rafmacalaba/datause-encoder-data huggingface dataset:
rest of the literature, e.g. Rajan and Zingales (1998) and Kroszner et al. (2007), we use as instrument for the interaction term the interactions of legal origin dummy variables with the ED variable and the GFC0810 dummy variable.<sup>29</sup> Given our use of a nonlinear model, we implement a control function approach instead of the standard two-stage least squares estimator. This consists of including the residuals of the first-stage regression, estimated by OLS, in our econometric model.<sup>30</sup> The positive sign of their coefficient suggests that the effect of the global financial crisis may have been underestimated. However, the lack of statistical significance of this coefficient indicates that we cannot reject the null hypothesis that the triple interaction term is exogenous.<sup>31</sup> In columns (4) and (5), we take into account that sectors not only vary in on external finance but also in firms’ ability to access external finance. Raising outside finance is likely to be easier for firms in sectors characterized by a high level of tangible assets. In column (4), we interact our measure of asset tangibility, the TANG variable, with the private credit to GDP ratio and the GFC0810 variable. The coefficient on the interaction term is large and statistically significant and has an expected positive sign, given that TANG is an inverse proxy of financial vulnerability.<sup>32</sup> It indicates that the ratio of relative outward FDI in financially vulnerable sectors during the global financial crisis to relative outward FDI in financially vulnerable sectors during non-crisis years was about 59% lower in a highly financially developed country like the United States than in a less financially developed country such as Italy.<sup>33</sup> In column (5), we include both the ED × CREDIT/GDP

> 29Data on legal origin dummy variables come from La Porta et al. (1999).

> 30If the instruments are valid, the first-stage residuals control for the potential endogeneity of the suspect variable, parameters are consistently estimated (as long some assumptions are satisfied), and the statistical significance of their coefficient provides the basis for a robust (to assumptions) endogeneity test (Wooldridge, 2010).

> 31The first-stage (cluster-robust) F -statistic indicates that the instruments are relevant, with a value slightly above the threshold identified as Staiger and Stock (1997) to qualify an instrument as strong (F ≈ 10). However, doubts have been raised about the validity of using legal origin dummy variables as exogenous instruments for financial development (Manova, 2013). Hence, results of column (3) should be interpreted as simply showing that our findings are robust to a common instrumental variables strategy in the Finance and Growth literature. 32We follow other papers (Braun, 2003; Manova et al., 2011; Manova, 2013) by not inverting this measure such that a higher value corresponds to a lower tangibility of assets. Hence, a higher TANG value indicates _lower_ financial vulnerability.

> 33

16
56 in 2010, two in 2011, and one in 2012, with no adjustments made for a year between February 2012 and February 2013. Meanwhile, in addition to rising world prices, the local currency began to depreciate markedly in 2011. At the beginning of 2008, the exchange rate was 0.97 cedis to the dollar. The cedi depreciated to 1.5 to the dollar in 2011, 2.0 in October 2013, 3.0 in July 2014, and 4.3 in June 2015, before recovering some.

In November 2014, Ghana introduced a new tax. Called a special petroleum tax, it is 17.5 percent of the ex‐depot price for gasoline, diesel, kerosene, and LPG. Premix (gasoline with 82 RON for exclusive use by fishing boats) and residual fuel oil are exempt from the tax. Because world petroleum product prices were falling at the time, the adoption of this large tax increased retail prices by only 3 percent, with the exception of LPG for which the price fell by 9.5 percent. Within six weeks, fuel prices were decreased by 10 percent or more on January 1, 2015.

The failure of the National Petroleum Authority to keep up with rises in world oil prices until recently can be seen in Figure 13, which plots price ceilings for diesel since 2009. Retail prices in Ghana needed to be substantially higher than the FOB price to account for transportation costs, various charges, taxes, and profit margins. The budget tables available on the government website (http://www.mofep.gov.gh) show that subsidies for petroleum products amounted to an estimated 623 million cedis (US$336 million) in 2012, 120 million cedis (US$58 million) in 2013, and 328 million cedis (US$108 million) in 2014, with 50 million cedis set aside for fuel subsidies in 2015. The actual spending was 474 million cedis (US$138 million) in 2014 but zero during the first six months of 2015 (Ghana 2015). In addition, there is a levy on all fuels except kerosene and premix—ranging from 0.03 cedis (US$0.01) a liter for local marine diesel to 0.08 cedis (US$0.02) per liter for gasoline and diesel—to help pay off the debt of the sole, state‐owned refinery in the country.

