rafmacalaba/data-use-mentions-extended huggingface dataset:
The patterns in Tables 4-7 are robust to many specification variants in addition to the variations described above when outlining our data and empirical strategy. One important test is that we find similar results when restricting to district-industries that have more than 50,000 employees, suggesting these outcomes are not sensitive to small sample sizes of female and male establishments.As another robustness check on our metric design, we find similar results when winsorizing our metrics at their 5% and 95% levels to weaken the influence of extreme values.

# **Instrumental Variable Analysis Using 1994 Incumbent Structures**

Our final analysis turns to the question of identification. Our analysis thus far takes the incumbent industrial structures as exogenous to predict the gender ratio of new entrants. There are several potential issues with this approach. First, reverse causality may be present, where the anticipation of many female entrants encourages females in the prior period to own businesses to link to the new firms. Second, omitted variable biases may be present. Our use of conditional estimations with district-year and industry-year fixed effects would require that such omitted factors exist at the district-industry level. An example would be a very inspirational female business leader in the district-industry that encourages both past and future women in the district to engage in firm ownership. Finally, measurement error likely exists that biases downward our estimates of the importance of incumbent industrial structures. This measurement error could be due to the sampled micro-data (vs. a census of local businesses) or incomplete metrics for how industries interact.

To analyze these challenges, we instrument in Tables 8 and 9 for incumbent industrial conditions in 2000 and 2005 using the industrial conditions that existed in 1994 in the manufacturing sector. The earliest year for which the female ownership question was asked is 1994. These data are only collected for the manufacturing sector.

Returning to the challenges facing the least squares estimates, the 1994 incumbent industrial conditions and the role of women are more exogenous to the entry conditions in 2000 and 2005 than the contemporaneous incumbent conditions. To the extent that
150<br>Source: Authors’ calculations based on HBS<br>consumption deciles<br><!-- End of picture text --> The right-hand panel of Figure 11 illustrates the incidence of social transfers (excluding old age pensions) from an ex post rather than an ex ante perspective and reveals a disturbing pattern: there is very little difference in the actual nominal per capita amount of social transfers across the expenditure distribution. Whatever quintile is evaluated, whether rich or poor, each (equivalent) individual receives on average 120-140 PLN per month. And all deciles except the 9<sup>th</sup> and 10<sup>th</sup> receive higher per capita transfers than the poorest decile. If these resources were applied with perfect targeting, rather than being spread more or less uniformly over the distribution, they would be more than double the amount needed to eradicate poverty .

The conclusion, therefore, has to be that Poland has a costly and inefficient system for distributing social transfers; less than half of which serves to bring household consumption up to the poverty line level. A major proportion of these resources is not going to the most needy. However, it might be the case that poverty reduction is not the only objective of this redistribution, i.e. some of the transfers serve an insurance function and are not necessarily designed to improve the well-being of the poorest. Another 14
facilities’ traditional input based budgets were increased by the average amount of P4P payments to treatment facilities on a quarterly basis during the entire 23-month treatment window. As a result, treatment and comparison facilities had the same levels of financial resources on average throughout the study. Therefore, the differences in outcomes between the two groups at followup must be attributed to the difference in incentive structures and not to a difference in available financial resources.

# **c. Estimation**

Given the reassignment of districts between the treatment and comparison groups before the start of the study, and the limited number of districts that could be assigned to the treatment and comparison groups, we view our study as quasi-experimental. While the sample is balanced at baseline on outcomes and characteristics, it is possible that the reassignment of districts was correlated with something unobservable to us and related to health outcomes. However, redistricting took place within the context of a decentralization agenda that was led by the Ministry of Local Government, and we find no evidence that it was driven by or related to health outcomes (MINALOC 2004).<sup>8</sup> Therefore, we think it is likely that any relevant unobservable factors were likely to be invariant over the time period of the intervention.

