rafmacalaba/datause-encoder-data huggingface dataset:
which is relatively high. Williams and Horodnic (2017) also found that the percentage of workers receiving unreported income is higher in Eastern Europe than Western Europe.

**Medina and Schneider (2019) estimated the size of the shadow economy for 158 countries from 1991 to 2015.** Their results show that, in the case of Romania, the informal economy has gradually decreased from 36% to 23%, except for the global financial crisis.  Davidescu & Schneider (2017) identify the main drivers of the shadow economy in the case of Romania: (i) unemployment rate, (ii) high share of selfemployment, (iii) indirect taxation, and (iv) lack of trust in authorities. Davidescu & Schneider (2019) argue that the minimum wage acts as a long-term supporting factor of the grey economy as enterprises will seek alternative methods of circumventing tax authorities, and at least part of them will pay their employees in cash (envelope wage). Davidescu (2022) identifies a significant impact of minimum wage increases on the magnitude of the shadow economy, but only in the long term. Stancu et al. (2020), based on the dynamic multiple indicators – multiple causes (DYMIMIC) approach, estimate the share of the shadow economy in Romania's GDP at over 15%. According to the authors, this share has declined since the early 1990s, when it was close to 25%. Schneider (2015) estimates the share of the shadow economy in the GDP to be over 25%, the second highest value (after Bulgaria) in Central and Eastern Europe.

**In this paper, we follow the second approach; in this approach, to determine the extent of tax evasion, we must compare the distribution of "true" incomes with the distribution of "reported" incomes.** Still, several challenges arise, and selecting and matching the appropriate datasets is necessary. Following the literature, we assume that true income is revealed in income or labor surveys such as the European Survey on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS), the Labor Force Survey (LFS), while "reported income" is the income reported to the tax authority. However, the main challenge with this approach is that there is no single data set that includes both the true and the reported income of individuals. Official tax return data only capture reported income and do not provide undeclared or tax-exempt income information, meaning it cannot accurately measure "true" income. Conversely, survey data does not provide reliable information on reported income to tax authorities. Therefore, it is necessary to match datasets to obtain a

7
commercial crop production. However, these could be available for bioenergy crops, but with appropriately discounted yields.

**Table 6: Total area of agricultural land in 1998 and the potential surplus** **<u>agricultural in 2050</u>**

|Region|Totalagriculturala|rea1998|Potentialsurplus|
|---|---|---|---|
|||Million Ha||
|North America|493||54-348|
|Oceania|480||216-428|
|Japan|5||0|
|West Europe|147||12-61|
|East Europe|66||4-40|
|CIS and Baltic States|574||113-491|
|Sub-Saharan Africa|991||104-717|
|Caribbean and Latin America|760||152-555|
|Middle East and North Africa|461||23-372|
|East Asia|765||15-510|
|South Asia|224||36-63|
|World|4,966||729-3,585|

Source: Smeets et al. (2007) Notice however, that the figures reported refer to the global technical potential for biomass to produce energy and should be viewed as a maximum contribution that can be expected. Only a portion of that biomass will be used for biofuel production (and some will be lost during the conversion process). Given the uncertainties involved in projecting the amount of land available for biomass for energy production and its yield, the actual technical potential may be outside the range reported by most authors.<sup>13</sup>

# **3    Production Economics**

The previous sections showed that second-generation biofuels can potentially make significant contributions to the energy mix. However, whether that potential will be realized depends on the economics of their production. In particular, biofuels will need to be cost-competitive with fossil fuels for their commercial scaling-up. The costs of different second-generation biofuels are reviewed in this section.

> 13 With the exception maybe of the authors reporting the widest ranges such as Hoogwijk et al. (2003) and IEA (2007).