**Figure 13: Comparison of retail diesel prices in Ghana with Western European FOB diesel prices**

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40 Ghana retail maximum<br>0.20 Western Europe FOB<br>0.00<br>US$/liter<br><!-- End of picture text -->

Jan 09 Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Jul 14 Jan 15 Jul 15 _Sources_ : <u>http://www.npa.gov.gh for Ghanaian prices, industry sources for Western Europe.</u> Infrequent price adjustments have sometimes been followed by sharp price increases. In June 2009, prices were increased by 30 percent across the board for all fuels. In January 2011, prices were again increased by 25 or 30 percent, with the exception of kerosene and premix, the prices of which were not changed. In February 2013, the price of premix was not changed but the prices of all other fuels were raised, ranging from 15 percent for kerosene to 20 percent for gasoline and diesel and 50 percent for
information set in her locality. We first note a strong negative relationship between the earnings ratio and the log of the migrant’s annual income in columns 1 and 2, which is in line with prediction 1 of our model. The results also hold when residuals from the previous Mincer regression predicting log annual earnings are used instead (see appendix table A.2).

<u>Table 2: Determinants of the Reported Earnings Ratio</u>

||<br>(1)|(2)|
|---|---|---|
|Migrant's annual earnings (log)|-0.290***<br>(0.06)|-0.330***<br>(0.06)|
|Ratio of wife to husband's account of annual remittance|-|0.003<br>(0.04)|
|Median monthly income of Gulf migrants (log)|-|0.176*<br>(0.10)|
|Wife's years of education|-|0.0184**<br>(0.01)|
|Wife is employed (Indicator)|-|0.134*<br>(0.07)|
|Wife is unaware of husband's overseas expenses (indicator)|-|-0.093<br>(0.06)|
|Disagreement over remittance use (Indicator)|-|-0.111***<br>(0.04)|
|Migrant spends on temptation goods (indicator)|-|-0.143***<br>(0.04)|
|Constant|-|1.891*<br>(1.07)|
|R-squared|0.14|0.35|
|Observations|108|108|

Notes: The dependent variable is the ratio of the wife’s report of the migrant’s monthly **e** arnings to that of the migrant. Bootstrapped standard errors (with 500 replications) are displayed in parent **h** esis, clustered at the district level. *** denotes statistical significance at the 1% level, ** at the 5% level and * at the 10% l **e** vel. _Source:_ Authors’ analysis based on data from QSKF.

Referring to column 2, the positive coefficient on the reported remittance gap is small and statistically not different from zero ( _p_ = 0.95 ). We also performed a similar exercise using the discrepancy in reported hours worked per week by the migrant, which is unobserved by the 22
# **Informal workers across Europe: Evidence from 30 European countries**<sup></sup>

## **_Mihails Hazans_**<sup>**_*_**</sup>

_University of Latvia and IZA_

## **JEL classification** : **_J08, J61, J71, O17, O52_**

**Keywords:** _informal employment, discrimination, human capital, minorities, immigrants,  tax morale._

>  This paper—a product of the Human Development Economics Unit, Europe and Central Asia Region—is part of an effort to understand the underlying factors that determine the size of informal employment in the shadow economy, providing background technical analysis for a forthcoming World Bank regional study ―In from the Shadow: Integrating Europe's Informal Labor‖. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The paper has greatly benefited from discussions with and suggestions by Truman Packard. I thank Alexander Tarvid for excellent research assistance. I thank Hartmut Lehmann, Jan Rutkowski, Indhira Santos and participants of IZA/World Bank Workshop _Institutions and Informal Employment in Emerging and Transition Economies_ (Bonn, June 2011) for useful comments and suggestions. The usual disclaimer applies.

> * mihazan@lanet.lv .
Prior to turning to the structural model, we first estimate a reduced form gravity regression linking cross-product variation in home bias to the characteristics of<sup>_−→_</sup> _Z_<sup>_k_</sup> . Our data/estimation strategy is somewhat unusual. Before moving to estimation we describe some of the challenges this particular setting imposes, and the way in which we manage them.

