rafmacalaba/datause-encoder-data huggingface dataset:
10

# **Table 1 Descriptive Statistics**

||||1988<br>N=65002|||1992<br>N=67880|||1998<br>N=74003||
|---|---|---|---|---|---|---|---|---|---|---|
|||Mean|S.D.|Median|Mean|S.D.|Median|Mean|S.D.|Median|
|Log (hourly|earnings)|0.447|1.090|0.351|0.375|1.015|0.294|0.596|1.008|0.498|
|Education (|years)|5.440|4.368|4.000|5.651|4.310|5.000|6.332|4.357|5.000|
|Experience|(years)|23.839|13.654|21.000|22.938|13.292|21.000|22.845|13.206|21.000|
|Education*|Experience|99.411|93.775|78.000|101.661|93.570|81.000|117.259|102.804|96.000|
|Region:|North|0.082|0.274|0|0.065|0.246|0|0.071|0.257|0|
||Centre East|0.129|0.335|0|0.114|0.317|0|0.118|0.322|0|
||South|0.161|0.368|0|0.186|0.389|0|0.184|0.387|0|
||North East|0.286|0.452|0|0.270|0.444|0|0.282|0.450|0|
|Metropolita|n|0.407|0.491|0|0.397|0.489|0|0.397|0.489|0|
|Urban||0.777|0.416|1|0.814|0.389|1|0.820|0.384|1|
|Race:|Black|0.055|0.228|0|0.058|0.235|0|0.062|0.242|0|
||Mixed|0.417|0.493|0|0.406|0.491|0|0.410|0.492|0|
|Log (hourly<br>– Log (hour|earnings)q90<br>ly earnings)q10||2.7081|||2.5107|||2.4849||
|Log (hourly<br>– Log (hour|earnings)q75<br>ly earnings)q25||1.3732|||1.2432|||1.2585||

Notes: Earnings are in constant 1998 consumer prices. The base region is the South East, and the base racial group is white.
frequency surveys and demonstrates that attrition is difficult to predict.<sup>7</sup> Nonetheless, we will attrition-weight our regressions when analyzing reporting differences. Survey retention rates between baseline and endline interviews were even higher, with only 2 of 954 individuals not interviewed at the in person endline and just 9 percent of respondents not available for an interview at the three-month follow-up conducted over the phone. Moreover, the overwhelming majority of participants (97.5 percent) indicated that they would be willing to participate again. These rates of compliance are much higher than in other studies (see, e.g., Dillon 2012; Croke et al. 2014; Garlick, Orkin, and Quinn 2015) and probably reflect a variety of factors, including a short questionnaire, flexible interview schedules, adequate compensation, and the cultivation of trust between respondents and enumerators.

# **4. Data**

# **4.1 Descriptive Statistics, Balance, and External Validity**

Table 2 assesses balance across treatment arms at baseline for the sample that participated in the survey (sampling was done before the survey was fielded, so examining balance at baseline is a strong test of whether randomization was successful), distinguishing between variables that were explicitly targeted in the randomization, presented in Panel A, and variables that were not, presented in Panel B. Sample sizes per treatment arm differ slightly from the target of 320, with 318 respondents participating in weekly in-person interviews, 315 in weekly phone interviews, and 321 in triweekly phone interviews. The null hypothesis that the treatment arms were balanced on all variables that were used to stratify the sample is not rejected, as is shown in Panel A.

Just under three-fifths of respondents in the experiment were women, and the average respondent age was 34. Approximately two-thirds of respondents were employed, and approximately three-fifths of those working reported being self-employed. Our survey population thus constitutes a group of fairly poor urban dwellers who are likely to be mainly reliant on labor income and are thus of particular interest for a study of this type which aims to think about how best to collect labor market data. Our sample is representative of urban labor markets in Ghana, as is shown in appendix table B5, which compares our sample with that of the respondents in the

> 7 The predictive power of models of interview completion presented in appendix table B4 is very low; the adjusted R2 of the best performing model is 0.013. Moreover, few of the covariates significantly predict attrition.

9
domestic poverty of higher food prices are minor or even pro-poor in the case where most of the poor are net sellers of food staples. High-income countries, for example, are well-placed to absorb price shocks because of the small shares of farm produce in the expenditures of their consumers, their producers’ access to risk management tools such as futures and options markets, and their relatively well-developed social safety nets. Even so, some high-income countries continue to use insulating policy instruments such as variable import levies, or even just specific tariffs whose _ad valorem_ equivalent varies inversely with the border price. Another example of this possibility is where large, poor, food-importing countries—for which insulation is more expensive because it turns their terms of trade against them—insulate less than would a small but otherwise similar country, and hence may not avoid adverse poverty outcomes because of inadequate domestic social safety nets.

