rafmacalaba/datause-encoder-data huggingface dataset:
# **Droughts occurring at historical rates lower annual GDP on average by 0.2 percent, but once every 40**

**years (or 2.5 percent of the time) GDP losses exceed 2 percent** (Figure 16).<sup>11</sup> This is based on 1,000 stochastic simulations that incorporate the cumulative impacts of droughts. This lowers the overall productive capacity of the economy over time, and median real GDP from these simulations further reduced relative to the baseline as a result. Lower annual GDP is driven by L&A value-added falling on average by 2.1 percent, but falling more than 17 percent every 40 years. This translates into exports falling on average by 0.5 percent, and over 5 percent every 40 years. Lower output and export revenues impact fiscal accounts, which fall by 0.1 percentage points on average and by 0.5 percentage points of GDP every 40 years.

**Losses are expected to worsen going forward as a result of climate change** . Running the same analysis with risk curves for the RCP 4.5 climate scenario produces similar median results but increases the lower bounds on economic outcomes. Figure 17 shows that the lower bound of the 95 percent confidence interval for real GDP falls between 0.3 and 0.4 percentage points from the original. Given that in the baseline scenario the lower bound was -2.1 percent, that represents a 14 to 19 percent increase in potential climate damages to GDP in the RCP 4.5 scenario. Likewise, the impact of climate change on the agriculture industry has potential climate damages increasing from -17 percent by 4 percentage points, or by around 23 percent.

> 11 Light-green shaded areas in Figure _16_ show percentiles 2.5 and 97.5. That is, 2.5 percent of the time (or once every 40 years) observations lie above the upper-bound of the light-green shaded area, and 2.5 percent of the time observations lie below the lower-bound of the light-green shaded area.

23
# Appendix Table A1 Initial occupation

|Estimation: Multinomial Logit|||
|---|---|---|
|Dependent: 1-digit occupation category<br>Sample: Age started >=15|(>=1 & <=9)||
|Occupation|1|2 3 4|
|Years of schooling|0.5359|1.5128 0.5797 0.6244|
||(3.00)|(8.82) (4.95) (8.88)|
|Female|0.3924|-0.1691 0.1754 1.2991|
||(0.41)|(0.40) (0.42) (4.91)|
|Occupation|5|6 7 8|
|Years of schooling|0.1818|0.0086 0.0484 0.1011|
||(5.40)|(0.10) (0.98) (1.54)|
|Female|0.4010|-1.6778 -1.6966 -1.1268|
||(3.19)|(3.80) (6.01) (4.00)|
|Number of observations|1703||
|R squared (pseudo)|0.1254||

Numbers in parentheses are absolute t values with Huber robust standard errors. The omitted case is occupation category 9. All the specifications include age started, total asset value (2000), mother/guardian’s age and years of schooling.

25
the “contest for aid.” The non-bold line in Figure 1 shows average policy – measured by the CPIA – across recipients for the same time period, and its dynamics over time exhibit a very similar pattern – particularly when considering the outlier adjusted measure. The change in the CPIA over the time period amounts to about one full point from 2.5 to 3.5. For example, on the trade policy sub-dimension, an increase from 2 to 3 implies a change from a “very discretionary and discriminatory trade regime” to a “somewhat discretionary and discriminatory trade regime,” and “high” taxation of imports and exports to “moderate” taxation. On the transparency sub-dimension, an increase from 2 to 3 corresponds to a change from “boundaries between the public and private sectors are poorly defined, and conflicts of interest abound” to “boundaries between the public and private sectors are moderately well defined, but violations are frequent and often not investigated or sanctioned.” Note that there is quite a bit of a variation in the policy level across aid recipient countries. For example, in Peru, Uganda, or St. Kitts and Nevis the CPIA index has improved by more than three points while in other countries such as Suriname or Iraq it hardly changed over this time period.

