rafmacalaba/data-use-mentions-extended huggingface dataset:
# **IV. Regression Analysis**

In this section, we turn to a more systematic analysis of the summary statistics using regression analysis. Our primary objective is to understand the relationship between growth, size, and age. Hence we run regressions of the form: _Growth = a + b1 Size + b2 Age + b3 Industry Dummies + b4 Year Dummies + b5 Country Dummies + e_ (1) Our main measure of growth is Employment Growth defined as the log difference between employment three years back and employment last year divided by two. We also use Sales Growth and Labor Productivity (Sales/Worker) Growth, constructed similarly, to see if there is an association between size and increase in sales and productivity. We use three dummies for size – 5-19 employees (reference category), 20-99 employees and 100+ employees that are consistent with the size stratification in the surveys. The size dummies are constructed in the base year. In addition to country and year fixed effects, we control for industry fixed effects since firm size distributions vary by industry as do net growth rate patterns. We also control for firm age since Haltiwanger et al. (2010b) find that there is no systematic relation between firm size and growth once age is controlled for although their evidence is based only on US data. Firm age in our data is defined as the number of years since the establishment began operations in the country.<sup>15</sup> While there are several approaches to the use of survey data in regression analysis, we follow the “model approach” (see Cameron and Trivedi, 2005) used in the literature which utilizes data collected in the sample directly, without weighing. Hence we use simple OLS

> 15 The year when the establishment began operations refers to the year in which the establishment actually started producing or providing services. If the establishment was privatized, then the date refers to when the original government-owned establishment began operations.

13
T1 >= T2 (p-value)|0.241|0.141|0.241|0.234|0.231|0.109|
|Endline control mean|||2.1|99|||
|Additional controls|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.051|0.505|0.010|0.107|0.018|0.225|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson models show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 4 (excluding lime). Additional control variables identical to those listed in notes of Table 2. Onesided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

**Table S4.12.** ITT effects on integrated adoption of the full ISFM package, using alternative measures.

|||Integrat<br>|ed adoption<br>|of full ISFM<br>|package<br>||
|---|---|---|---|---|---|---|
||At least<br>|out of 5<br>|3 out<br>|of 3<br>|Region-<br>|specific<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T1 (extension)|0.152*
Table A.16: Dynamics of the effects using 2018 data. Estimating the effect of migrants’ legal status on wages after return, unconditional on return

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|-0.001|-0.048|0.008|-0.003|
||(0.001)|(0.044)|(0.011)|(0.006)|
|Age squared|0.000**|0.000|-0.000|0.000|
||(0.000)|(0.001)|(0.000)|(0.000)|
|No education|0.001|0.412***|0.138***|-0.334***|
||(0.003)|(0.158)|(0.040)|(0.031)|
|Primary or preparatory|0.002|0.175|0.124***|-0.329***|
||(0.003)|(0.184)|(0.044)|(0.031)|
|Secondary|0.000|0.267**|0.091***|-0.222***|
||(0.002)|(0.135)|(0.032)|(0.024)|
|Rural|0.003|-0.013|-0.059**|-0.007|
||(0.002)|(
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data Equations (a) to (c) show the relationship between the tax office sales/raw materials/labor costs and the true sales/raw materials/labor costs with equation (a) forming the analytical underpinning for a measurement equation for S . It’s reasonable to expect that firms never report more than what they sell ( ).<sup>20</sup> Therefore we can transform the first-order condition for sales into log linear form: The marginal effects of P and on sales are: Reported sales increase with the penalty multiplier, probability of audit, detection efficiency, and the costs associated with misreporting, but decreases with corporate income tax rate. The patterns conform to our expectation and are in line with the predictions derived from other tax compliance models.

The values of the parameters P and are unknown, but can be approximated by a vector of observable proxy variables Χ2 (see section 4). Observable proxy variables may also be used for the tax rate , if this is interpreted as an unobserved and firm-specific effective tax

> 20 holds if , i.e. if the tax rate is higher than the marginal cost of tax evasion; and holds as long as the firm perceived tax office efficiency is not too small. can be larger than one if ) or smaller than one if ). Accordingly, firms may under-/over- report labor costs.

18
# Methodolot,

**The** central question at issue is would the results of **the** earlier Borus study, relating to only two **years** of labor market experience following training (and, subsequently, three years army service), **he** confirmed when longer follow up periods **ar-** employed? Given **the** far greater input of general educational studies **and** training theory in the more formal industrIC ehools and, particularly, vocational secondary schools, it might bea -ugtht that, over the longer term, graduates of **these** institutions would achieva higher earnings than those of job-related, informal but perfunctory training modes. The wider scope of the educational programs offered in the more forml training institutions would mark out their graduates **as** those who would **be** better equipped to learn

from experience on-the-job, be more likely to be selected for junior supervisory tasks and, generally, to be more upwardly mobile within the internal labor market of the firm.

