rafmacalaba/datause-encoder-data huggingface dataset:
Table 12: Impacts <u>of hosting refugees on whether households are negatively affected by alternative price changes</u>

||A fall in<br>|prices of fo<br>|od items<br>|A rise in<br>|prices of f<br>Hous<br>|ood items<br>eholds<br>|A rise in p<br>|rices of agricu<br>|ltural inputs<br>|
|---|---|---|---|---|---|---|---|---|---|
||All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|
|Refugee Intensity|-0.098|-0.096|-0.112|0.006|-0.063|0.266**|0.209**|0.213**|0.096|
||(0.076)|(0.087)|(0.126)|(0.069)|(0.079)|(0.125)|(0.091)|(0.106)|(0.066)|
|Constant|0.893|0.945|0.711|0.415|0.705|-0.973|-1.050*|-1.011|-0.501|
||(0.561)|(0.666)|(0.745)|(0.459)|(0.527)|(0.904)|(0.629)|(0.746)|(0.398)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.019|0.021|0.058|0.043|0.054|0.070|0.026|0.036|0.064|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|13520|9734|3786|13518|9732|3786|13520|9734|3786|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on whether a household is negatively affected by a fall in prices of food items (the first three columns), a rise in prices of food items (the middle three columns), and a rise in prices of agricultural inputs (seed and fertilizer) (last three columns). Household sample weights from ESS2 applied. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

50
# Figure 2: U.S. MFN and Column 2 tariffs

## (A) A comparative snapshot of U.S. tariffs (B) U.S. Column 2 tariffs

<!-- Start of picture text --> Percent Rate increase Frequency Cumulative %<br>40 Column 2 rate 3500 100%<br>35 MFN tariffs 3000 90%<br>30 80%<br>25 2500 70%<br>20 60%<br>2000<br>15 50%<br>10 1500<br>40%<br>5<br>1000 30%<br>0<br>20%<br>500<br>10%<br>0 0%<br>Tariff rate<br>Steel<br>Autos<br>Washing machines Solar panels Aluminum and parts All products<br>Number of products<br>0 5 10 15 20 25 30 35 40 45 50 55 60 >60<br><!-- End of picture text --> Source: USITC. A. Trade weighted averages.

B. Based on the 2018 8-digit HTSUS harmonized tariff schedule of the United States and 11,796 product lines. Specific tariffs are not represented. Tariffs on washing machines and solar panels: above-quota safeguard tariffs to be applied in the first year. Tariffs on autos and parts are not yet official.

imports from countries of the Soviet Union and others under communist control, except Yugoslavia. The Jackson–Vanik amendment to the Trade Act of 1974 withheld NTR status from communist, non-market economies that restricted the freedom of emigration and other human rights.

China, the biggest in the group of non-market economies, was first granted non-discriminatory MFN status in 1980. This was, however, subject to annual renewal and even in the absence of actual changes in U.S. tariffs, introduced a great deal of uncertainty and political controversy in US-China trade relations. Every year between 1990 and 2001, the U.S. Congress introduced, but failed to pass, legislation to revoke China’s NTR status. Finally, in 2000, China was granted permanent normal trade relations (PNTR) status, also facilitated by its accession to the WTO in 2001. Close to two decades after granting China PNTR status, there is still substantial controversy that surrounds China’s non-market economy status as well as the impact of China’s WTO accession and PNTR status on the decline in U.S manufacturing.

