rafmacalaba/datause-encoder-data huggingface dataset:
Consequently, even where panel data exist, the estimates of poverty mobility based on these panel data are often found to be affected by various data quality issues such as national representativeness or cross-country comparability.<sup>5</sup> Still, financial constraints aside, it is common knowledge that a certain level of technical and logistical capacity is required to implement a high-quality consumption survey. For example, fielding an LSMS-type (Living Standards Measurement Survey) household survey is a demanding task that often involves a number months—if not years—of preparation, which consists of a variety of steps ranging from pre-survey stages (e.g., questionnaire design, sample selection, enumerator training, survey firm selection) to post-survey stages (e.g., data entry, data cleaning and checking, post-survey weighting or stratification).<sup>6</sup> Put differently, available resources may provide the necessary condition, but it is technical/ logistical capacity that forms the sufficient condition for producing high-quality survey data. The latter issue is perhaps a common challenge for which most national statistical agencies are keen on finding a solution.

Furthermore, development practitioners working on smaller-scale projects have been more and more interested in collecting consumption data, say, for project evaluation purposes. Such projects more often than not lack the financial and technical resources, nor the intention, to implement an LSMS-type regular household consumption survey. But properly evaluating such projects would clearly rely on good-quality consumption data. As such, data gaps and the need to come up with innovative measurement methods—particularly those that can be implemented at scale—to fill in these gaps are increasingly receiving more attention.

> 5 Beegle _et al._ (2016) offer a review of studies that use the existing panel data for African countries and find much variation in the estimates for chronic poverty and transient poverty. Furthermore, it is argued that a considerable proportion of these estimates of poverty mobility are subject to measurement errors in income or consumption (Dercon and Krishnan, 2000; Glewwe, 2012; Lee _et al._ , 2017). See also Dercon and Shapiro (2007) for a brief discussion of various panel data sources for developing countries.

> 6 See Grosh and Glewwe (2000) for a systematic treatment of the different steps involved in fielding an LSMS-type survey.

5
Policy Research Working Paper 6113

# **Abstract**

The authors use a new dataset on export transactions for a large set of Latin American and Caribbean and comparator countries to assess the extent of “export entrepreneurship” during periods of fast export growth (2005–2007) and depressed external demand (2008– 2009). Export entrepreneurship is equated with the extensive margin of exports, namely the advent of new exporting firms, new export products, and new export market destinations. The main findings are: (1) annual exporter entry, exit, and survival rates in Latin America and the Caribbean are quite similar to what is observed in other countries, and entry rates across sectors are quite similar but survival rates appear to be highest in agriculture; (2) the relative size of entrants into export markets (relative to incumbents) tended to be lower for natural resource-abundant countries during 2005–2007, but less so during the crisis years of 2008-2009; (3) entry rates tend to be lower in sectors in which a country has revealed comparative advantage, however, exit rates and survival rates of new exporters are higher in those sectors; and (4) the low growth of exports during the global recession of 2008–2009 in Latin America and the Caribbean was due to lower growth in exports of incumbent firms’ pre-existing products and destinations, while new products and destinations tended to attenuate the recession’s effects. Overall, the data suggest that the Latin American and Caribbean region appears to be no less entrepreneurial in terms of the extensive margins of exports than comparator countries.

This paper a joint product of the International Trade Department, Poverty Reduction and Economic Management Network; Office of the Chief Economist, Latin America and the Caribbean Region: and the Trade and International Integration Unit, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and dlederman@ 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
we purposively select roads within each town. After that, SSCBTs were randomly selected within markets and roads. The analysis is based on a sample of 1,159 respondents, after data cleaning. Evidence from Table 1 reveals that the majority of respondents (84.38%) Table 1: SSCBTs socio-economic characteristics by adoption status

