rafmacalaba/data-use-mentions-extended huggingface dataset:
identical, but close, to the corresponding value of 0.29 for ρ based on the actual panel.<sup>13</sup> We will return in the next section to more discussion on estimation results using this value of ρ from the IHDS panel.

Table 1 compares point estimates of IHDS consumption dynamics in the IHDS true panel and the synthetic. We use data from the 2011/12 second survey round ( _x_ i2) as the base year for predictions. See the Appendix for detailed estimation results. We calculate the bootstrap standard errors by bootstrapping (yij, xij) from its empirical distribution function (1,000 times), adjusting for the complex survey design of the IHDS (including stratification, cluster sample, and population weights), and applying the estimated parameters for equation [1] from the original samples.

We provide two ways to evaluate the goodness-of-fit for estimation results. First, we consider the precision of the synthetic panel point estimates by enumerating the number of times they fall within the 95 percent confidence interval (CI) around those from the actual panels. A more demanding test is to consider similar statistics on a narrower band of one standard error around the actual panel estimates. Table 1 reveals that of the 15 possible cells, the synthetic panel method produces estimates that fall within the 95 percent CI of the true panel estimates in 10 cases (and in half of those, the estimates fall within one standard error). Even in those cases where the estimates fall outside the 95 percent CI, scrutiny of the estimates reveals that they are actually quite close. For example, while 21 percent of the population in the actual panel was estimated to be vulnerable in both 2004/05 and 2011/12, the corresponding estimate in the synthetic panel was 19.3 percent.<sup>14</sup> Similarly 18.6 percent of the population in

> 13 Monte Carlo simulations provided in Dang and Lanjouw (2013) suggest that estimates on poverty dynamics based on
# **APPENDIX B.**

# **B.1 International securities**

In Section 4, we analyze the main drivers of foreign holdings. An outstanding question is the type of securities that managers use to access international markets. Table 1 provides some suggestive evidence. For instance, pensions funds with a higher share of fixed income investments tend to invest more domestically. It is possible that these funds have a higher concentration in domestic government securities and thus allocate only a small portion of their portfolios internationally. According to Table 1, funds with the lowest exposure to foreign assets (in quartile 1) invest up to 40% of their assets in treasury bills and in bonds. Conversely, it appears that funds that allocate a larger portion of their investments through investment companies, and in particular, via mutual funds, invest more internationally. The evidence however is only suggestive since OECD does not collect detailed portfolio information.

To examine the international investment strategies of pension funds, we use detailed security-level data collected from local supervisory authorities for a sample of Latin American countries – Chile, Colombia, Mexico, and Peru.<sup>15</sup> We differentiate between direct holdings of securities among these investors, and delegated assets into other investment funds. For example, pension fund investments into fund of funds, mutual funds, or in Exchange Traded Funds (ETFs). Finally, to examine the level of active management among these investors, we examine portfolio allocations to passive strategies –investment in equity and fixed-income funds that passively track a benchmark index.

> 15 Complete portfolio data is available for Colombian funds between 2008 and 2018. For Chile, the data covers the years 2013-2018 and for Peru, 2014 to 2018.
# **6.5 Human Capital**

**Some of Togo’s human capital indicators are relatively weak but do not stand out compared to the rest of the region.** Figure 39 suggests that Togo fares relatively well with respect to health – infant mortality rate is the second lowest in the group and the share or birth attended by a skilled health worker is the second highest. With regard to education, Togo has a relatively high primary enrollment rate but the primary completion rate is relatively weak, which indicates high repetition rates and a high number of dropouts.

**Figure 39: Health and Mortality Indicators, Togo and benchmark countries, 2007.**

<!-- Start of picture text --> 180<br>160 Mortality rate, infant (per 1,000 live births) 10090 Total enrollment, primary (% net)Primary completion rate, total (% of relevant age group)<br>140 Births attended by skilled health staff (% of total) 80<br>120 70<br>60<br>100<br>50<br>80 40<br>60 30<br>40 20<br>10<br>20<br>0<br>0<br><!-- End of picture text --> _Source:_ DDP data, World Bank.

