rafmacalaba/datause-encoder-data huggingface dataset:
Some interesting differences are also observed when we use alternative definitions of the phases of the reforms. This is specially so under R<sup>1</sup> and R<sup>2</sup> .<sup>15</sup> In the Central province, for example, there is a large increase in productivity during the success phase, but no statistically significant changes during the failure phase of 2000. In contrast, in the Eastern province there is a significant decline in the failure phase of 2000 (R<sup>1</sup> ), but there is not any significant change during the success phase. Finally, the Southern province shows a sharp decline (of 34.8 percent) in failure phase, and a sharp increase (of 55.7 percent) in the success phase.

# 6 Conclusions

This has paper has investigated the relationship between market structure in cotton and farm productivity. We have used unique farm surveys for rural Zambia, the Post Harvest Survey, spanning the 1997-2002 period. We have exploited a marketing reform whereby the Zambian government eliminated the cotton marketing board in 1994. Entry and exit into the market and the development of the outgrower scheme gave rise to interesting dynamics in market organization. Starting with a baseline period in 1997-1998, there was a subsequent failure of the outgrower scheme in 1999 and 2000. Further entry and competition into the sector led to an improvement in the outgrower scheme in 2001-2002.

We have estimated the impacts of the different phases of the reforms by building a modified difference-in-differences estimator. The first differences are taken across the different phases of the reforms. An equation of maize productivity, a major staple produced by virtually all households, provides the second difference. In the presence of entry and exit into cotton farming, and in the presence of cotton-specific farm unobservables, the estimated average productivity can be biased. To correct for these dynamics effects, we introduce a model of selection into cotton that provides proxies for unobserved productivity. These proxies are given by land cotton shares (i.e., the shares of total land allocated to cotton) “purged” of the effects of observed covariates. This modified difference-in-differences model delivers consistent estimate of the impacts of the reforms on farm productivity.

> 15R1 includes only 2000, and R2 includes 2001 and 2002 (as does F 2). The difference is that the introductory phase now includes 1999.

24
respondents are more likely to overstate the number of outages, though during widespread outage events, survey-based estimates of outage duration were more accurate than during more typical periods.

The results contribute to a growing literature investigating the sensitivity of survey results to design features and other potential sources of bias. For instance, Beegle et al. (2010) find that reference periods and mode (i.e., recall vs. diary-based data collection) can materially impact the results of consumption and welfare surveys, Bardasi et al. (2010) find similar relationships for labor force surveys, and Heath et al (2021) show large reference period and interview frequency effects in labor force surveys. In all cases, these studies establish the extent of qualitatively large and economically significant differences due entirely to survey design.

# I.II – Electricity Provision in Tajikistan

The Soviet Central Asian Power System (CAPS) was built in the 1970s and included what is now the independent country of Tajikistan. The system provided nearly universal electricity in the region, but production and distribution investments were optimized without regard to country borders. With independence, Tajikistan and other countries in Central Asia entered complex bilateral agreements for fuels, electricity, and water. Disagreements between the member countries led to a partial disintegration of the CAPS, and ultimately, Tajikistan’s exit from most cross-border arrangements including for electricity and gas. Official policy aimed for energy self-sufficiency with heavy reliance on hydroelectric power. However, low administered prices and a lack of alternative sources led to widespread use of electricity for heating in Tajikistan, leading to serious recurring electricity shortages in the winter months.

**Figure 1: Satisfaction with Electricity Provision in Europe and Central Asia**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Total Rural Urban<br><!-- End of picture text --> _Source: Life in Transition Survey, 2016_ When last measured, traditional survey measures indicated that Tajikistan was among the worst performers in the ECA region in terms of the reliability of electricity supply to households (figure 1). Tajikistan also suffered from a uniquely large divergence in satisfaction between urban and rural areas 3
all other channels that have been highlighted in previous research: common creditors, trade links, dependence on a common overexposed fund investors, and the market size of the crisis country. These control variables are interacted with the crisis dummy for the initial-crisis countries, which are the same set of countries used in the first step of our analysis. Additionally, _ZB,t−_ 1 represents a set of potential domestic drivers of crises, such as the lag of mean of growth expectations and the lag of stock market volatility. As before, _INTt−_ 1 represents the interest rate in advanced economies, and _δB_ country effects.