|Characteristics|Total sample|Adopters|Non-Adopters|
|---|---|---|---|
|Size|1159|522|637|
|Proportion of female SSCBT(%)|84.38|83.91|84.77|
|Age (average)|36.51|37.7|35.52|
|Number of years as SSCBT (average)|9.04|9.81|8.42|
|Education:||||
|No formal education (%)|12.94|12.26|13.50|
|Primary school (%)|38.40|33.91|42.07|
|Secondary school (%)|45.04|51.15|40.03|
|Tertiary school (%)|3.62|2.68|4.40|
|proportion of Maried (%)|68.00|72.80|65.62|
|Living in urban (%)|97.50|98.66|96.55|
|Trade as mains source of income (%)|94.82|96.55|93.41|
|Have a fx place in the market (%)|94.39|97.51|91.84|
|Member of an association (%)|62.81|91.00|39.72|
|Household size (average)|6.61|6.84|6.42|
|Nord Kivu|516|229|287|
|South Kivu|643|293|350|

Source: Our own computation based on survey data.

and 83.91% of the adopters of CTA were female. At the time of the survey, the average age of the SSCBT was 36 years. The average household size of respondents (both adopters and non-adopters) was 6 people per family; about 95% of respondents were living in urban areas and had spent an average of about 9 years in cross-border trade. The educational level of the SSCBTs was different between adopters and non-adopters. Whereas 55.5% of the non-adopters had at least primary school level of education, only 46% of adopters had a similar level of education. However, at least 54% of adopters reached secondary education against 44% of non-adopters. In addition, the majority of respondents, both adopters and non-adopters have a fix place in the market where they sell their goods. The adoption rate in the two provinces is 44.3% and 45.5% in North Kivu and South-Kivu, respectively. To implement the CTA, the GLTFP worked with mass media (radio and television), officials, existing SSCBTs’ associations and groups of interest to spread the awareness and sensitize SSCBTs in order to organize themselves in groups. In DRC, the dissemination of CTA was implemented in only a few selected markets and regions. This means that the overall population of SSCBTs was not equally exposed to the CTA (the instrument for the policy intervention was not randomly distributed). On the other hand, SSCBTs exposed to the CTA had full control over their decision to adopt or not to adopt (i.e. the receipt of the treatment is endogenous). According to the impact assessment literature, the most plausible assumption to make in this case is that of selection on 8
welcomed foreign investment into “special economic zones,” but it is important to note that some of these were very large, amounting to urban areas of 20 million people or more. The positive impact of foreign investment in these locations led to a more general opening up of the economy to foreign investment, with the result that China has become the largest recipient of direct investment flows in recent years. Thus, compared to other labor-abundant countries in Asia, China has been more open to foreign trade and investment.

The opening up measures would not have had such substantial impact if they had not been accompanied by improvements in investment climate. This is probably one of the least understood features of China’s recent development. There are literally dozens of Chinese coastal cities that have developed quite good investment climates. In these cities the private sector accounts for 90% or more of manufacturing assets and production. A genuine Chinese private sector has emerged that is highly profitable: in 2005 average pretax rate of return for domestic private firms was the same as that for foreign-invested firms (Dollar and Wei, 2006). World Bank investment climate surveys have documented the differences in the objective conditions of production in Chinese cities, compared to ones elsewhere in developing Asia. For example, firms lose a lot of output as a result of unreliable power supply: 3.3% of output in Indonesia, 4.9% in Pakistan, 5.9% in Philippines, 7.9% in India. The figure for coastal Chinese cities was 1.0% (Table 3). Similarly, most manufacturing firms are importing some parts and material: customs clearance time for imports is low in Chinese cities (3.2 days) compared to those in Indonesia (4.8), India (6.6), Philippines (7.2), Bangladesh (10.6), or Pakistan (17.1).<sup>3</sup> On a whole range of practical matters that affect production, Chinese coastal cities outperform the best locations in Bangladesh, India, Indonesia, Pakistan, and the Philippines. The only large Asian countries that have similarly good investment climate indicators are Thailand and Vietnam, both of which have grown quite well – though not as fast as China – in the recent period.