**Despite the prolonged economic stagnation, public expenditures per capita have been maintained at least a few years back.** Public health expenditures are more or less constant and even though public education expenditures as a share of government revenues and GDP may have a downward trend, the expenditure per capita is more or less constant (Figure 40).

**Figure 40: Public health and education expenditure, Togo 2002‐2006.**

<!-- Start of picture text --> 2 Health expenditure, public (% of GDP) (lhs) 12 5 25<br>Health expenditure, public
Fante languages. The term was first used during the electricity rationing program associated with a severe drought in 1997, and gained prominence again after the West Africa Gas Pipeline damage in 2012.<sup>4</sup> The Electricity Commission of Ghana (ECG) published official substation-level load shedding schedules from late 2012 to March 6th, 2015 which typically classified service areas in 6, 12, or 24 hour blocks as lights on, lights off, or standby. For two reasons, this study focuses on firm-owner self-reports, including time (and firm) fixed effects that extract plausibly exogenous variation not predicted by the official load shedding schedules. First, publicly-available load shedding schedules concluded on March 6th, 2015, only seven weekdays into the panel data collection period, and due to rolling entry into the weekly survey sample, only three days into the portion of the panel data collection period that includes all firms in the sample. Secondly, ECG load shedding schedules block together all firms in the sample under the Hohoe substation as a single service area. The authors, the research team, and the firm owners in the sample, however, can attest from experience that variation across firms within 12 hour blocks was the norm.<sup>5</sup> Anecdotally the load shedding schedules became less and less reliable as the crisis wore on into late 2014 and into the hot harmattan season of early 2015. Protests were widespread and though the study focuses on the tail-end of the 2012-15 crisis, it studies a period of long, erratic, and politically-salient blackouts.

# **2.2 Garment Making**

Bespoke garment making firms are ubiquitous in many parts of Africa and the developing world. Ghana is no exception. Nearly all garments produced by these small firms are made-to-order, for special occasions like funerals and weddings, as dress attire for church, for work in government offices on African-wear Fridays, or simply as everyday clothing. A fraction of shops also produce ready-made garments or supply larger school uniform contracts, but
DHS data are used to estimate the World Health Organization (WHO) measures of malnourishment (WHO Multicentre Growth Reference Study Group and others (2006)).<sup>8</sup> Male and female growth curves and distributions of height/length -for-age, weight-for-age, and weight-for-height, are estimated by the WHO using samples of 882 children (longitudinal survey) and 6669 children (cross-section survey) from six countries (Brazil, Ghana, India, Norway, Oman and the United States). The medians and standard deviations from these distributions are used by the DHS to calculate z-scores for every child.<sup>9</sup> Children under 5, with a z-score below -2 standard deviations (with respect to their age) for a given metric are considered to be malnourished with respect to that metric.

The measurement of child anthropometrics is challenging and several caveats are worth noting.<sup>10</sup> First, it is assumed that children with similar rearing, health care, and exposure to environmental hazards will grow in exactly the same manner. This, in turn, assumes that ethnicity is not related to anthropometrics. For obvious reasons, this is complicated to delve into, but remains an assumption, rather than an established empirical fact.<sup>11</sup> However, there is some reassuring evidence that differences (between African and Indian populations) in anthropometrics that cannot be explained by income, may be explained by the disease environment (Coffey et al. (2013)) and culture (Jayachandran and Pande (2017)). Second, the measurement of a child’s age is challenging (Larsen et al. (2019)). Third, the surveys are implemented by national statistical offices with differing degrees of expertise. Taken together, these facts suggest that we should expect that any empirical analysis using these measures will involve a relatively low signal-to-noise ratio.

Our focus here is on designing a prototype drought-contingent targeting system, rather than providing a
whole set is only _ρ_ = 0.17. In a second exercise, we calculate the correlation of the blood test result against an index of equally-weighted symptoms that are commonly associated with blood poisoning (convulsions, diarrhea, dizziness, eye irritation, headaches, shortness of breath and vomiting). The composite correlation is even weaker: _ρ_ = 0.05. We conclude that our evidence does not support the use of self-reported symptoms as indicators of actual pesticide poisoning. Given the high incidence of poisoning in the sample, however, our results strongly support regular medical testing for farmers who are exposed to highly- or moderately-toxic organophosphate and/or carbamate pesticides.

