rafmacalaba/datause-encoder-data huggingface dataset:
Table <u>8: Impact of hosting refugees on salaried employment</u>

||||Individ<br>|uals<br>|||
|---|---|---|---|---|---|---|
||All|All|All|All|Rural|Urban|
||OLS|FE|FE|FE|FE|FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Refugee Intensity|-0.073**|-0.083**|-0.093**|-0.092**|-0.109**|0.068|
||(0.032)|(0.038)|(0.039)|(0.042)|(0.046)|(0.062)|
|Constant|0.830***|0.890***|0.650***|0.329|0.229|0.054|
||(0.194)|(0.236)|(0.220)|(0.248)|(0.270)|(0.387)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|WOREDA|EA|ID|ID|ID|ID|
|Controls|NO|NO|NO|YES|YES|YES|
|R-squared|0.115|0.124|0.026|0.030|0.044|0.010|
|# of EAs|433|433|433|433|290|143|
|N|33691|33691|28806|28548|20907|7641|

_Notes:_ This table provides estimates of the impact of logarithm refugee intensity on whether an individual has had salaried employment. Individual sample weights from ESS2 applied. The first four columns report results for all individuals while columns (5) and (6) report results for rural and urban individuals, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for individual (ID) FEs. Columns (4) to (6) additionally control for 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. I report the overall and the within R-squared in the first two and other columns, respectively. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

46
The volume-based safeguard (Q-SSM) can be used when imports in a year exceed “base imports”—a rolling average of imports in the preceding three year period<sup>2</sup> . The duty that can be applied increases as imports exceed this base. Imports between 110 and 115 percent of the base allow an additional duty of 25 percent of the current binding or 25 percentage points. Imports between 115 and 135 percent of the base allow an additional duty of 40 percent of the binding, or 40 percentage points; while imports above 135 percent of the base allow an additional duty of 50 percent of the binding, or 50 percentage points. A volume-based safeguard can only be imposed for two years, and, if it is used twice in succession, cannot be used for another two years. If an SSM duty is imposed, and imports are lower than in the period before imposition, the trigger level is not reduced—thus avoiding a potential outcome where use of the duty itself causes the trigger to decline.

The draft Modalities do not, in general, permit total applied duties to exceed the pre-Doha limit. A major focus of debate has been on exceptions to this limit for the quantity safeguard, and two specific proposals have been advanced. The “Lamy compromise” would have permitted duties to exceed the bindings by 15 percentage points on 2.5 percent of tariff lines when imports exceeded the base by 40 percent (ICTSD 2008). The proposal by the G-33 (2008) and its negotiating partners would permit tariffs up to 30 percent (or percentage points) above the pre-Doha bindings on 7 percent of tariff lines, when imports exceed 110 percent of base levels. The draft modalities (2008a, para 145) consider increases of 15 percent above the bound rate or 12 percent above the bound rate.

In the next section, we examine the _qualitative_ implications of using the quantity and price-based safeguards as a guide to understanding the model-based results in subsequent sections.

# **Qualitative Impacts of Using the Price and Quantity Triggers**

The impacts of using a _price-based safeguard_ in a small, trading economy are straightforward. To see this, it is useful to consider first the market for a single imported

> 2 Since imports in any one year are compared against a three-year moving average of past imports, steady growth in imports of 5 percent per year compounds to a “surge” in imports of over 10 percent, against which a safeguard can be imposed.

6
Figure A.2: Data and Outliers <!-- Start of picture text --> Outliers: Devaluations and Hyperinflation<br>1980m1 1985m1 1990m1 1995m1 2000m1 2005m1 2010m1 2015m1<br>date_m<br>1980m1 1985m1 1990m1 1995m1 2000m1 2005m1 2010m1 2015m1<br>REER Terms of trade (psoy_cpiae)<br>Productivity diff. (cpi_ppi) Reserves (res_cpi) Consumer Price Index NEER<br>Trade openness (trade_cpi) REER<br>12<br>100<br>10<br>50<br>8<br>0<br>6<br>monthly growth rate (%) -50<br>4<br>-100<br><!-- End of picture text --> <u>Table A.4: Robustness to Outliers and Lags</u>

||<br>Baseline|<br>Dummies|<br>2 Lags|13 Lags|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Terms of trade|0.642***|0.863***|0.645***|0.688***|
|Productivity differential|1.456***|0.903**|1.530***|0.476***|
|Reserves|0.312***|1.880***|0.123*|1.688***|
|Trade openness|-0.634***|-3.237***|-0.336***|-2.442***|
|Constant|-2.720|5.290|-3.733|3.486|

