rafmacalaba/datause-encoder-data huggingface dataset:
47 i2V See Benbow and Stanley for the genetic<sup>explanations. See L. Brush</sup> [Encouraging Girls in Mathematics:<sup>The Problem and the Solution (Cambridge,</sup> MA: Abt Books, 1980)]; Pallas and Alexander;<sup>and J. Sherman and E. Fennema</sup> ["The Study of Mathematics by High<sup>School Girls and Boys: Related Variables."</sup> American Educational Research Journal,<sup>14(2) (1972):159-168] for environmental</sup> explanations.

i{/ See S. Walker and L. Barton<sup>(Eds.) [Gender. Class, and Education.</sup> (Barcombe, Lewes, Sussex (UK): Falmer,<sup>1983)] and J. White, R. Deem, L. Kant</sup> and M. Cruickshank (Eds.) [Girl<sup>Friendly Schooling. (London: Methuen, 1985)]</sup> in Britain, for example.

!iY Since Jimenez and Lockheed<sup>analyzed their data separately by sex,it</sup> is not possible to compute the sex<sup>differences after the considerable statistical</sup> adjustments introduced for school<sup>selection.</sup> !WU Lockheed, et al.

AJ9 See Buchi Emecheta. Double<sup>Yoke (1982) for a poignant account of female</sup> gender-socialization in Nigeria.
Table 5: Marginal probability effects using instrumental variables and alternative definitions of the employment variables

|Dependent Variable: Labor Force P|articipation||||
|---|---|---|---|---|
||Model C|Model D|Model D2|Model D3|
|Age|0.046***|0.052***|0.049***|0.049***|
|age_sq|-0.001***|-0.001***|-0.001***|-0.001***|
|schooling|-0.033***|-0.038***|-0.039***|-0.040***|
|schooling_sq|0.002***|0.003***|0.003***|0.003***|
|marital_dummy|-0.058***|-0.060***|-0.053***|-0.053***|
|log_land|0.033***|0.007***|0.012***|0.013***|
|log_land_sq|0.004***|-0.001**|-0.001*|-0.000|
|log_hhsize|-0.053***|-0.011|-0.018***|-0.018***|
|children_under_6|-0.080***|-0.085***|-0.053***|-0.054***|
|children_above_6|0.060***|0.117***|0.114***|0.113***|
|female_adult|0.033|0.040|0.012|0.014|
|female_dependent|0.056***|0.046**|0.002|0.002|
|male_dependent|0.141***|0.140***|0.118***|0.119***|
|female_hh_dummy|0.134***|0.141***|0.136***|0.136***|
|max_schooling|-0.010***|-0.012***|-0.014***|-0.014***|
|st_dummy|0.200***|0.135***|0.162***|0.164***|
|sc_dummy|0.077***|0.077***|0.083***|0.082***|
|obc_dummy|0.066***|0.050***|0.056***|0.056***|
|hindu_dummy|-0.056***|-0.023***|-0.025***|-0.021***|
|muslim_dummy|-0.164***|-0.095***|-0.104***|-0.102***|
|Survey|-0.115***|-0.003|-0.041***|-0.095***|
|rank==2|-0.020***|0.012**|0.014**|0.003|
|rank==3|-0.032***|0.024***|0.019|-0.007|
|rank==4|-0.114***|0.084***|0.096***|0.106***|
|rank==5|-0.165***|0.030***|0.036|0.048|
|rank==6|-0.157***|0.033*|0.074**|0.118***|
|Gap|-0.052***|-0.051|-0.100**|-0.109**|
|all_farmers_share||1.617***|1.020***|0.592***|
|all_non_farm_self_share||1.645***|0.709***|0.142*|
|all_casual_share||1.525***|0.743***|0.410***|
|all_non_farm_regular_share||1.559***|0.344***|-|
|District Fixed Effects|No|Yes|Yes|Yes|
|Number of Observations|317046|316978|316978|316978|
|Wald Chi Sq|15831|33169|28553|27625|
|Pseudo R2|0.14|0.28|0.23|0.23|
|note: ***p<0.01,**p<0.05,*p|<0.1||||

28
They establish price and income support (usually de-couple payments based on historical areas planted), tariffs, quotas, public agricultural research, provision of infrastructure (irrigation), export subsidies, export credit, subsidized loans and insurance, etc. The 2002 Farm Bill is expected to be in place for the next six years.

