rafmacalaba/datause-encoder-data huggingface dataset:
geographical unit. Thus, satellites have enabled development economists to study geographic entities that were previously inaccessible because of insufficient data coverage. In future, new commercial satellite projects with continually improving spatial and temporal coverage will only reinforce the importance of remote sensing for academic studies.<sup>1</sup> Especially the use of nighttime lights as a proxy for economic activity is important to scholars in economics and other social sciences. Nightlight intensity has been used to approximate economic activity as light is believed to be a normal good with its consumption increasing at higher incomes. Henderson et al. (2012) have found strong correlation with GDP at the national level and researchers have gone on to use nightlights at smaller geographic units. For example, Bleakley and Lin (2012) use nighttime light intensity to measure economic activity in their study on the economic persistence of defunct portage sites and Alesina et al. (2016) combines them with sub-national ethnolinguistic maps. Similarly, Storeygard (2016) employs nightlights to study intercity transport costs and their impact on income of sub-Saharan African cities, while Harari (2016) uses them to measure shapes of urban areas in India.

However, the predictive power of nighttime light at geographies smaller than the national level has recently been disputed in the literature. For example, while Mellander et al. (2015) conclude that nightlight data have a reasonably high correlation with economic activity in the developed world context of Sweden, Bickenbach et al. (2016) find severe parameter instability between regions in India, Brazil, and the US, and cast doubt that the correlation between nightlights and GDP at the country level carries over to the subnational level. Chen and Nordhaus (2011) find similar issues when scaling down from national to grid cell levels. The reasons for this are threefold: The common nightlight data produced by the National Oceanic and Atmosphere Administration (NOAA) is only available at a geospatial resolution of 30 arc seconds (about 1km). This resolution might be too coarse to capture

> 1Private satellite ventures likes Planet and Digital Globe have recently pushed the limits to spatial resolution of up to 0.5 meters/pixel.

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# **5 Conclusions**

We provide new evidence suggesting that opportunity entrepreneurs have more profitable businesses, manage them better and have higher measures of cognitive and of some non-cognitive skills, compared to necessity ones. Using a discriminant analysis, we show that combining these characteristics allows us to separate both types of entrepreneurs, although not perfectly, and identify those that even though they started their business out of necessity are more similar in observables to their opportunity counterparts. Given that the discriminant analysis only increases 10 percentage points of the prediction between necessity and opportunity entrepreneur compared to a random assignment, the characteristics of the firm and of the entrepreneur considered in the analysis cannot fully explain when an entrepreneur is either an opportunity or a necessity one. In this way, we proceed to analyze this characteristic with an instrumental variable approach, which confirms that businesses led by opportunity entrepreneurs are significantly more profitable than those led by necessity entrepreneurs, in part because of better management practices. These results are relevant for improving the targeting of business support programs. For instance, these programs could obtain larger impacts by concentrating on the entrepreneurs with the highest growth potential, whereas other types of interventions might help low-performing necessity entrepreneurs improve their prospects for salaried employment.

# **References**

- [1] Angrist, J. D. and A. B. Krueger (2001). “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments.” Journal of Economic Perspectives, 13(2):69-85.

- [2] Angrist, J. D. and A. B. Krueger (1994). “Why Do World War II Veterans Earn More than Nonveterans?” Journal of Labor Economics, Vol 12, No.1 74-97.

- [3] Bruhn, M. (2009). “Female-owned Firms in Latin America: Characteristics, Performance, and Obstacles to Growth.” World Bank Policy Research Working Paper Series 5122.

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short samples. When it comes to consumption, they often having only one year of seasonal data, rendering it impossible to discern regular seasonal patterns from (idiosyncratic) intra‐ annual variation. This paper revisits this agenda, methodologically and empirically, taking 19 years of food price data over the past two decades and three waves of nationally representative panel data on household consumption over the past decade from Tanzania. Three insights emerge.

First, methodologically, the paper has shown that the most widely used technique of estimating the seasonal gap either from a dummy variables regression or by averaging deviations from a moving average trend can give upwardly biased estimates of the seasonal gap when implemented on samples covering less than 20 years. The bias results when non‐systematic high or low prices are mistakenly taken as identifying seasonal peaks or troughs. This problem is particularly severe for crops in which the seasonal pattern is poorly defined. The alternative, trigonometrically based procedure for estimating seasonal factors on short data samples proposed here, gives more reliable results.