Against this background, it is hard to ignore similarities between the recently imposed U.S. tariffs and the general protectionist nature of Smoot-Hawley-inspired Column 2 tariffs (Fig5
_Table 11: Dependent variable – The firm is credit constrained_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|GOB|0.1699|0.2069*|0.605**|0.579**|0.610**|0.578**|0.533**|0.512*|
||(0.1133)|(0.1144)|(0.287)|(0.286)|(0.290)|(0.288)|(0.271)|(0.268)|
|GOBx(1-IQ)|||-1.029*|-0.897|-1.061*|-0.912|-0.974*|-0.824|
||||(0.544)|(0.547)|(0.576)|(0.574)|(0.518)|(0.508)|
|ROA||0.0201||0.006||0.008||0.010|
|||(0.0290)||(0.028)||(0.028)||(0.029)|
|LTA||-0.0750*||-0.055||-0.059||-0.068*|
|||(0.0395)||(0.039)||(0.039)||(0.039)|
|ln(SIZE)|-0.0190|-0.0236|-0.024|-0.026|-0.023|-0.026|-0.021|-0.025|
||(0.0345)|(0.0335)|(0.034)|(0.033)|(0.034)|(0.033)|(0.034)|(0.033)|
|ln(AGE)|0.1476***|0.1512***|0.152***|0.154***|0.152***|0.154***|0.150***|0.153***|
||(0.0569)|(0.0554)|(0.057)|(0.056)|(0.057)|(0.056)|(0.057)|(0.055)|
|SOE|-0.1295|-0.1036|-0.038|-0.036|-0.001|-0.003|-0.043|-0.038|
||(0.1644)|(0.1610)|(0.164)|(0.164)|(0.176)|(0.175)|(0.172)|(0.168)|
|FEM|0.0946|0.1007|0.111|0.113|0.108|0.111|0.100|0.104|
||(0.1091)|(0.1086)|(0.110)|(0.109)|(0.109)|(0.109)|(0.109)|(0.109)|
|Const|-0.3456*|0.2480|-0.341*|0.101|-0.342*|0.124|-0.343*|0.201|
||(0.1874)|(0.3527)|(0.187)|(0.346)|(0.187)|(0.345)|(0.187)|(0.350)|
|N. Obs|14,656|14,656|14,656|14,656|14,656|14,656|14,656|14,656|
|R2|0.0821|0.0871|0.091|0.093|0.089|0.092|0.085|0.089|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule|of Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

28
lower and higher saturation groups respectively. In control transformers, no study households were treated. In lower-saturation transformers, 60% of study households were treated. In higher-saturation transformers, 80% of study households were treated. As mentioned in the sampling discussion above, 20 percent of the households in each transformer were sampled into the study. Thus, the first stage randomization results in approximately 12% to 16% of all households within treated transformers being assigned to treatment.

In the second stage, a total of 1,000 study households within these 110 transformers were randomized into either control or treatment status. This resulted in 457 and 543 households in each group respectively. By design, as depicted in Figure 2, treated households are found only in treated transformers, in the proportions set by each transformer’s treatment intensity. Control households, however, can be in either control or treated transformers. The two stages of randomization also induced spatial heterogeneity in the location of the treated households, leading to variation in the proximity to and number of treated neighbors.

# **3.3 Intervention and household survey**

In the spring of 2013, all study households were visited and invited to participate in a baseline survey regarding electricity use. Their treatment status assignment was not disclosed at the time of the baseline survey. All households were given 150 Kyrgyz soms (approximately 3.26 USD) to compensate them for their time, after finishing the survey.<sup>31</sup> After the baseline survey, baseline interaction with the control households was complete. In contrast, households randomly assigned to the treatment group were offered the opportunity to purchase up to 4 CFLs at a highly subsidized, randomly-drawn price, via a willingness to pay experiment.<sup>32</sup> The set of possible prices was _{_ 0, 5, 10, 15, 20 _}_ . The market price for CFLs was a minimum of 100 KGS, so treated households were paying a maximum of 20% of market price. The market price for incandescent lightbulbs was between 15 and 20 KGS. On average, treated households received 3.2 CFLs from the study intervention. Take-up of CFL treatment was not perfect, as some treated households opted to stop after the survey.

> 31As of 2011, the average monthly nominal employee wage was 9,352 KGS per month (or an estimated 467 KGS per day of work) (National Statistical Committee of the Kyrgyz Republic, 2012).

> 32We provided up to four CFLs as pilot surveys indicated that, on average, households had five to six lightbulbs at baseline. We sought to replace most of their incandescent bulbs with CFLs. The experiment, which utilizes the Becker-de Groot-Marschak methodology to measure demand for CFLs, is explained in Meeks (2016).

12
In this context, IFC investments can be procyclical and profitable in countries experiencing expansions, whereas IFC investment can be countercyclical and riskier in countries facing recessions with the asset quality and profitability of the IFC overall portfolio still healthy. The same reasoning applies to IFC investments at the industry level within a country, especially in large EMDEs which are strongly diversified. It is perfectly plausible that even if a country faces an economic slowdown, there could be sectors experiencing expansions and the IFC portfolio in this country is still healthy ad profitable. Of course, the quality of projects in terms of individual profitability and development impact is critical in ensuring that global and country portfolios are resilient to short term economic fluctuations.

