rafmacalaba/datause-encoder-data huggingface dataset:
of establishments in non-rural areas.<sup>8</sup> Enumerators conduct door-to-door visits and data collection is not restricted to registered (or formal) businesses. Establishments are included regardless of their size (even establishments without employees), as long as the economic activity takes place within delimited fixed building structures.<sup>9</sup> The census is administered every five years. We exploit the five waves between 1998 and 2018. We consider only establishments in manufacturing, wholesale and retail, and non-financial services.<sup>10</sup> In these three broad sectors, the number of establishments in the census increased from 2.5 million in 1998 to 4.3 million in 2018. In 2018, 14% of establishments were in manufacturing, 52% were in wholesale and retail, and the remaining 34% were in (other) non-financial services. Average establishment size in the census has hovered at about 4.5 employees (about 7.5 excluding establishments with no employees).<sup>11</sup> 96% of establishments employ 10 or fewer employees (a fraction that has not changed since 1998) and accounted for 44% of employment in 2018; less than 0.5% of establishments employ more than 100 workers but in 2018 accounted for 36% of employment (Table 2 below and Table B.3 in the appendix).

# **2.1 Formal and Informal Plants**

The 2018 questionnaire introduced two new questions where the respondent is asked whether the establishment is registered with the central tax authority SAT ( _Servicio de Administración Tributaria_ ) and with the social security administration IMSS ( _Instituto Mexicano del Seguro Social_ ). SAT collects corporate and personal income taxes (similar to the IRS in the US), value added taxes, and federal excise taxes. IMSS, on the other hand, collects social security contributions, which employers are required both to contribute to and to withhold on behalf of their employees.<sup>12</sup> The two questions on registration

> 8Rural areas have fewer than 2,500 residents but INEGI does include rural areas in the census when they are the capital of the municipality.

> 9For example, businesses located inside residential properties are included, but street vendors are

not.

10To be precise, our analysis excludes agriculture (11 in the NAICS classification system); mining (21); utilities (22); construction (23); finance (52); real estate (53); management services and administrative support (55-56); education (61); healthcare and social assistance (62); postal services (491); religious and civic organizations (813); private households (814); and employment services (5613).

> 11We measure employment by adding white collar and blue collar employees, unpaid workers, third-party employees (who don’t have an official employment relationship to the establishment), and contractors.

12IMSS also delivers some social services. The share of employers’ social security contributions amount to a tax of about 24% of profits (World Bank Group Doing Business 2020).

4
Policy Research Working Paper 9663

# **Abstract**

Climate change and weather shocks pose major challenges for household income security and well-being, especially for smallholder farmers’ communities. In such communities, imperfect risk insurance and labor markets may induce households to use traditional institutions such as polygyny to harness their size and composition to their resilience strategies against these shocks. This paper tests this hypothesis by analyzing how polygyny’s interaction with droughts affects crop yields. For identification, the paper relies on the spatial variation in polygyny’s prevalence across Mali’s rural communes and the randomness of drought episodes.

The findings show that polygynous communities are more resilient to drought-induced crop failure. Exploration of the mechanisms shows that polygynous communities diversify their income sources more than monogamous ones, including via child marriage—a phenomenon known to undermine women’s outcomes. As the literature links polygyny to underdevelopment, interventions to eliminate it should make formal resilience and adaptation strategies available to drought-prone communities. Failure to do so may entrench political opposition to enforcing a ban on polygyny and child marriage.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at mtiberti@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
**<u>Table 1. Who qualifies as a migrant?</u>**

||**UK born**<br>**UK nationality**|**UK born**<br>**Irish nationality**|**Irish born**<br>**UK nationality**|**Irish born**<br>**Irish nationality**|
|---|---|---|---|---|
|**Residin in UK**|**DOES NOT**<br>**QUALIFY AS**|**Not**foreign<br>population by<br>country of birth|Foreign<br>population in UK<br>by country of<br>birth|Foreign<br>population in UK<br>by country of<br>birth|
|**g**|**MIGRANT**|Foreign<br>population in UK<br>bynationality|**Not**foreign<br>population by<br>nationality|Foreign<br>population in UK<br>bynationality|
|**Residin in**|Foreign<br>population in<br>Ireland by<br>country of birth|Foreign<br>population in<br>Ireland by<br>country of birth|**Not**foreign<br>population by<br>country of birth|**DOES NOT**<br>**QUALIFY AS**|
|**g**<br>**Ireland**|Foreign<br>population in<br>Ireland by<br>nationality|**Not**foreign<br>population by<br>nationality|Foreign<br>population in<br>Ireland by<br>nationality|<br>**MIGRANT**|

One problem arising from these definitional issues is encountered when dealing with former colonies. For example, many Portuguese nationals are born abroad in one of Portugal’s former colonies. As outlined in table 2, the Portuguese census reports nearly 50,000 people born in Brazil, but only 32,000 with Brazilian nationality. Care should therefore be exercised since a large proportion of foreign-born migrants will have a domestic nationality.