<!-- Start of picture text --> 1985 1995 2005 2015<br>Year<br>60 3.6<br>3.4<br>40<br>3.2<br>3 CPIA, overall<br>20<br>% of Policy-Selective Aid<br>2.8<br>0<br>2.6<br><!-- End of picture text --> Bold lines shows the percentage of policy-selective aid in the global aid budget (GPS) between 1985 and 2014. Dashed bold line shows GPS without outliers. Solid line shows the average overall CPIA measure among recipient countries. The large jump between 1997 and 1998 reflects also the change from a 5- to a 6-point scale in the CPIA measurement.

Figure 1: Share of Policy-selective Aid and CPIA 7
# **Appendix Table B1:** Detailed description of construction of outcome variables

|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Highest Grade Completed|I|Core<br>Respondent|Yes|2,3,4|Highest grade completed is the self-reported<br>highest grade attended/completed by the core<br>respondent at the time of the household survey.<br>In rounds 2 and round 3 this is the highest<br>grade attended and in round 4 in is highest<br>grade completed.|
|Passed Primary School<br>(PSLC)|S1|Core<br>Respondent|No|2,3,4|=1 if core respondent passed the Primary<br>School Leaving Certificate (PSLC) at the end<br>of 8th grade.|
|Passed Junior Secondary<br>School (JCE)|S1|Core<br>Respondent|No|2,3,4|=1 if core respondent passed the Junior<br>Certificate of Education at the end of 10th<br>grade|
|Passed Secondary School<br>(MSCE)|S1|Core<br>Respondent|No|2,3,4|=1 if core respondent passed the Malawi<br>School Certificate of Education at the end of<br>12th grade|
In comparison to similar studies in other contexts, the businesses in this study are smaller in size, reflecting the less developed nature of the country and small size of most informal businesses. In the study in Malawi (Campos et al, 2015), businesses had on average two employees and monthly profit of USD 214, while in the study in Sri Lanka (de Mel et al. (2013)), businesses had on average three employees and monthly profit of USD 300.

# **3.3 Experimental Design**

The 3,596 informal businesses<sup>7</sup> were randomly allocated into three treatment groups and one control group. The first group of informal businesses received package A of incentives, the second group packages A and B of incentives, and the third group packages A, B and C.

The randomization was done in the office using STATA and the following methodology was used for stratification:

- (1) 16 strata were created using the following variables: business owner gender, business operating in Dantokpa market, trader, and business owns a bank account.

- (2) Inside each stratum a Z-score was created as the average of standardized profits, turnover and number of employees. Based on this Z-score, triplets of businesses were created and inside each triplet, businesses were randomly allocated to 3 groups, each of 1,200 firms.

- (3) The 1,200 businesses in one group were then randomly allocated further into a first treatment group with 301 businesses, and second treatment group with 899 businesses.

As a result, 301 businesses were allocated to receive package A (treatment group 1), 899 to receive packages A and B (treatment group 2), 1,199 to receive packages A, B, and C (treatment group 3), and 1,197 to the control group. Figure A2 describes the organizational chart of the interventions. We decided to allocate fewer firms to the first treatment based on the existing literature which had shown limited impact of simplification of business registration procedures and cost reduction alone. Our goal was to retain more firms in the treatment groups that we thought would have higher impacts on formalization, in order to provide sufficient power to estimate the impact of formalization on firm performance.

> 7 The sample was initially composed of 3,600 businesses, but 4 businesses were in fact duplicates of other businesses in the sample and were dropped from the sample.

10
allow for better quantification of changes in physical activity levels related to travel and the intensive (per hour) and extensive (number of hours) physical margin of domestic and market work that are harder to quantify with the methodology and data adopted here. Another promising alternative to costly household surveys is the use of technology for the purpose of capturing TEE. Most modern smartphones can track physical activity levels reasonably well and simple devices that monitor heart rates offer the potential to estimate variation in activity levels for larger samples, greatly increasing the scope for estimating activity levels for populations outside the laboratory relative to the prohibitively expensive chemical methods that prevail in the nutrition literature.