This paper empirically assesses the cyclical patterns of IFC investment activity in the last two decades. Using quarterly data on IFC investments and GDP in 35 client countries from 2000 to 2019, the paper examines how the cyclicality of IFC investment has evolved over time and across countries, regions and industries. To determine how these dynamics interact with the IFC’s financial performance objectives, the analysis compares the average ex post portfolio performance, as measured by nonperforming loan (NPL) ratios and risk-adjusted return on capital (RAROC),<sup>2</sup> of countries in which IFC investment has been procyclical with those in which it has been countercyclical. Finally, the paper highlights IFC’s recent corporate initiatives which may contribute to reconciling the objectives of providing countercyclical support to its clients, when they need it most, while preserving financial sustainability.

The paper is organized as follows. Section 2 briefly reviews the existing research on the cyclicality of capital flows to EMDEs and situates this report in the context of existing literature. Section 3 presents the empirical approach used in the analysis, including the definition of key variables and comments on the data treatment. Section 4 presents the stylized facts for IFC investment cyclicality observed over the last two decades, including disaggregated results by subperiod, region, and industry sector. Section 5 discusses the empirical findings and uses ex post portfolio metrics to assess the implications of investment cyclicality for IFC business performance. The final section concludes the report by exploring lessons for IFC investment strategy in the aftermath of the

> 2 RAROC is defined as (%) = i i i i i i i +f i i ib i ii i <u>b</u> i i fi ii − bbi bbi b i ii − iiooi ooi ii b iieeooi ii − iieeooi ee lli iio ooll

4
defecation. The program made children taller even in households who did not themselves own latrines, and there is no evidence that the effect of the program differed across children in households which did or did not.

# **3.4.3 Differences throughout the height distribution**

Were the final differences between the treatment and control groups concentrated on taller or shorter children? Randomization only ensures an unbiased estimate of the average treatment effect, not of the full distribution of outcomes or treatment effects, but recognizing this, it still could be informative to compare the height distributions in the treatment and control groups.

Panel A of figure 3 plots height CDFs in the treatment and control groups in Ahmednagar in the baseline data, from before the program. The lines are very close to each other, as we would expect, with the slight separation at the bottom suggesting that the shorter children in the control group were not as short as the shorter children in the treatment group before the program.

Panel B presents the same CDFs from the endline data, after the program. Almost throughout the range, the treatment group distribution has moved to the right of the control group distribution (especially keeping in mind their baseline separation among shorter children). This suggests that improved sanitation helped relatively tall and short children both. If so, this is consistent with open defecation being a public bad with consequences for many people and with widespread stunting and enteropathy in the population, such that even relatively tall children have room to grow relative to their genetic potentials. A Kolmogorov-Smirnov test for equality of distribution rejects that the treatment and control distributions of height are the same after the program ( _p_ = 0 _._ 03) but does not using the data from before ( _p_ = 0 _._ 23).

Finally, figure 4 plots quantile regression estimates of the difference-in-differences “effect” of the program at deciles 0.1 through 0.9. Thus, these are quantile regression equivalents 22
# References

Abdel-Basset, M. _et al._ (2021) ‘A new hybrid multi-criteria decision-making approach for location selection of sustainable offshore wind energy stations: A case study’, _Journal of Cleaner Production_ , 280, p. 124462. doi: 10.1016/j.jclepro.2020.124462.

Abdullah, L., Chan, W., Afshari, A., 2019. Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. _J Ind Eng Int_ 15, 271–285. https://doi.org/10.1007/s40092-018-0289-z Ahmed, A., Sutrisno, S.W., You, S., 2020. A two-stage multi-criteria analysis method for planning renewable energy use and carbon saving. Energy 199, 117475. https://doi.org/10.1016/j.energy.2020.117475 Ali, G., Abbas, S., Pan, Y., Chen, Z., Hussain, J., Sajjad, M., Ashraf, A., 2019. Urban environment dynamics and low carbon society: Multi-criteria decision analysis modeling for policy makers. Sustainable Cities and Society 51, 101763. https://doi.org/10.1016/j.scs.2019.101763 Barbier E.B., <u>Greening the Post</u> ‐ <u>pandemic Recovery in the G20.</u> _Environmental and Resource Economics_ (2020) 76:685–703.

