rafmacalaba/datause-encoder-data huggingface dataset:
home (see for example Anukriti et al., 2020).

Conducting treatment effect analysis by gender suggests that the observed treatment effects are driven by the female respondents. In the female subsample, T1 makes choosing the gender equal bequest almost 13 percentage points more likely, a finding which is significant at the 1 percent level (see table 4 in the Online Appendix). All other treatment effects of T1 are positive and of economically important size, yet statistically not significant, possibly reflecting the smaller sample size. T2 has a statistically significant and positive effect on willingness to use gifting: among the female subsample, the likelihood to favor gifting increases by 10 percentage points (significant at the 5 percent level). This is driven by the female respondents who underestimate support as visible in table 6 of the Online Appendix: they are 11 percentage points more likely to favor gifting as a means of reaching a gender equal bequest allocation, again significant at the 5 percent level. I conclude that female respondents respond more strongly to the informational treatment and show stronger evidence of belief updating. A potential interpretation is that the perceived social norm could act as a constraint on the preferences of female respondents, but less so for male respondents.

Personal interest in inheritance matters is likely to play a decisive role. At baseline, a majority of respondents declared that they never think about inheritance issues. Excluding these respondents from analysis yields interesting results: table 5 in the Online Appendix illustrates that T1 is associated with a statistically significant and sizeable increase in support for legal reform in the subgroup of respondents who sometimes, often or very often think about inheritance. The other treatment effects are also positive but shy of statistical significance. Treatment 2 leads to a large increase in willingness to use gifting which is significant at the 10 percent level. Table 7 in the Online Appendix shows that this increase in willingness to use gifting is driven by respondents who hold correct beliefs, which is in line with the interpretation that their preferences are more malleable. Given the limited sample size, this finding should be taken with a grain of salt. Yet, it suggests that individuals who hold an interest in inheritance matters substitute away from gender equal inheritance towards gifting.

# **4.3.4 Second Endline**

More than 90 percent of respondents who were interviewed agreed to being recontacted and 45 percent answered the call, yielding a sample size of N=672. The Online Appendix includes 34
# **2. Model and Data**

We developed a multi-country, multi-sector, recursive dynamic, global computable general equilibrium model for the purpose of this study. The model is flexible enough to accommodate new regions/countries or sectors. Although the database represents 57 sectors and commodities, we have aggregated some sectors and disaggregated other to arrive at the 27 sectors and commodities as needed for this study. Similarly, the database includes 113 countries, but we have regrouped the countries into 25 countries/regions in the present version of the model<sup>2</sup> . The key features of the model are as follows<sup>3</sup> : (i) it has flexible production structures with nested constant elasticity of substitution (CES) representation thereby allowing different substitution possibilities at different tires; (ii) it fully disaggregates the energy sector identifying biofuels as separate sub-sector to incorporate feedback of oil price shock through the substitution possibility between petroleum products and biofuels; (iii) it explicitly represents all energy-intensive manufacturing sectors, such as iron & steel, pulp & paper; (iv) it has a fuller representation of land types and uses, including crop lands, forest lands, grass lands, etc., as this feature is also essential to capture the substitution possibility between biofuels and petroleum products; (v) it assumes a representative household maximizing its utility, using a non-homothetic Constant Difference of Elasticities (CDE) function, subject to the budget constraint; (vi) it represents both bilateral and international trade through Armington assumption; (vii) it differentiates capital by vintage allowing new capital to move perfectly across sectors thereby insuring a uniform rate of return; and (viii) it follows the macroeconomic balance where total investment equals to total savings including household savings, government savings and foreign borrowings.

The model uses the GTAP database. However, the database has been substantially updated for the purpose of this study. For the detailed discussion on data update, please refer to Timilsina et al. (2010).

> 2 Please see Figure 2 and Table 3 of this paper for the list of sectors/commodities and countries/regions represented in the model.

> 3 For details of the model, please refer to Timilsina et al. (2012) and Timilsina et al. (2011a, b).