# **A Estimation of Resting Energy Expenditures (REE)**

The best anthropometric predictor of REE is fat-free mass, but as this is rarely measured in the field, prediction equations have been developed based on regressing laboratory measured REE on more commonly observed variables ((St. Jeor and Stumbo 1999)). A standard approach, adopted by FAO (2001) and (Indian Council of Medical Research 2009), is to use prediction equations that regress REE on gender, age, weight, and in some cases height. Estimating separate equations by age and sex, and incorporating height, helps account for systematic average differences in fat-free mass, e.g. women, older, and shorter individuals tend to have lower fat-free mass for a given weight. Equations provided in (Henry 2005) use height and weight and are estimated separately for several age/sex groups. Results are not very sensitive to the use of alternative prediction equations. In Appendix Table S.1 Panel A, alternative REE estimates are generated using the prediction equations used by (Indian Council of Medical Research 2009). Similar results can be obtained using the prediction equations from (St. Jeor and Stumbo 1999). There have been laboratory measurements of REE in India but none for a large or representative sample of individuals and none generating specific predictive equations. (Ferro-Luzzi et al. 1997) attempt a validation of the FAO equations on Indian data and find a reasonable fit.

The method used here for imputing REE to individuals in other data sets is to regress the REE predicted by the (Henry 2005) equations using the 19
The model is well specified, as indicated by the diagnostic tests in Table 4. Moreover, recursive estimates of the coefficients for [ _e_ + _wfp_ − _pc_ ] _t_ − 1 _agt_ − 1 and ∆ _mt_ − 2, over 2002:1-2008:11, reported in Figure 10, show that they are empirically stable. The recursive estimates also reveal that lagged money growth only becomes clearly significantly different from zero at the 5 percent level in 2007.

We thus conclude that cereal and food prices seem to be determined by the exchange rate and foreign prices in the long run. The domestic agricultural market also plays an important role by both generating supply shocks, and possibly by creating backward and forward-looking expectations about future price changes. Money growth also affects food inflation in the short run, but the evidence is not as strong as for the other effects.

Figure 10: Recursive estimates of selected coefficients in the food inflation model, 2002:1-2008:11

# _5.3 Non-food Prices_

The specification of the general model for non-food inflation includes the money market term ( _m_ − _p_ ) − 0.65 _y_ + 2.48 ∆ 12 _eus_ , and the foreign sector error correction term _e_ + _wp_ − _pnf_ − _trend_ , where _trend_ is obtained with the Hodrick31
TABLE 1. Politically Motivated Aid and Growth, OLS, 1974–2009

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ΔAid (t−1)|0.116|0.473**|0.142|0.345|
||[0.085]|[0.208]|[0.139]|[0.329]|
|ΔAid squared (t−1)||‐0.010**||‐0.006|
|||[0.004]||[0.006]|
|UNSC (t−2)|‐1.384|‐1.368|‐1.737|‐1.732|
||[0.832]|[0.836]|[1.216]|[1.247]|
|UNSC (t−2)* ΔAid (t−1)|‐0.981**|‐1.289***|‐1.446***|‐1.547***|
||[0.429]|[0.379]|[0.295]|[0.338]|
|ΔGDP p.c. at start of period|‐3.607*|‐3.731**|‐1.472|‐1.770|
||[1.904]|[1.801]|[4.068]|[3.745]|
|ΔAssassinations|‐0.198|‐0.184|0.695|0.486|
||[0.179]|[0.169]|[0.942]|[0.969]|
|ΔAssassinations * ΔEthnolinguistic|0.337|0.307|‐3.610|‐3.172|
|Fractionalization|[0.341]|[0.327]|[3.354]|[3.417]|
|ΔM2/GDP|‐0.003|‐0.004|0.043*|0.039*|
||[0.016]|[0.014]|[0.024]|[0.020]|
|ΔPolicy|0.923***|0.940***|0.918**|0.913***|
||[0.160]|[0.165]|[0.330]|[0.324]|
|Sample|All|All|Africa|Africa|
|Adj. R‐Squared|0.171|0.190|0.139|0.142|
|Number of Observations|393|393|145|145|
|Number of countries|54|54|24|24|

**Notes:** The dependent variable is growth of real GDP per capita and covers the 1974–2009 period. All regressions use averages over four years, include variables in first differences (except for the UNSC variable) and include period dummies. Robust standard errors in brackets (clustered at the recipient country level): * p<0.10, ** p<0.05, *** p<0.01.