16
Table 3: Agricultural outcomes <u>(insured</u> crops) at time _t_ + 1

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Panel A: First Stage_|||||||
|||payment =|1||||
|Below trigger|0.194<sup>_∗∗∗_</sup>|0.197<sup>_∗∗∗_</sup>|0.201<sup>_∗∗∗_</sup>||||
||(0.0578)|(0.0673)|(0.0553)||||
|_Panel B: Reduced form_|∆|log ha sow|ed||log yield||
|Below trigger|0.0765<sup>_∗∗_</sup>|0.0571|0.0604|0.0733|0.107|0.0242|
||(0.0357)|(0.0404)|(0.0448)|(0.0596)|(0.0755)|(0.0518)|
|_Panel C: 2SLS_|∆|log ha sow|ed||log yield||
|�<br>% ha receiving payment|0.394<sup>_∗∗_</sup>|0.291|0.301|0.377|0.545|0.120|
||(0.194)|(0.201)|(0.218)|(0.340)|(0.466)|(0.260)|
|Bandwidth|50|70|all|50|70|all|
|Polynomial|linear|linear|quadratic|linear|linear|quadratic|
|N|1916|2502|4311|1916|2502|4311|

_∗ ∗∗_ Note. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _p <_ 0 _._ 10, _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Observations are at the municipality-crop-year-level. Agricultural production is for the corresponding insured crop (maize, beans, sorghum, and barley). All regressions include crop fixed effects. Panel A shows results from the OLS estimation of the first stage, which corresponds to estimating equation 1. Panel B shows results from the OLS estimation of the reduced form, which corresponds to estimating equation 2, and panel C displays 2SLS estimates. Columns 1 and 4 are estimated with a linear polynomial on a sample of observations within the optimal bandwidth of 50 mm. Columns 2 and 5 are estimated with a linear polynomial on a sample of observations within an alternative bandwidth of 70 mm. Columns 3 and 6 are estimated using a quadratic polynomial on the entire sample.

27
-13 -

# Table 3: DISTRIBUTION OF PRICE RATIOS: SHARE OF RATIOS IN A GIVEN RANGE IN THE TOTAL NUMBER OF RATIOS - (Total number of price ratios 1) /a

||Develope|d countries|Develop|ing countries|
|---|---|---|---|---|
|Range|1982|1984|1982|1984|
|0.00-0.25|0.10|0.11|0.14|0.15|
|0.26-0.50|0.28|0.29|0.22|0.23|
|0.51-0.75|0.26|C.25|0.22|0.21|
|0.76-1.00|0.18|0.17|0.19|0.20|
|1.01-1.25|0.08|0.08|0.10|0.09|
|1.26-1.50|0.04|0.04|0.05|0.04|
|1.51-9999|0.07|0.07|0.09|0.09|