**-** **<u>Table 2. Disparities between the foreign born and foreign migrants in Portugal</u>**

|**Former colony**|**Migrants from**<br>**colony defined as**<br>**foreign-born**|**Migrants from**<br>**colony defined as**<br>**foreign citizens**|**Migrants born in the**<br>**colony but with**<br>**Portuguese nationality**|
|---|---|---|---|
|Brazil|49,891|31,869|18,022|
|Mozambique|76,017|4,685|71,332|
|Angola|174,210|37,014|137,196|
|Cape Verde|44,964|33,145|11,819|
|Guinea-Bissau|21,435|15,824|5,611|
|Sao Tome and<br>Principe|12,490|8,517|3,973|
|Macau|2,882|71|2,811|
|Timor Leste|2,241|137|2,104|
|**Total**|**384,130**|**131,262**|**252,868**|

# **3. About the Data**

## **_Sources of Migrant Stock Data_**

Immigrant stocks<sup>7</sup> are usually recorded by demographic methods that measure the total population, and the most important of these are the population censuses. The census, a

> 7 This paper omits detailed discussion of immigrant flows. Readers are referred to OECD 2002a and b and 2003.

6
fashion. They find some positive, though heterogeneous impact of EPAs on firms’ export performance, particularly on export value, new destination markets and new products. However, one question that still demands further attention at the firm level is whether EPAs’ services impact the probability of non-exporting firms to become exporters. Although some studies are quoted as showing evidence that EPA activities are not effective in promoting new exporters, this is not clear for two reasons: a) they do not identify benefiting firms (e.g. Bernard and Jensen (2004)); b) the support they are analyzing is production-related subsidies that are not necessarily targeting export promotion (e.g. G ̈org et al. (2008); Girma et al. (2009a,b)).

Bernard and Jensen (2004) find no impact of export promotion on the probability of exporting for firms from the United States. However, they use the log of export promotion expenditures per plant for each state as covariate, which does not allow them to identify the groups of treated and untreated firms. G ̈org et al. (2008) find little evidence on Irish firms that grants encourage non-exporters to start exporting. Yet, they find that if grants are large enough they can be effective to make already exporting firms more competitive on the international market. Girma et al. (2009b) find a positive correlation between firms that export and those which receive subsidies in Germany, but they find no impact of subsidies on the probability of starting export and weak evidence that subsidies affect the share of exports in total sales. Girma et al. (2009b), Girma et al. (2009a) and G ̈org et al. (2008) focus on the impact of production-related subsidies, which are not necessarily a typical service provided by EPAs for improving the matching between domestic producers and foreign buyers.

This paper is closely related to Schminke and Van Biesebroeck (2013), who identify firms that receive support from EPAs in Belgium and find positive effect on firms’ propensity score to export. However, Belgium is a relatively small country in the European Union (an important common market), where exports account for approximately 80% of its GDP. Thus, there is a lack of evidence on the effectiveness of EPAs on promoting new exporting firms in a large middle income country. Furthermore, when compared to other countries with a similar level of GDP per capita, Brazil shows a relatively low number of exporting firms per capita (see figure 1). It also has one of the lowest average new exporters entry rate when compared to other countries available in the World Bank firm-level export dynamic database (see Canuto et al. (2013) for further discussion on this topic). Therefore, further understanding of policy impacts aimed to promote new exporters in this context is much needed.

5
estimate equations analogous to (1). We also estimate a regression analogous to equation (2) to check for a pre-reform correlation between the level of distrust and the change in one of our other four outcome variables: mean employment size. Column 3 of table 6 shows the coefficient δ for this regression. The coefficient indicates that level of distrust was not correlated with the change in mean employment in new firms over the pre-reform years 2003 and 2004. We cannot perform the pre-trend analysis for survival and growth rates since we only have two pre-reform years and would thus have to use post-reform years to calculate these rates.

**Table 6: Pre-Trend Analysis**

||Log number of new<br>firms registered|Dependent variable:<br>Log number of new<br>firms entered|Log mean employment<br>in new firms|
|---|---|---|---|
||(1)|(2)|(3)|
|yr2004* Distrust|0.0715|0.186|-0.881|
||(0.317)|(0.476)|(3.165)|
|Observations|24|24|24|
|R-squared|0.116|0.298|0.603|
|Number of regions|12|12|12|

Notes: All regressions include region fixed effects and a dummy variable for year 2004. The outcome variable in column 1 assigns new firms to the year they report as their registration year. The outcome variable in column 2 assigns new firms to the year when they first appear in the database. Mean employment in column 3 is based on year of entry, not year of registration (since it is only observed once a firm enters the dataset). Bootstrapped standard errors clustered at the region level in parentheses; *** p<0.01, ** p<0.05, * p<0.1 <u>Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey</u>