Second, the current neglect of price seasonality is premature. Wholesale maize prices are estimated to be 27 percent higher than those during the troughs (on average across the 20 wholesale markets). The seasonal gap is about half as large for rice (15 percent), which is partly irrigated and more widely traded. Seasonality explains 7 to 13 percent of overall price volatility (for rice and maize respectively), underscoring that many other sources of domestic food price volatility remain. Nonetheless, seasonal variation is 2.5 to three times larger than on the international reference markets (SAFEX for white maize and Bangkok for rice). This suggests substantial scope for improved access to secure storage and other measures to reduce seasonality, even more so when targeted to those markets displaying the highest seasonal gaps. Across markets in the country, they vary between 13 and 46 percent for maize and between 3 and 22 percent for rice, indicating substantial heterogeneity in food price seasonality across the country.

Third, there appears price‐induced seasonality in caloric intake (controlling for other seasonal factors), though not for everyone. When taken on average across the country, seasonality in 23
Figure 1b: Non-anonymous Growth Incidence Curve - Synthetic vs. Actual Panel Data Nicaragua 2001 - 2005 <!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>0 5 10 15 20<br>Ventiles<br>Truth Lower Bound Upper Bound<br>Growth in Consumption Per Capita (%)<br><!-- End of picture text --> Data source: EMNV, 2001 - 2005.

Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

33
**Table 2.3 Convergence and Non‐Convergence Experiences, 1960‐2014**

|Economy|Real GDP per<br>capita<br>relative to<br>the US in<br>1960|Real Gdp<br>per capita<br>relative to<br>the US in<br>2014|Year the<br>economy<br>graduated to<br>high‐income|Average GDP<br>per capita<br>growth rate,<br>during middle<br>income|Average per<br>capita GDP<br>growth rate,<br>1960‐2014|
|---|---|---|---|---|---|
|_Graduated to hig_|_h income_|||||
|Chile|0.29|0.413|2011|0.0243|0.0244|
|Cyprus|0.216|0.547|1979|0.0585|0.0343|
|Greece|0.275|0.497|1970|0.076|0.0235|
|Hong Kong SAR,||||||
|China|0.213|0.991|1976|0.0564|0.044|
|Ireland|0.368|0.933|1971|0.0381|0.0321|
|Japan|0.304|0.676|1967|0.0819|0.0327|
|Korea, Rep.|0.067|0.671|1993|0.0665|0.05568|
|Malaysia|0.147|0.443|2011|0.0399|0.0398|
|Malta|0.128|0.605|1991|0.0558|0.0434|
|Portugal|0.238|0.545|1990|0.0423|0.028|
|Seychelles|0.363|0.494|1979|0.0466|0.0323|
|Singapore|0.151|1.388|1980|0.0678|0.0499|
|Spain|0.326|0.648|1967|0.0693|0.0265|
|Taipei China|0.137|0.848|1986|0.0644|0.0555|
|_Not graduated to_<br>|_high‐income_|||||
|China (PRC)|0.066|0.239|..|..|0.0451|
|India|0.059|0.1|..|..|0.0322|
|Indonesia|0.054|0.186|..|..|0.0336|
|Mauritius|0.205|0.343|..|..|0.0315|
|Panama|0.155|0.377|..|..|0.0338|
|Romania|0.08|0.398|..|..|0.0397|
|Sri Lanka|0.157|0.198|..|..|0.0374|
|Thailand|0.064|0.267|..|..|0.047|
|Tunisia|0.09|0.198|..|..|0.0308|

Note: Sample consists of 75 middle‐income economies in 1960. A convergence success refers to an economy that advanced from a middle‐income status to a high‐income status during the period 1960‐2014, or an economy whose per capita GDP increased at an average annual growth rate over 3.0 per cent over the period, even though it has not graduated to a high‐income status. _Source_ : Lee (2018).

# Box 2.1 Causes of Middle‐Income Traps

The causes of middle‐income traps are multiple. They include diminishing returns to capital, exhaustion of cheap labor and imitation gains, poor quality of human capital, inadequate contract enforcement and intellectual property protection, misallocation of talent, inadequate access to advanced infrastructure, and financing constraints. Some of these causes are consistent with the evidence that views productivity slowdowns as the main source of middle‐income traps; they differ from each other mainly in terms of the reason why these slowdowns occur. As such, they are likely to be complementary in nature, that is, they could be present in combination in any particular case.