# /a The sums may not add up to 1 due to rounding.

Another sensitivity test was carried out to account for different weights of individual price ratios at the original level of disaggregation. The results of the test are summarized in Table 4. For this test we have eliminated all products which had an EEC market share smaller than 0.01X. In addition, we have also eliminated all price ratios which were smaller than 0.5 and greater than 2. In other words, we have retained in the sample only those products which were exported to the EEC markets at prices 502 below the comparable EEC export prices as well as prices which were double or more of EEC export prices. The idea was to test for the effects of heterogeneity within the given product groups at the original level of disaggregation by eliminating all woutliers," that is price ratios, which characterized large price differences. The table shows for a given product group the average relative price of CPEs' exports and frequency of observations lying in the predetermined price range, defined as ratio R with the value of 0.5 <- R <- 2.00. The test was carried out again for both years.
**Table 13.2 Explaining Social Norms – Males only sample**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**PNB Index -**<br>**HH Roles**|**PNB Index -**<br>**Public Space**|**SNE Index -**<br>**HH Roles**|**SNE Index -**<br>**Public Space**|**Sanctions**<br>**Index**|
|Respondent’s age in years,|0.00|0.00|0.00|-0.00*|-0.00|
|squared|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Years of education, derived from|-0.00|-0.01|0.00|-0.02* (0.01)|-0.05|
|education level|(0.01)|(0.01)|(0.01)||(0.03)|
|Indicator for respondent having|-0.01|0.10* (0.06)|-0.04|0.01|0.40|
|childyounger than 5years|(0.02)||(0.04)|(0.06)|(0.33)|
|Respondent lives in urban area|-0.01|-0.01|-0.09|-0.06|-0.96*|
||(0.05)|(0.12)|(0.05)|(0.14)|(0.49)|
|Log of household's total income<br>minus working female’s earned<br>income(NPR)|-0.06**<br>(0.02)|-0.06<br>(0.04)|-0.04<br>(0.03)|-0.05<br>(0.05)|-0.07<br>(0.21)|
|Madhesh Province|1.11***<br>(0.08)|0.71***<br>(0.12)|0.98***<br>(0.05)|0.85*** (0.12)|-0.24<br>(0.49)|
|Gandaki Province|-0.19***<br>(0.07)|-0.36***<br>(0.08)|-0.25***<br>(0.06)|-0.35*** (0.09)|-1.96***<br>(0.4)|
|Sudurpashchim Province|0.45***|0.44***|0.49***|0.25|-2.19**|
||(0.09)|(0.14)|(0.15)|(0.15)|(0.79)|
|Respondent belongs to Madheshi|0.06|0.07|0.15***|0.11|0.16|
|(non-Dalit) ethnic<br>group|(0.09)|(0.17)|(0.05)|(0.15)|(0.30)|
|At least one among MIL and FIL|-0.02|-0.07|0.00|-0.05|0.00|
|<br>live in the HH|(0.02)|(0.05)|(0.02)|(0.05)|(0.22)|
|Indicator for ever migrated|0.09<br>(0.06)|-0.03<br>(0.12)|0.07<br>(0.04)|0.01<br>(0.11)|0.80**<br>(0.32)|
|Constant|0.96***<br>(0.31)|1.14**<br>(0.54)|0.97**<br>(0.36)|1.53** (0.62)|18.56***<br>(2.46)|
|Observations|850|850|850|850|850|
|Adjusted R-squared|0.64|0.26|0.60|0.33|0.09|

_Notes_ : (1) Reference group for province is Bagmati Province. (2) SEs clustered at the ward level. (3) Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. SEs clustered at the ward level.

48
2 studies imply that, at best, we can only expect to unearth suggestive empirical regularities. Even settin8 aside measurement difficulties and issues of data consistency across countries and time, numerous interpretational problems plague cross-country investigations. It is not clear that we should include vastly different countries in the same regression. Regression analysis presupposes that observations are drawn from a distinct population, but as argued by Arnold Harberger (1987), Thailand, the Dominican Republic, Zimbabwe, Greece, and Bolivia may have little in common that merits their being put in the same regression. Thus, the statistical basis upon which we draw inferences from cross country analyses may be in doubt.

Furthermore, it ic conceptually difficult to interpret the coefficients on regressions that involve data for over 100 countries averaged over thirty years during which time business cycles, policy changes, and political disturbances have influenced economic activity. Many papers interpret the coefficients as elasticities, suggesting that if a policy indicator changes by one percent, growth will change by a percent corresponding to the coefficient on the policy indicator. These types of conceptual experiments should be treated skeptically as crose country regressions do not resolve causal issues, nor do the regressions ".. describe a single piece of machinery through time." (Harberger, 1987, p. 256) Cross country regression **s** hould be viewed as evaluating the strength of partial correlations and not as behavioral relationships tvi-t suggest how much growth will change when policies change.

"Looking at the facts" becomes even more opaque when the objects of analysis are national policies. In theoretical models of policy and growth, economists typically represent policy distortions with the greek letter tau. Not only do international data sets such as the International Financial Statistics and the Sumuers and Heston (1988) data not contain data series called tau, but it is very difficult to construct
the Commonwealth of Independent States (CIS).<sup>2</sup> The original 2005 database is comprised of about 9,500 firms, with 200-600 firms per country. The sample was constructed by stratified random sampling from a national registry of firms. In each country, the sectoral composition of the sample in terms of manufacturing versus services was determined by their relative contribution to GDP. Firms that operate in sectors subject to government price regulation and prudential supervision, such as banking, electric power, rail transport, and water and waste water, were excluded from the sample. The sample includes only registered firms (i.e., not informal firms), and their size varies from as few as 2 employees to as many as 9,999 employees. Moreover, firms in rural areas and small towns are represented since at least 10% of firms in the sample are in a small city or the countryside (i.e., population under 50,000 inhabitants).