It is clear from these examples that, other things equal, alterations in trade restrictions could increase or reduce the national and global poverty impacts of higher international prices. Only by looking at data on the changes in agricultural distortions during periods of rapid increases in international food prices, and estimating the impacts of consequent domestic price changes on poverty in different countries is it possible to ascertain the net effects on national and global poverty.

This paper begins by looking at data on the consumption patterns and income sources of low-income households in a sample of thirty developing countries, where three-quarters of the world’s poor live, in order to assess which commodities are likely to be important in affecting poverty through changes in their commodity prices. We then turn to data on agricultural price distortions, and in particular on relative movements in domestic and international prices, to 3
**Table 8: Weight-for-age Z-score (Tanzania)**

|||**Model 1**<br>|**- Without Co**<br>|**ntrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unco|nditionalqua|ntile regres|sion|
|||10th|25th|50th|75th|
|Per capita kcal|-0.017|0.000|-0.028|-0.013|-0.051|
||(0.041)|(0.071)|(0.057)|(0.043)|(0.055)|
|1-SSR|0.049|0.086|0.051|-0.000|0.075|
||(0.038)|(0.061)|(0.051)|(0.039)|(0.052)|
|1-SSEXR|0.089**|0.151**|0.125**|0.038|0.110**|
||(0.037)|(0.064)|(0.051)|(0.039)|(0.051)|
|HDDS|0.142***|0.192***|0.171***|0.101**|0.205***|
||(0.037)|(0.068)|(0.054)|(0.040)|(0.052)|
|Per capita||||||
|<br>expenditure|0.045|0.038|0.038|0.048|0.059|
||(0.041)|(0.063)|(0.054)|(0.046)|(0.061)|
|Food share|-0.098***|-0.185***|-0.086*|-0.050|-0.092|
||(0.038)|(0.050)|(0.050)|(0.041)|(0.058)|
|||**Model**|**2 - With Con**|**trols**||
|VARIABLES|OLS|Unc|onditionalqu|antile regres|sion|
|||10th|25th|50th|75th|
|Per capita kcal|-0.034|0.012|-0.028|-0.033|-0.093|
||(0.044)|(0.077)|(0.062)|(0.046)|(0.059)|
|1-SSR|0.034|0.054|0.017|-0.010|0.056|
||(0.039)|(0.060)|(0.053)|(0.040)|(0.054)|
|1-SSEXR|0.033|0.081|0.061|0.002|0.048|
||(0.036)|(0.063)|(0.052)|(0.040)|(0.052)|
|HDDS|0.065|0.091|0.094|0.029|0.124**|
||(0.041)|(0.068)|(0.058)|(0.044)|(0.058)|
|Per capita||||||
|<br>expenditure|0.016|0.049|0.029|0.011|0.000|
||(0.047)|(0.075)|(0.063)|(0.050)|(0.066)|
|Food share|-0.031|-0.118**|-0.021|0.001|-0.012|
||(0.037)|(0.048)|(0.055)|(0.044)|(0.065)|

# **4.5 Uganda**

Finally, we present the main results for height-for-age and weight-for-age for Uganda in Tables 9 and 10, respectively. In the parsimonious model, for height-for-age, only four out of five food security measures are statistically significant in the OLS specification, and only two out of the same five are statistically significant at the 10<sup>th</sup> quantile. Similarly, in the weight-for-age regressions, three out of the five food security measures considered are statistically significant in the OLS estimate, while at the lower end of 26
19 results (not shown) are qualitatively unchanged, the sign reversion disappears for debt and moves further to the upper extreme of the distribution for reserves.

# _6.1 Robustness: omitted variables_

Could the negative indirect effect of workers’ remittances be spuriously due to other unobserved country-specific features that are correlated with both workers’ remittances and current account reversal?

For example, the degree of development of financial markets could sustain the flows of workers’ remittances – especially those channeled through the banking system and, as such, more likely recorded in the official balance of payments statistics – and, at the same time, reduce the probability of financial crises. Similar effects could descend from a higher flexibility of exchange rates and greater freedom in capital movements. Another potential channel for a spurious relationship is political instability that could reduce migrants’ willingness to send money back home and, at the same time, be correlated to financial instability. To the extent that there is some relationship between the literacy rate of migrants and their propensity to remit<sup>25</sup> , a spurious correlation might arise if education<sup>26</sup> , through its positive effects on potential growth, reduce the probability of financial crises.