It is now conventional to estimate gravity regressions with vectors of fixed effects that sweep out important variation in the data. In a cross-sectional regression with multiple origins and destinations, origin-product fixed effects control for systematic variation in the supply of a product, while destination-product fixed effects control for variation in expenditure levels and geographic remoteness of the destination. In a time series context origin-product-year and destination-product-year fixed effects sweep out heterogeneous supplies and demands, and shocks to either.

We use data from a single destination, PR.<sup>29</sup> Including destination-product fixed effects in the regression would sweep out useful cross-product variation in the data. Instead we parameterize import demand. Since our identification strategy relies on interactions between product characteristics and _HOMEj_ , the key threat to identification is if cross-product variation in the level of import demand is correlated with the product characteristics of interest, the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s. We address this problem in a manner that is conventional in applied econometrics; we include the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s themselves in the regression, along with their interactions. In this way we control for cross-product variation in the level of demand that might bias the coefficients on the interactions. The way in which the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s affect total import demand becomes important when estimate in the sample of upstream products.

29In principle we could have included other destinations in the sample (especially other countries in the Caribbean), but since the U.S.-PR flow would be the only domestic U.S. flow this strategy would lead the _HOMEj_ coefficient to compare U.S.-PR flows to all U.S. flows to the Caribbean, rather than to ROW-PR flows alone. If the demand structure of PR were typical of the Caribbean, this might be preferable, but the effects of U.S. sovereignty are likely to have made the structure of PR’s import demand different than those of other Caribbean states.

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**Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments**

Hai-Anh Dang, Dean Jolliffe, and Calogero Carletto<sup>*</sup> Key words: poverty, mobility, imputation, consumption, wealth index, synthetic panels, household survey JEL: C15, I32, O15

* Dang (hdang@worldbank.org; corresponding author) is an economist in the Survey Unit, Development Data Group, World Bank, and a non-resident senior research fellow with Vietnam’s Academy of Social Sciences; Jolliffe (djolliffe@worldbank.org) is a lead economist in the Survey Unit, Development Data Group; Carletto (gcarletto@worldbank.org) is the manager of Center for Development Data, Rome, Italy. All authors are with the World Bank. We would like to thank Alemayehu Ambel, Andrea Brandolini, Grant Cameron, Shatakshee Dhongde, Sam Freije-Rodriguez, John Gibson, Peter Lanjouw, Haizheng Li, Michael Lokshin, David Newhouse, Franco Peracchi, Kinnon Scott, Jacques Silber, Alessandro Tarozzi, Renos Vakis, and participants at the China meeting of the Econometric Society (Wuhan) and seminars at Georgia Tech and World Bank for helpful discussions on earlier versions. We are grateful to the UK Department of International Development for funding assistance through its Knowledge for Change (KCP) Research Program.
by students near the minimum proficiency threshold and those driven by students at the very bottom of the learning distribution. Students who are further away from the minimum proficiency threshold likely have different learning needs than those closer to the threshold.

**Learning Poverty Severity** indicates inequality of learning among those below the threshold. It brings together the concepts of Learning Deprivation Severity and Schooling Deprivation Severity. Learning Deprivation Severity measures learning inequality among learning deprived children in school. Compared to the gap measure, the severity measure is more sensitive to changes in learning levels of learning deprived children who are further away from the minimum proficiency threshold and changes in children who are out of school.

For example, as shown in Figure 3, Vietnam and Myanmar are expected to have similar increases in the Learning Poverty Gap under an intermediate scenario, but the two countries have very different expected changes in Learning Poverty Severity. This implies that both countries may need different levels of policy focus on identifying the diverse learning needs among children below minimum proficiency and on providing flexible and tailored learning opportunities. As school systems reopen, it will be critical to meet students at their point of need and to monitor changes in the learning distribution among the learning poor; for that, Learning Poverty Severity is the appropriate measure.

**Figure 3. Relative Changes in Learning Poverty Gap and Learning Poverty Severity**

<!-- Start of picture text --> 400%<br>350%<br>300%<br>250%<br>HIC<br>200%<br>UMC<br>150% LMC<br>LIC<br>100%<br>VNM<br>MMR<br>50%<br>0%<br>0% 100% 200% 300% 400% 500% 600% 700%<br>Change in Learning Poverty Severity (%)<br>Change in Learning Poverty Gap (%)<br><!-- End of picture text --> _Note: Figure shows relative percent change from the baseline under the intermediate scenario._ Based on the simulation results, Learning Poverty Severity will rise by 1.7 percentage points under the intermediate scenario for low- and middle-income countries (Table D3). The increase in Learning Poverty Severity is highest in Europe and Central Asia, followed by East Asia and the Pacific. Tackling Learning Poverty Severity will require a focus on differentiated and targeted learning interventions for children at the bottom based on the learning level of the child.