The European Union intervenes in cotton production to provide support to Spanish and Greek producers. Under the Common Agricultural Policy of the European Union, support is given to cotton growers based on the difference between the market price and a guide (i.e. support) price. Advance payments are calculated on the basis of estimated cotton production. Ginners receive these payments and pass-through them to producers in the form of higher prices.

The effects of the removal of cotton distortions in world markets have been widely researched. Some of the literature is reviewed by Baffes (2004). He reports results from FAPRI (2002), which showed that under global agricultural liberalization world cotton prices would increase, on average, by 12.7 percent. Using similar methods, Sumner (2004) reports price increases following different scenarios of cotton reforms. On average, his findings indicate an expected increase of 11.58 percent in world cotton prices. These numbers are in line with those reported by Baffes (2004). Notice, however, that the latter paper focuses mostly on the impact on world prices of the elimination of US domestic support. The comparison of Baffes and Sumner results would indicate that most of the price changes that can be expected from the liberalization of world markets would be generated by US policies. This is confirmed by Hoekman, Nicita and Olarrega (2004), who report much lower cotton price changes from a Doha scenario that considers the elimination of trade barriers without changes in domestic support. It is unclear whether the reforms needed to achieve these increases in prices are reasonable or even feasible. Nevertheless, in section 2 below, we use these estimates to simulate the poverty effects on rural 7
are also more likely than women to have informal employment. The average age of workers in Colombia is approximately 36 years. The highest proportion of workers is in the 25 to 34 years of age category. Informal workers are less educated than formal workers. Furthermore, 37% of formal workers have achieved some university education, whereas the corresponding number for informal workers is only 10%. We find that 39% of formal workers are married, in contrast to only 26% of informal workers. Cohabitating is more common among informal workers. The average household size and the proportion of other relatives and non-relatives living in the household are higher for informal workers.

We create a variable for the proportion of potential beneficiaries, which we use to test for heterogeneous effects in section IV. We define potential beneficiaries as household members who are not in the nuclear family, including married children and their spouses, grandchildren, grandparents and their spouses, siblings of the household head, and other relatives. These household members would be eligible for the SR but not the CR. We also define a group of vulnerable people within the household as the proportion of children under 1 year of age and the proportion of elderly members (65 years and older). An informal worker has, on average, a higher proportion of potential beneficiaries in the household and the same proportion of vulnerable members.

# **The Onset of SISBEN Interviews**

To determine whether the SR increased informal employment, we use variation across municipalities and compare the levels of informal employment before and after the onset of SISBEN interviews. We use the SISBEN onset dates as a proxy for the introduction of the SR because the law required that to become enrolled in the SR, people needed to have been interviewed for the SISBEN. Specifically, we use the SISBEN onset date as recorded in the SISBEN database 10
# **The stock market as a source of fmance: A comparison of U.S. and Indian firms**

In a market economy, the stock market performs three basic functions: (i) a source for financing investment; (ii) a signalling mechanism to managers regarding investment decisions; and (iii) a catalyst for corporate governance. This paper analyzes the financing practices of U.S. and Indian firms with regard to sources and uses of funds, based on their balance sheets.' The primary objective of the study is to pinpoint the role of the stock market in financing firm expenditures. The analysis in this paper is based on data for an aggregate of firms in the U.S. and India. The paper is divided into two main sections. Section I outlines the analytical issues and Section II presents and discusses the empirical results.

-'-

There are several reasons for undertaking a comparative analysis of sources and uses of funds for Indian and U.S. firms. For one, India is one of the fastest-growing emerging stock markets. In fact, India has the second largest number of listed firms on its stock exchanges after the U.S., though the Indian stock market is much smaller than several others in terms of market capitalization. It is also interesting to explore corporate finance issues in the context of a developing country like India from a theoretical perspective, even as a pure comparative exercise in scholarship, especially given the extensive research on corporate finance for the U.S..2 There are a number of interesting issues that can be posed in a study of sources and uses of funds. For instance, what is the relationship between the different components of the sources l Samuel (1995a) deals with the signalling role of the market and Samuel (1996a) deals with the governance role of the market.