<u>Table A.5: Test for Residual Normality</u>

|Component|Statistic|Prob.|
|---|---|---|
|Joint Skewness Chi-sq|10.0|0.076|
|Joint Kurtosis Chi-sq|280.7|0.000|
|Joint Jarque-Bera (Normality)|290.7|0.000|

<u>Table A.6: Robustness to DOLS and FMOLS</u>

||Pre-|2008|RE|ER||Full sa<br>|Adj-<br>mple|REER|||
|---|---|---|---|---|---|---|---|---|---|---|
||DOLS|FMOLS|DOLS|FMOLS||DOLS|||FMOLS||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Terms of trade|0.649***|0.642***|0.573***|0.567***|0.564***|0.590***|0.508***|0.548***|0.580***|0.504***|
|Productivity diff. (CPI/PPI)|1.380***|1.375***|1.513***|1.512***|1.253***|||1.253***|||
|Reserves|0.215***|0.223***|0.217***|0.223***|0.039|0.109*|0.289***|0.028|0.102*|0.298***|
|Trade openess|-0.425***|-0.431***|-0.591***|-0.598***|-0.089|-0.333***|-0.733***|-0.068|-0.322***|-0.748***|
|Constant|-3.200***|-3.155***|-2.122***|-2.082***|-3.279***|-3.033***|-0.924*|-3.256***|-3.023***|-0.877*|
|Prod. diff (Adj CPI/PPI)||||||1.460***|||1.465***||
|Prod. diff.(Large Adj. CPI/PPI)|||||||1.428***|||1.429***|
|Jarque-Bera|7.3|11.2|3.4|22.0|74|20|1|47.5|37|8.3|
|P-value|0.026|0.004|0.182|0.000|0.000|0.000|0.634|0.000|0.000|0.016|
|Hansen instability|0.015|3.422|0.003|2.599|0.002|0.003|0.003|3.990|3.706|2.689|
|P-value|> 0.2|< 0.01|> 0.2|< 0.01|> 0.2|> 0.2|> 0.2|< 0.01|< 0.01|< 0.01|
|Overvaluation as of Oct 2015|||||||||||
|No HP filter|-|-|31%|31%|78%|58%|25%|76%|57%|25%|
|HP filter|||27%|28%|69%|49%|20%|67%|49%|21%|
|Overvaluation using coefficients pr|e-2008||||||||||
|No HP filter|-|-|31%|11%|101%|59%|4%|76%|57%|25%|
|HP filter|||27%|5%|94%|50%|-7%|94%|50%|-7%|

36
our sample, Kyrgyz Republic comes out at the top with an estimated sibling correlation of 0.38 which is also the lowest among the 53 countries in Table 1. Turkiye (previously known as Turkey) and Armenia share the unfortunate distinction of the lowest intergenerational mobility in this region with an estimated sibling correlation of 0.55.

# (5.2) Evolution Over Time: Estimates from Decade Wise Birth Cohorts

As noted earlier, in many countries, the sample size for the 1960s birth cohort is too small for credible estimation of sibling correlation. We thus focus on the three decade wise birth cohorts, from the 1970s to the 1990s. The children born in the 1970s are likely to face signicantly dierent economic and educational policies when compared to the children born in the later decades. There were two major policy developments in the 1980s and the following decades that might have aected the educational opportunities of children. First, there were economic liberalization and reform across many developing countries including trade liberalization, privatization and deregulation. The reform yielded impressive economic growth and substantial reductions in poverty in many countries, but at the same time increased income inequality (World Bank (2006)). Second, there were dramatic expansion of schools in many developing countries.<sup>34</sup> Many countries also implemented compulsory primary and secondary schooling in the decades of 1980s-2000s. As a result, signicant gains in school enrollment and schooling attainment were achieved over these decades (World Bank (2018)). Did the poverty reduction and the expansion of schooling and other educational policies outweigh the countervailing eects of inequitable growth and might have actually expanded the educational opportunities for the children in the later decades? Are there important regional dierences in the evolution of inequality of educational opportunities over these decades? We make some progress on these questions in this section.