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businesses surveyed during the follow-up survey. Impact rates in groups 2 and 3 are always significantly higher than in group 1, and impact rates in group 3 slightly higher than in group 2 (but not significantly).

Table 7 shows heterogeneous impact by business characteristics. We find that male business owners formalized much more than female business owner. Slightly above 10 percent of businesses owned by women formalized in groups 2 and 3, compared to around 20 percent for those owned by men. This result could be correlated with the fact that a large majority of businesses operating in Dantokpa market are owned by women. However, column 7 of the table shows that it is also true outside Dantokpa Market for women not operating in trade.

In all groups, formalization rates were 7-9 percentage points higher outside Dantokpa market than inside the market. One explanation to this result is that formalization could be less attractive in the market as businesses are already registered with the public company in charge of all markets in Cotonou (SOGEMA- “ _Société de Gestion des Marchés Autonomes_ ”). They are also more interconnected as they usually have representatives they can address in case of problems with the administration.

Businesses operating in the trade sector had lower formalization rates than in other sectors. This result is probably correlated with the fact that almost all businesses in Dantokpa market are traders, but it is also true outside the market.<sup>15</sup> One possible explanation which was mentioned during qualitative interviews is that before the program implementation, traders already had access to a “trader card” that provides a formal status with specific benefits (see Table 1), whereas no such specific card existed for other sectors.

The program was more effective on businesses with an owner who went at least to secondary school. This is not surprising if we consider that business owners with more education may benefit more from trainings and counselling services. Formalization also requires doing some basic accounting, which could be more difficult with a low level of education.

Formalization rates were higher among informal businesses that were similar to formal businesses before program implementation. Using species classification techniques (de Mel et

> 15 Results not shown but available upon request

21
The analysis on the patterns of economic mobility has already shown that during both periods when economic growth was positive and labor markets generated jobs and higher earnings, a significant number of households managed to escape poverty. This hinges towards the role of education and labor markets as drivers for poverty reduction – not only in facilitating transitions out of poverty but also in building resilience towards idiosyncratic and aggregate shocks. Among households with higher educational attainment, chronic poverty is less frequent and the share of households staying out of poverty is also higher (Figure ). In addition, higher education seems to protect against churning as the fraction of households falling back into poverty decreases monotonically in the level of education. This pattern does not differ for the two periods and suggests that independent of the nature of growth, education plays an important role in facilitating a sustainable path out of poverty.

Figure 8: The link between economic mobility and educational attainment and labor force status of the household head <!-- Start of picture text --> Panel A: 2010 to 2015: Education Panel B: 2010 to 2015: Labor Force Status<br><!-- End of picture text --> <!-- Start of picture text --> 80% 60%<br>70% 50%<br>60% 40%<br>50%<br>30%<br>40%<br>20%<br>30%<br>10%<br>20%<br>0%<br>10%<br>0%<br>basic and high college university<br>general school and<br>vocational<br>poor-poor poor-nonpoor poor-poor poor-nonpoor<br>nonpoor - poor nonpoor - nonpoor nonpoor - poor nonpoor - nonpoor<br><!-- End of picture text --> _Source: Author’s calculations based on ILCS 2004, 2008, 2010 and 2015._ Employment status is also linked to transitions in and out of poverty (Figure 8). The share of households that are chronically poor is lower among households in which the head is employed than for households where the household head is out of the labor force (10.4 versus 17.9 percent). Moreover, the probability of belonging to the group of households which lived above the national poverty line in both periods was highest among households where the household head is employed. The sector of employment (Figure 9) also plays an important role in determining dynamic patterns of poverty. Between 2010 and 2015, the share of chronic poor was highest among households working in the agricultural sector (14.7 percent), and significantly lower for 14
We include these targeting category dummy variables as instrumental variables for household receipt of aid, omitting the correct inclusion, poor district dummy under shocks and the correct exclusion, non-poor district under no shocks.