Baumann, M., Weil, M., Peters, J.F., Chibeles-Martins, N., Moniz, A.B., 2019. A review of multi-criteria decision making approaches for evaluating energy storage systems for grid applications. Renewable and Sustainable Energy Reviews 107, 516–534. https://doi.org/10.1016/j.rser.2019.02.016 ‐ Chen, Z., Marin, G., Popp, D. and Vona, F., Green Stimulus in a Post pandemic Recovery: the Role of Skills for a Resilient Recovery. _Environmental and Resource Economics_ (2020) 76:901–911. <u>https://doi.org/10.1007/s10640-020-00464-7</u> Cystat (2020a). <u>GDP growth rate, 2</u><sup>nd</sup> <u>quarter 2020. Statistical Service of Cyprus, online data.</u> Cystat (2020b). Mean annual consumption expenditure per household by main categories of goods <u>and services, district, and urban/rural residence. Statistical Service of Cyprus, online data.</u> DFID (United Kingdom Department for International Development), 2019. <u>Synthesis Report on the</u> “ ” <u>Fourth Roundtable Discussion on Strategic Energy Planning , August.</u> EEA (European Environment Agency), 2020. _The European Environment – State and outlook 2020_ , Copenhagen. doi: <u>10.2800/96749</u> Elliott, R. J., Schumacher, I., & Withagen, C., 2020. Suggestions for a Covid-19 post-pandemic research agenda in environmental economics. _Environmental and Resource Economics_ 76(4), 1187-1213. <u>https://doi.org/10.1007/s10640-020-00478-1</u> Engström G., Gars J., Jaakkola N., Lindahl T., Spiro D. and van Benthem A.A., 2020. What Policies Address Both the Coronavirus Crisis and the Climate Crisis? _Environmental and Resource Economics_ 76:789–810. https://doi.org/10.1007/s10640-020-00451-y European Council, Conclusions of the European Council Meeting of 12 December 2019, Brussels.

European Council, Conclusions of the Special Meeting of the European Council of 17-21 July 2020, Brussels.

Eurostat (2020). Household saving rate. Online data available at - - <u>https://ec.europa.eu/eurostat/en/web/products datasets/ /TEC00131</u> 27
other factors such as transport and installation costs, are added to these quality-adjusted import prices, we conclude that several African nations tend to face relatively high prices for imported digital goods, especially some low-income countries where the tariff burden tends to be higher.<sup>4</sup> We then examine the likely effects of AfCFTA on digital goods imports. AfCFTA is an important trade agreement encompassing 54 African Union member states. It seeks to foster economic integration by progressively eliminating tariffs on 90 percent of traded goods, promoting trade in services, and implementing rules of origin to ensure preferential treatment of products. By 2030, the AfCFTA has the potential to create a single market of over 1.3 billion people with a combined GDP exceeding $3 trillion.<sup>5</sup> To assess the likely effects of AfCFTA on digital goods imports, we draw on the detailed tariff schedule of the agreement, and use the framework of Hoekman, Ng, and Olarreaga (2002) to estimate the total demand for imported goods broken down to the 6-digit level of the Harmonized System (HS). The model simulates a counterfactual trade flow resulting from tariff liberalization and compares it with the base year. Despite the comprehensive liberalization expected from the AfCFTA, our analysis indicates that the agreement is unlikely to have a significant impact on reducing tariff protection on digital goods in Africa. The average import-weighted tariffs on these products is expected to decrease by only 0.29 percentage points due to the agreement, from 6.26 percent to 5.97 percent. This result is partly due to the absence of tariff reductions on extra-zone imports of digital goods, which constitute the majority of digital goods imports in AfCFTA, and the limited intra-regional trade in these products. As a result, imports of digital goods are expected to increase only by 0.3% for the whole region.

We then estimate the effects of a full import tariff liberalization on digital goods imports of AfCFTA members towards all countries. We find that such a scenario would lead to an 8% increase in the imports of digital goods. These findings suggest that AfCFTA nations should consider including tariff concessions on digital goods imports from non-member countries, potentially lowering costs significantly, especially in the poorest countries with the highest tariffs on digital goods.

In addition to the articles cited above, our paper is related and contributes to several strands of existing research. First, our work relates to a broader literature on product quality and trade, including Verhoogen (2008), Bastos and Silva (2010), Crozet, Head, and Mayer (2012), Manova and Zhang (2012), Brambilla, Lederman, and Porto (2012), Johnson (2012), Hallak and 4This is aligned to patterns of prices for digital machinery, equipment, ans software observed in the ICP data, which are higher in Africa than other regions, including high-income economies (Cruz, Cirera, Hjort, Lang, Konte, and Edet, 2024).