We address some of these issues by adding to our empirical specification the interactions between international reserves and external debt on one side, and indicators of financial development, exchange rate arrangements, political instability, literacy rate of working-aged residents and country size on the other side. As a further robustness exercise, we also add interactions with dummy variables for African and Asian developing scountries to prove that our results are not specific to any particular geographical area, and for small countries. Given the results in the previous section, we present, for both measures of reversals, only the most clear-cut results based on the 75<sup>th</sup> percentile workers’ remittances-dummy variable.

> 25 As far as we know, only Faini (2002) empirically tests how migrants’ skills affect their propensity to remit and finds a not very robust negative relationship.
inadequate, such that results communicated to the European Commission could not be considered accurate and reliable.

**<u>Table 7. Problems identified in European Commission inspection, November 1998:</u>**

|**Issue**|**Details**|
|---|---|
|Inspection recording|•Insufficient recording of monitoring of ‘own checks’<br>byfishprocessingfacilities|
|Fisheries inspectors|•Lack of expertise and technical direction to undertake<br>a thorough inspection of processing facilities with<br>properly documented records<br>•Generally insufficient training and experience<br>•Lackofconfidenceinenforcement|
|Identification marks|•Lack of identification marks on individual boxes of<br>product destined for export to the EU|
|Processing facilities|•Poor structural maintenance<br>•Inadequate vermin control<br>•Use of static hyper-chlorinated water<br>•Cross-contamination risk from use of hand-operated<br>suspended hoses|
|Laboratory facilities|•Poor level of maintenance<br>•Inadequate cleaning standards<br>•Lack of temperature control<br>•No facilities to undertake chemical analysis<br>• No facilities to undertaken total plate count at 22<sup>o</sup>C|
|Landing sites|•Unhygienic storage of fish on vessels<br>•Lack of potable water<br>•Lack of perimeter fence able to exclude general<br>public, verminand stray animals|

_Source: European Commission (1998)._ In March 2002, a European Commission mission again visited Kenya, both for the purpose of assessing compliance with EU hygiene standards with a view to inclusion in List I and monitoring measures relating to pesticide residues in fish (European Commission, 2003). The overall impression of the inspection team was positive, although some relatively ‘minor’ noncompliances were identified. In particular, the pesticide monitoring program that had been communicated to the European Commission by the Kenyan Government had not been fully implemented and improvements were required in procedures for laboratory analysis. Some nonconformities were identified in processing establishments, most of which had already been recognised by the Competent Authority although no action had been taken. Finally, hygienic conditions at landing sites were considered to be in need of improvement. The Kenyan Government provided written confirmation to the European Commission regarding the necessary 42
steady state of our economy, which implies that our development paths are characterized by reductions in entrepreneurship, and increases in the average rm size. Lastly, because of our focus on growth accelerations, we follow a dierent strategy for parameterizing the pre-reform stationary equilibrium, appealing to rm-level data in low-income countries to discipline the choice of distortions that hinder output and productivity.

Lastly, our consideration of China's growth acceleration as a case study merits a discussion of the closely related work of Song et al. (2011). The authors propose a model with a private entrepreneurial sector and state-owned enterprises to understand the behavior of the savings rate, the rate of return on capital, and capital ows, during China's economic transition. As in Buera and Shin (2017), their emphasis is on nancial frictions, which limit the access to credit by private entrepreneurs and encourage the accumulation of internal sources of nancing for investment. Credit is mostly devoted to state-owned enterprises. In a context of heterogeneous but exogenous rm-level productivity, the authors show that the downsizing of the public sector leads to excess demand for nancial assets that result in capital outows. In our paper, we approach the Chinese acceleration from a dierent angle. While we propose a more reduced-form specication of entry distortions to implement features of a communist regime, we leverage this tractability to characterize more sharply the interaction between the underlying distortions and the innovation incentives of rms in a context of costless reallocation.