In summary, China, India, and the rest of developing Asia entered this recent era of globalization with similar per capita GDP and wage levels. But China has done more to open its economy to the global market, while significant numbers of its coastal cities have created sound investment climates for private investment. The result has been a 5
has helped establish such school clinics, and given the apparent effects that VHI has had on anthropometric measures of health status among younger children, this is a cause for concern.

_Table 7: Double-difference estimates of VHI impact on contact probability among sick_

|Age in<br>1992/93|Treatment<br>effects|z-stat|F-test consumption<br>quintile differences|Prob> F|
|---|---|---|---|---|
|0-4|0.0389|0.95|F(4, 432) = 2.28|0.060|
|5+|0.0473|3.41|||
|All|0.0433|3.38|||

We can get richer utilization information by focusing on the 1997-98 survey. In so doing, we are forced to use the single-difference matching estimator, and are therefore unable to eliminate any biases introduced by selection on time-invariant unobservables as we were able to with the double-difference approach. Table 8 reports results separately for under-fives and individuals over the age of five. For both age groups, VHI appears to stimulate use of commune health centers (CHCs). Among the under-fives, VHI appears also to encourage visits to pharmacists to obtain prescription medicines, and to reduce visits to pharmacists to obtain advice and non-prescribed medicines. Among individuals aged five and over, but not among young children, VHI encourages the use of hospital care—including inpatient care—and to a much lesser extent the use of polyclinics. With the exception of visits to CHCs, impacts do not differ significantly across the consumption distribution; VHI only has a significant impact on the use of CHCs among the poor. These results are broadly similar to those reported by Trivedi (2003), though he did not report separate results for children and adults, or for the two different types of pharmacy visit. He also found VHI increased utilization of CHC and hospital care, and decreased pharmacy visits overall. However, unlike us, he found VHI caused substitution away from the private sector.

15
and its input substitutability is limited. The third, and perhaps most important, reason is the rapid and massive expansion of cotton production that took place in China and India, as a direct consequence of these countries’ adoption of biotech cotton. During the five‐year period 2002‐07, China increased its cotton output by 56 percent (from 5.2 to 8.1 million tons) while India increased its output by 126 percent (from 2.3 to 5.2 million tons). Today, China and India account for more than half of global cotton production, mostly from biotech varieties.

Econometric evidence confirms that lint prices have diverged from cotton by‐product prices, as proxied by the agricultural commodity price index. The degree of co‐movement between cotton prices and the agricultural commodity price index was analyzed with the following ordinary least squares regression, using annual data from 1960 to 2009: _Pt_<sup>_COTTON_</sup> and _Pt_<sup>_AG_INDEX_</sup> denote the price of cotton and the agricultural commodity price index in year _t_ (expressed in nominal dollar terms), _MUVt_ denotes the def‐ lator, _t_ is the time trend, and _εt_ denotes the error term; _μ_ , _β1_ , _β2_ , and _β3_ are para‐ meters to be estimated. The model is expressed in logarithms to facilitate inter‐ _1_ preting the estimated parameters as elasticities. The estimate of _β_ is expected to be positive while _β2_ and _β3_ can take any sign depending on the relative impact of technological progress and inflation on the particular commodity sector. Notice that while the price of cotton is part of the agricultural commodity price index, its contribution is only 3.9 percent, thus not driving the results.<sup>**4**</sup> The parameter estimates of [1] are reported in the top row of Table 1. Spe‐ cifically, the estimate of _β1_ is 0.61 and highly significant with an adjusted‐ _R_<sup>_2_</sup> of 0.91 and an ADF statistic of ‐6.03, implying a very strong long‐run co‐movement between cotton prices and the agricultural commodity price index. The inflation coefficient, _β2_ , is highly significant as well. The time trend is highly significant and negative, with a relatively large estimate, implying that cotton prices have been declining faster than the agricultural price index, even after accounting for inflation.