Figure 2 presents the estimates of sibling correlation for the six regions dis-aggregated by the decade of birth (1970s-1990s birth cohorts).<sup>35</sup> The rst impression that jumps out of

> 34For a comprehensive discussion on the school expansion in developing countries, see chapter 2 titled The great school expansion- and those it has left behind in World Bank (2018). For recent analysis of the eects of school construction on intergenerational mobility, see Mazumder et al. (2019) and Ahsan et al. (2023).

> 35The countries in a region in Figure 2 may vary from Figure 1, as we included only those countries for which estimates for all three decades are available. For example, Figure 2 does not include Brazil where the last DHS survey was done in 1986, and as a result, we do not have enough observations for the 1980s and 1990s birth cohorts. A cohort-wise graph including all countries can be found in the online appendix.

19
**Regulatory Arbitrage and Cross-Border Syndicated Loans**<sup>**1**</sup>

Asli Demirgüç-Kunt World Bank Bálint L. Horváth University of Bristol Harry Huizinga Tilburg University and CEPR

**Keywords:** Regulatory arbitrage, creditor rights

**JEL classification:** G21, G38

> 1 <mark>This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Iftekhar Hasan and participants at the LAPE-FINEST 2019 Spring Workshop for useful comments.</mark>
**Appendix 4: Vignette Regressions (Ordered Probit)** **<u>Vignette 1</u>**

|(1)|(2)<br>PCE +<br>household||
|---|---|---|
|PCE|characteristics||
|Coef.<br>se|Coef.|se|
|Log expenditure per capita<br>0.008<br>0.038|0.044|0.043|
|_Household Demographics_|||
|Female headed household|0.047|0.059|
|Age of household head|-0.009|0.011|
|<br>Age of household head squared|0.000|0.000|
|Household size|0.030**|0.014|
|Number of children|-0.017|0.020|
|Number of older adults|-0.073|0.054|
|Number of migrants|-0.006|0.032|
|<br>_Ethnicity (Reference: Tajik)_|||
|<br>Uzbek|-0.074|0.052|
|Russian|0.041|0.166|
|Other|0.146|0.151|
|_Education (Reference: No Education)_|||
|Primary|-0.072|0.121|
|Basic|-0.207*|0.112|
|General Secondary|-0.023|0.109|
|Special Secondary|0.018|0.119|
|Technical Secondary|0.037|0.123|
|<br>Higher Education|-0.020|0.118|
|Graduate School|-0.098|0.458|
|_Employment Characteristics of Household, Head’s Occu_|_pation_||
|_(Reference for Occupation: Unemployed)_|||
|Number of employed|-0.020|0.021|
|Agriculture / Fishing / Forestry|0.074|0.074|
|Manufacture / Mining|0.101|0.103|
|Services|0.341**|0.173|
|Construction|0.046|0.095|
|Public Administration / Education / Health|0.021|0.082|
|Sales and Services|-0.009|0.072|
|Other|0.077|0.116|
|_Agriculture (Reference for holdings: No Land)_|||
|Small Holding|0.168**|0.072|
|Medium Holding|0.086|0.092|
|<br>Large Holding|0.057|0.089|
|Anylivestock, poultry, etc|0.039|0.062|
|_Geography (Reference: Dushanbe)_|||
|<br>Sogd Urban|0.095|0.082|
|<br>Sogd Rural|0.009|0.087|
|<br>Khatlon Urban|-0.670***|0.133|
|Khatlon Rural|-0.115|0.083|
|RRP Urban|0.101|0.120|
|RRP Rural|0.132|0.080|
|Gbao Urban|-0.121|0.123|
|Gbao Rural|-0.281***|0.088|
|Number of observations<br>4,771|4,771||
|Pseudo R2<br>0.000|0.022||

31
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Under the trade expansion scenario, several SAPP countries would end up importing more than half their power needs (figure 3.5). At one extreme, Botswana would import almost all its power from neighbors. A second group comprising Namibia, Angola, and Malawi would import around half their power consumption. A third set of countries, including Zimbabwe and Lesotho, would not see a significant shift in their pattern of power imports as a result of trade. South Africa’s power imports, though large in absolute terms at 36 TWh annually, would cover no more than 10 percent of domestic consumption. Most of these importing countries would be reliant on the Democratic Republic of Congo and (to a far lesser extent) Mozambique to meet their power sector needs.