# **5.  Determinants of Cholinesterase Inhibition**

Using the blood cholinesterase test results, we conduct a regression analysis to identify the major determinants of pesticide poisoning. According to the medical literature, the types and severity of cholinesterase inhibition symptoms depend on the toxicity of the pesticide, the amount of pesticide involved in the exposure, the route of exposure, and the duration of exposure (Extension Toxicology Network, 2004). The health effects of pesticide use can often be reduced significantly by appropriate use of protective items such as gloves, hats, jackets, eye glasses or masks (Cropper, 1994). A priori, we would expect farmers’ self-protective behavior to depend on their perception of risk, along with the quality of their information about safe handling of pesticides. Taking these factors into account, we specify and estimate the following regression model with dummy variables to control for provincial differences.

# POISONING = f (PESTAMT, PWHOIab, PWHOII, PWHOIII, PWHOU, PTRAIN, NPROTECT, IPMD, Provincial Dummies*)

where POISONING = 1 if acute/chronic poisoning indicated by cholinesterase test, 0 otherwise; PESTAMT = total amount of pesticides applied (kilograms); PWHOIab = % of pesticides classified as highly hazardous (WHO category Ia or Ib); PWHOII = % of pesticides classified as moderately hazardous (WHO category II); PWHOIII = % of
# **Figure 17:** **<u>Cargo Dwell Time, Monopoly-oligopoly versus Competition, 2011</u>**

<!-- Start of picture text --> 12<br>9.66<br>10<br>7.86<br>8<br>6<br>4<br>2<br>0<br>monopoly/oligopoly competition<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

In all countries except South Africa, monopoly-oligopoly situations are likely to keep cargo dwell times lower (Figure 18). However, shorter dwell time does not necessarily translate into lower prices, since the main objective is to maximize profits. Only in South Africa does the high degree of competition play an important part in keeping dwell time lower: since importers in a competitive situation cannot afford to reflect the costs of delays in their prices because they are afraid of losing customers, they protect the customers from price increases due to cost increases. These findings can be explained by the maturity of the South African economy.

**Figure 18: Cargo Dwell Time in Select African Countries, Monopoly-oligopoly versus Competition, 2011**

<!-- Start of picture text --> monopoly/oligopoly competition<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

# **Analysis by C&F Agents’ Professionalism**

# **Level of Information Provided by C&F Agents about the Clearance Progress**

Overall, when C&F agents provide accurate forecasts and real-time information about progress or delay in the clearance process well in advance (even if unexpected events might arise),
and sample selection

The sampling frame for wave 2 of the SHFS is the exhaustive list of sampling units for every stage in the multi‐stage selection process (denominated according to the stage of selection, i.e. primary sampling units (PSUs) in the first stage, secondary sampling units (SSUs) in the second stage, and so on) employed in the survey’s sampling strategy. Sampling units are listed separately by stratum. Each sampling unit must have information concerning the population residing in it to allow for selection proportional to size (United Nations Statistical Division, 2005). In the absence of a recent population census, no readily useable enumeration areas and population estimates existed. To overcome these challenges the SHFS drew from a variety of data sources and GIS techniques to create a population sampling frame, strata boundaries, and a comprehensive list of enumeration areas.

## _Strata boundaries_

In line with stratification at the intersection of administrative regions and population type, the following GIS data sets were combined to spatially demarcate strata boundaries:

- (i) Pre‐war region boundaries;

- (ii) IDP settlement boundaries;

7
index by district matches the WHO estimate rankings for Ebola cases. In our sample, Port Loko has the highest disruption measure, Pujehun has the lowest. Second, a variance decomposition of the disruption index reveals that within district variation accounts for the majority (70%) of the overall variation. Using the dummy measure to refer to high and low disruption, we see that within all districts, there are both high and low disruption villages, with the greatest incidence of high disruption being in Port Loko.