Therefore, we will use the difference-in-differences methods that control for unobserved time invariant characteristics. This method compares the change in outcomes in the treatment group to the change in outcomes in the comparison group. By comparing changes, we control for observed and unobserved time-invariant characteristics as well as for time-varying factors that are common to the treatment and comparison groups. We show below that the final assignment to the treatment and comparison groups is orthogonal to pre-intervention observable variables, leading us to assert that there should be no correlation between this assignment and nonobservables that drive program effects.

All of the individual outcomes relate to pregnancies and many women do not give birth in both waves of the survey. Hence, we treat the 2006 and 2008 household surveys, described below, as repeated cross-sections and
<!-- Start of picture text --> BEthiopia ~~ _<br>~<br>~ .<br>.<br>a“<br>-- ~e ee SS~<br>FJ aa “Sa Indonesia<br>2 & “Ethiopia ‘ \mindonesia<br>& ~ :<br>s \ N *SalombiaSS gChile<br>2 \ ~~ :<br>zg4 BGolombia’“N\ Chile<br>Be 7 Coldmbi<br>m4 _sironesia or .<br>eae ‘#Chile<br>re) ae<br>2 _-77<br>e£thiopia — ~~<br>6 7 8 9 10<br>Logged average GDP per capita for the estimation period<br>° TFPQ —-—-—-—- TFPQ fitted<br>= Marginalcost _——-—-—- Marginal Cost fitted<br>« Wage ——-—-- Wage fitted<br><!-- End of picture text -->

|Country|Data source|Implementing agency|Time period|Spatialunit<br>(count)|Plant count|
|---|---|---|---|---|---|
||Encuesta Nacional IndustrialAnual (Annual|Institute Nacional deEstadisticas||Municipalities||
|Chile|National Industrial Survey—ENLA)|(INE)|2001-2005|(208)|6.344|
||Encuesta Annual Manufacturera (Annual|Departamento Administrative||Municipalities||
|Colombia|Manufacturing Survey<br>—EAM)|NacionaldeEstadistica(DANE)|2000-2009|(267)|12,397|
||Survei Tahunan Perusahaan Industri|Badan Pusat Statistik (BPS-||Districts and||
|Indonesia|Pengolahan(Annual Manufacturing Survey)|Statistics Indonesia)|2000-2015|metros (291)|34,
This paper uses three panel datasets for Chile, Nicaragua, and Peru to validate the methodology proposed by Lanjouw et al. (2011). By providing a wide range of new sensitivity analyses and robustness checks, this paper shows further that the technique is remarkably flexible with respect to the model specification and the choice of variables. Results indicate that the methodology performs well in predicting actual mobility in and out of poverty by means of two rounds of cross-sectional data; true mobility lies within the two bounds most of the time. Specification of the underlying model of income/consumption matters for reducing the bounds, which can be narrowed considerably via the introduction of interaction terms and asset ownership as controls in the underlying model of consumption/income. More important, the method appears equally well-suited to the estimation of short term mobility as long term mobility.

# **References**

Antman, Francisca, and David McKenzie. 2007. “Earnings mobility and measurement error: a pseudo-panel approach.” _Economic Development and Cultural Change_ , 56(1): 125-161.

Bourguignon, Francois, Chor-ching Goh, and Dae Il Kim. 2004. “Estimating individual vulnerability to poverty with pseudo-panel data.” World Bank Policy Research Working Paper No. 3375.

Bourguignon, Francois. 2010. “Non-anonymous growth incidence curves, income mobility and social welfare dominance.” Journal of Economic Inequality, DOI: 10.1007/s10888-010-9159-7.

ENAHO. 2004-2006 and 2008-2009. National Household Surveys. Lima, Peru. Peruvian Statistics Bureau (INEI).

Lanjouw, Peter, Jill Louto, and David McKenzie. 2011. “Using Repeated Cross-Sections to Explore Movements in and out of Poverty.” World Bank Policy Research Working Paper 5550.