# 3 Motivating Facts

We set the stage for the quantitative model presenting some evidence characterizing aggregate and micro-level features of economic transitions. We consider separately two types of convergence episodes: sustained growth accelerations in the post-war period, identied appealing to the methodology of Hausmann et al. (2005), and postcommunist transitions.<sup>2</sup> As we shall explain in greater detail below, we proceed in

> 2In Hausmann et al. (2005) a growth acceleration starts in year _t_ only if the following three conditions are met: (1) the average growth rate in the seven ensuing years (years _t_ through _t_ +6 ) is above 3.5 percent; (2) the average growth rate in the seven ensuing years is at least two percentage points higher than in the preceding seven years (years _t −_ 7 to _t −_ 1 ); and (3) the output per-capita in the ensuing seven years is above the previous peak. If more than one contiguous years satisfy all three conditions, the start of the growth acceleration is chosen to be the one for which a trend regression with a break in that year provides the best t among all eligible years, in terms of the F-statistic. A sustained growth acceleration is one for which the average growth rate in the decade

7
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Corridor serves as a link to other important corridors that branch off from it, such as the Trans-Kalahari, Beira, Lobito, Dar es Salaam, and Nacala corridors (Curtis 2009). Along the corridor, the core road artery runs 7,500 km from Dar es Salaam in Tanzania to Durban in South Africa, via Lusaka, Zambia, through the Chirundu border crossing and on to Harare, Zimbabwe, and then into South Africa at Beitbridge. But as a result of recent political unrest in Zimbabwe, an alternative route has opened up, bypassing Zimbabwe and going through Botswana instead.

The road sector went through important institutional reforms in 2000–01. Before the reforms most of the financing for roads came from government appropriations out of the national budget. But in the early 2000s dedicated road-user charges were created and assigned to the road fund, to be managed by the Zimbabwe National Road Administration (ZINARA). But maintenance and rehabilitation is constrained by insufficient funding, aggravating the extent of undermaintenance.

# **Challenges**

While Zimbabwe boasts one of the most extensive road networks in the region, its condition has deteriorated rapidly over the past two decades. As of 2008 Zimbabwe has one of the lowest percentages of roads in good condition the region (figure 11). The roads in poor condition include key regional arteries, such as the corridor leading to the port of Beira in northern Mozambique, hindering the competitiveness of the port even though it is significantly closer to Zimbabwe than the port of Durban. The 2010 Zimbabwe Road Condition Survey paints an even more dismal picture of the current quality of classified roads (table 10). Only 34 percent of total classified roads are in fair or good condition and only 14 percent in good condition. The quality of paved/sealed roads is somewhat better than that of gravel and earth roads; nevertheless, a daunting 50 percent of classified paved roads are in poor condition and require costly rehabilitation. Based on the results of this survey, the government estimates that $2.9 billion would be needed to rehabilitate Zimbabwe‘s entire classified network.

**Figure 11. Benchmarking road conditions against regional peers 2006–08**

<!-- Start of picture text --> South Africa<br>Mauritius<br>Swaziland<br>Malawi<br>Namibia<br>Botswana<br>Mozambique<br>Madagascar<br>Zimbabwe<br>Zambia<br>Angola<br>Lesotho<br>0% 25% 50% 75% 100%<br>Percentage main road network length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD road sector database on southern sub-saharan african countries (2008).

30
Most countries in the world are affected by forced displacement. There are at least 150 countries that host at least 100 refugees each and there are at least 141 countries that generated at least 100 refugees each. If we add the numbers for the internally displaced, almost no country in the world can be said to be immune from forced displacement.

Some countries and some regions are more affected than others. Over 53 percent of all refugees come from only three countries (Somalia, Afghanistan and the Syrian Arab Republic) and the great majority of displaced people are hosted in the Middle East and North Africa (39%), Africa (29%) and Asia and the Pacific (14%) regions.<sup>6</sup> Europe accounts for only 6% of the forcibly displaced and the Americas account for 12%, most of which are IDPs in Colombia.

The incidence of refugees on host countries is greater in low and middle‐income countries and this is true whether we consider population or GDP statistics, refugees or IDPs. Figure 2 shows the number of refugees per 1,000 inhabitants and it is visible that Lebanon is by far the country with the highest incidence of refugees followed by Jordan. Almost all countries in the figure are either middle or low income countries and almost all are in the MENA or Africa region. Figure 3 shows the number of refugees per USD estimated in Purchasing Power Parity (PPP) per capita. Ethiopia and Pakistan lead this particular ranking and the MENA and African region together with South Asia are the regions with countries showing the highest incidence. This principle also applies to IDPs. The top ten countries with the largest populations of IDPs are Colombia, the Syrian Arab Republic, Iraq, Sudan, the Democratic Republic of Congo (DRC), Azerbaijan, Somalia, Bosnia and Herzegovina, Afghanistan and Pakistan, essentially low and middle income countries. Quantitatively speaking, the heavy burden of refugees and IDPs is carried by low and middle income countries and not by wealthy OECD countries. Forced displacement is therefore a problem that complicates the question of economic development in directions that are not yet fully understood.