> **4** An alternative specification would have been to deflate both _PtCOTTON_ and _PtAG_INDEX_ by MUVt, which would effectively restrict the sum of the price and inflation coefficients to unity (i.e. _β1_ + _β2_ = 1). The advantage of having the inflation as a separate explanatory variable is that the homo‐ geneity restriction is relaxed and a direct estimate of the effect of inflation can be obtained (Hou‐ thakker 1975).

**_Page 7_**
# **Table 10. Heterogeneity in impacts on key outcomes**

|Outcome|Husband<br>employed|Woman<br>employed<br>|Women's<br>labor income|<br>Total<br>household<br>income<br>(ln)|Per capita<br>food hh<br>expenditures<br>(ln)|Per capita<br>total hh<br>expenditures<br>(ln)|
|---|---|---|---|---|---|---|
|**Panel A: Predicted average**<br>Average intent to treat<br>(beta1)|**treatment e**<br>0.039|**ffect and th**<br>0.542***|**e heterogen**<br>3,270***|**eity paramet**<br>0.357***|**er, midline**<br>0.115|0.086|
|Heterogeneity (beta 2)|0.147|1.148**|0.447|0.181|-0.218|-0.39|
|Best ML Method|Boosted<br>tree|Elastic net|Random<br>forest|Elastic net,<br>exponential|Boosted<br>tree|Elastic net|
|**Panel B: Mean predicted im**|**pacts by qu**|**intiles of pr**|**edicted imp**|**act (using m**|**idline) (GATE**|**S)**|
|Mean least affected (Q1)|-0.036|0.385***|2,710***|0.159|-0.046|-0.05|
|Mean most affected (Q5)|0.109|0.681***|3,839***|0.524***|0.273*|0.204|
|Most-least affected (Q5-Q1)|0.158|0.296**|1,129|0.37|0.332|0.27|
|<br>**Panel C: Predicted average**<br>|**treatment e**<br>|**ffect and th**<br>|**e heterogen**<br>|**eity parame**<br>|**ter, endline**<br>||
|Average intent to treat<br>(beta1)|0.01|-0.038|-18|-0.021|0.038|0.033|
|Heterogeneity (beta 2)|-0.389|0.399|0.065|0.147|-0.106|0.299|
|Best ML Method|Random<br>forest|Elastic net|Elastic net|Random<br>forest|Elastic net,<br>exponential|Elastic net,<br>exponential|
|**Panel D: Mean predicted im**|**pacts by qu**|**intiles of pr**|**edicted (usi**|**ng endline)**|**(GATES)**||
|Mean least affected (Q1)|-0.056|-0.126|-274|-0.212|-0.079|-0.111|
|Mean most affected (Q5)|0.078|0.064|282|0.181|0.152|0.165|
|Most-least affected (Q5-Q1)|0.136|0.179|596|0.423|0.258|0.299|

Notes _:_ Heterogeneity analysis based on the approach in Chernozhukov et al. (2020) (see discussion in Section 6 and Appendix C). Panel A (respectively C) show estimates on the main average treatment effects (beta 1) and the heterogeneity loading parameter (beta 2) at midline (endline). Panel B (and panel D) shows impacts per quartile of the predicted treatment effects at midline (respectively endline).

Predictions are estimated for each sample split, values are the medians across 50 sample splits. Significance is based on adjusted p-values to account for partition uncertainty (* p < .1, ** p < .05, *** p <.01). Weekly Income variable is in FDJ.