2 Based on International Finance Corporation's (IFC) recent project on corporate financial patterns in industrializing countries, Singh and Hamid (1992) and Singh (1995) have studied India and other developing countries.
**Table 12. Results of log-linear models. Youths from 18-35 years of age of ENCEL 2017. Comparison by treatment intensity.**

||||Consta<br>|nt fluidity<br>|||Non-con<br>|stant fluid<br>|ity|
|---|---|---|---|---|---|---|---|---|---|
|Type of model||g.l.|G<sup>2</sup>|X<sup>2</sup>|BIC|g.l.|G<sup>2</sup>|X<sup>2</sup>|BIC|
|1. Independence||50|339.35|336.47|125.52|||||
|2. Homogeneous diagon|al|49|147.85|136.09|-61.71|48|147.66|136.36|-57.62|
|3. Diverse diagonal||47|133.21|119.4|-67.79|44|130.67|118.21|-57.51|
|4.<br>Symmetry<br>(2p)<br>homogeneous diagonal|with|48|144.99|135.56|-60.29|46|144.76|136.06|-51.97|
|5.<br>Symmetry<br>(3p)<br>homogeneous diagonal|with|47|144.39|134.36|-56.61|44|144.17|134.87|-44.01|
|6. Symmetry (4p) with<br>diagonal|diverse|46|132.6|119.54|-64.13|42|130.04|118.41|-49.58|
|7. Symmetry (5p) with<br>diagonal|diverse|45|132.02|118.53|-60.43|40|129.48|117.48|-41.58|
|8.<br>Corners<br>(2p)<br>homogeneous diagonal|with|48|146.85|135.61|-58.43|46|146.65|135.92|-50.07|
|9.<br>Corners<br>(3p)<br>homogeneous diagonal|with|47|143.96|133.27|-57.04|44|143.05|132.99|-45.12|
|10. Corners (4p) with<br>diagonal|diverse|46|130.03|116.39|-66.7|42|127.34|115.21|-52.28|
|11. Corners (5p) with<br>diagonal|diverse|45|125.33|111.97|-67.12|40|122.37|110.41|-48.69|

Note: g.1. = degree of freedom; G<sup>2</sup> = Deviance; X<sup>2</sup> = Pearson’s chi square; BIC = Bayesian Index Criteria. Source: Own elaboration from ENCEL 2017 (n=3,084).

Nonetheless, model 3, which specifies different inheritance parameters according to occupational stratum, provides some distinct elements for both groups. The youths of the subgroup with higher treatment intensity have more favorable inheritance patterns than those belonging to the subgroup with lower intensity: if their origin is NM they are 4.07 times more likely to inherit their provider’s stratum than to be in another stratum ( _dp1=4.07, p<0.001_ ), and if their origin is agricultural, they are 2.37 times more likely to inherit rather than have a destination in another stratum ( _dp6=2.37, p<0.001_ ). This contrasts with the subgroup with lower treatment intensity, for which no evidence of intergenerational inheritance was found when their origin is NM ( _dp1=1.91, ls_ ) but, in the case of origin being at the base of the stratification, the probability to inherit the provider’s stratum is 2.95 times higher than it is to be in another stratum ( _dp6=2.95, p<0.001_ ), slightly higher than that of the high treatment intensity group.

Although this evidence is limited and must be regarded with some caution (since the level of φ is so close to 1), the results suggest that there might be differences in favor of the group with high treatment intensity. Both groups are similar in terms of experiencing a strong intergenerational inheritance, but it seems that a higher intensity of PROSPERA’s treatment 40
likely to grow a vegetable crop in all three seasons (Figure 3a) and they allocate a larger portion of their cultivable land to HVCs (Figure 3b).

**Figure 3** **_a._ Probability of cultivating a high-value crop across beneficiaries and control groups in 2020-21**

<!-- Start of picture text --> 89%<br>81%<br>67%<br>60%<br>39%<br>31%<br>24% 27%<br>Kharif 2020 Summer 2020 Rabi 2021 Overall<br>Johar Beneficiary Non-Beneficiary<br><!-- End of picture text -->