**Table 3:  Targeting Categories and Errors in 2016**

||**IH**<br>|**PS**|**FI**<br>|**AS**|
|---|---|---|---|---|
||# Obs|Percent|# Obs|Percent|
|**Shock Occurred**|1003||569||
|Moderate+ distribution, poor district|351|35%|293|51%|
|Limited distribution, non-poor district|232|23%|71|12%|
|Moderate+ distribution, non-poor district|229|23%|149|26%|
|Limited distribution, poor district|191|19%|56|10%|
|**Shock Did Not Occur**|1504||383||
|No distribution, non-poor district|552|37%|16|4%|
|No distribution, poor district|262|17%|52|14%|
|Positive distribution, poor district|456|30%|244|64%|
|Positive distribution, non-poor district|234|16%|71|19%|

# **4.2. Instrumental Variables, Zero Stage Results**

Table 4 presents select coefficient results from the zero stage BRGLMs for the IHPS sample and using the current period shock specification.<sup>20</sup> The second column gives results for the probability that a household receives aid (Aid), while the third column gives results for the probability of receiving aid when a shock occurred (Aid*Shock). We first note that experiencing a weather shock increases the probability of receiving aid and receiving aid under a weather shock. Aid distributions also have a significant impact on the probability that a household received aid but have no impact on aid receipt under a weather shock (column 2). The interaction term of aid distributions and current shock also has no significant impact on the probability that a household receives aid under shocks (column 3). Thus, while a shock occurring at the household-level does increase the probability of household aid receipt, aid distribution at the TA-level

> 20 Results are similar across the different samples and shock specifications; full results are given in Online Appendix 4.

25
# **Table 24: DID Regression (10)**

||No. of stud<br>Elementar|ents<br>y|Secondary||University||No. of sick|HH members|
|---|---|---|---|---|---|---|---|---|
|D|-0.007|(0.132)|-0.194|(0.092) **|-0.004|(0.032)|0.085|(0.140)|
|T|-0.161|(0.140)|-0.009|(0.117)|-0.009|(0.043)|-0.808|(0.143) ***|
|D*T|0.143|(0.175)|0.237|(0.137) *|0.001|(0.049)|0.076|(0.178)|
|lnHH size|1.151|(0.118) ***|0.455|(0.087) ***|0.119|(0.040) ***|0.547|(0.116) ***|
|Adult share|-4.475|(0.257) ***|-0.471|(0.181) ***|0.193|(0.096) **|0.274|(0.252)|
|HH head sex: Male|-0.294|(0.140) **|-0.143|(0.086) *|0.103|(0.050) **|0.107|(0.140)|
|HH head education: Elementary|-0.127|(0.094)|-0.095|(0.072)|0.031|(0.021)|-0.200|(0.104) **|
|HH head education: Secondary|-0.857|(0.188) ***|0.271|(0.169) *|0.076|(0.062)|-0.352|(0.175) **|
|HH head education: University|-0.523|(0.340)|-0.206|(0.248)|0.617|(0.316) *|-1.006|(0.214) ***|
|Appliance ownership: Refrigerator|0.033|(0.139)|0.155|(0.102)|0.023|(0.030)|-0.227|(0.149)|
|Appliance ownership: Color TV|0.253|(0.126) **|0.162|(0.099) *|0.006|(0.043)|-0.100|(0.138)|
|Appliance ownership: Radio|0.043|(0.091)|-0.030|(0.067)|0.035|(0.023)|0.035|(0.093)|
|Appliance ownership: Gas stove|-0.079|(0.124)|0.144|(0.073) **|0.001|(0.017)|0.201|(0.127)|
|Appliance ownership: Washing|||||||||
|machine|-0.023|(0.122)|0.102|(0.107)|0.059|(0.048)|0.171|(0.127)|
|Gas use for cooking|0.036|(0.114)|0.096|(0.092)|0.080|(0.039) **|0.026|(0.114)|
|Power use for lighting|0.014|(0.138)|-0.114|(0.098)|-0.004|(0.035)|0.416|(0.137) ***|
|Tap water for cooking|0.221|(0.093) **|-0.104|(0.070)|-0.011|(0.026)|-0.084|(0.097)|
|Constant|-0.416|(0.401)|-4.251|(0.267)***|-5.016|(0.144)***|-4.181|(0.381)***|
|Obs.|2,064||2,064||2,064||2,064||
|R-squared|0.445||0.082||0.038||0.065||
|F-stat|165.58||10.48||1.44||9.84||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

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