4
# **1. Introduction**

Small and medium-size enterprises (SMEs) provide jobs and incomes to millions of individuals in the developing countries (see ILO 2019, ILO and GIZ 2013, Ayyagari et al. 2011, 2014). According to the World Bank’s Enterprise Surveys data on formal firms in 125 developing countries, SMEs (5-99 workers) account for about 47 percent of the total formal sector employment and 52 percent of the total output on average across countries. The rest is accounted for by large firms (>99 workers). Ayyagari et al. (2014) report that over 95 percent of the new formal sector jobs are created by SMEs in the developing world and only 5 percent by large firms. SMEs are also expected to promote inclusive growth (see OECD 2019). The contribution of SMEs is even higher if we include the self-employed, micro firms (fewer than 5 workers), and the unregistered or informal firms (see ILO 2019). Thus, it is not surprising that most countries have implemented policies to promote SMEs.

However, a lingering concern with SMEs is that they may be unduly affected by economic downturns and crises, such as the COVID-19 pandemic. That is, SMEs are less resilient to crises than large firms. This may happen for several reasons that are discussed in detail in Section 2. The relative underperformance of SMEs in periods of crises implies that SMEs act as destabilizers, magnifying the adverse impact of crises. Furthermore, while it may not be a surprise that SMEs were considerably affected by the COVID-19 pandemic, what is interesting is uncovering the potential contributors that favored large firms while debilitating SMEs. A rigorous comparison and quantification of SMEs’ and large firms’ performance during a crisis in the context of developing countries is lacking (see Section 2). This study attempts to fill this gap in the literature. It analyzes the difference in the decline in sales due to the COVID-19 pandemic between SMEs and large firms (henceforth, the “sales decline gap” or the “gap”) in 19 developing countries spread 2
their village.<sup>10</sup> Experience from qualitative fieldwork would suggest that this problem is a real one. In many cases, authority figures would fail to mention significant conflicts that were only later discovered after the number and type of informants was increased. Further, lack of disclosure may have a moral as well as rational basis. Conflict, in Indonesia as elsewhere, is often seen as a sign of failure, with harmony in village life particularly valued.<sup>11</sup> As such, it is not surprising that many village heads might be economical with the truth when describing unrest in their villages.<sup>12</sup> What are the implications for how we interpret PODES and to what extent does it limit the dataset’s utility in mapping conflict and assessing its causes? The first clear implication is that levels of conflict may in fact be significantly higher than the PODES data would suggest. This conclusion is backed up by other case study research – using qualitative and secondary data 10 Under the New Order, the village head was appointed by, and accountable to, the district government. While this fundamentally changed under the regional autonomy law (Law 22/1999), with village heads elected and accountable to the elected village council (BPD), in reality changes have been slow coming, with the new system only recently implemented in some districts, and still awaited in others (Antlov 2001).

11 Many anthropologists of Indonesian life stress the value local communities place upon harmony and social cohesion. See, for example, Tule (2000) who emphasises the importance in Flores of the ‘house’ as a provider of harmony: “for the Keo of Flores and other tribes in NTT [the house] is a cultural force which plays a centripetal role in uniting and incorporating its members into a social unit ... [a] system has been built up which supports the harmonious relationship among house members, both Christians and Muslims.” While the extent to which such harmony actually exists in Flores, or elsewhere in Indonesia, is a subject for hot debate, the degree to which the discourse frames discussion of social relations in many villages is incontestable.

12 Comparing the PODES data with the results of the qualitative research reveals that there is less of a difference between the two data sources on less sensitive issues. For example, the data on the characteristics of village leadership broadly matched that derived from the qualitative fieldwork. Aside from some discrepancies in the age and education of the village head (they tended to report themselves as younger and more educated than they actually were!) the two data sources generally verified each other.