The coefficient of interest here is _β_ 2, which captures the effect of uncertainty regarding fundamentals on the probability of a crisis. The use of the interaction term, however, carries the risk that the coefficient simply picks up the direct effect of the crises in _A_ rather than the effect of uncertainty. To ensure that this is not the case, we estimate a separate set of regressions where we enter the uncertainty variable and the initial crises variables separately: The variables have the same interpretation as before.

Since probit models do not lend themselves to consistent estimates of the coefficients in a fixed effects regression, we use a pooled probit with dummies for country effects. For robustness, we run the same sets of regressions assuming a logistic distribution. In addition, we estimate a linear probability model to overcome the potential incidental-parameter problem.

# **4.2 Data**

We build a rich data set comprising monthly observations from December 1993 to September 2005. The sample comprises 38 countries—15 developed and 23 emerging—where the selection of the period and countries reflects availability of data. The set of countries is listed in Table 4 in the Appendix.

The explanatory variable that is of interest in the current analysis is uncertainty regarding fundamentals. Based on our model, uncertainty is defined as the variance of private signals around the true value of the fundamentals that affect the rate of return on investments. Such data, however, is not directly observable. Since the return on investment typically correlates strongly with the growth rate of output, we follow Prati and Sbracia (2002) and use the standard deviation of GDP growth forecasts from Consensus Economics as a reasonable proxy.<sup>10</sup> To match our empirical analysis with the model presented earlier, we define a crisis as an episode that features a sharp drop in stock returns. Monthly stock market returns,

> 10See Table 5 in the Appendix for a description of the exact construction of the variable. In the main analysis, we use a weighted average of current and following year forecasts as described in Table 5. However, as a robustness check we repeat all estimations using only current year forecasts, and only following-year forecasts, separately. The results are qualitatively the same and quantitatively similar.

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<!-- Start of picture text --> Income o-convergence across municipalities, 1992-2014<br>=Total Urban Rural = Border = Non-border<br>0.6<br>=EI9 AN<br>80<br>2<br>20.5g ae<br>A9°8<br>a<br>zs<br>aa}§<br>H80.4<br>aoS§8<br>on°<br>a<br>n<br>0.3<br>1992 2000 2005 2010 2014<br>‘Year<br><!-- End of picture text -->
2005). If poverty urbanizes in the future consistently with the relationship modeled above, then the urban share of “$1 a day” poverty will reach 39% at that date, with a standard error of 1.6%. (This rises to 43% for the truncated sample with poverty rates over 2%.) For the higher poverty line, the urban share of the poor will be 51% by 2030 with a standard error of 0.7%.

For the $1 a day line, these estimates are very close to what one obtains by the simplest linear extrapolation. At the rate of increase in the urban share of the world’s “$1 a day” poor of 0.6% points per year implied by Table 3, the share will rise from 25% in 2002 to 42% by 2030. A majority of the poor will be found in rural areas until about 2040. However, at the pace of urbanization found for the $2 poverty rate that we find in Table 4, a majority of the poor will live in rural areas for another 80 years or so! The signs of deceleration in the urbanization of the $2 a day poor in Table 4 also point to a slower future rate than suggested by the above calculations based on the WUP projections and our PUC’s.