> 6 http://www.unhcr.org/en‐us/figures‐at‐a‐glance.html.

4
inequality aversion parameter v equal to 2, the extended concentration index reverts to the standard concentration index. By setting a value of v above 2, the analyst raises the weight attached to the poor (compared to the weight in the standard concentration index) and reduces the weight attached to the better off. Reducing the parameter v below 2 has the opposite effect.

The paper also showed how inequality, as measured by the extended concentration index, can be combined with information on the average to measure overall health achievement. It was shown that by measuring achievement as a weighted average of health levels, where the weights are the same as used in the extended concentration index, the resultant index is in fact simply equal to the product of the average and the complement of the extended concentration index. In the case where the measure of health is a measure of ill health, and ill health is higher amongst the poor and hence the concentration index is negative, pro-rich inequality raises the level of achievement (or "disachievement") above the mean, by a percentage that is equal to the value of the extended concentration index.

The methods were illustrated using distributional data on under-five mortality, child malnutrition and adult fertility for 44 developing countries. The results illustrate two important points, each of which has an important implication. First, levels of inequality and the rankings of countries can both be sensitive to how far one deviates from the implicit value judgements underlying the concentration index. In countries where the health of the poor is very much worse than that of the rest of the population, the increase in measured inequality when one weights more highly the health of the poor can be quite marked. This suggests that in future empirical work on health inequalities, especially in contexts where there is a specific concern with the health of the poor, more attention should be paid to the sensitivity of results-including country rankings-to the weighting scheme used in the health inequality measure. The second important point to emerge is that noteworthy changes-including major rank changes-result when one moves from an assessment of achievement based solely on the average to an index of achievement that captures both the average and the extent of inequality between the poor and better-off. These changes are especially pronounced when the weight attached to the _13_
**Table 4: Dependent variable: Inflow FDI (EC2SLS)**

||1|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|CFDI|-0.457<br>(0.357)|0.174<br>(0.179)|0.023<br>(0.231)|0.280<br>(0.496)|0.070<br>(0.239)|0.301<br>(0.527)|
|FDIt-1|0.651***<br>(0.039)|0.241***<br>(0.039)|0.392***<br>(0.062)|0.408***<br>(0.132)|0.380***<br>(0.064)|0.404***<br>(0.136)|
|GPCGR|-0.052|-0.007|-0.006|-0.034|-0.008|-0.035|
||(0.032)|(0.016)|(0.020)|(0.032)|(0.020)|(0.034)|
|GDP||0.770***|0.749***|0.881***|0.727***|0.880***|
|||(0.110)|(0.136)|(0.234)|(0.139)|(0.237)|
|WFDI|0.363|0.556***|0.269|0.038|0.335*|0.057|
||(0.288)|(0.151)|(0.177)|(0.242)|(0.194)|(0.283)|
|WAGE|0.125|-0.041|0.085|-0.114|0.133|-0.101|
||(0.215)|(0.121)|(0.156)|(0.170)|(0.166)|(0.197)|
|ILLIT||-0.026|0.009|-0.891***|-0.021|-0.903**|
|||(0.028)|(0.029)|(0.343)|(0.046)|(0.357)|
|OPEN|-0.005|0.004|0.009**|0.014***|-0.010**|0.014***|
||(0.04)|(0.003)|(0.004)|(0.005)|(0.004)|(0.005)|
|TARIFF||||0.015||0.015|
|||||(0.048)||(0.049)|
|INCTAX||||-0.015||-0.016|
|||||(0.017)||(0.018)|
|PSR|||0.008|0.025|0.005|0.025|
||||(0.018)|(0.022)|(0.019)|(0.023)|
|TELE|||||-0.272|-0.065|
||||||(0.329)|(0.097)|
|CRISIS||-0.003|-0.019|-0.122***|-0.026|-0.124***|
|||(0.017)|(0.019)|(0.044)|(0.021)|(0.047)|
|ACCESS||<br>0.299*|<br>0.301|<br>0.276|<br>0.283|<br>0.274|
|||(0.177)|(0.202)|(0.222)|(0.204)|(0.224)|
|Obs.|147|123|93|59|93|59|
|R-squared|0.706|0.782|0.804|0.806|0.806|0.806|

# Notes:

(1). Standard errors are reported in parentheses. ***, ** and * indicate coefficient significant at 1, 5 and 10%, respectively.

36