The data enable us to identify the gender of the largest shareholder (owner) of individually or family owned firms. Hence in this paper we define male v. female entrepreneurs as “male v. female sole or principal owner of individual or family owned firms.”<sup>3</sup> These individuals are also the main managers of the firm. Other strengths of these data from our perspective include the fact that the same survey instrument was administered in 26 countries in the same year; that there are a host of performance variables for each firm; and there are a set of questions dealing with institutional factors, especially in the area of finance, which may affect the relative performance of male and female-owned business. The weaknesses include a) the small number of firms sampled in each country; b) inability to identify the gender of the other owners of the firm when there is more than one; c) lack of demographic information on the entrepreneurs; and d) the numerous missing answers to some variables of interest (e.g., capital).

> 2 The 15 CEE countries are: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia. The 10 CIS countries are: Armenia, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Ukraine and Uzbekistan. Note that Turkmenistan never entered the BEEPS sample.

> 3 These are existing firms, rather than “nascent” firms.  To the extent that there are gender differences in the survival of firms, we may have a biased sample.  However, given Kallebert and Leicht (1991) and Ruderl and Preisendorver (1998) have found no significant differences between the survival rates of male- and female-owned businesses, our sample should not be biased on this dimension. If survival bias is stronger among women, then our results comparing existing firms owned by men v. women might be upwardly biased for all women entrepreneurs.

9
# **1 Introduction**

Traditionally, the study of poverty dynamics requires tracking the same individuals or households over time. However, in many developing countries, panel data is scarce, which prevents analysts from observing the economic trajectories of individuals or households over an extended period of time.

Following the path opened by Deaton (1985), papers by Bourguignon, Goh, and Kim (2004), and Guell and Hu (2006) explored the use of synthetic panels at the household level. However, these two approaches require certain assumptions that may not be easily satisfied in available cross-sectional data. The former method requires at least three rounds of cross-sectional data, and assumes a first-order auto-regression process where past household or individual incomes (earnings) can affect present outcomes; the latter is exclusively restricted to duration analysis. To overcome these limitations, Dang et al. (2014) constructed synthetic panels from as few as two rounds of household-level crosssectional data, providing lower-bound and upper-bound estimates of poverty transitions. More recently, Dang and Lanjouw (2023) proposed a method that delivers point estimates of poverty transition probabilities (households shifting in and out of poverty, or preserving their status) in the absence of longitudinal data.

This paper takes advantage of these new developments, as well as some critiques that have recently been raised regarding some aspects of the methodology (e.g., Herault and Jenkins, 2019; Garcés‐Urzainqui, Lanjouw, and Rongen. 2021, Colgan, 2023), to delve deeper into the characteristics of poverty dynamics in Bhutan during the five-year period from 2017 to 2022. According to Amendola et al. (2023), during this time the stark decrease of poverty in Bhutan at the national and sub-national levels seems to be linked mainly to redistributive processes in the monetary metric of expenses, rather than to economic growth. The fact that the key driver of poverty reduction is a reshuffling of households along the distribution curve, rather than a movement of the curve itself, justifies the need for tools capable of identifying who changes their position within the income (or expenditure) scale during the period considered. Methods based on pseudo-panels allow for precisely this kind of analysis, and significantly contribute to our understanding of the relative importance of the underlying drivers of poverty dynamics. In the case of Bhutan, the results are interesting and, hopefully, useful to policy makers: education level and residence in rural areas are associated with greater overall mobility, both upward and downward, but only residence in rural areas is informative regarding the “structure” of mobility. Families living in rural 2
less intensive presence of INPRES schooling expansion relative to Duflo (2001). Nonetheless, the working sample still captures 38 districts in which the number of schools planned (per capita) is larger than the equivalent number measured at the national level (1.8 per 1,000). Therefore, we focus on the contrast between mothers born in districts with “intensive” INPRES presence and those born in “non-intensive” INPRES districts. In Table A2 we also show that the variables influencing INPRES allocation of schools across space correspond to the same ones examined in Duflo (2001), and that this relationship is not significantly different in our working sample. INPRES allocation was indeed remedial, targeting districts where more children could be served, where enrollment rates had been lower, and where distances to be traveled (to existing schools) were larger (indirectly measured by population density).