20
|**Table 7: Summary of Results (results are percentage**<br>**No initial rent capture case**|**change fro**|**m initial eq**|**uilibrium, unles**<br>|**s otherwise**|**indicated)**|||Unilateral<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||||EU|||||Discrimina||**Unilateral**|
|**Scenario definition**|Benchmark|**EU FTA**|Discriminatory<br>Services|EU Tariffs|**Africa FTA**|**EU-Africa**<br>**FTA**|**Unilateral**|tory<br>Services|Unilateral<br>Tariffs|<br>**&**<br>**Domestic**|
|50%reduction of discriminatory barriers on EU services firms|No|**Yes**|Yes|No|**No**|**Yes**|**Yes**|Yes|No|**Yes**|
|50% reduction of discriminatory barriers on African services firms|No|**No**|No|No|**Yes**|**Yes**|**Yes**|Yes|No|**Yes**|
|50% reduction of discriminatory barriers on ROW services firms|No|**No**|No|No|**No**|**No**|**Yes**|Yes|No|**Yes**|
|50% reduction of regulatory barriers for all services firms|No|**No**|No|No|**No**|**No**|**No**|No|No|**Yes**|
|Removal of tariffs on EU sourced goods|No|**Yes**|No|Yes|**No**|**Yes**|**Yes**|No|Yes|**Yes**|
|Removal of tariffs on ROW sourcedgoods|No|**No**|No|No|**No**|**No**|**Yes**|No|Yes|**Yes**|
|**Aggregate welfare**|||||||||||
|<br>Welfare (EV as % of consumption)||**0.7**|0.5|0.2|**0.3**|**1.0**|**3.6**|1.5|2.0|**10.3**|
|Welfare (EV as % of GDP)||**0.6**|0.4|0.1|**0.2**|**0.8**|**3.0**|1.3|1.7|**8.6**|
|**Government budget**|||||||||||
|Tariff revenue (% of GDP)|3.6|**2.1**|2.9|2.1|**2.9**|**2.1**||2.9|||
|Tariff revenue||**-29.0**|-0.1|-28.9|**-0.1**|**-29.1**|**-100.0**|-0.3|-100.0|**-100.0**|
|**Aggregate trade**|||||||||||
|Real exchange rate||**0.9**|0.3|0.6|**0.2**|**1.2**|**4.0**|0.9|3.1|**5.8**|
|Aggregate exports||**3.2**|0.1|3.1|**0.3**|**3.5**|**12.6**|0.5|11.9|**15.4**|
|**Factor Earnings**|||||||||||
|Skilled labor||**2.2**|0.7|1.5|**0.5**|**2.7**|**9.0**|2.2|6.5|**15.3**|
|Semi-skilled labor||**1.1**|0.5|0.6|**0.3**|**1.4**|**5.6**|1.5|4.1|**10.3**|
|Unskilled labor||**1.5**|0.6|0.9|**0.3**|**1.9**|**7.4**|1.9|5.3|**14.3**|
|Capital||**1.5**|0.5|0.9|**0.3**|**1.8**|**7.0**|1.7|5.1|**12.4**|
|Land||**2.6**|0.4|2.2|**0.5**|**3.0**|**7.7**|1.4|6.1|**10.0**|
|**Factor adjustments**|||||||||||
|Skilled labor||**0.5**|0.3|0.3|**0.2**|**0.7**|**2.1**|0.9|1.3|**4.2**|
|Semi-skilled labor||**0.7**|0.2|0.7|**0.1**|**0.8**|**2.5**|0.6|1.9|**4.5**|
|Unskilled labor||**0.2**|0.1|0.1|**0.0**|**0.2**|**0.7**|0.2|0.5|**1.3**|
|Capital||**0.3**|0.1|0.3|**0.0**|**0.3**|**1.3**|0.3|1.2|**2.2**|
|Land<br>Source: Authors' estimates.||**1.0**|0.5|0.7|**0.4**|**1.4**|**3.7**|1.4|2.2|**7.2**|

51
# **4. Data**

The data for this study come from the 2005 Albania Living Standards Measurement Study (ALSMS05) survey conducted by the Albanian Institute of Statistics (INSTAT), with technical assistance from the World Bank, between April and November, 2005. The sampling frame for the survey was stratified into four regions – namely coastal, central, mountain and Tirana, the capital – and a total sample of 3,640 households from 455 census enumeration areas (EAs) was drawn based on a multi-stage cluster design.

The ALSMS05 includes both a community level survey and household questionnaire which covers general household demographics, education levels, asset ownership, expenditures and labor market participation. The central role of migration in Albanian society led to the inclusion of a set of unique survey modules on migration, which collected comprehensive migration histories for current and past household members. For children no longer living in the household or temporarily absent, parents were interviewed as proxies and asked to provide migration histories since 1990 on the timing of moves, destinations, and current location. Basic demographic and socio-economic data were also collected for these individuals through proxy.