Systematic errors in the UN’s projections for the urban population share will, of course, bias these forecasts for the future urbanization of poverty. As already noted, the critical assessments of the UN’s forecasts have argued that they are likely to overestimate the pace of future urbanization. The alternative forecasts by Bocquier (2005) predict that the urban population share will only rise to 49% by 2030. Inserting this into our PUC implies that the urban share of the “$1 a day” poor will rise to only 31% by that date (standard error of 1.4%), while for the $2 line it rises to 39% (s.e.=0.7%).

These projections should clearly not be taken too seriously. Narrowing down the range of estimates would certainly require a credible economic model, since the pace of urbanization will undoubtedly depend on the extent and pattern of future economic growth. However, from what we currently know, it appears very likely that the bulk of the poor will still be living in rural areas for at least a few decades to come.

# **6. Conclusions**

Widely heard concerns about the urbanization of poverty in the developing world have been neither well informed by data nor cognizant of the broader economic role of urbanization in 26
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- And Northern Africa Region,” _Review of Middle East Economics and Finance_ Vol. 6 : No. 2, Article 2, 2010.

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- Carbó-Valverde, S., Rodriguez-Fernandez, F., Udell, G., 2009. “Bank market power and SME financing constraints.” _Review of Finance_ 13, 309-340.

- Cetorelli, N., 1999. “Competitive analysis in banking: appraisal of the methodologies,” Federal Reserve Bank of Chicago Economic Perspectives, first quarter, 2-15.

- Chong, T.T.L., Lu, L., Ongena, S., 2012. “Does banking competition alleviate or worsen credit constraints faced by small and medium enterprises? Evidence from China.” _CentER Discussion Paper_ , 2012-013.

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Delis, M.D., 2012. “Bank competition, financial reform, and institutions: The

- importance of being developed.” _Journal of Development Economics 97(2)_ , pp. 450–465.

16
# **List of references**

- Bardasi, Elena, Shwetlena Sabarwal, and Katherine Terrell. “How Do Female Entrepreneurs Perform? Evidence from Three Developing Regions.” _Small Business Economics_ 37, no. 4 (2011): 417–41.

- Banerjee, Abhijit, Emily Breza, Esther Duflo and Cynthia Kinnan “Does Microfinance Foster Business Growth? The Importance of Entrepreneurial Heterogeneity,” 2014

- Berge, Lars Ivar Oppedal, Kjetil Bjorvatn, and Bertil Tungodden. “Human and Financial Capital for Microenterprise Development: Evidence from a Field and Lab Experiment.” _Management Science_ , 2014.

- Berge, Lars Ivar Oppedal, Kjetil Bjorvatn, and Bertil Tungodden.  “Human and Financial Capital for Microenterprise Development: Short-Term and Long-Term Evidence from a Field Experiment in Tanzania.” _Dartmouth University_ , 2012.

http://www.dartmouth.edu/~neudc2012/docs/paper_173.pdf.

- Bjorvatn, Kjetil, and Bertil Tungodden. “Teaching Business in Tanzania: Evaluating Participation and Performance.” _Journal of the European Economic Association_ 8, no. 2– 3 (2010): 561–70.

- Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts. “Does Management Matter? Evidence from India.” National Bureau of Economic Research, 2011.

- Bloom, Nicholas, and John Van Reenen. “Why Do Management Practices Differ across Firms and Countries?” _The Journal of Economic Perspectives_ , 2010, 203–24.

- Bloom, Nick, and John Van Reenen. “Measuring and Explaining Management Practices across Firms and Countries.” National Bureau of Economic Research, 2006. http://www.nber.org/papers/w12216.

- Bruhn, Miriam, Dean Karlan, and Antoinette Schoar. “What Capital Is Missing in Developing Countries?” _The American Economic Review_ , 2010, 629–33.

- Bruhn, Miriam, Dean S. Karlan, and Antoinette Schoar. “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico.” _World Bank Policy Research Working Paper_ , no. 6508 (2013).

- Calderon, Gabriela, Jesse M. Cunha, and Giacomo De Giorgi. “Business Literacy and Development: Evidence from a Randomized Controlled Trial in Rural Mexico.” National Bureau of Economic Research, 2013. http://www.nber.org/papers/w19740.