Ravallion, Martin & Chen, Shaohua, 2003. "Measuring pro-poor growth," Economics Letters, 78(1):93-99 21
# **Inflation adjustments**

As already discussed, we are working on the assumption that the researcher has only one cross-section survey and that simulations are conducted only with this survey. We cannot therefore model general equilibrium effects considering all agents and all markets. We can, however, make price adjustments using changes in the Consumer Price Index (CPI) generated by changes in price of the subsidized good.

Changes in the CPI that imply only the change in price of the subsidized good can be estimated using any price index. For instance, using the Laspeyres price index, we find that: where is the average share of expenditure on the good . In our case, the average level of prices is 1 in the initial period and the increase in average prices is . To be noted here, when is relatively small, tends to zero and its impact can be neglected. This would be the case of most subsidized products that occupy a relatively small share of the CPI basket.

Note that, when the elasticity is the same for all households, we find that: This formula may help to find the appropriate increase in price to generate a predefined level of change in real revenues.

To be noted here that in the case where the interaction effect is neglected, the predefined level of change in real revenues is: For many products such as primary or intermediate products (fuel or electricity for example), we should also expect that a change in price will result in changes in prices of other products. This effect can be simulated using input-output tables or general equilibrium models or this information may simply be available from previous studies. In these cases, we can simply import the CPI change into our model.

22
growth rates from national accounts – growth in income from agriculture, industry, and services –to rural and urban areas, which requires several strong assumptions. Growth in the agricultural sector is allocated to rural households. Growth from industry is applied to urban households, which is a reasonable assumption at least in most developing countries (which are the countries for which we use the HFPSs). The service sector is the most difficult to allocate. On the one hand, both urban and rural household benefit from government services such as education and health care services. On the other hand, financial services, retail, ITS and more, are all likely to be predominantly present in urban areas. Even government services are likely to be overrepresented and of greater value in urban areas. In our analysis we use growth in service sector income, but the argument likely carries over from levels of service sector income to its growth. One extreme assumption would be to assign all service sector growth to urban areas. Another option would be to assign the growth according to population weights. We opt for a middle way by assuming that the growth (or more likely, declines) in service income in low- and middle-income countries experienced in 2020 is distributed according to the urban and rural _income_ shares. Since urban areas are richer than rural areas, using the income share gives a higher share of service growth to urban areas compared with using population shares. The results are very similar whether income or population shares are used (Table B.4).

12
879|0.879|0.879|0.872|0.872|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

Looking at the income group specific results, we observe again that upper middle and high-income countries drive these forward links results, but, overall, the results suggest that connectivity is particularly important for economies that have strong forward links. If these countries want to benefit from GVC participation, connectivity is an important policy field and must be included in GVC strategies.

21
- is not correlated with the source of unobserved heterogeneity, condom use can be instrumented to correct for the bias.

As explained above the data for this analysis are from a random sample of 612 sex workers in Sonagachi from a survey conducted in 1993. The sex workers were surveyed by a team social workers under the supervision of the All India Institute of Hygiene and Public Health (AIIH&PH). AIIH&PH had a presence in Sonagachi for more than a year at the time of the survey and were therefore able to establish an excellent rapport with the sex workers and thus elicit accurate responses<sup>5</sup> . While the survey instrument was primarily focussed on medical evaluations, a number of socio-economic questions were also asked. It should be noted that there are two main limitations in the survey from the perspective of estimating compensating differentials. Clients were not surveyed, which prevents us from saying anything about the demand side of the market, and questions on prices were not based upon different See the AIIH&PH report (1997) for more details.

8
dimensions of taxation in low- and middleincome countries. Much of the literature discusses the gender implications of personal income tax and payroll taxes (see Elson 2006; Grown and Valodia 2010; Joshi et al. 2020; Lahey 2018; Stotsky 1997). Grown and Valodia (2010) also examine gender issues in indirect taxes using expenditure data for Argentina, Ghana, India, Mexico, Morocco, South Africa, Uganda, and the United Kingdom. Informal taxes have a gender dimension in Nigeria (Akpan and Sempere 2019) and Zambia (Ligomeka 2019). To the best of our knowledge, the genderdifferentiated burdens of land taxation in low- or middle-income countries have not been studied. We aim to elicit new evidence in this relatively unexplored topic.