This, Is illustrated in Figure 1. While in the initial years of post-training experience (between B and C) the cross-sectional earnings of both apprenticeship and vocational secondary school course completers may be observed to lie at roughly the same level, over the longer term the average age-earnings profile of vocational school graduates would assume a steeper slope, generating a positive earnings (and significant productivity) gap between vocational school and apprenticeship course completers. Only longitudinal earnings data (as at year D) and unavailable to Borus, would pick up this earning gap. Of course, the identification of such an earnings gap would not, in itself, indicate that the policy conclusions stemaing from the earlier work were incorrect; but it would point to the need fcr a wider investment appraisal of the varioua training program alternatives, in terms of a comparison of the net present value of the long-term earnings stream minus costs for each training mode.

6
TO EQUITY<br>(CHANGE IN GINI POINTS)<br>REGRESSIVE <-----> PROGRESSIVE MARGINAL CONTRIBUTION<br>Croatia (2014) Poland (2014) USA (2016) Russia (2014) Brazil (2009) Chile (2013) Mexico (2010) Argentina (2012) Turkey (2016)<br><!-- End of picture text --> Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott 2013); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018); US (Higgins et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to equality is the difference between the Gini coefficient without the particular fiscal intervention and the Gini coefficient of all income components together. There is no marginal contribution calculated for Argentina. USA results are preliminary.

# **_6.3 Taxes and Transfers and Their Effects on Poverty_**

Panels (a) and (b) of Figure 12 show the increase in poverty due to each direct and indirect tax, respectively. These results vary widely depending on the taxes considered. On the direct tax side, the payroll income tax has the highest poverty-increasing effect, raising the share of poor by about 30
Labor Force Survey (LFS) and the Household Expenditure and Income Survey (HEIS), with a suitable coverage of Afghan refugees.

# **4. Research design**

In this section, we first describe data sources and the matching strategy. Then we explain the key variables and how they are constructed. Finally, we elaborate our identification strategy to assess the unequal impact of macro shocks in Iran on Afghan refugees and the host communities.

## **4.1. Data**

Our data is drawn from two surveys regularly conducted by the Statistical Center of Iran, the main governmental body to run sample surveys: the Labor Force Survey (LFS) and the Household Expenditure and Income Survey (HEIS). They both have nationwide and provincial representative samples of rural and urban households, and the latest quinquennial population census is used as their framework. As there is no camp for Afghan refugees in Iran, they spread over specific regions of the country, and the national household surveys have good coverage of them.<sup>4</sup> LFS has been conducted in the middle of each quarter since spring 2005 and is the primary source of employment statistics in Iran. The information available in LFS covers a wide range including household member’s demographic and employment status, such as education, migration, working hours, industry, occupation, and experience (but not wage and income). Importantly for this research, the nationality of each household member is also inquired in this survey. The sampling of LFS is on a rotating panel basis in the sense that each household is sampled in two consecutive seasons of two consecutive years. This feature enables us to observe each individual’s change in employment status and compare it between two communities. Table 1 lists the LFS rounds with the available sample of Afghan refugees.

HEIS has been conducted annually since 1963, but its raw data is available from 1984 onwards. Its enumeration is uniformly spread over the year such that in each month, a representative
begin, and more likely to experience peaceful settlement attempts if claims do begin, between two democracies as opposed to dyads with less democratic forms of government. Tir and Ackerman (2009) make a similar conjecture and find that dyads with joint democracies are more likely to negotiate water treaties.

Despite this democratic peace rationale, which suggests that democratic countries are better stewards of the environment and will be more likely to cooperate with other democracies, the literature has also found insignificant and even negative and significant results (Brochmann 2012).

We test the democracy argument and calculate a combined democracy score for the relevant period of each dyad ( **_Democracy_** ) based on the Polity IV database (Marshall, Gurr, and Jaggers, 2013), which ranges from -10 for institutionalized autocracies to 10 for institutionalized democracies. The data for the democracy variable are derived for the 1945-2008 period.

# _<u>Overall Relations: Alliances</u>_

Overall political relations between countries should also affect their hydropolitical relations (Yoffe, Wolf, and Giordano, 2003: 1117; Brochmann and Hensel, 2009: 415). We use measures of a history of alliances to proxy for overall relations between states sharing a river basin.

Following Tir and Ackermann (2009), we expect states with common security interests to form military alliances, which afford them the opportunity to enhance security, stability and order. In turn, these shared interests can lead to an increase in mutual trust and result in the attainment of additional objectives in other realms (e.g. environmental) (Keohane 1984). We therefore hypothesize that countries with an overall robust history of alliances will be more likely to experience cooperative events and less likely to experience conflict. The intensity of these events should also be affected. To measure the existence and robustness of alliances, we use the Correlates of War (COW) data set (Militarized Interstate Disputes, v3.1). We include a dummy variable ( **_Alliance_** ) depicting
if the right questions are being asked, they are not being asked in a sufficiently large number of countries. Perhaps now is the time to launch a new wave of _specialized_ household surveys of financial service usage, building on what has been learnt from the specialized cases mentioned in Section 2 above, and seeking co-financing from financial service providers who could make use of the results for market development.