As a third and final step, we provide evidence that the disruption measure picks up localized variation in the intensity of the aggregate shock, rather than capturing more permanent features of local infrastructure, economy or state capacity. Panel A of Figure A3 shows within district partial correlations between the disruption index and the village level covariates related to the location of the village, distance to key infrastructures, political alignment, poverty, size and exposure to NGOs (from Panel A in Table 1): we see insignificant correlations with all covariates although a standard deviation increase in village poverty increases the chance of being high disruption by 4 _._ 8pp. All these village covariates are conditioned on in throughout our analysis. Panel B of Figure A3 then shows within district partial correlations between the disruption index and historic measures of health services or knowledge, as measured at the Chiefdom level using DHS2013 data or the 2007 National Public Survey. Reassuringly, the dummy index of disruption is uncorrelated to multiple past health related measures, including actual health behaviors, health outcomes, access to health facilities, and state capacity related to the health sector.

Of course, the quantitative results are sensitive to the exact construction of the disruption dummy, _Evd_ . We later show our core results are qualitatively robust to three alternative constructions: (i) using the continuous disruption index; (ii) defining disruption to be within-district, so that a quarter of villages in all districts, by construction, are high-disruption; (iii) additionally using information on the disruption of
25

|Country,<br>L<br>economy, or<br>territory|ast rtishe<br>P<br>Date|d<br>censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, untless indicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|Yean,e People's<br>Dem.<br>Rep. of|May 73|1590 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Yugoslavia<br>Zaire<br>Zambia|Nar81<br>Jul84<br>Sep 80|22425 J<br> 29671 F<br>5680 F|UNPVSR<br>7/86 (official est.)<br>U.N. 1985 revision (prelim.)<br>Bank projection from census|BasedanUNPVSR<br>4/87<br>U.N. 1968 revision (prelim.)<br>1980-85, Bank sector report;<br>1985-90, Bank projection|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (pretim.)<br>1975-80, Bank sector report;<br>1985-90, Bank projection|
|Ziffbabwe|Aug<br>02|<br>7SS0 F|Bank<br>projection<br>from census|Bank est. bared on official<br>contraceptive<br>prevalence<br>rate<br>of 26X|U.N. 1988 revision (pretim.)|

Notes: When a source for an estinmte is
|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Subsidies<br>1,170|731|-439|
|Average transfer per capita, daily (IRR)<br>0|8,380|8,380|
|Transfers, universal<br>0|300|300|
|Net total budget<br>1,170|1,030|-139|
|Source: Authors’ calculation usingforecasted HEIS 2018/2019.|||

It is important to mention that cash transfers may lose their value if not adjusted for inflation in time. This happened after 2012 in the Islamic Republic of Iran, when due to due to high inflation the real value of cash transfers diminished and was the key factor behind the increase in poverty in 2012-2014 (KarakurumOzdemir et al. 2016). Regular indexation of cash transfers to keep their real value unchanged along with introducing targeting mechanisms could help the poor to cope with the social-economic shocks.

# 6. Conclusions

In this paper we studied the impact of the gasoline price reform of 2019 on poverty, inequality and government spending using the most recent household budget survey from the Islamic Republic of Iran conducted in 2018/19 and updated to reflect the prices in 2019/20. We showed that households spend 2.1 percent of their expenditures on gasoline, with the middle deciles consuming the most gasoline directly, relative to their total household budget. The benefits of subsidies are highly regressive, with the richest decile benefiting 10 times the poorest decile.

We separate the effect of the price increase into direct and indirect impacts. We find that the reduction in the monetary well-being of households due to the direct impact is 2.8 percent, but due to the indirect impact it is 3.4 percent on average. The average loss in welfare from indirect impacts is higher than the direct impacts for all deciles. The overall increase in poverty, measured by the 5.5 2011 PPP USD
# **Figure** **<u>3.4 Rural Investment Climate Assessments by RIC Indicators</u>**

<!-- Start of picture text --> Farm<br>Nonfarm<br>Security<br>Business/Agriculture service Corruption<br>Management Transportation<br>Technology Water<br>Labor Market<br>Finance Electricity<br>Telecommunication Government<br><!-- End of picture text --> _Source:_ Authors’ Calculation Based on Yemen RIC Survey 2010 _Notes:_ Percentile ranking methodology of “Doing Business” was applied to calculate the level of respective RIC indicators; 100 is the worst level among all communities; 0 is the best level of all the communities. The best and worst levels of communities were obtained from 2010 RIC survey data of Yemen, Burkina Faso, Nigeria, and Mozambique.