_Diminishing returns to capital_ . In the initial stages of development, high rates of investment (especially by the <u>public sector) generate strong gains in terms of growth ‐‐ in the case of public investment either directly throug</u> 34
Figure A1 – Educational Paths – Males <!-- Start of picture text --> Compulsory Education Secondary Edu Higher Education<br>General 4y- Post<br>Primary Preparatory Secondary University Grad<br>5y-<br>3y-Voc. Agr.<br>University<br>Post<br>Secondary<br>3y-Voc. Ind<br>3y-<br>Voc.Com<br>5y-Voc Edu<br>2.12 4.08 7.7 5.2 4.4 9.8 3.4 5.9 6.7<br>5.7 5.4 10.6 11.8 16.2<br>Labor Market -Rates of Return (%) – Males 2006<br><!-- End of picture text --> 34
Policy Research Working Paper 4960

# **Abstract**

Using detailed data on trade and tariffs from 1992–2007, the authors examine how the ASEAN Free Trade Agreement has affected trade with nonmembers and external tariffs facing nonmembers. First, the paper examines the effect of preferential and external tariff reduction on import growth from ASEAN insiders and outsiders across HS 6-digit industries. The analysis finds no evidence that preferential liberalization has led to lower import growth from nonmembers. Second, it examines the relationship between preferential tariff reduction and MFN tariff reduction. The analysis finds that preferential liberalization tends to precede external tariff liberalization. To examine whether this tariff complementarity is a result of simultaneous decision making, the authors use the scheduled future preferential tariff reductions (agreed to in 1992) as instruments for actual preferential tariff changes after the Asia crisis. The results remain unchanged, suggesting that there is a causal relationship between preferential and MFN tariff reduction. The findings also indicate that external liberalization was relatively sharper in the products where preferences are likely to be most damaging, proving further support for a causal effect. Overall, the results imply that the ASEAN agreement has been a force for broader liberalization.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to explore effects of regional trade agreements. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at cfreund@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Political Economy of Village Sanitation in South India: Capture or Poor Information?**

_Radu Ban, Monica Das Gupta, and Vijayendra Rao_<sup>1</sup> Development Research Group The World Bank Washington, DC 20433

> 1 Emails: <u>rban@worldbank.org, mdasgupta@worldbank.org, vrao@worldbank.org</u>

_Disclaimer_ : These are the authors’ views and should not be attributed to the World Bank or affiliated organizations or member states.
to construct the index we believe are particularly subject to this second interpretation (Questions d and e in Appendix 1). As a robustness check, we construct the index without these two questions, and the results do not change.

# **4.4 Response to Economic Shocks**

The program could have also improved enterprise performance by helping enterprises to better cope with the 2008 economic crisis. In the follow-up survey, about 89% of enterprises—both in the treatment and control group—reported that they had been affected by the crisis. We asked these enterprises what changes they made in response to the crisis. Table 7 reports the answers to these questions and examines whether the responses differed across the treatment and control groups. The results show that treatment enterprises are eight percentage points (s.e. = 4 percentage points) less likely than control enterprises to report that they had to cut production in response to the crisis. The ability to weather shocks more effectively could be a result of being able to more proactively engage in marketing activities and better control finances, as shown in the previous section. Enterprises that are less well trained in these skills might experience economic shocks more passively and do not have tools to counteract a shortfall in demand.

Other changes in response to the crisis were not statistically significant across the treatment and control groups, but one of magnitude (but not statistical significance) to note is a positive impact on seeking government assistance (a 5.6 percentage points increase, s.e. = 4.4 percentage points, relative to an average of 12.8% in the control group). For enterprises that reported seeking government assistance, we asked which program or agency they contacted and most answers indicated state or federal programs that provide funding or subsidies to micro, small, and medium sized enterprises.

# **5. Cost-Effectiveness: Why Don’t More Enterprises Use Consulting Services?**

Given the large increases in productivity, and eventual growth in employees, we ask why more firms do not use consulting services. In particular, a cost-effectiveness calculation suggests that the returns to hiring a consultant may be well worth the cost. The measured effect of the program on the daily wage bill of U$99 implies an increase in the annual wage bill of US$99 x 365 = US$36,135. The annual cost of the consulting services was US$11,856. Since the program was highly subsidized, participating enterprises only had to pay between 10% and 30% of this cost (depending on firm size). Among the enterprises in the treatment group, only 53% chose to participate in the subsidized consulting program once offered a spot. Although we do not attempt to translate the job growth to firm 16
the matching algorithm, but it would not necessarily render the methodology inapplicable.<sup>36</sup> In any event, our analysis suggests that this approach could prove useful for both regulatory authorities and researchers.