# _National basic access indicators_

In addition to providing the essential raw material for research, both of the fundamental, academic type, the policy-oriented and that useful for the market, there is a need for a set of basic high-profile national summary indicators – the GDP, M2 and CPI of the world of access. These indicators will not by themselves prove anything, but in the early years of

> used.  (By the qualification ‘independent’ I mean disregarding multiple respondents from the same law firm or accountancy practice).  Nor is the Chinese case unrepresentative in this regard.

> 32 Recent co-ordinated efforts to document the flow of international remittances provide a good example of the combined use of multiple sources (cf. the papers prepared for a recent APEC conference at <u>http://www1.worldbank.org/finance/html/amlcft/ARS%20Research%20Papers.htm).</u>

22
an average of 74 percent (Gourlay et al., 2021;Table 1).

_Table 1. Response rates in round 1 of IPA Recovr surveys based on RDD (top panel) and HFPS surveys based on contact information from previous surveys (bottom panel)_

|**_Country_**|**_Respondents_**|**_Attempted_**<br>**_contacts_**|**_Response rate_**|
|---|---|---|---|
|**_Recovr - Rando_**|**_m Digit Dialing (RDD)_**|||
|Burkina Faso|1,356|2,284|59%|
|Colombia|1,507|6,984|22%|
|Côte d'Ivoire|1,329|3,022|44%|
|Ghana|1,357|10,781|13%|
|Mexico|1,330|29,876|4%|
|Philippines|1,389|8,378|17%|
|Rwanda|1,482|4,234|35%|
|Sierra Leone|1,304|3,831|34%|
|**_HFPS - Existing _**|**_phone contactfrompre_**|**_vious survey_**||
|Burkina Faso|1,968|2,500|79%|
|Ethiopia|3,249|5,374|60%|
|Malawi|1,729|2,337|74%|
|Nigeria|1,950|3,000|65%|
|Uganda|2,227|2,386|93%|

_Sources: Innovation for Poverty Action (IPA, 2020) and Gourlay et al. (2021). Based on survey round 1 in each country. The IPA Recovr surveys used random digit dialing of a nationally representative sample of phone numbers.
Policy Research Working Paper 10217

# **Abstract**

Measuring the economic impact of a war is a daunting task. Common indicators like casualties, infrastructure damages, and gross domestic product effects provide useful benchmarks, but they fail to capture the complex welfare effects of wars. This paper proposes a new method to estimate the welfare impact of conflicts and remedy common data constraints in conflict-affected environments. The method first estimates how agents regard spatial welfare differentials by voting with their feet, using pre-conflict data. Then, it infers a lower-bound estimate for the conflict-driven welfare shock from partially observed post-conflict migration patterns. A case study of the conflict in Eastern Ukraine between 2014 and 2019 shows a large lower-bound welfare loss for Donetsk residents equivalent to between 7.28 and 24.79 percent of life-time income depending on agents’ time preferences.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eartuc@worldbank.org and honder@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Table 1. Basic statistics about the countries and surveys</u>**

||_GDP per capita_<br>_PPP_|_Population <$1 a_<br>_day_|_Under-5_<br>_mortality_|_Number of households_<br>_surveyed_|
|---|---|---|---|---|
|Burundi 2000|590|55|190|3,979|
|Cambodia 1999|1710|34|135|6,001|
|Cambodia 2000|1804|34|135|12,236|
|Indonesia 2003|3213|8|48|65,762|
|Jamaica 1998|3366|2|20|7,375|
|Jamaica 2000|3395|2|20|1,800|
|Mongolia 2000|1620|27|75|6,000|
|Mozambique 1996|700|38|206|8,250|
|Myanmar 2000|-|-|110|25,545|
|Romania 1995|5965|2|15|24,560|
|Sierra Leone 2000|464|-|186|3,916|

_Source_ : World Bank, World Development Indicators. Poverty rates are for the following years: Burundi 1998; Cambodia 1997; Indonesia 2002; Jamaica 1999 and 2000; Mongolia 1998; Mozambique 1996; Romania 1998. Under-5 mortality data are for 2000 except Romania which is for 1995.