**Source** _:_ Own calculations; see supplemental appendix S1 for variable definitions and sources.

24
with a work requirement. While not properly an expansion of a pre-existing program, it still brought adversely affected households under the cover of a social protection policy. The only thorough study of the program (Galasso & Ravallion 2004) shows somewhat mixed results: It failed to properly target the intended beneficiaries, reaching only about one-quarter of eligible families. Moreover, about one-third of benefit recipients were not in the labor force before the crisis. On the other hand, it succeeded in lowering the unemployment rate by 2.5 percentage points. A second study, and the only one to look at a vertical expansion of a pre-existing program, comes from the Fiji Islands after the country was hit by hurricane Winston in February 2016. The vertical expansion consisted of a top-up to three existing social assistance programs, worth around three months of the programs’ regular payments. An impact evaluation of this intervention (Ivaschenko, Doyle, KIm, Sibley & Majoka 2020) implements a regression discontinuity design around the observable eligibility threshold for the cash-transfer program on a sample of households in the most affected regions. It finds that beneficiary households recovered significantly faster from the shock compared to nonbeneficiary households around the eligibility threshold. The former were 26 percent more likely to have replaced a lost dwelling, and 13 percent more likely to have repaired damaged walls at the time of the survey. The study also finds that most of the funds were spent on necessities such as food and repairs, that female-headed households had a higher recovery rate compared to male-headed ones, and, importantly, that market-access was crucial to a 8
approach triggers the "sampling without replacement" effect presented previously. This may explain why practitioners have experienced political difficulties with the case-by-case strategy. Lipton and Sachs (1990, 298) note that "in almost all countries where privatizations have been attempted, there have been major political obstacles to the case-by-case approach". Boycko, Shleifer, and Vishny (1993, 148) state that reforms that proceed at a rather slow pace are likely to reach a deadlock. As we argue later in this paper, "spatial gradualism" (reforming one _region_ after another, as China did by installing Special Economic Zones) can avoid the "sampling without replacement" problem. Thus, the mechanisms explored in this paper may help explain why gradual reform strategies have been more successful in China than in Latin America and Central and Eastern Europe.

The outline of this paper is as follows. We first describe various examples of reforms for which support dynamics have been counterintuitive. Next, we construct a learning model that provides an explanation for these puzzling dynamics. We then consider the question of why gradualism worked quite well for China, although this was not the case for most Latin American and Central and Eastern European countries. Finally, we conclude.

# **Support Dynamics for Economic Reforms: a Short History**

In addition to the case of Slovakia discussed in the introduction, there are many examples of economic reforms that lost support despite their initial success (and vice versa). Stokes (2001) provides a thorough analysis of support dynamics related to various reforms. In that volume, several authors examine the public's reactions to reforms in Spain, East Germany, Poland, Mexico, Peru, and Argentina. In her summary of the study, Stokes (2001, 25) notes that "[their] most startling result is that in every country people sometimes reacted to economic deterioration by supporting the government and its economic program more strongly. Conversely, they sometimes reacted to economic improvement with pessimism and opposition". Similar findings are reported by the econometric studies of Remmer (1991) and Tucker (2000), who analyzed data from 12 Latin American and five post-communist countries, respectively, and reported a negative causal effect of economic performance on support for incumbents.

Stokes (2001) provides various specific examples of these counterintuitive dynamics. For example, in all three Latin American countries studied (Mexico, Peru, and Argentina), economic expansion (measured by either wage or GDP growth) was followed by pessimism 5
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-25 -

correlation in that city between SO% and temperature, and tempeature and mortality. No association between particulates and morality was detected in either city.