In this paper, we use the detailed migration histories collected for all sons and daughters of the household head and household head’s spouse, whether currently living in the household or abroad.<sup>1</sup> The data from this module enable us to construct time varying measures of past migration of sons and daughters, i.e. our left-hand-side variable, which is a annual series of dichotomous variables indicating whether the individual had migrated for the first time in that particular year. While we focus on the timing of first-migration, supplementary analyses mentioned below also differentiate the outcome variable by destination (Greece and Italy versus other countries including other European destinations and North America) and by whether the migration was temporary or permanent.

> 1 All sons and daughters who are no longer living in the households but resided in Albania at the time of the survey are excluded.  These individuals may have migrated internationally in the past but no information on the first year of migration is available.

8
||Coef.|Coef.|Marg.impact|
|---|---|---|---|
|Early parenthood<br>Work<br>Female<br>Age<br>Hunger<br>Race (non-white)<br>Schooling mother (>=interm. complete)<br>Cons|1.053***<br>(.333)<br>-1.660***<br>(.160)<br>-.128<br>(.132)<br>.216***<br>(.043)<br>.338**<br>(.152)<br>-3.496***<br>(.668)|1.253***<br>(.277)<br>-1.624***<br>(.166)<br>-.109<br>(.132)<br>.206***<br>(.042)<br>.305**<br>(.150)<br>.078<br>(.126)<br>-.039<br>(.261)<br>-3.424***<br>(.668)|.463***<br>(.096)<br>-.399***<br>(.054)<br>-.033<br>(.039)<br>.065***<br>(.014)<br>.105**<br>(.054)<br>.025<br>(.041)<br>-0.012<br>(.080)|
|Log likelihood<br>Wald test on joint signif. coefs.<br>Number of obs.<br>Wald test of exogeneity|-405.56<br>2<br>)<br>5<br>(<br>χ<br>=245.76<br>399<br>2<br>)<br>2<br>(<br>χ<br>=278.20|-403.35<br>2<br>)<br>7<br>(<br>χ<br>=247.49<br>397<br>2<br>)<br>2<br>(<br>χ<br>=300.32||

Table 2 - Probability of being out of school, instrumental variable probit model.

Note: Early parenthood and work are modeled as endogenous variables. Instruments include the declared ideal age for first having sexual relationships (coded as one if that age is below or at 17 years old) and the reference wage (see the text for further details). Level of significance: *** 1 percent; ** 5 percent; * 10 percent; standard errors in parenthesis. The reference to compute the marginal impacts is a male, with the mean age, white, whose mother did not complete intermediate schooling, who has not suffered hunger and declared a reservation wage equal to the national minimum wage. Impacts change little once alternative reference points are considered. For the dummy variables, the impact of a discrete change from 0 to 1 is reported.

Source: Own calculations based on World Bank, Fortaleza Survey 2003.

As opposed to most of the previous literature, we find that, in this particular urban context, having worked at some point during the past year does not have a detrimental effect on school attendance. As suggested when looking at raw figures, dropping out of school leads most often to inactivity, and not to work. This could be a result of the acute lack of employment opportunities currently faced by inhabitants of these neighborhoods. It could as well be that working for some hours allows funding for schooling. In fact, the field work suggests that not having as little money as that required to take the daily bus to go to school can lead to school drop-out.

Age has, as expected, a significant impact on school abandonment, with older teenagers more likely to give up school. After controlling for the above factors and the endogeneity of parenthood and work status, differences in drop-out rates between females and males are no longer significant. Similarly, the likelihood of being out of school does not differ significantly across race groups. This evidence is consistent with the reports by Perlman (2004: 128) who, 13
out by HJM, as defined in equation (2a) in section 2 above. Using the initial size as the base (columns 1-4), the dummy for large establishments is negative and significant, indicating lower net employment growth compared to small establishment, all else equal. This holds even after age is introduced as a control.<sup>31</sup> These findings closely follow those by ADM, with largely comparable coefficients (note that here employment growth figures are scaled by 100). Likewise once establishment age is used as a control, it is associated with lower net employment growth. These findings also remain significant once the SME size threshold is raised to 250 employees (columns 3-4). However a divergence is also clear: when using the period-average size to categorize establishments (columns 6-7), the coefficient of large versus SMEs is notably smaller (though still significant). In fact, once the size threshold is raised to 250, in fact, there is no statistical difference between SMEs and large establishments. By contrast, the effect of an establishment’s age is negative and persistent across all specifications, indicating a strong effect that older establishments are less dynamics in terms of employment growth.