> 5See: `https://au-afcfta.org/`

3
Table 5: Effect of Treatment on the Treated <u>(IV</u> Results)

|**Panel A: Health Insurance**|Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
|---|---|---|---|---|---|---|
||Without|Price Interac|tion: Eq 1|With Pr|ice Interaction|: Eq 3|
|Received a Pamphlet|0.009|0.012|0.011|0.079**|0.094***|0.099***|
||(0.013)|(0.013)|(0.013)|(0.035)|(0.034)|(0.034)|
|Received a Pamphlet*Price||||-1.009**<br>(0.465)|-1.188***<br>(0.452)|-1.263***<br>(0.452)|
|No. of Observations|4,587|4,587|4,587|4,587|4,587|4,587|
||Without|Price Interac|tion: Eq 2|With Pr|ice Interaction|: Eq 4|
|Received a Pamphlet|-0.031|-0.026|-0.029|0.048|0.032|0.038|
||(0.021)|(0.020)|(0.020)|(0.046)|(0.045)|(0.045)|
|Received a Pamphlet*No-Contract|0.066**|0.059**|0.064**|0.104|0.125*|0.126*|
||(0.027)|(0.026)|(0.026)|(0.071)|(0.071)|(0.071)|
|No. of Observations|4,587|4,587|4,587|4,587|4,587|4,587|
|No-Contract Treatment Efect|0.035**|0.033**|0.035**|0.152***|0.157***|0.164***|
|RP+[RP*No-Contract]|[0.043]|[0.048]|[0.040]|[0.005]|[0.003]|[0.002]|
|**Panel B: Pension**|Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
||Without|Price Interac|tion: Eq 1|With Pr|ice Interaction|: Eq 3|
|Received a Pamphlet|0.002|0.012|0.013|-0.009|0.002|-0.006|
||(0.017)|(0.016)|(0.016)|(0.049)|(0.047)|(0.047)|
|Received a Pamphlet*Price||||0.068|0.060|0.111|
|||||(0.280)|(0.267)|(0.266)|
|Observations|2,925|2,925|2,925|2,925|2,925|2,925|
||Without|Price Interac|tion: Eq 2|With Pr|ice Interaction|: Eq 4|
|Received a Pamphlet|-0.038|-0.030|-0.033|0.030|0.041|0.033|
||(0.024)|(0.023)|(0.023)|(0.072)|(0.071)|(0.070)|
|Received a Pamphlet*No-Contract|0.076**|0.070**|0.075**|-0.047|-0.052|-0.052|
||(0.034)|(0.032)|(0.032)|(0.102)|(0.098)|(0.098)|
|Observations|2,925|2,925|2,925|2,925|2,925|2,925|
|No Contract treatment efect|0.038*|0.040*|0.042*|-0.017|-0.011|-0.019|
|RP+(Rp*No contract)|[0.096]|[0.076]|[0.057]|[0.804]|[0.868]|[0.778]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, gender, education, marital status, number of children, dummies for selfemployment, dummy for working and an interaction between self-employment and lagged (2015) dependent variable and dummy for “willingness to remain in the city permanently if policy permits.”), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

33
in DDI for fully liberalized non-commodity sectors following the reform, although there seems to be a decrease in manufacturing DDI when er account for Covid-19, suggesting that DDI was less resilient and may have faced some crowding out in the manufacturing sectors, but not in fully liberalized sectors. The latter is most relevant in regard to the policy analysis thus the liberalization policy may have been a more crucial factor in driving investments.

Following this, it can be concluded that despite the Covid-19 restriction, the effect of the policy reform remained important, and these results are not necessarily being impacted by economic recovery in 2021.

Table 8: Robustness Check- Possible Effects of Covid-19 Recovery on Realized, Planned FDI and DDI

||FDI||Planned F|DI|DDI||
|---|---|---|---|---|---|---|
|Manufacturing Sector (1vs0)|0.266***||0.190***||-0.124***||
||(0.0244)||(0.0225)||(0.0361)||
|100% Liberalized (1vs0)||0.0712***||0.243***||0.114***|
|||(0.0169)||(0.0169)||(0.0315)|
|Observations|168,329|122,670|141,937|104,328|168,329|151,926|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects and controlled for Covid-19 global lockdown. The sample period is 2019q1 to 2022q3.

Source: Author estimations.

# **Accounting for Data Seasonality and Endogenous Investment Policy Effect**

The FDI investment policy is an endogenous intervention, which means that FDI inflows are likely to be influenced by other industry growth, among other factors. Furthermore, the FDI data exhibit some seasonality. While our model for the main results largely addresses these, we further adjusted for the suspected effect by including GDP and nominal exchange rate variables as covariates in our main estimation. Controlling for GDP and exchange rate, we found that our results remain consistent (Table 8). There is an increase in the fully liberalized sectors in realized FDI, realized DDI, and planned FDI.

22