006|2000~2003|2004~2006|2007<sup>*</sup>|1995-2006|
|**I. Sub-Saharan Africa**||||||||
|A. Regional simple average|2.9|3.3|4.6|4.3|5.1|5.3|4.9|
|w/o Zimbabwe|2.9|3.3|4.9|4.5|5.3|5.6|5.0|
|B. Regional weighted average|2.3|2.1|4.6|3.9|5.9|6.1|4.1|
|Nigeria|1.7|2.8|5.6|5.2|6.2|7.3|4.4|
|South Africa|2.2|1.4|4.1|3.4|5.0|5.0|3.5|
|Rest of SSA|2.7|2.5|4.9|4.0|6.5|6.1|5.1|
|**II. Oil Exporting Countries**|3.5|5.9|8.4|8.4|8.4|8.2|9.1|
|**III. Oil Importing Countries**|2.8|2.7|4.1|3.7|4.7|5.0|4.1|
|A. By endowment & location||||||||
|Resource intensive countries|3.2|1.3|5.7|6.1|5.1|4.9|3.8|
|Costal countries|2.9|3.4|3.8|3.4|4.4|5.0|4.3|
|Landlocked countries|2.9|2.5|3.0|2.1|4.2|4.3|3.6|
|B. By income level & fragile states||||||||
|Middle income countries|5.6|4.5|3.7|3.5|4.0|4.7|4.2|
|Low income countries|2.1|3.5|4.9|4.3|5.6|6.1|5.2|
|Fragile countries|2.2|0.5|2.8|2.4|3.2|3.6|2.5|
|**IV. Regional Groups**||||||||
|EAC|3.4|3.9|5.1|4.5|6.0|6.4|5.0|
|SADC|3.6|2.6|4.0|3.1|5.2|5.7|4.0|
|CFA franc zone|2.8|4.9|4.9|5.1|4.7|4.3|5.9|
|WAEMU|2.1|3.5|3.2|2.9|3.5|4.0|3.6|
|CEMAC|3.8|6.8|7.3|8.0|6.4|4.7|9.0|
|ECOWAS|2.4|2.8|4.7|4.8|4.5|5.2|4.4|
|ECCAS|2.8|3.6|6.3|6.0|6.9|6.6|7.1|
|COMESA|3.0|2.3|3.5|2.4|5.0|5.8|3.8|
|IOC|2.9|3.5|2.4|2.1|2.9|5.1|3.0|
|<br> **V. Others**||||||||
|Flexible exchange rate regime|2.3|2.4|5.1|4.3|6.1|7.1|5.1|
|Fixed exchange rate regime|3.5|3.9|4.3|4.3|4.4|4.1|5.1|
|MDRI|2.1|2.7|5.3|5.1|5.6|6.0|5.0|
|Source: World Bank WDI, ADI and IMF WEO<br>*Estimates based on AFRCE briefs, World Bank|database. All stati<br>GEP, and IMF (20|stics are annual p<br>07).|ercent rates and si|mple averages un|less otherwise m|entioned.||
|<br>Notes : ECOWAS: Economic Community of W<br>CEMAC: Economic and Monetary Com|<br>est African States;<br>munity of Central|<br> <br>Africa;|WAEMU: West<br>ECCAS: Econo|Africa Monetary<br>mic Community o|Union of the CF<br>f Central African|A franc zon<br>States;|e;|
|<br>SADC: Southern African Development<br>EAC: East African Community;|<br>Community ;<br>|<br> <br>|<br>COMESA: Com<br>IOC: Indian Oce|<br>mon Market for<br>an Commission.|<br>Eastern and South|<br>ern Africa;||

6
# Box 1: Effects of Changing Remittances Modeling in El Salvador

Some refinements on how non-labor income is projected may result in a more accurate estimation of the changes along the income distribution. This study modeled international remittances as neutral distribution using the inflows growth rate between the baseline year and the simulated year from the World Bank's Macro Poverty Outlooks. However, as stated in the methodology presented earlier in this paper, international remittances can also be modeled using the proposed method in Olivieri et al. (2014). This methodology consists of a two-step assignment rule. First, the projected amount of international remittances is calculated as the initial level of remittances times the change between the baseline and the projected year; second, the difference in the amounts between the base year and simulated year is randomly assigned to households within each region, considering population growth while maintaining the original regional distribution of international remittances.

To assess the effect of these two different international remittance approaches on income distribution, this document simulates the poverty and vulnerability headcounts and the Gini coefficient for El Salvador 2017 - 2019. It is important to clarify that El Salvador is selected due to its high level of international remittances. In 2019, this non-labor income component accounts for 61.6% of the average per-capita non-labor income of the country<sup>23</sup> ; hence, a change in the way international remittances is modeled is expected to impact the overall performance of the model. **Figure 11** presents the results for this exercise using the _new model without rescaling_ . In the results, Neutral distribution corresponds to the first approach while Random Allocation refers to the methodology in Olivieri, et al. (2014).

<!-- Start of picture text --> Figure 11 Neutral Distribution vs. Random Allocation to Simulate Remittances in El Salvador<br>A) Differences Between Projected and Actual B) Poverty MSE<br>Data<br>2.50 2.00<br>2.00<br>1.50<br>1.50<br>1.00<br>1.00<br>0.50<br>0.50<br>0.00 0.00<br>2017 2018 2019 2017 2018 2019 2017 2018 2019<br>Poverty at $6.85 Gini Neutral distribution<br>Neutral distribution Random allocation Random allocation<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

As expected in a country like El Salvador, results suggest that using random allocation to model international remittances slightly improves the model's performance along the whole time series compared to neutral distribution. The difference between the simulated and actual poverty rate at 6.85 USD reduces for all years by around 0.14 percentage points (p.p.) (Figure 11.A). Still, the reduction in inequality is smaller (approximately 0.08 p.p.). In addition, results suggest that the average MSE for poverty and vulnerability is also lower with the random allocation approach (Figure 11.B), especially in years where the model's bias is larger such as 2017 and 2019.

> 23 Own calculations using SEDLAC 2019.

27
12 Partnership (ELP) Systems Research program. Managed by the World Bank and financed by the Government of the UK, ELP Systems Research currently supports country research teams working in Ethiopia, Jamaica, Liberia, and the province of Punjab, Pakistan. These country research teams have conducted early learning system diagnostics in each country and evaluations of system-level interventions since 2017.

The countries were selected for the ELP Systems Research program on the basis of the potential impact of the research, judged by the presence of ongoing or prospective scale-up of ECE services, policy maker demand for the research, and the presence of potential local research and implementation partners. As noted in Authors (2021), this tends to lead _prima facie_ to a sample of countries with relatively favorable conditions for prioritization of ECE. The country set differs slightly from the countries analyzed previously due to a change in the focus countries between Phase 1 and Phase 2 of the ELP research program. The work in Tanzania did not continue in Phase 2 due to changes in the country context, while a new focus country, Jamaica, was added.

Between September 2020 and February 2021, the four Country Research Teams collected data from: (a) reviews of government policies, plans, and other relevant documents (e.g., news articles) and (b) semi-structured phone interviews with key government, school, and civil-society informants. These findings were synthesized into country reports organized around six themes: prioritization; coherence of response; learning at home; teachers; equity; and return to school. Given that this is an evolving policy area, we complemented the Country Research Team data collection with a targeted set of semi-structured interviews to fill in data gaps, including those related to recent policy developments.

# **Data analysis**