The second panel of Table 7 presents regression results that are weighted by each establishment’s employment share.<sup>32</sup> While columns 9-12 hold closely to the results in the first panel, when using the average size to classify establishments, once age is controlled for, the difference between SMEs and large establishments becomes insignificant (column 14). This aligns with the conclusions arrived at by HJM, that while establishment size is negatively associated with employment growth, this effect loses significance after controlling for age. The fact that size becomes less important when using the average size – even though all specifications control for age – underscores the importance of how establishment size is defined.

# [Insert table 7 here]

It is also worth noting that part of this divergence is definitional. Columns 1-8 present establishmentlevel results, and thus are indicative of the average experience at this level; they show, in other words, young and small establishments on net are the most dynamic. By weighting observations by their employment share, columns 9-16 provide results in broader strokes, that is, in terms of the aggregate effects on the whole private sector. This distinction is likely to be of particular relevance for informing policy makers, as the former results speak to firm-level interventions (such as training programs), while the latter lend themselves to more broadly administered policies (for example, lowering policy barriers to firm entry).

Tables 8 presents similar results for the sub-set of establishments that report positive employment growth, so-called gross job creation. Here there is no apparent distinction between using either initial

> 31 Table 7 includes categorical age variables; Appendix II (online) shows results are robust to using the continuous measure of age.

> 32 This is the weight as determined by the joint survey and employment weight.

22
The Colombian data confirms our presumption that when the fundamontal determinants of the Trade Surplus are included, the Real Exchange Rate does not add significantly to the explanatory power of the regression. This point is shown in the following regression were we have added the PT/PH version of the Real Exchange rate as an additional explanatory variable in (21).

# (22)

TS/GDP= -19.8 + 4.81 Ln(TofT) + 0.57(Fiscal S./GDP) +4.68 Ln(RER) (T-Value)(-0) (1.4) (2.7) (0.87) R2 Adj.:0.76 D.W.:2.19 1970-1986 The results show that the coefficient of the RER is not significantly different from zero and that the coefficients of the Fiscal Surplus and the Terms of Trade do not change their values significantly.

In conclusion, the Real Exchange Rate is as much an endogenous variable as the Trade Surplus and therefore one nould not use one to affect the other. The proper policy assignment to improve the Trade Surplus is to reduce absorption relative to income (i.e. through the fiscal deficit or other appropriate exogenous variable) and then allow for the required real devaluation that should accompany the improvement in the Trade Surplus.

49
# **7 Impact on firm performances**

We first examine the extent to which formalization resulted in any of the key purported benefits of formalization, and then turn to examining impacts on the main outcomes of profitability, sales growth, and employment.

# **7.1 Impact on intermediate outcomes**

Table 6 examines whether formalizing is leading firms to be more likely to access banks, improve accounting and other business practices, be less harassed for taxes, or access new customers. It does this through estimation of equations (3) and (4) using our two rounds of follow-up surveys. The top of the table presents the yearly intent-to-treat impacts of the different interventions, while the bottom of the table presents the impact of formalization for those who respond to treatment.

Despite the facilitation of access to bank accounts in treatments 2 and 3, and the creation by banks of a special account for _entreprenants_ , column 1 shows no significant impact of formalizing on whether the business has a bank account. 25 percent of the control group had accounts, suggesting that in practice the requirement to be formal was not always binding, and that those who signed up for the accounts through our intervention were substituting from accounts they would have opened anyway. Treatment group 2 is 5 percentage points more likely to have received a loan in the second year, but there are no other significant impacts on loan usage. As a result, the overall instrumented impact of formalizing on loan receipt is positive, but not statistically significant.

Columns 3 and 4 do show significant impacts of formalizing on the likelihood of attending business training in the past year (67 percentage points), with this impact coming from treatments 2 and 3 who were offered this service. Formalized firms are more likely to be doing any form of accounting (15 percentage points), but this did not translate into improved overall business practices.<sup>15</sup> One possible explanation is that there was some crowding out effects, and better accounting practices were offset by worst marketing and stock control practices.

Formalization also reduced significantly perceived tax harassment. This result is interesting as it is also valid for businesses in group 1 and 2. It means that it was not due mainly to the tax mediation performed by the CGA but instead that all newly formalized businesses faced less

> 15 Measured using the same 26 questions on business practices as in McKenzie and Woodruff (2015).

24