# **5. Results**

We find a positive effect of the reform on the number of new firms. This effect is statistically significant at the 1-percent level (table 7, columns 1 and 2). That is, we find that the post-2005 increase in new firm registration was greater in regions with high distrust in courts than in regions with low distrust in courts. The magnitude of the coefficient in column 1 implies that going from the lowest level of distrust (0.43) to the highest level (0.77), the reform increases the number of registered firms by 34% (exp(0.861*0.77)/exp(0.861*0.43)-1=0.34 ). Since the average number of firms registering in a region-year cell is 559, this increase corresponds to 190 additional firms per 12
et al. (2017) approach energy poverty through a vulnerability lens and develop six factors that relate to energy poverty and vulnerability (Figure 2). First, households might face poor access to energy. However, in the context of EU countries, access to energy is usually not the main problem. Second, they might face constraints based on the limited affordability of energy. This constraint may be particularly binding in the current inflationary context because of the Ukraine conflict. Third, they might be subject to poor flexibility, which is the inability to switch to energy sources that are more appropriate to their needs. Fourth, energy efficiency is a potential vulnerability factor because households might experience high energy losses due to poor energy efficiency at home. Fifth, some households might not be able to meet their energy demands from a cultural or socioeconomic perspective. Lastly, households might suffer from a lack of or limited policies addressing energy poverty. With respect to measurement possibilities, Thomson et al. (2017) detail the same categories presented in Figure 1.

_Figure 2: Energy poverty through a vulnerability lens_

|Access|Affordability|Flexibility|Efficiency|Needs|Practices|
|---|---|---|---|---|---|
|•Poor<br>availability|•Poor<br>affordability|•Inability to<br>switch|•High loss of<br>energy|•Mismatch<br>with needs|•Lack of<br>policies|

_Source: Compilation of findings by_ _<mark>Thomson et al. (2017)</mark>_

**In Table 1, we summarize different indicators measuring energy poverty gathered by Gouveia et al. (2022) and add several indicators identified from our review of the relevant literature.** The table reveals that energy poverty indicators in the EU mainly rely on three different types of data sources: EU-SILC (European Union Statistics on Income and Living Conditions), HBS (Household Budget Survey), and BSO (Building Stock Observatory) (Gouveia et al., 2022). We add several indicators from our literature review, which we outline in the next subsections.   Most indicators can be classified into three different measurement types (expenditure, consensual, and direct measurement), and some additional ones are more related to the building stock features and energy prices.

_Table 1: Different indicators measuring energy poverty_

|**Type of**<br>**Measure**|**Indicator**|**Definition**|**Source**|**Availability –**<br>**Bulgaria**|
|---|---|---|---|---|
||Ind<br>M/2 measure|icators from Gouveia et al.(202<br>Absolute (equivalized)<br>household energy<br>expenditure below half<br>the median|2)<br>HBS|Yes|
|Expenditure-<br>Based|2M measure|Share of household<br>energy expenditure<br>(compared to disposable<br>income/expenditure)<br>above twice the national<br>median|HBS|Yes, when<br>using HBS<br>income|

7
rather than the means from the surveys used to measure poverty, as appears to be standard practice in measuring relative poverty. Anchoring to the survey means also has the advantage that one can set separate relative poverty lines for urban and rural areas or sub-national regions.

If one sets the constant of proportionality in a strongly relative line based on the regression on survey means it would be 0.560 (s.e.=0.044).<sup>17</sup> Recall that the corresponding number using national accounts’ consumption was 0.382. The higher value using survey means reflects the fact that national accounts consumption includes things that are not typically included in survey-based measures. Under reporting/non response in surveys or over-estimation of consumption in national accounts, could also be playing a role.<sup>18</sup> Following the same procedure but using survey means we are led to use _k_ =1/2, which is consistent with past practice in setting strongly relative poverty lines. As before, we set the absolute line at $1.25 a day. The minimum cost of social inclusion must be in the interval (0, * $1.25). We set it half way, i.e., _Z_  $1.25 / 2 . Thus we use the following schedule of poverty lines for country _i_ at date _t_ (in $’s per day at the 2005 PPP for household consumption): _Z_ ( _M it_ )  max[$ 1.25 , ($ 1.25  _M it_ ) / 2]  $1.25 / 2  max[$ 1.25 / 2, _M it_ / 2] (3) These choices conform well with the empirical relationship between national poverty lines and survey mean consumption or income. Using the aforementioned constrained threshold estimator with surveys means instead of national accounts gave _Z_ ˆ * =$1.17 (s.e.=0.17) and ˆ _k_  0.472 (s.e.=0.07); n=70. Our chosen parameter values are not significantly different from these estimated coefficients.

