rafmacalaba/datause-encoder-data huggingface dataset:
Comparing the magnitude of results on additional market work detected and switching suggests a sizeable portion of those women who are detected in market work only in the enterprise questions were in fact classified as subsistence workers in the 3-month individual question approach (consistent with H2). We test this question (H2) directly in Table 3, which is restricted to women not in 3-month market employment. We include a covariate for engaging in subsistence work in the 3-month period to test H2. While the baseline probability of being reclassified is only 0.9 (Tunisia) to 1.0 (Egypt) percentage points, there are large differences for women who were classified as subsistence workers. In Tunisia, the probability of being reclassified into market work in the enterprise approach is 13.9 percentage points higher for those who were engaged in subsistence work and in Egypt the change is 8.7 percentage points. Differences are all statistically significant (confirming H2). The magnitude of differences also underscores how market work is often misclassified as subsistence work only; the enterprisebased approach, asking about sales in the past 12 months, may be particularly important for improving detection along this margin.

# **Table 3. Marginal effects from logit models of changes in market employment status adding enterprise questions for women aged 15-64, not in 3-month market employment**

||**Tunisia -**<br>**Market**|**Egypt,**<br>**Arab Rep.**<br>**- Market**|
|---|---|---|
|**Subsistence work (no work omit.)**|||
|Subsistence work|0.139***|0.087***|
||(0.020)|(0.007)|
|**Baseline probability**|0.009***|0.010***|
||(0.001)|(0.001)|
|**N (Obs.)**|4131|14981|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Subsistence” is a covariate for having been classified as in subsistence work only in the 3-month period.

We now focus on which individual characteristics predict under-detection; which women are we missing in traditional approaches to employment detection? We present marginal effects for the outcomes of being reclassified into market employment and extended work going from the 3- month to enterprise-added definitions in Table 4. These analyses test H3. In the appendix, Table 5 presents employment rates for women by individual characteristics and Table 6 presents these same outcomes descriptively by individual characteristics, as well as separately for urban and rural areas.

The reference, baseline probability for an urban, 15–19-year-old, single, higher educated woman is quite low (less than 0.1% to 0.3%). However, the marginal effect for rural is large and significant, ranging from 1.8 percentage points to 3.3 percentage points increase across countries and market employment/extended work. Compared to the reference 15–19-year-old, older age 19
# **A.1.4. Government and Taxes**

In the model, the government has two major roles. First, it sets plan prices and output quotas and allocates investment funds. Second, it imposes taxes, purchases commodities, and redistributes resources. Public revenue comes from direct taxes on capital, labor, value-added taxes, indirect taxes on output, tariffs on imports, the externality tax, and other non-tax receipts: where _Dj_ is the depreciation allowance and _X i_ and _M i_ are the exports and imports of good _i_ .

Externality taxes, such as those on SO2 or CO2, may be on the value of output, or on the quantities. We allow for both, the total revenue from the externality tax on output is: In one application of the model described in Ho and Nielsen (2007, Chapter 10), the externality tax rate is set proportional to the marginal air pollution damages ( _MD_<sup>_O_</sup> ) from output _j_ : (A63) _t xjt_  _MDOjt_  1 When we consider a tax on fossil fuels based on the carbon content, the externality tax per unit of fuel _j_ is: where<sup>_c_</sup> _j_<sup>is the carbon content per unit of fuel of type</sup><sup>_j_.</sup> The nontax payments to the government are set as a fixed share of household income:

- (A65) _FEEt_  _NHHYt p_

Total government expenditure is the sum of commodity purchases and other payments: Government purchases of specific commodities are allocated as shares of the total value of government expenditures, _VGG_ . For good _i_ : ‐58‐
|**Table 4: OLS and FE Regres**|**sions with VA**<br>|**T Revenue**<br>|**as share**<br>Dep<br>VA<br>|**of GDP as**<br>endent vari<br>T Revenue/<br>|**dependen**<br>able<br>GDP<br>|**t variable**<br>|<br>|
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|
|Independent variable|<br>**OLS**|<br>**FE**|<br>**FE**|<br>**FE**<sup>**1/**</sup>|<br>**FE**<sup>**1/**</sup>|<br>**FE**<sup>**1/**</sup>|<br>**FE**<sup>**1/**</sup>|
|L[VAT Revenue/GDP]|0.90***|0.61***|0.44***|0.43***|0.42***|0.40***|0.37***|
||(0.02)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|
|Net Exports/GDP|-0.02***|-0.03***|-0.05***|-0.05***|-0.05***|-0.06***|-0.05***|
||(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|GDP Growth|||-0.00|-0.02*|-0.01|-0.01|-0.01|
||||(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|Terms of trade, index|||0.01**|0.01**|0.01**|0.11**|0.01*|
||||(0.00)|(0.01)|(0.01)|(0.00)|(0.00)|
|Output gap/GDP|||||-3.04*|-6.60***|-8.35***|
||||||(1.75)|(1.87)|(1.96)|
|(Net Exports/GDP)*(Output Gap/GDP)||||||-0.41**|-0.42***|
|||||||(0.10)|(0.09)|
|Log of GDP per capita|||||||1.16***<br>(0.44)|
|Constant Term|0.83***|2.94***|2.90***|2.91***|2.82***|3.02***|-5.38|
||(0.17)|(0.27)|(0.64)|(0.67)|(0.67)|(0.64)|(3.26)|
|R-sq|0.90|||||||
|Adj. R-Sq|0.90|||||||
|R-sq: within||0.67|0.59|0.64|0.65|0.68|0.69|
|# of observations<br>_Notes:_|279|279|221|221|221|221|221|

This table reports beta coefficients and standard errors from OLS and FE regressions. All regressions exclude oil exporters.

L is the lag operator and L (Xt) = Xt-1.

***, ** and * denote significance at 1%, 5% and 10% levels respectively.

1/ Country and time fixed effects.

2/ Regression exclude EU11 countries.

3/ Regression include EU11 countries only.

17
||**Ta**|**ble A13: Inclu**|**ding all interac**|**tion terms simu**|**ltaneously**||||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell|0.768|0.887*|1.037*|1.160**|1.073**|0.685*|0.656*|0.629*|
|average)*Herfindahl-Hirschman<br>Index|(0.491)|(0.493)|(0.539)|(0.501)|(0.513)|(0.360)|(0.367)|(0.374)|
|Exports (%, cell average)*Courts|-0.076*|-0.089**|-0.074*|-0.070*|-0.096**|-0.075*|-0.077*|-0.077*|
|<br>Obstacle (cell average)|(0.045)|(0.044)|(0.044)|(0.042)|(0.044)|(0.045)|(0.045)|(0.045)|
|Exports (%, cell average)*Women|0.163*|0.156*|0.146|0.149|0.136|0.186**|0.191**|0.192**|
|<br>can work the same night hours as<br>men Y:1 N:0|(0.098)|(0.093)|(0.093)|(0.091)|(0.090)|(0.090)|(0.092)|(0.092)|
|Exports (%, cell average)*Women|0.115|0.157|0.146|0.157|0.166*|0.200**|0.194*|0.201**|
|<br>can work in the same industries as|(0.101)|(0.098)|(0.098)|(0.097)|(0.100)|(0.099)|(0.100)|(0.096)|
|men Y:1 N:0|||||||||
|Exports (%, cell average)|-0.548|-0.313|-0.425|-0.603|-0.707|-0.760|-0.735|-0.755|
||(0.625)|(0.614)|(0.631)|(0.525)|(0.532)|(0.496)|(0.491)|(0.499)|
|Exports (%, cell average)*GDP|0.059|0.034|0.044|0.061|0.078|0.075|0.072|0.079|
|per capita (logs)|(0.069)|(0.068)|(0.070)|(0.058)|(0.059)|(0.055)|(0.055)|(0.071)|
|Herfindahl-Hirschman Index|-9.772**|-11.543***|-12.715***|-13.130***|-15.763***|-10.768***|7.869|8.584|
||(4.495)|(4.053)|(4.110)|(3.871)|(4.275)|(3.803)|(7.985)|(7.882)|
|Courts Obstacle (cell average)|0.000|0.151|-0.049|0.456|0.589|0.120|0.043|-0.040|
||(0.932)|(0.903)|(0.843)|(0.794)|(0.833)|(0.827)|(1.426)|(1.421)|
|Women can work the same night|<br>-8.005***|<br>-7.419***|<br>-7.169***|<br>-5.233*|<br>-5.004*|<br>2.012|<br>0.334|<br>-0.669|
|<br>hours as men Y:1 N:0|||||||||
||(2.570)|(2.591)|(2.572)|(2.726)|(2.785)|(3.065)|(3.966)|(3.982)|
|Women can work in the same<br>industries as men Y:1 N:0|<br>-3.586|<br>-3.368|<br>-3.268|<br>-2.308|<br>-1.489|<br>-3.658|<br>-2.687|<br>-3.203|
||(2.218)|(2.074)|(2.064)|(2.051)|(2.340)|(2.412)|(3.123)|(3.067)|