- De Mel, Suresh, David McKenzie, and Christopher Woodruff. “Business Training and Female Enterprise Start-Up, Growth, and Dynamics: Experimental Evidence from Sri Lanka.”

18
method estimate gets “diluted” as it compares a large pool of individuals assigned to the treatment group where only a handful were effectively treated, to a large pool of individuals assigned to the control group. By using several rounds of administrative data, our approach also allows the verification of the parallel trends assumption required for the application of non-experimental approaches.

The combination of experimental and non-experimental methods along with rich administrative data present a new avenue for empirical applications of impact evaluations when take-up is low. Examples of widespread reach out efforts with small response rates include credit card offers in the U.S. where response rates were estimated to reach 0.2% in 2006,<sup>15</sup> and the banking/finance industry in the U.K., where the overall click rate (i.e. the percent of clicks that are made among all the emails sent) of email marketing campaigns was estimated at 0.48% in 2016.<sup>16</sup>

> 15 Federal Deposit Insurance Corporation “Credit Card Activities Manual”

> <u>https://www.fdic.gov/regulations/examinations/credit_card/ch5.html [accessed October 15h 2017].</u> 16 Sign-up.to 2017 report https://www.signupto.com/email-marketing-benchmarks/email-benchmark-2017/  [accessed October 15<sup>th</sup> 2017].

17
to Syria’s national absolute poverty line, estimated at $2.2/day in 2003-04 (El-Laithy and AbuIsmail, 2005).

Given the objective of presenting cross-country comparisons, we fix the poverty line at $2 a day (in 2005 PPP$) for all the countries in our sample. This line is close to the national absolute poverty thresholds for Syria and Egypt, although it is above the national absolute poverty lines in Yemen and below the national poverty lines in Tunisia, Jordan, and the Palestine territories. Therefore, it overestimates poverty in Yemen but underestimates poverty in Tunisia, Jordan, and Palestine (Figure 1).

Table 3 indicates some progress with poverty reduction in the 2000s. Poverty rates declined in Tunisia, Syria, Jordan and the Palestine territories and increased in Egypt and Yemen. The subjective well-being data, however, reveals a different picture from the household survey data. It shows a uniformly bleak situation in the period before the Arab Spring (Figure 2, top panels), although we do not make strict comparisons of the quantitative changes observed in each country because of the differences in the survey years captured with the objective and subjective data.<sup>19</sup> In all developing MENA countries on which we have sufficient information, the percentage of dissatisfied (unhappy) people – those with life evaluation scores below the lower cutoff point of 4 or 5 as discussed in Table 1 – was high and increased between 2007 and 2010. The deterioration was particularly large in the Arab Spring countries, especially Egypt and Syria. By the end of the decade almost half of the population in Syria and Egypt were unhappy about their life (Figure 2, top panels). Importantly, unhappiness rates in Arab Spring countries were mostly higher than those in other countries in the region (Figure 2, top panels).

> 19 Data are not available for all countries in 2007. Thus we use data in 2006 for Lebanon, and 2008 for Algeria, Iraq, Syria, and Tunisia. We also show estimates for nine countries with larger sample sizes only. Estimates using a larger sample consisting of other countries provide a qualitatively similar result.

15
10. Including the word "intent" in the injury clause of the 1921 antidumping act would have made little difference in how it was interpreted. Anyone who offers goods for sale does so with the intent of winning the sale from someone else. So, in an administrative context, free from the precedents of the legal system, it is unlikely that the word "intent" would have limited w'"en injury would be found.

11. Palmeter (1991) explains that the logic behind the "best information available" provision is that without it the respondent could block an investigation by refu&iig to coopevate. He poIlItS out that the provision was rarely used until recently. But in recent casos information requirements put to respondents have been so difficult that they were either unable to comply or estimated that complying to be so costly that they chose not to.