**Figure 3b. Percentage of cultivable land allocated to high-value crops in 2020-21**

<!-- Start of picture text --> 34%<br>29%<br>26%<br>22%<br>19%<br>17%<br>15% 15%<br>Kharif 2020 Summer 2020 Rabi 2021 Overall<br>Johar Beneficiary Non-Beneficiary<br><!-- End of picture text --> _Source: Created by authors using primary data collected from farmers_ Column 1 in Table 6 shows regression results from a linear probability model where the dependent variable is whether a farmer cultivated any high-value crops across all three seasons in 2020-21. Column 2 reports results on the effects of JOHAR irrigation on the share of cultivable land under HVCs. Whether the farmer is a beneficiary of a JCLIS is the main variable of interest in both the regressions. We also control for access to irrigation from other sources (own or rented pump or gravity irrigation), distance to markets (because HVCs are grown for the market), whether the 20
Table 8 Decomposing Equality Gains (Continued)

||Consum|ption Eq|uality Gains|Income|Equality|Gains|
|---|---|---|---|---|---|---|
||_ε_= 0_._5|_ε_= 1|_ε_= 10|_ε_= 0_._5|_ε_= 1|_ε_= 10|
|Bangladesh|0.1|0.2|0.0|-0.3|-0.5|-2.2|
|Bhutan|0.3|0.7|1.7|-0.4|-0.9|-6.7|
|Cambodia|1.0|1.6|-0.2|-1.2|-2.3|-9.7|
|Indonesia|0.1|0.2|0.7|-0.3|-0.7|-3.8|
|Iraq|0.2|0.4|-0.6|-0.5|-0.9|-2.9|
|Jordan|-0.1|-0.3|-4.3|-0.2|-0.5|-5.0|
|Kyrgyz Republic|-0.1|-0.1|-0.3|-0.2|-0.4|-0.7|
|Mongolia|-0.0|-0.1|1.4|-0.3|-0.5|-2.5|
|Nepal|-0.1|-0.1|2.3|-0.2|-0.4|-2.4|
|Pakistan|-0.1|-0.2|0.3|-0.2|-0.5|-2.8|
|Papua New Guinea|-0.4|-0.8|-2.6|0.0|0.0|-0.3|
|Sri Lanka|0.0|-0.0|-0.9|-0.4|-0.8|-2.8|
|Tajikistan|0.2|0.4|0.7|-0.6|-1.3|-3.2|
|Uzbekistan|0.1|0.1|-0.1|-0.6|-1.0|-3.7|
|Vietnam|0.2|0.3|0.1|-0.7|-1.4|-6.1|
|Yemen, Rep.|-0.4|-0.7|-1.2|-0.2|-0.4|-1.5|
|Azerbaijan|-0.0|-0.2|-0.5|-0.6|-1.3|-6.4|
|Georgia|-0.2|-0.4|-1.1|-0.4|-0.7|-3.3|
|Moldova|-0.3|-0.5|-1.9|-0.4|-0.7|-2.4|
|Ukraine|-0.1|-0.2|-4.3|-0.5|-1.1|-0.5|
|Bolivia|-0.5|-1.1|-3.8|-0.2|-0.4|-2.7|
|Ecuador|0.1|0.1|-0.7|-0.8|-1.8|-5.3|
|Guatemala|-0.4|-0.7|-3.3|-0.4|-0.8|-3.4|
|Nicaragua|0.9|1.9|0.5|-2.6|-4.5|-9.4|
|Average|0.2|0.3|0.1|-0.3|-0.6|-2.6|
|Pop. weighted av.|0.1|0.2|0.4|-0.3|-0.6|-2.8|
|GDP weighted av.|0.1|0.2|0.2|-0.3|-0.6|-2.7|

Notes: The table presents the decomposition of the equality gains from trade _G_ ( _ε_ ) _− G_ (0). The first three column report the average consumption equality gains from trade for different values of inequality aversion ( _ε_ = 0 _._ 5, _ε_ = 1, and _ε_ = 10). These consumption equality gains are calculated by assuming that liberalization only impacts consumption and not income. The three remaining columns report the income equality gains from trade for different values of inequality aversion ( _ε_ = 0 _._ 5, _ε_ = 1, and _ε_ = 10), calculated by assuming that liberalization only impacts income but not consumption.

53
16

# REFERENCES FOR MALNUTRITION TABLE FOR CHILDREN IN DEVELOPING COUNTRIES

- A. Bengoa, J.M. and G. Donoso (1974) "Prevalence of Protein-Calorie Malnutrition, 1963 to 1973." PAG Bulletin 4(l):26-29.