82
III. RESULTS

# _Baseline Estimations_

Table 2 presents the results of baseline PPML estimations, with robust, country-pair clustered standard errors. Column (1) regresses international co-inventorship against the focal variable, plus individual-country and time FE, as well as the list of controls.<sup>17</sup> The effect of the variable is positive and statistically significant. In particular, column (1) shows an elasticity of 0.20. That is to say, a 10% increase in the size of the inventor diaspora abroad is associated with a two-percent increase in international patent collaborations, which is also economically meaningful. This result is of the same order of magnitude as estimates for the case of diasporas and trade (Felbermayr and Toubal 2010; Felbermayr and Toubal 2012; Head and Ries 1998; Rauch and Trindade 2002, although slightly larger than in Aubry et al. 2014) and diasporas and FDI (Aubry et al. 2014).

17. In unreported results it is included bilateral (country-country) fixed-effects—results provided from the authors upon request. In a nutshell, in those estimations all time-variant variables dramatically decrease their coefficients, becoming statistically nonsignificant. However, regressions with only country FE and time FE are preferred because: (1) including bilateral FE removes 35% of the observations of the original sample; and (2) both diaspora networks as well as international co-patenting move slowly over time, which makes it difficult to identify any effect coming from within-pair variation—therefore identification relies on cross-country-pair differences only.

21
**Table 4: Testing for an income effect**

||Women no|t yet offcially|promoted|Single|women|
|---|---|---|---|---|---|
||(1)<br>Women’s<br>expenditures|(2)<br>Girls’<br>expenditures<br>(All outcomes|(3)<br>Remittances<br>as % in house|(4)<br>Women’s<br>expenditures<br>hold income)|(5)<br>Remittances|
|selected|3_._59**|0_._92|3_._12*|0_._35|_−_3_._74|
||(1_._42)|(0_._85)|(1_._69)|(2_._09)|(6_._86)|
|N|256|103|255|49|49|
|Nominated mean|5_._49|1_._50|6_._29|11_._58|19_._32|
|Nominated SD|4_._65|1_._92|8_._90|14_._70|20_._45|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Columns 1-3 is limited to those respondent who had not yet been officially promoted at the time of the household surveys. The sample in Columns 4-5 is limited to those respondent who are unmarried with a household size of 1.

**Table 5: Effects on attitudes and beliefs**

||(1)<br>Overall self-beliefs<br>(Index)|(2)<br>Confdence<br>(Index)|(3)<br>Negotiating<br>(Index)|(4)<br>Gender attitudes<br>(Index)|(5)<br>Aspirations<br>(Dummy)|
|---|---|---|---|---|---|
|selected|0_._22*|0_._15|0_._13|0_._07|0_._07|
||(0_._13)|(0_._13)|(0_._11)|(0_._09)|(0_._07)|
|N|363|363|363|363|363|
|Nominated mean|_−_0_._17|_−_0_._12|_−_0_._10|0_._04|0_._49|
|Nominated SD|1_._14|1_._11|0_._95|0_._74|0_._50|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