The paper also contributes to the empirical literature on the tax burdens of agricultural land taxes in low-income countries. Most of the empirical evidence on land taxation focuses on vertical equity (for example, Hill et al. 2017; Kalkuhl et al. 2018; Mesfin and Gao 2020). Specifically, Hill et al. (2017) and Mesfin and Gao (2020) impute the tax burden of the rural land fee and agricultural income tax in Ethiopia by assuming a constant perhectare tax rate across all land area classes. Building on this evidence, we examine the horizontal equity from a gender perspective. Our findings on vertical and horizontal equity are relevant for countries where land taxation is area-based.

In what follows, Section 2 discusses the conceptual framework on gender and taxation and provides an overview of the rural land use fee and agricultural income tax, and of gender and land rights in Ethiopia. Section 3 describes the data and methodology, and section 4 presents the results and discussion. Section 5 provides a summary and draws conclusions.

# **2. Gender and Taxation: A Conceptual Perspective, and the Ethiopian Context**

# **2.1 Vertical and Horizontal Equity of Taxation from a Gender Perspective**

Vertical equity and horizontal equity are principles considered in assessing the
**Table 6 (Continued)**

||1|**Food C**<br>IHPS<br>2|**onsumption Score**<br>1|FIAS<br>2||
|---|---|---|---|---|---|
|**Weather Shocks†**||||||
|Weather Shock|-1.625||1.149|||
||(1.170)||(1.919)|||
|Current Shock||-1.368||1.084||
|||(1.201)||(2.004)||
|Sequential Shock||-6.616|***|-2.619||
|||(2.089)||(2.962)||
|**Aid * Shock**||||||
|Aid|-3.352|-4.327|4.645|3.649||
||(3.362)|(3.443)|(4.370)|(4.501)||
|Aid * Weather Shock|-1.861||-2.697|||
||(3.674)||(4.875)|||
|Aid * Current Shock||-3.085||-2.711||
|||(4.124)||(5.849)||
|Aid * Sequential Shock||10.583|**|5.220||
|||(4.779)||(5.537)||
|**Explicit HH Targeting Characteristics**||||||
|Dummy, Female Head|-0.972
girl_ * _Female_ * _Post1_|-0.506|-1.924*|-1.952|-0.804|-1.388|-2.524**|2.899|0.969|
||(1.890)|(0.957)|(3.093)|(1.036)|(2.675)|(0.997)|(3.093)|(1.210)|
|_Firstborn girl_ * _Female_ * _Post2_|-0.864|-2.716**|-1.894|-1.198|-0.889|-3.411***|1.681|0.907|
||(2.638)|(1.050)|(3.267)|(0.973)|(2.879)|(1.161)|(2.993)|(1.388)|

NOTES: This tables reports estimates from the specification in column 3 of Table 3 for various sub-samples. Each column within a panel is a separate OLS regression. SC denotes scheduled castes. The wealth categories are based on the national household wealth distribution. Standard errors in parentheses are clustered by state. Baseline mean refers to under-5 mortality for children born in the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
to the model.

Five “raw” variables are included. The values for the “share of advanced, intermediate, and basic digital skills” (1) are the fundamental characteristics that encode an occupation’s digital level. The sum of these three, i.e., the “total share of digital skills" (2), provides a summary of how much digital skills is required by an occupation across levels of digital skills complexity. We also include three variables that represent the share of combinations of digital skills levels: advanced and intermediate digital skills share (3), advanced and basic digital skills share (4), and intermediate and basic digital skills share (5). Notably, all 127 occupations have at least 1 percent of their skills that are digital skills.