12. Wilson, near the end of his last term, had vetoed the same bill that, when passed again by the next Congress, became the antidumping law of 1921.

13. At the end of December 1989, a comparable tally, covering only Australia, Canada, the United States, and the European Community, came to 530 (Low 1991, 22).

14. The EC, U.S., Canada, and Australia chapters of Jackson and Vermulst (1989, 69, 146, 193, 262) verify this point.

15. Each industry study author, in his first draft, made a major point of how unreasonable was the interpretation that applied antidumping to that industry's exports. The point receives less emphasis in the final versions because the authors and I have agreed that the overall lesson is not that each application is an abarration from the norm, but that there is no norm. Thus, for example, the Swedish stainless steel case is not a misapplication of antidumping rules; antidumping rules are a misapplication of economic policy and of legal principles of even-handedness and due process.

16. Bellis is, however, critical of the way the EC has put the rule into practice.

17. The description given by the Antidumping Authority (1990, 6-7) indicates that Australian practice for determining the noninjurious price level is similar. Enforcement practices seem, however, to be quite different. In essence, an Australian antidumping order posts the noninjurious import price: exporters can avoid paying any duty by pricing at or above that level. Under E ei foccmerat practice (as described by Norall 1986, 108) the exporter would have to raise its price to .lie no _dumping_ level, rather than to the no-injury level, to avoid the duty.

- 46 -
|Overdue on obligations to<br>financial institutions|-0.015|-0.534|-0.089|-1.641**|-0.371|-0.436|1.211*|0.006|
|---|---|---|---|---|---|---|---|---|
||(0.130)|(0.409)|(0.483)|(0.792)|(0.519)|(1.179)|(0.689)|(0.585)|
|Ever temporarily closed during<br>the COVID-19 outbreak|-0.132|0.115|-0.670|-0.012|0.174|-0.118|-0.855***|-0.209|
||(0.173)|(0.075)|(0.496)|(0.654)|(0.554)|(0.621)|(0.284)|(0.619)|
|Adjusting or converting their<br>production or services|0.040|-0.036|0.297|2.185***|-0.211|0.917|-0.243|0.650|
||(0.105)|(0.067)|(0.494)|(0.627)|(0.353)|(0.739)|(0.309)|(0.611)|
|Started or increased online<br>business activity|-0.024|-0.215***|0.273|-0.961*|-0.555|-1.173***|-0.588|-1.106|
||(0.110)|(0.079)|(0.446)|(0.519)|(0.359)|(0.377)|(0.403)|(0.768)|
|Started or increased delivery of<br>goods, services or carryout|-0.087|0.288***|0.106|0.771*|0.113|0.356|-0.263|0.214|
||(0.092)|(0.064)|(0.390)|(0.427)|(0.264)|(0.335)|(0.516)|(0.697)|
|Started or increased remote work|0.289***|-0.077|-0.807*|-1.281*|-0.089|-0.293|0.034|0.141|
||(0.103)|(0.082)|(0.454)|(0.729)|(0.598)|(0.703)|(0.282)|(0.493)|
|Exporting directly (at least 10%<br>of sales)|-0.104|-0.152***|-0.430|-0.171|-1.052*|-0.462|0.301|1.538***|
||(0.152)|(0.050)|(0.493)|(0.592)|(0.613)|(0.892)|(0.726)|(0.559)|
|Constant|-0.901**|0.120|-2.375***|-2.710|0.647|0.007|-0.996|-2.732***|
||(0.399)|(0.195)|(0.556)|(1.845)|(0.424)|(0.851)|(0.873)|(1.030)|
|Number of observations|630|262|551|223|641|264|630|261|
|r2_w|0.455|0.531|||||||
|r2_b|0.023|0.007|||||||
|r2_o|0.003|0.034|||||||
|rho|0.775|0.858|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

57