- B. Keller, W. and C. Fillmore (1983) "Prevalence of Protein-Energy Malnutrition." World Health Statistics Quarterly 36:150-161.

- C. United Nations Administrative Committee on Coordination/Subcommittee on Nutrition (1987) "First Report on the World Nutrition Situation." Rome, **FAO.** pp. 63-64. Supplement (1988).

- D. World Health Organization (1987) "Nutrition: Global Surveillance Through Anthropometric Measurements." Weekly Epidemiological Record 7-11.

- E. "Pakistan National Nutrition Survey 1985-1987" (1988) Nutrition Division; National Institute of Health.

- F. Ministerio de Salud Publica, Republic de Honduras, Instituto de Nutricion de Centro America y Panama (1988). "Encuesta Nacional de Nutricion, Honduras, 1987." .

- _G._ CUnited Nations Administrative Committee on Coordination/Subcommittee on Nutrition (1989) "Update on the Nutrition Situation: Recent Trends in Nutrition in 33 Countries."

- H. Burleigh, Elizabeth (1989) "Trends in Nutrition--El Salvador-1965-1988." USAID.

- I. Demographic and Health Surveys. Bolivia (1989), Burundi (1987), Colombia (1986), Dominican Republic (1986), Egypt (1988), Ghana (1988), Guatemala (1987). Mali (1987), Morocco (1987), Nigeria 41986), Senegal (1986), Sri Lanka (1987), Thailand (1987), Togo (1988), Trinidad and Tobago (1987), Uganda (1988-1989), Tunisia (,1988). Institute for Resource Development/Westinghouse, Columbia, Maryland.

- J. Food and Agriculture Organization (1987-1989) "Nutrition Country Profiles." Rome, Italy.

- K. Global Nutritional Status Data: Anthropometric Indicators Update 1989; Nutrition Unit, Division of Family Health, World Health Organization, Geneva.

- L. Government of Malawi and UNICEF (1987) "The Situation of Children and Women in Malawi." September.

- M. Elder, John (1989) "Nutritional Status of Children Under Five in Mauritania. The Social Dimensions of Adjustment Unit. The World Bank.
10 Table 2 Selected Financial Ratios for the Aggregate Data

||Pvt Ltd. Co.s<br>1984-1986|Pub. Lt<br>1984-1986|d. Co.s<br>1987-1989|Large Co.s<br>1987-1989|
|---|---|---|---|---|
|Current Ratio (cr)|1.08|1.21|1.24|1.32|
|nwc/K (liq)|0.052|0.092|0.107|0.132|
|Cash flow/K (cfk)|0.055|0.051|0.054|0.062|
|L.t. Borrowing/Total Borrowing<br>(matl)|0.343|0.656|0.656|0.691|
|L.t. Borrowing/Total Borrowing<br>(including trade dues) (mat2)|0.165|0.383|0.402|0.426|
|Fixed assets/K (assm)|0.273|0.463|0.445|0.455|
|Bank Borrowing/Total Borrowing<br>(banktb)|0.619|0.380|0.372|0.335|
|D.F.I. Borrowing /Total<br>Borrowing (dfitb)|0.108|0.247|0.271|0.253|
|D.F.I. Borrowing/Total U.t.<br>borrowing (dfiltb)|0.316|0.376|0.413|0.392|
|S.t. Bank Borrowing/<br>Total Bank Borrowing|0.835|0.780|0.801|0.799|
|Total Borrowing/K (lev)|0.374|0.397|0.440|0.421|
|L.t. Borrowing/Fixed assets|0.470|0.563|0.648|0.640|
|L.t. Borrowing/Equity|0.583|0.827|0.962|0.874|
|Trade dues etc./Total Liabilities|0.395|0.275|0.267|0.251|
|S.t Borrowing/Inventories|0.776|0.534|0.601|0.529|