Chinn et al.'<sup>3</sup> investigated the association between mortlity of people aged 45 tO 74 and air pollution in London, England and Wales. Mean SO2 and smoke levels for 1971 are not provided explicitly, but visual inpectio of the relevant graph in the tcxt suggests means of .06 ppm and 80 pg/mt. respectively. Two age groups for bothmn and women were analyzed: those aged 45 to 64 and 65 to 74In addition to total mortality, several specific causes of death were considered. including hypertensive disease, influenza, and chronic brnchitis. There was little correlation between either SCQ or smoke and morUtliy, prompting the authors to suggest this was a negative study. However, one particularly significant finding was a correlation between S02 and morality from chronic bronchitis among men above 65 (r= .22) and women betwe 45 and 65 (r = .26).

Kyrzanowski and Wojtniak" examined the association between individual-specific daily mortality and air pollution over a ten-year period for residents living in Cracow, Poland versus dtose living just outside the city. The results indicated a significa sttical relationship between air pofluidon, measured as particulate matter and SO2, and all-cause mortality for men.

Hatzakis et al.<sup>6</sup> explored the relationship between daily morality and air polution in Athens. Greece during 1973-1982. Mean daily levels of 50 and British smoke were 85 pg/n<sup>3</sup> and 63 g/rn<sup>3</sup> , respeivey, with an average of 28.48 deaths per day. The poflutmnts were firly strongly correlated (r = .55). Mortality was adjusted for seasonal pa over time by calculatig an observed minus predicted measure. Regression analysis was used to control for temp humidity. holidays, and annual. seasonal, monthly and weeldy trends. S02 and excess all-cause mormlity were correlated when all other independet variables were taken ito accounlt Particulates, however, were not related to mortality. The high estimate of the mortality efect is taken from the crude (i.e. no covariates included) regressionresults of this study. The linear coefficient relating S% to daily deats was 0.0346. Thus a
advertisements and websites, operating complaints hotline, receiving complaint statistics from financial institutions, conducting consumer interviews and focus groups, and engaging in mystery/incognito shopping. Rather, the vast majority—67 percent—of those with the authority to enforce financial consumer protection monitor compliance through on-site inspections. This is not surprising, as compliance with consumer protection regulations is addressed as part of business conduct supervision which uses financial examination as the main tool. Yet this approach may not be sufficient to fully capture the scope and nature of the violations and direct contact with the user of services. Other methods mentioned above can enhance monitoring effectiveness of the responsible agency. Moreover, monitoring the trends in customer complaints can provide insights to the regulators for necessary adjustments in rules and regulations as financial products constantly evolve.

# **3.3 Dispute Resolution Mechanisms**

In retail financial transactions, especially those involving low-income individuals, power imbalances between providers and users of financial services are substantial. An individual is unlikely to initiate and go through a legal process when subjected to unfair treatment due to insufficient resources and/or understanding. A modern financial consumer protection framework relies on two key mechanisms to address this concern. First, it involves financial institutions establishing an effective mechanism to receive and resolve customer complaints. Second, in case the complaint is not resolved within a reasonable time or the customer is not satisfied with the outcome, the complaint may be referred to a third party independent dispute resolution mechanism such as an ombudsman or a mediation service.

Surprisingly, only around one-third of the countries in the survey report the existence of laws or regulations that set standards for internal complaint resolution mechanisms for financial institutions. These standards include certain processes to be implemented, standards on the timely handling of complaints, and accessibility. The fact that there are no clear standards on handling and processing complaints in most countries raise doubts on the ability of the supervisory agency to monitor and assess how well financial institutions manage these complaints.

More than half of the countries in the survey (58 percent) report having a third-party dispute resolution mechanism. 30 percent of countries have a specialized financial ombudsman while 21 percent have a general ombudsman that handles financial consumer protection issues along with others. Different types of third-party dispute resolution mechanisms are not mutually exclusive and some countries have more than one type. Mediation services tend to be more common in Asia, where about half of the countries report their existence compared to a quarter or less in other regions. While 57 percent of high-income countries have financial ombudsmen, only 5 percent of low-income countries have them (see Figure 9).

13