**Figure 3.5 Net imports as a share of domestic demand (percentage)**

<!-- Start of picture text --> 100<br>50<br>0<br>-50<br>-100<br>-150<br>-200<br>-250<br>-300<br>-350<br>-400<br>Trade expansion Trade stagnation<br>Net imports of power as percentage<br>Mozambique Zambia Lesotho Botswana<br>domestic consumption<br>DRC South Africa Zimbabwe Malawi Angola Namibia<br><!-- End of picture text --> _Source:_ Rosnes and Vennemo 2009.

The possibility of accelerating regional power trade in the SAPP hinges entirely on the ability of the Democratic Republic of Congo to deliver the massive investments in hydropower that would be needed. The implementation of the trade expansion scenario described here depends on the rapid development of 7,640 MW of additional hydropower resources in the Democratic Republic of Congo. There are a host of technical, financial, and political challenges that make this a difficult prospect. First, from a technical perspective, the envisaged scale-up is more than three times the country’s existing installed generation capacity, which amounts to little more than 2,450 MW, making this a huge technical challenge for the country. Second, the cost of developing these hydropower schemes would be $892 million annually for a decade, which is equivalent to almost 15 percent of the Democratic Republic of Congo’s GDP, and would not be financially tenable without massive capital contributions from those countries that would ultimately import the power. Third, for many years the Democratic Republic of Congo has suffered from political instability and weak governance, a fact that does not make it an attractive destination for investments of this magnitude.

45
# **Appendix B: Further description of the food and non-food consumption aggregates in the TZNPSs and the experiment**

The main consumption aggregate in NPS consists of the food and non-food consumption expenditure aggregates.

# _Food component_

Food consumption is made up of expenditures on thirteen food categories: cereals and cereal products; starches; sugars and sweets; dry pulses; nuts and seeds; vegetables; fruits; meat products and fish; milk and milk products; oils and fats; spices and other foods; non-alcoholic beverages; and alcoholic beverages. It includes all sources of consumption, such as purchased food, meals eaten outside, food produced at home, and gifted food. The values of food that has been eaten and nonpurchased food that was consumed are incorporated into the welfare measurement.

# _Non-food component_

NPS collects data covering a wide array of non-food items, including health, education, utilities, and energy sources (including water, kerosene, electricity, matches, bulbs, and charcoal), furnishings and household items, clothing, transport, communication, recreation, and other non-food consumption. Each non-food category has a specific reference period based on its frequency of purchase or consumption: expenses for mobile phones and personal care are gathered over the last month, and expenses on furnishings over the past twelve months.

The information about some non-food goods and services was excluded from the consumption aggregate in earlier rounds of NPS because those items were not collected or due to incomparability with other rounds. Based on this information, we can identify two methods for defining the consumption aggregate. The new classification method includes three additional items in non-food consumption - the use value of durables, imputed rent on user-occupied property, and clothing expenses - while the old method excludes them. Hence, the new method yields higher consumption figures for non-food and total consumption, by the inclusion of durables and imputed rent. We employ the new method to construct the consumption aggregates for analysis. The descriptive statistics for the consumption aggregate calculated using the new method and the old method are shown respectively in Table 1 in the main text and Table B.1 below.

In all NPS rounds as well as the experimental round TA 1, food consumption can be disaggregated into 83 individual commodities, while in TA 2, only 17 items are reported. Quantities and unit costs are recorded from which total expenditures are computed. Finally, in TA 3, only monetary expenditures on 13 basic food categories are surveyed, without surveying quantities or prices.

71
Table 9. Program impact on health outcomes: Under-...ve child deaths.

|Dependent variable|Child death (children < 5 year)|Under-5 mo|rtality rate|
|---|---|---|---|
|Speci...cation|(1)<br>(2)|(3)|(4)|
|Program impact|-0.016*<br>-0.017**|-48.0**|-46.0*|
||(0.01)<br>(0.009)|(24.2)|(25.8)|
|Constant|0.049***|144.9***||
||(0.006)|(16.9)||
|Controls|No<br>Yes|No|Yes|
|District ...xed e¤ects|No<br>Yes|No|Yes|
|Observations|2922<br>2922|50|50|
|R<sup>2</sup>|0.002<br>0.01|0.08|0.18|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable (columns 1-2) is a dummy variable indicating whether any children under ...ve in the household have died during the last year and estimated under-5 mortality rate in the community expressed per 1,000 live births (columns 3-4). c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas. d. Control variables: see note (d) in Table A.8.