42
Policy Research Working Paper 4956

# **Abstract**

The impacts of international migration on development in the sending countries, and especially the effects on remaining household members, are increasingly studied. However, comparisons of households in developing countries with and without migrants are complicated by a double-selectivity problem: households self-select into migration, and among households involved in migration, some send a subset of members with the rest remaining while other households migrate en masse. The authors address these selectivity issues using the randomization provided by an immigration ballot under the Pacific Access Category of New Zealand’s immigration policy. They survey applicants to the 2002–05 ballots in Tonga and compare outcomes for the remaining household members of emigrants with those for members of similar households that were unsuccessful in the ballots. The immigration laws determine which household members can accompany the principal migrant, providing an instrument to address the second selectivity issue. Using this natural experiment, the authors examine the myriad impacts that migration has on remaining household members, focussing on labor supply, income, durable assets, financial service usage, diet, and physical and mental health. The analysis uses multiple hypothesis testing procedures to examine which impacts are robust. The findings indicate that the overall impact on households left behind is largely negative. The findings also reveal evidence that both sources of selectivity matter, leading studies that fail to adequately address them to misrepresent the impact of migration.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to study the impacts of migration and remittances. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at dmckenzie@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
Panel A in Table 9 shows that SMEs are more severely affected in their growth from bribery. Note that in this estimation, the requirement of the minimum number of firms in a cluster is reduced to 20 instead of 30. This is done because ES sample design divides the total sample evenly by firm size; small, medium, and large. An average SME is predicted to have sales growth reduced by 2.5 percentage points if they are bribed, which is much lower in magnitude than for the entire sample of firms and is also insignificant. SMEs have a lower dispersion in their sales, and the difference between the bribery impact for firms at the 25<sup>th</sup> and 75<sup>th</sup> percentile of sales is smaller than for the entire sample; bribed SMEs with sales at the 25<sup>th</sup> percentile have sales growth that is 2.6 percentage points less than SMEs with sales at the 75<sup>th</sup> percentile.

When analyzing all firm sizes, the interaction between bribery and age was insignificant in predicting growth. When analyzing only SMEs, we find that SMES older than 10 years are less affected by bribery (columns 3 and 4). This result shows that being in the market for longer periods allows firms to acquire experience that is needed to handle market distortions. Interestingly, older SMEs actually grow 4.4 percent higher than young SMEs when bribed. For SMEs, age is more important than sales levels in terms of characteristics that can help compensate distortions.

The data includes many small countries with a low number of bribery clusters that are used as an instrument. We exclude countries that completed Indicator Surveys, a smaller variation of Enterprise Surveys. Indicator Surveys conducted in mostly Caribbean economies targeted only 150 firms due to small country size. Variation in bribery measure across location-sector cells is low in these countries. There are eighteen countries in this sample compared to 29 countries in the whole region. Excluding these countries shrinks the sample size to 5,586 observations, which is only about one thousand observations fewer than in the entire region. Bribery continues to distort sales growth and more severely for small and young firms (Panel B, Table 9). The differential effect of bribery for firms in the 25<sup>th</sup> and 75<sup>th</sup> percentiles is smaller, 18.5 percent in 18 countries compared to 24 percent for all countries.

# <u>B. Definition of Bribery</u>

Table 10 show results when considering a different definition of bribery. Results are shown for version 2 of the Bribery Indicator where refusals to respond to bribery survey questions are 20
# Table 3. Regression Eguations for Agricultural SuRpIv in<sup>Sub-Saharan Africa: Country models.</sup>

|||||Dependan|t variable:|total agri|cultural ex|ports||
|---|---|---|---|---|---|---|---|---|---|
||Ethiopia|Ghana|Kenya|Malawi|Nigeria|Senegal|Tanzania|Togo|Zimbabwe|
|Producer Price a/|-0.84*<br>(-2.3)|1.17*<br>(3.8)|-0.48<br>(-2.0)|-0.07<br>(-0.14)|0.22<br>(0.56)|1.52<br>(1.83)|0.42<br>(0.58)|2.1**<br>(3.3)|0.86*<br>(2.48)|
|Real effective exchange rate a/|-1.64*<br>(-2.55)|0.32<br>(1.6)|0.35<br>(0.27)|2.13*<br>(2.99)|-1.03**<br>(-4.4)|1.65<br>(0.22)|-1.07*<br>(-2.6)|0.24<br>(0.29)|0.29<br>(1.75)|
|Disasters variable|-0.0016<br>(-0.15)|-0.019*<br>(-2.68)|-0.046<br>(-0.75)|_-0.005_<br>(-0.12)|4.18<br>(1.52)|0.0008<br>(0.126)|-0.007<br>(-0.24)|0.068*<br>(2.51)|-9.03<br>(-0.66)|
|Weather variable|-0.029<br>(-0.069)|-0.40<br>(1.84J|0.96*<br>(2.23)|-0.023<br>(-0.049)|-0.40<br>(-0.66)|1.36<br>(2.02)|-0.65<br>(-0.66)|-0.146<br>(-0.38)|0.56**<br>(4.08)|
|Intercept|15.9<br>(379)|-2.15<br>(-2.17)|5.2<br>(0.80)|-4.90<br>(-3.78)|&16**<br>(3.1)|-10.07<br>(-1.45)|7.54<br>(1.62)|-6.17<br>(-1.01)|-0.73<br>(-.41)|
|Degree of freedom|12|12|12|12|12|12|12|12|12|
|R square|0.66|0.85|0.72|0.85|0.81|0.64|0.75|0.78|0.62|

Note: Exports, producer price, exchange rate, and the rainfall variable<sup>have been expressed in logarithms. Their coefficiants may</sup> be interpreted as elasticities.