For completeness, we also present the contrast between main demographic characteristics of the full ECED sample and our working sample in Appendix Table A3. It is clear that the main difference between the two comes from the restriction we impose in terms of cohort-of-birth. Therefore, mothers in our working sample are older, slightly less educated (and so are the children’s fathers) than those in the full sample. It is important to note that since the ECED evaluation design was based on sampling children between the ages of 6-9, our exercises are indeed drawing from a sample of children with older mothers, larger sibships, and likely higher parity than the overall sample in the Indonesia ECED Project. It is key to take these points into consideration when discussing limitations to the external validity of our results.

Appendix Table A3 reports that in our working sample, mothers are 41 years old on average, with 32 percent being born in years 1966-1970 and 68 percent in years 1971-1975. On average, they have completed 7.4 years of schooling. Approximately 80 percent of them report having completed primary education and being able to correctly read the sentence “rain came late this year” in Bahasa Indonesia. These mothers (who are close to completing their fertility if not so already) report having given birth to 4.3 children on average. Focus children are on average 7.7 years of age and 50 percent of them are boys. In about 9 percent of the cases, the father is reported to not reside in the household with them. This includes those who report that the father is deceased.

7
Other studies apply a variance decomposition analysis over time. The objective is to split up the variance over time into its components, the variance within and between groups of the same education and experience. Changes in the returns to observed skills, such as education, change the distance between the mean wages of different population sub-groups. An increase in the returns to higher education will drive the sub-group means further away from each other, thus increasing earnings dispersion. SBTC might be the reason for such an increase in returns to education. Since very few individual skills are observed in the data, individuals with heterogeneous unobserved skills will look alike to the econometrician. If the returns to some unobserved skill change, this will be noted as changes in the earnings dispersion within sub-groups, the residual variance. With certain assumptions in the decomposition process one can infer changes in the returns to unobserved skills. People who argue for SBTC have also claimed that SBTC may also change unobserved skill returns, and that changes in these returns may be taken as indications for SBTC.

Lemieux (2006) points out the role that changes in the composition of the workforce have for the residual variance. Taking composition effects appropriately into account, he finds that an increase in returns to unobserved skill may have occurred in the 1980s but did not in the 1990s when technological progress is widely believed to have taken place. The fact that the returns to unobserved skill increase only in the 1990s for the Unites States is also incongruent with the consistent increase in the returns to education over both the 1980s and 1990s. Overall, Lemieux concludes that the pattern of changes in the returns to unobserved skill in the United States does not lend support to the SBTC hypothesis.

In the literature in the U.S. and other countries, the lack of evidence for other explanations is interpreted to imply that SBTC may be behind increases in the returns to education. The same holds true for Argentina. Several reviews suggest that changes in technology are the proximate cause of changes in returns to education in Argentina. Giovagnoli, Fiszbein, and Patrinos (2005) suggest that increased demand for skills may have driven the increasing returns to education observed in the 1990s. Analysis in World Bank (2004) also shows that the patterns observed for that decade are consistent with skill-biased technical change. Acosta and Gasparini (2007) show that the wage premium for a college education increased more in manufacturing industries with higher rates of physical investment. They also find that this premium grew more in sectors which faced strong import competition.

In an extensive analysis of labor market data from Gran Buenos Aires, Gasparini (2003) presents many pieces of evidence in favor of SBTC as an explanation for the increase in 8