54
further to 71 percent in 2011. Overall, the trend of placing bank supervision in the central bank seems to have increased for both crisis and non-crisis countries.

The supervisory quality index,<sup>15</sup> our second institutional variable, was developed by Anginer, Demirgüç-Kunt, and Zhu (2013) and quantifies whether the supervisory authorities have the power and the authority to take specific preventive and corrective actions. They found that <mark>good bank supervision can foster systemic stability and lessen the unintended consequences of deposit insurance on systemic bank risk during good times.</mark> The average index does not differ significantly across “crisis” and “nocrisis” countries (figure 3).

Our third institutional indicator measures whether and for how long (in years) the central bank has been publishing a financial stability report. The underlying binary (0/1) variable of whether a central bank published an FSR in a given year was constructed by Čihák et al. (2012).<sup>16</sup> They find that higherquality FSRs tend to be associated with more stable financial environments but find only a weak empirical link between a simple publication of FSRs and financial stability. The data on FSR publication are available for only 78 countries, covering 22 of the 25 crisis countries. The data on the quality of FSRs as rated by Cihak et al. are not publicly available. We assume that quality can improve with time and use the length of FSR publication as a proxy for the quality of the reports.<sup>17</sup> As of 2008, the average length (in years) of publishing an FSR was significantly higher for crisis countries than for the noncrisis countries (figure 4). At the same time, the shares of crisis and noncrisis countries that published an FSR in 2007 are similar—86 percent of the crisis countries and 82 percent of the noncrisis countries, respectively.

> 15 We followed Anginer, Demirgüç-Kunt, and Zhu’s (2013) methodology and computed the index by aggregating the answers to 14 selected questions on supervisory powers that were collected in the 2003, 2007 and 2011 surveys conducted by Barth, Caprio, and Levine (2008).

> 16 The methodology for assessing central banks’ FSRs was introduced by Čihák (2006) in the first worldwide survey of FSRs. 17 Alternatively, one could use as the control the availability or even better the actual implementation of macroprudential tools prior to the 2008 crisis. However, this type of data for the number of countries covered in this study is not available. See Claessens, Ghosh, and Mihet (2013) for an example of a study that collected data on the use of macroprudential tools for 48 countries.

13
but rather to measure revisions of intertemporal plans and to shed light on the determinants of such revisions.

# 5. **Conclusion**

The consequences of making low quality intertemporal choices can be dire, especially among the poor in developing countries. Low-income farmers can go hungry for long periods if they fail to make good smoothing decisions. They can fail to rise out of extreme poverty if they miss opportunities to make high return investments. We conducted an experiment among Malawian farmers to investigate why their intertemporal choices may appear not to serve their individual self-interest. More precisely, we provide the first field evidence on the causes and correlates of decisions to revise prior intertemporal choices. The experiment allowed subjects to make an intertemporal allocation of substantial funds they would receive at two future times 30 days apart. This future 30-day period was timed to occur during a period of low income and low food stores, and thus consumption smoothing of substantial amounts of future income is very difficult. Several weeks later, prior to the first disbursal of funds, we revisited study participants and allowed them to revise their previous allocations over the same 30-day period. We examine these revisions of allocations for evidence of self-control problems as well as other potential mechanisms behind intertemporal choice revision.

We provide new evidence for the existence of self-control problems. Revisions of allocations toward the present are positively associated with measures of “present”-bias from an earlier baseline survey, as well as the (randomly assigned) closeness in time to the first possible date of money disbursement.<sup>50</sup> These results complement existing evidence for self-control problems that are based on demand for commitment devices (e.g., Ashraf et al., 2006, and Beshears et al., 2011). In contrast to pre-existing work on demand for commitment devices, our approach has the advantage of allowing even naïve individuals (who are not aware of their self-control problems) to contribute to estimates, since naïve as well as sophisticated respondents can display revision behavior. In addition, analysis of revision behavior avoids problems of low demand that may arise if commitment devices are poorly designed.

> 50 This result is reminiscent of Kaur, Kremer, and Mullainathan’s (2011) finding that worker effort increases as a worker’s randomly-assigned payday comes closer.

31