Notes:(I) s.t.=short term; (ii) l.t.=long term; (iii) D.F.I.s = Devt. Finance Institutions; (iv) Current Ratio<sup>=</sup> Current Assets (CA)/Current Liabilities (CL); (v) CA = Inventories + Loans and advances and<sup>other</sup> debtor balances + Book value of Quoted Investments + Cash and Bank Balances + Advance<sup>of Income</sup> Tax; ((vi) CL = Tax and other current provisions + s.t. Bank borrowing + s.t. other borrowing + Trade dues and other current liabilities; (vii) NWC = CA - CL; (viii) K = Fixed assets + Current Assets; (ix) L.t. borrowing = Borrowing from all D.F.I.s + Borrowing from govt./semi-govt. bodies + Borrowing<sup>from</sup> companies + L.t. borrowing from banks and others; (x) S.t. borrowing = Total borrowing - U.t. borrowing; (xi) Equity = Paid up capital + forfeited shares + reserves and surplus; (xii) Inventories = Raw materials, components etc. + Finished goods + Work-in-progress + Stores & spares + Misc.; (xiii) Trade dues<sup>etc. =</sup> Trade dues and current liabilities; (xiv) Cash flow = accounting profits + depreciation.
# **5.2 Results**

The results of the panel data regressions, using the OLS and FHS models (i.e., equations (2) and (3)) are presented in Table 2.

Columns (1) to (4) in Table 2 consider the relationship between gender equality under the law and the female labor force participation rate. Columns (1) and (2) present the OLS results, under a parsimonious model and after the inclusion of several, potentially endogenous, covariates. In both cases, the relationship is positive and significant; the inclusion of the additional covariates has very little impact on the coefficient on the WBL index. The FHS results, presented in columns (3) and (4), imply that the relationship between gender equality and female labor supply is slightly stronger than the OLS results would suggest, although the OLS and FHS models do not yield statistically different results. Regardless of the model used, the magnitude of the effect is modest – a one‐point increase in the WBL index is associated with an increase in female labor force participation of between 0.047 and 0.053 percent. On average, implementing one WBL reform increases the index by 2.9 points; thus, the reform of a single one of the 35 WBL data points would be associated with an increase in female labor supply of between 0.14 and 0.15 percent.

Columns (5) to (6) in Table 2 show that, according to OLS estimation, improvements in gender equality under the law are associated with a reduction in the wage gap between men and women. Again, while the relationship between legal gender equality and women’s economic outcomes is positive (in that it is associated with a lower wage gap), it is modest; a one‐point increase in the WBL index is associated with a 0.09 to 0.1‐percentage point reduction in the wage gap (the median gender wage gap in our sample is 15.64 percent). However, according to the FHS model results (columns (7) and (8)), there is no statistically significant relationship between the WBL index and the pay gap. As the FHS model is designed to identify the causal impact of a policy variable, we can infer from these results that there is no causal effect of the WBL index on the gender wage gap. Similar results hold for occupational segregation between genders (columns (9) through (12) in Table 2). The OLS results with other covariates included (column (10)) suggest that increases in the WBL index are associated with lower levels of occupational segregation between genders, but the results under the FHS model show no significant effect (columns (11) and (12)).

19
The construction of these outcome variables is detailed in Table A.1 in the Appendix. Descriptive statistics are shown in Table A.2 in the Appendix.

# **4.4 Internal validity**

We discuss four threats to the identification of causal impacts: imbalances between the treatment and control groups, spillovers, attrition, and multiple testing.

**Balance** The treatment and control groups are well balanced across a series of baseline demographic and economic characteristics (Table A.2 in the Appendix). For all but one variable analyzed, the magnitude of the differences between the treatment and control groups is smaller than 0.1 standard deviations of the control group, which indicates good balance (Imbens and Rubin 2015, p. 310). Only two out of 75 differences are statistically significant at conventional thresholds. The p-values of omnibus F-tests of joint significance are above 0.90 for both treatment arms. Overall, these results suggest that the control and treatment groups are well-balanced.

**Spillovers** Important spillovers between the treatment and control groups are unlikely given the relatively small scale of the program—only 0.3% of the rural population of Jendouba received a cash grant.

**Attrition** Attrition could be an issue in this study, especially since the endline survey took place about 2-2.5 years after the intervention and 4-4.5 years after the baseline survey. Furthermore, previous research has shown that cash transfer interventions can affect migration decisions (Angelucci 2015; Adhikari and Gentilini 2018; Imbert and Papp 2019; Gazeaud et al. 2021a).

The overall attrition rate in our study is 8.8% (Table A.3), which is relatively low and on par with comparable studies in other developing country contexts. The attrition rate in the control group is 11.2%, which is significantly larger than the attrition rate of 6% and 6.8% observed in the two treatment groups. Differential attrition is mainly driven by higher migration rates in the control group. While the survey firm made its best efforts to 13