# **VI: Conclusions**

The results of this paper challenge the dominant view that Bitcoin is little used for transactions purposes (other than buying other cryptocurrencies), and that its value is almost entirely based on speculation. Exploiting extraordinarily rich and detailed public data on trades in 135 fiat currencies, we have presented what we believe to be the most systematic and detailed evidence to date on how crypto is used as a vehicle for international capital transfers, as well as for domestic transactions. Our main identification technique takes advantage of the fact that trades are reported to eight decimal places, and the data set gives a timestamp, price, and currency for each trade. To bolster our analysis, we have also looked an interesting natural experiment of a three-day power outage in the Venezuela, where crypto has been a popular vehicle for private citizens to evade draconian capital controls, and perhaps also for official entities to avoid US sanctions. We find that the power cut had a massive footprint not only in the Venezuelan Bitcoin market, but also in the Bitcoin markets of its major destination and remittance-sending countries. Indeed, although we do not know whether the Venezuelan experience can be extrapolated to other markets, it suggests that our estimate of share of trades that are crypto vehicle trades is very much a lower bound. As such, our work complements and considerably extends previous studies that indirectly attempt to identify transactions use based on network analysis of on-chain transactions, which do not include payment currency and are only a small share of the Bitcoin trade universe.

Even though crypto exchanges are subject to regulation in the country where they are based, offchain transactions, particularly smaller ones, are difficult to systematically audit, especially where the regulator in one country needs information on a centralized exchange in another, Finland in the case of

> 36 A similar comment applies to those exchanges that report holdings only to six digits instead of eight digits.

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|**Authors**|**Topic and country of the**<br>**study**|**Dimensions of**<br>**aspirations**|**Underlying**<br>**concept of**<br>**aspirations**|**Measures of aspirations**|
|---|---|---|---|---|
|Stutzer (2004)|Link between income<br>aspirations and happiness of<br>households in Switzerland|Income|Sufficient needs|Two continuous variables based on the following questions:<br>1. Sufficient income: “What income would you indicate as good or bad in your<br>circumstances? Please try to state what income per month (before taxes) for your<br>entire household you consider to be [...] sufficient.”<br>2. Minimum income: “What household income per month would you consider<br>an absolute minimum in order to make ends meet and without running into debt<br>even if you reduce your needs to a minimum? We do not only mean<br>housekeeping allowance but all essentials, including insurance, taxes and so on”|
|Young Lives<br>longitudinal<br>surveys (Pasquier-<br>Doumer and Risso<br>Brandon 2015;<br>Favara 2017;<br>Favara and<br>Sanchez 2017;<br>Ross 2019)|Link between aspirations in<br>adolescence and later<br>outcomes and differences in<br>aspirations across subgroups<br>in Ethiopia, India, Peru, and<br>Vietnam|Education|Wishes without<br>constraints|One dummy variable equal to 1 for children aspiring to university based on the<br>question "Imagine you had no constraints and could study for as long as you<br>liked, or go back to school if you have already left. What level of formal<br>education would you like to complete?"|

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<!-- Start of picture text --> 100%<br>90%<br>BOM<br>rite)<br>60%<br>50%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>O%<br>1979-1992 1994 1995 1996 1997 1998 3861999 #2000 2001 2002 2003 2004 #2005 2006 2007 2009 «2010 2011 2014<br>——East Asia & Pacific ——Europe & Central Asia ——Latin America & Caribbean — Middle East & North Africa ——=South Asia ————Sub-Saharan Africa<br><!-- End of picture text -->
As table 8 shows, the gap between large and small firms’ growth is relatively higher in sectors with more interactions with officials and in locations where deals are less orderly. This is particularly true when looking at firms by size. And the effects are more significant for bribes paid than for management time spent with officials.

# **Table 8: Growth outcomes are relatively more disparate when disordered deals are prevalent, particularly in sectors where more interactions with officials are needed**

Difference-in-difference at the location-sector cell level

|Dependent variable:|Gap in grow<br>versus sm|th of large<br>all firms|Gap in gr<br>versus yo|owth of old<br>ungfirms|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Std_Bribeslocation* Intensity of Govt Interactionssector|0.0133**<br>(0.0066)||0.0065<br>(.0074)||
|Std Management Timelocation*Intensity of Govt Interactions|sector|0.0053*<br>(0.0029)||0.0047<br>(.0036)|
|Location dummies|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|
|R<sup>2</sup>|0.33|0.31|0.24|0.28|
|N|260|260|285|285|

Prevalence of disordered deals: i) std dev. of bribes paid in a location (metropolitan areas within countries) ii) standard deviation of management time spent with officials in a location Sectors are ranked by their 'intensity of interactions with officials', using Germany as the benchmark Robust standard errors are reported; * ** significant at the 10 and 5 percent levels Overall, these results underscore that just looking at the average level of deals does not capture the story and that uncertainty about the policy implementation environment is itself an important dimension affecting firms’ behavior.

40