# **4. Enterprise Dynamic, Performance, and Migration**

By combining major obstacles perceived by entrepreneurs and major constraints assessed by RIC indicators, the following areas were identified as critical for improvement: market demand, access to markets, access to finance, business/agriculture services, management, and technology. Labor, telecommunication, and government-related issues (permits, licenses, registration, and tax rate and administration) could be added to the list. The following analyses are supplementing the above assessments including enterprise dynamics of entry and exit, performance, and community level migration to further fine tune the needs for improvement of the RIC.

# **4.1 Enterprise Entry and Closure**

Reardon et al. (2000) conclude that entry barriers to nonfarm enterprises in rural areas cause rural income inequality because the poor cannot utilize alternative sources of income to improve their livelihoods or well-being. Entry barriers must be eliminated for promotion of rural income generation activities. Some entry analyses find that entry regulation matters for urban enterprises because most of those analyzed are formally registered businesses (Klapper et al. 2006; Djankov 9
Table D.15. Quality of School Teachers

||Total Enrollment|Total Teachers|Certifed Teachers|Certifed Teachers with<br>High-School|Teachers with<br>High-School|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|9.764|0.519|0.320|0.350|0.452|
||(35.31)|(1.155)|(1.123)|(0.969)|(0.991)|
|Observations|1,522|1,522|1,522|1,522|1,522|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|386.7|13.42|12.78|11.51|11.88|

_Notes:_ This table shows the effects of guerrilla control on school size (Columns 1 and 2) and quality of school teachers (Columns 3–5). Data was obtained from the 2013 teacher census provided by the Ministry of Education. “Total enrollment” and “Total teachers” refer to the total number of students and teachers at the school level, respectively. “Certified teachers” refers to teachers who have received a formal accreditation in pedagogy from the Ministry of Education.

Table D.16. Simpson’s Index

||All plots|Commercial plots|Subsistence plots|
|---|---|---|---|
||(1)|(2)|(3)|
|Guerrilla control|0.0402*|0_._0399<sup>_†_</sup>|1.28e-05|
||
of protection. The first stage _F_ -statistics (see Table A2) lie between 4 and 5 for the imports sample, so that LIML proves to be the more robust estimator for this sample. The last two columns indicate that the preference parameter _a_ lies between 20.8 (Overall protection model) and 62.5 (All tariff model) across the three measures and the variety of _I_ measures – a relatively smaller range of estimates than with the full sample, but not significantly so.

Table 6 presents LIML estimates from the exports sample, where the dependent variables are the two subsidy measures. The first stage _F_ -statistics (see Table A3) range from a low of 0.54 (model with _I_ 80) to a high of 5.27 (model with _I_ 60). The LIML estimator is therefore robust to the weak instrument problem for only models with intermediate measures of political organization. Therefore the lower limit of the _a_ estimates (which come from the model with _I_ 80) are quite unreliable. It would be safest to infer that _a_ exceeds 100 (and is less than 250) from the subsidy data. The results are therefore quite robust to differential rates of tradeoff between lobbying contributions and welfare that may characterize U.S. government preferences across policy instruments.

The main implication from the estimates of the preference parameter _a_ in Tables 2-6 is that the observed values of _z_ / _e_ and their interactions with _I_ can only lead to the observed levels of NTB protection and subsidies if government puts great weight on welfare. That is, the U.S. government is approximately a welfare maximizer. But welfare maximization would lead to free trade, and trade in agriculture is by no means free! The deadweight losses from protection are considerable. Table 7 compiled from Hufbauer, Elliott, and Schott, indicates the extent of those losses from protection in U.S. agricultural products.<sup>47</sup> They estimate the annual deadweight losses from protection due to sugar