The datasets covered in this study are all most closely consistent with an impairment definition of disability—and as such fall under ICF’s “body functioning and structure” domain. The definition does not include mental health, chronic illness or the inability to carry out specific activities. The latter approach is an alternative that is attractive since it is arguably easier to verify. Indeed, selective misreporting of morbidity has long been recognized as a potential problem in studies of the relationship between health and other socio-economic characteristics (Gertler, Rose and
<!-- Start of picture text --> 20000<br>18000<br>16000 2008-2016<br>14000<br>2000-2007 2008-2016<br>12000<br>10000 2000-2007<br>8000<br>6000<br>2000-2007<br>4000 2008-2016<br>2000<br>0<br>0 20 40 60 80<br>Employment share (% of total)<br>agriculture industry services<br>Source: World Development Indicators<br>Output per worker (constant 2010 US$)<br><!-- End of picture text --> Broadening the focus to other developing EAP countries shows that also Myanmar, Cambodia and Mongolia witnessed significant structural transformation. Figure 10 summarizes structural transformation for developing EAP countries. The horizontal axis captures _change_ in employment share between 2000-2007 and 2008-2016 (note that the change is not annualized). The vertical axis is _percentage change_ in real output per worker over the same period (again, the change is not annualized). In the northeast (southwest) quadrant are country-sector pairs that have observed an increase (decline) in both productivity and employment. In the northwest quadrant are country-sector pairs where productivity has grown but the employment share has fallen. Comparing performances within country and across sectors informs about the structural transformation dynamics. Specifically, we find that across the region workers move out of agriculture towards more productive sectors. The employment shift towards services tends to be more pronounced. It is worth noting that the service sector encompasses activities that typically vary widely in terms of productivity, such as retail and IT. Whether workers move from agriculture towards more or less productive jobs in the service (and industry) sectors has important implications for future growth, as anticipated in the discussion about Figure 7 regarding the labor decomposition based on de Vries et al. (2015).

**Figure 10 Structural transformation of all developing EAP countries**

13
# 1. Introduction

Entrepreneurship can foster innovation, productivity growth, and employment (Schumpeter 1934, Acemoglu and Robinson 2012). According to the Global Entrepreneurship Monitor Survey, over half of the women in developing countries are, or aspire to be, entrepreneurs (Elam et al. 2021). However, most lead subsistence-oriented micro-businesses, which are not seen as key drivers of innovation and growth (La Porta and Schleifer 2014).<sup>2</sup> The narrative among policy makers regarding women’s entrepreneurship is gradually shifting from encouraging the creation of a high number of startups to focusing on supporting women who are well positioned to lead growth-oriented enterprises (Elam et al. 2021). While innovative women entrepreneurs have the potential to be agents of change and offer novel solutions to global challenges, they encounter a range of unique constraints that can limit the expansion of their businesses.

This paper reviews evidence on interventions designed to support women-led businesses by addressing their differential constraints and aiding them in making decisions that foster business growth. The focus is on women who lead growth-oriented micro, small, and medium enterprises (MSMEs) with aspirations to scale up their operations, although some interventions may also benefit women managing subsistence enterprises. The paper does not discuss issues covered by other reviews related to differential selection into entrepreneurship between women and men or the role of non-economic outcomes such as work-life balance and community service desires (Campos et al. 2019, Love et al. 2023). It also does not address policies for large, limited liability companies, where the evidence remains scarce.

The review uses a simple analytical framework by which women entrepreneurs face differential constraints that affect their strategic business decisions such as how much to invest in production factors and what sector or market to enter, which in turn determine their business outcomes. Such constraints can also affect returns to their investments, thus directly impacting outcomes. Interventions are organized into four categories of constraints: 1) Human capital, encompassing gender gaps in access to skills and networks
median, maximum or quantiles by region). These aggregates are used to build an area-level predictive model calculated for each area (e.g. poverty headcount). A relatively small sample size (for example, Guatemala’s 338 _municipios_ ) means that internal validation, such as cross-validation or hidden test data, is difficult, and some external validation may be required.

Poverty rates were calculated for each _municipio_ . Aggregate rural, urban and overall poverty rates were available for 2006, but only rural poverty rates were available for 2011.<sup>8</sup> The study used aggregated and encrypted CDR data for August 2013, which overlaps with the ENCOVI survey period. In 2013 Guatemala had 140 cellular accounts for every 100 people.<sup>9</sup> The model tested two types of prediction: (i) same-survey prediction, such as predicting the 2006 urban poverty rates based on a model of the relationship between the 2006 ENCOVI data and the 2013 CDRs; and (ii) different-survey prediction, such as predicting the 2011 rural poverty rates based on a model of the relationship between the 2006 ENCOVI data and the 2013 CDRs. Same-survey

> 7 See Elbers, Lanjouw and Lanjouw (2003).

> 8 These data come from a 2011 census of rural areas designed to gather information for social-protection programs. No national census was conducted that year.

> 9 World Development Indicators, 2016. This reflects multiple accounts per person. While this does not indicate that every person has a cell phone, it suggests that cell phone use is relatively high.

**8**