# **3. Household survey data and poverty measures**

We have estimated all the poverty measures ourselves from the primary (unit record or tabulated) sample survey data rather than relying on pre-existing poverty measures. And all our previous estimates have been updated to ensure internal consistency. Households are ranked by either consumption or income per person. The distributions are weighted by household size and sample expansion factors. Thus our poverty counts give the number of people living in

> 17 Again, the intercept was suppressed to assure that the calculation was consistent with a strongly relative poverty line. A practical disadvantage of this method is that we lose 5 data points.

> 18 For further discussion of the discrepancies between these sources see Ravallion (2003) and Deaton (2005).

10
<!-- Start of picture text --> 100 40<br>35<br>80<br>30<br>25<br>60<br>20<br>15<br>40<br>10<br>20 5<br>0<br>0<br>Total Consumption as a % of GDP Investment Rate Public Sector<br>Private Consumption as a % of GDP<br>Private Sector<br>Govt. Consumption as a % of GDP<br>4 C: Share of exports and imports in GDP has 4 D: Consumption growth remains a key<br>increased as the economy has progressively contributor to growth, followed by investment<br>opened up<br>35<br>Contribution to GDP Growth<br>30<br>8<br>25<br>6<br>20 4<br>15 2<br>0<br>10<br>-2<br>5<br>-4<br>0<br>Consumption Investment<br>Exports as a % of GDP<br>Imports as a % of GDP Net Exports<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: Years refer to respective fiscal year. Investment rate is defined as gross fixed capital formation as a percentage of GDP. Net exports are the difference between exports and imports of goods and services.

# **2.4  Sources of Growth: Inputs and Productivity Growth**

To understand the underlying determinants of India’s growth trend, one may decompose GDP growth into input usage and total factor productivity (TFP). A common and simple growth accounting exercise decomposes GDP growth into use of labor and capital and total factor productivity using a Cobb-Douglas production function. Total factor productivity is estimated as the residual after accounting for labor and capital: 11
will go up by 52% and finally, for a 20% crop _loss,_ the costs of production in Guntur will more than double if the externality is taken into account.

# Catastrophic Crop Losses

It is amply clear that pesticide overuse in Guntur has made present ecological conditions much more favourable to an outbreak of Heliothis devastation. In other words, the probability of a catastrophic crop loss has increased. Once again there is no hard quantitative evidence regarding the extent of the increase, but by looking at some indirect evidence we put this probability at one in seven<sup>6</sup> . As regards which crop/s is/are most vulnerable to crop failure, evidence from India clearly shows that pulses are the most prone to Heliothis infestations (this may partly be due to the fact that their flowering stage occurs when cotton starts losing its vigour). Regarding the extent of catastrophic damage, it is clear from Indian evidence and evidence from elsewhere that a 50-60% damage is quite feasible. Thus, if it is assumed that there is a field failure on all (and only) pulses, in Guntur and in other

> 6 It is believed (Srivastava et al., 1991, Rao et al., 1990) that rainfall deficit years have a dominant impact on the build up of Heliothis populations but there is no quantitative evidence linking population build up and catastrophic field failures. If we consider the weather data, then from 1980 (when SPs were introduced) to 1989, there have been 7 rainfall deficit years and 2 catastrophic years giving a field failure probability due to high populations of 2/7. From 1960 to 1989 there have been 15 rainfall deficit years thus giving the probability of 1/2 for a deficit year. Hence the probability of catastrophic loss due to SP resistance may be taken as 2/7 x 1/2 = 1/7. Admittedly, this is preliminary and more research is required to get reliable numbers.

63
|Tax rates: Obstacle severity|0.252|0.240|0.224|
|---|---|---|---|
|(0-4 scale)||||
||(0.224)|(0.217)|(0.222)|
|Firm has own website Y:1|0.495|0.357|0.416|
|N:0||||
||(0.549)|(0.538)|(0.541)|
|Fertility rate||0.290|21.056|
|||(0.990)|(12.991)|
|Gender parity in workplace||0.069***|-0.280|
|laws||||
|||(0.019)|(0.363)|
|Gender parity in primary||24.517*|67.958|
|education enrollment||||
|||(13.185)|(306.924)|
|Freedom from business||0.240|0.362|
|regulations||||
|||(0.532)|(0.556)|
|Urbanization rate (%)||0.004|0.010|
|||(0.032)|(0.033)|
|Women in Parliament (%)||0.088|0.107|
|||(0.061)|(0.065)|
|Paternity leave (logs)||0.251|0.669|
|||(0.459)|(0.465)|
|Square of Maternity leave|||-0.956|
|(logs)||||
||||(0.718)|
|Square of Females: 20-29 yrs.|||1.334**|
|old (%)||||
||||(0.548)|
|Maternity leave (logs)*GDP|||-7.049**|
|per capita (logs)||||

62