Several derived variables are included to increase the complexity of the model.<sup>20</sup> In particular, the ratio of a level of the digital skill share (i.e., the ratio of advanced digital skills to the total share of digital skills) intuitively provides a quantitative measure of the intensity of the digital skill (e.g., advanced)

> 19 It was not possible to directly compare the digital scores and occupational ranking by digital scores using the Malaysia data to that used by O*Net due to differences in methodology for generating digital scores between the two data sets. 20 The complexity of the model, in this context, pertains to the additional information that is used to fit the model. For example, a regression model with 100 variables is more complex than another regression model that uses only 10 variables.

16
25,000; 50% of deposits over 25,000; total coverage not to exceed 100,000. Currently, the limits are tied to minimal wage (MW) which is at 100 rubles. Thus, the figures above re-expressed in terms of MW become 100% of deposits up to 20 MWs; 90% of deposits between 20 MWs and 250 MWs; 50% of deposits over 250 MWs; total coverage not to exceed 1000 MWs. The law allows both ex-ante and ex-post funding. ARCO provided the initial capital of Rb 3 billion. The Deposit Insurance Agency is planned to be constructed and take over the administration. The premiums are planned to be no more than 0.15%, and in emergency up to 0.3% which will go down to formed 0.05% once the fund reaches 5% of the insured deposit base.

_Sources_ : Agency for Restructuring Credit Organizations-Russia (2004), OECD (2004).

**Serbia and Montenegro.** ( _Agency for Deposit Insurance and Bank Rehabilitation, Bankruptcy and Liquidation, Serbia; Deposit Protection Law, Montenegro_ ) The deposit insurance system of Serbia came into force in 2001. The coverage limit is set at 5,000 dinars per depositor per bank which extends to foreign currency deposits as well. On the other hand, Deposit Protection Law was adopted on July 11<sup>th</sup> , 2004 by Montenegro which provide protection up to EUR 5,000 per depositor per bank and the Fund can increase this amount up to EUR 20,000 depending on its resources and the amount of protected deposits. _Sources_ : Bank Rehabilitation Agency-Serbia (2004), Own survey of deposit insurers.

**Slovak Republic.** ( _Deposit Protection Act, No. 118/1996 to 340/2003_ ) The scheme of Slovak Republic was established in March 1996 and was revised in 2001 in accordance with the EU directive 94/19/EC. The system is jointly administered and funded. The central bank made
repayment and overdue amounts are documented. This allows us to generate both loan-specific data and aggregated data for each of the loan officers from 2006 to 2010.

As described above, we use average loan size to reflect the depth of outreach to poorer borrowers.<sup>12</sup> From the CFPA Microfinance central database, we rely on the size of each loan as a dependent variable in the loan-level regression analysis and the average size of the loans disbursed in each year by each loan officer from 2006 to 2010 for the loan-officer-level regression analysis.

Our second data source is derived from a questionnaire that was administered to all of the CFPA loan officers. CFPA Microfinance’s inventory database provides information for 383 loan officers who extended loans at some point during the period 2006-2010. We first sent a questionnaire and an instruction letter through courier to each loan officer.<sup>13</sup> We then conducted telephone interviews with each loan officer during which we reviewed with them the entire questionnaire and the instruction letter. In several sections of the survey each loan officer was asked for personal information (e.g., age, gender, education, and career background prior to working at CFPA microfinance). Loan officers then sent completed questionnaires back to us through e-mail or courier. In the end, we received completed questionnaires from 379 loan officers.

Third, we rely on a county-level per capita income variable taken from the best secondary sources available to reflect economic conditions near the branches where loan officers are located. Specifically, we surveyed the _Yearbook of Rural Social and Economic Statistics_ and collected data on the annual income per capita among the rural population from 2006 to 2010.<sup>14</sup>

> 12 We acknowledge that there are limitations in using changes in loan size to measure mission drift. Non-poor borrowers may still be attracted by small loans when larger loans are expected in the future, as part of dynamic repayment incentives (Christen, 2000; Hishigsuren, 2004). Non-poor borrowers can