49
||**Tax morale **|**Tax fine**|
|---|---|---|
|(Intercept)|2.63<sup>***</sup>|2.37<sup>***</sup>|
||(0.03)|(0.03)|
|_Treatment_|||
|People pay|0.07|0.01|
||(0.04)|(0.04)|
|Rich people pay|0.15<sup>***</sup><br>(0.04)|0.31<sup>***</sup><br>(0.04)|
|Companies pay|0.11<sup>**</sup>|0.12<sup>**</sup>|
||(0.04)|(0.04)|
|Business owner|0.07|0.14|
||(0.12)|(0.12)|
|People pay * business owner|-0.11<br>(0.16)|0.05<br>(0.17)|
|Rich people pay * business owner|-0.25<br>(0.16)|-0.24<br>(0.17)|
|Companies pay * business owner|-0.31<br>(0.16)|-0.35<sup>*</sup><br>(0.17)|
|R<sup>2</sup>|0.00|0.01|
|Adj. R<sup>2</sup>|0.00|0.01|
|Num. obs.|6053|6053|
|RMSE|1.00|1.04|

**_***_** _p < 0.001, ** p < 0.01, *_ p < 0.05

Table 5 Treatment Effects among business owners and nonbusiness owners (OLS) This interaction effect also provides evidence against another alternative explanation: that respondents may see rich people as having better _information_ about the benefits and costs of tax evasion. If the information mechanism was at work, this would be the most relevant to business owners (because the information is most applicable), but instead the _companies pay_ and _rich people pay_ treatment effects are weakest among business owners.

A variant of the social norms argument may still be possible however. If people are not in fact most influenced by the behavior of people similar to them, but by _role models_ with higher status, then perhaps people are simply learning their tax paying norms from the highest status members of society such as rich people. This interpretation is not that plausible in this study. Our manipulation check variables ask how common it is for the different groups to cheat on their taxes. We can use the control group to assess how reliably these groups were seen as paying their taxes. Among respondents in the control group, rich people are ranked the worst (mean 2.8 on a 1-4 scale), followed by companies (2.7), and then ordinary people 2.5 (all these differences are statistically significant). This does not suggest that rich people are being held up as positive role models to aspire to and is more consistent with the treatment working by slightly improving respondents’ beliefs about the behavior of rich people.

A final mechanism that is more difficult to dismiss is that the experiment could be _priming_ certain types of taxpayers rather than changing beliefs about them. Therefore, if people are thinking primarily about rich people, they may be more likely to say that tax paying is important
**System and Demographic Dependency Ratios.** The more pressing problems confronting the social pension systems of Central and Eastern European countries stem from faulty design issues and structural factors that are independent of the progressive demographic aging. All four countries have much higher system than demographic old age dependency ratios<sup>2</sup> . In 1992 the system dependency ratios were 46% in Russia, 49% in the Czech Republic and Poland, and 59% in Hungary. In contrast, the respective demographic old age dependency ratios were 31%, 32%, 28% and 36%. Thus, the gap between the two ratios was 15% in Russia, 17% in the Czech Republic, 21% in Poland and 23% in Hungary<sup>3</sup> .

Table 1 System and Demographic Old Age Dependency Ratios, 1992

||SDR|DDR|Gap|
|---|---|---|---|
|Czech Republic|49|32|17|
|Hungary|59|36|23|
|Poland|49|28|21|
|Russia|46|31|15|
|USA|31|30|1|
|Switzerland|43|34|9|

SDR: System Dependency Ratio (given by the number of pensioners, including widows, orphans and disability pensioners, divided by the number of contributors).

DDR: Demographic Old Age Dependency Ratio (given by the number of people 60 years and over divided by people aged between 20 and 59 years).

# Source: World Bank Staff Estimates

> **2** Summary data on dependency ratios, contribution and replacement rates, future demographic structures and other aspects of the public pension systems of the four countries reviewed in this paper are set out in Table 4.

> **3** It should be noted that the reported ratios are estimates and are not based on very hard data because the pension systems of the four countries have rather weak record systems. There are also some conceptual and definitional problems with the data, such as the proper treatment of survivors' and disability pensions. Including widows, orphans and disability pensioners in the total number of beneficiaries inflates the system dependency ratio but lowers the average replacement rate. A rise in unemployment and evasion may also increase the system dependency ratio. In fact, system dependency ratios may vary significantly from year to year.

_7_