Years refer to split crop years (eg. 1980181). Since marketing<sup>occurs in the second of the two calendar years, export data</sup> for the 1981 calendar year correspond to prices, rainfall,<sup>etc for 1980.</sup>

- indicates statistically significant at the 95% leveL

- ** indicates statistically significant at the 99%O leveL

- a/ Two-year moving average.
**Figure 2. 3  Interest rates on checking, savings and CD services**

_Source: Superintendencia Bancaria, June 2002_ 14. **Cost barriers for checking accounts are high.** A bank such as Colmena requires a minimum balance of COP300,000 to open a checking account, corresponding to almost one minimum monthly salary. Additional account fees can add up to about 5% of one minimum salary. While fees are similar to those of savings accounts, the minimum balance requirements provide a prohibitively high barrier to entry for low income clients, in particular for the 50% of the unbanked who earn less than half a minimum wage, and for the additional 20% of the unbanked which earn less than one minimum wage (see Chapter 3). However, checking accounts have a relatively high cost even for higher income quintiles. The use of additional services such as ATM transactions costs low income earners an additional 0.4% to0.8% of their monthly income per transaction, while comparable fees for income earners in the highest income quintile correspond to a significantly lower cost of 0.1% of monthly income per transaction. However, some banks such as Conavi, Granahorrar, Megabanco, Santander or Banco Superior offer ATM withdrawals from their own network free of charge<sup>32</sup> .

> 32 Marulanda, 2004.

37
distribution pays. The case of the and Married Couple Dependents care group is interesting because if goes from being the largest net beneficiary of the system when in-kind benefits are included to being the large net contributor when they are excluded from the analysis. It is also interesting to note that while they receive the largest amount of direct social protection transfers<sup>15</sup> and indirect subsidies (electricity and water) of all fiscal and care groups, they are also the largest absolute payers of direct (PIT) and indirect taxes (GST and SST).<sup>16</sup> Figures 6 and 8 show how much is paid and received as a percentage of the households’ market income. The standing of some groups as large net beneficiaries or contributors to the fiscal system changes when assessing the impact of the fiscal system relative to the households’ income. In the fiscal categories, the total net benefits are similar for the No Workers no Dependents group and the Workers with Children in School group, while in absolute terms they were almost three times as high for the latter group. In the care categories, the change is more dramatic, as the total impacts of the fiscal system are much smaller for the Married Couple Dependents group relative to their income than in absolute terms. Also, for the Other Care Categories group, the benefits of the fiscal system rise in relative terms. Overall, the highest total beneficiaries of the fiscal system in Jordan are households in the fiscal groups with school-aged children. When excluding in-kind education and health benefits, most groups’ net benefits or contributions are very small, less than four percent of their incomes. As for the gender categories, the No- Income group receives net benefits corresponding to about 15 percent of their household income (this is also the only group who is net cash beneficiary of the system because of larger receipts of social protection transfers). Meanwhile, the Female Earner households receive 5.4 percent and the Male Earner ones, 8.5 percent of their incomes, and neither is net cash beneficiary of the system.

Two salient findings emerge in comparison to the fiscal incidence analysis in Rodriguez and Wai-Poi (2021). Firstly, in both studies the receipt of in-kind benefits, primarily education, is what drives which fiscal, care or gender majority groups have the largest net benefits from the fiscal system. Secondly, when focusing only on the cash benefits, much of the progressivity of the fiscal system was driven by the poorest households having a more concentrated share of direct transfers compared to richer ones, especially relative to their incomes. But when looking at fiscal or care categories, direct transfers are more spread across all groups.

> 15 Although much of it comes from the now discontinued bread transfer.

> 16 Figures 18 to 27 in the Annex present the absolute and relative incidence of each type of fiscal instrument separately.