rafmacalaba/data-use-mentions-extended huggingface dataset:
0.004|0.042|0.048|0.005|0.044|0.021|0.085|
|Hangzhou|0.091|0.095|0.004|0.080|0.088|0.009|0.083|0.080|0.088|
|Ningbo|0.047|0.051|0.004|0.041|0.048|0.007|0.043|0.029|0.068|
|Wuhan|0.095|0.101|0.005|0.096|0.097|0.000|0.096|0.120|0.055|
|Chengdu|0.092|0.097|0.005|0.102|0.108|0.007|0.104|0.125|0.067|
|No. of Observations|4244|2152||3005|1582||4587|3112|1475|
|P-value of F-test for Joint Sig.|||0.138|||0.236||||

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Note: Standard deviation in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

29
Once direct taxes and all social contributions are subtracted and direct transfers are added, the extreme poverty headcount for disposable income declined further, to 5.5 percent. However the legal poverty headcount increases somewhat to 8.6 percent, on account of the relatively large burden of direct taxes and social contributions for all but those under the extreme poverty line.<sup>13</sup> Once indirect taxes are incorporated into the analysis, all of the improvement in poverty that takes place through direct transfers is canceled out. In fact, indirect taxes result in a 3.5 percentage point increase in extreme poverty when compared to disposable income and a 2.9 percentage point increase when compared to market income plus pensions. Similarly, both the poverty gap and the severity of poverty decline with pensions and social transfers, but increase once indirect taxes are taken into account. Similar results hold when the international US$5-a-day (2005 PPP) and the US$2.5-a-day (2005 PPP) poverty lines are used.

How does this look for different types of households? As shown in Table 5, there is quite a bit of heterogeneity in terms of the impact of taxes and transfers on poverty across different types of households. Although poverty increases with indirect taxes for all types of households, households with children are especially hard hit, with extreme poverty being higher for consumable income than for market income for households with more than one child and the at-risk-of poverty rate being higher for single parents and for couples with children.

Table 5. Main Scenario: Changes in Poverty on account of taxes and transfers by household type

||Market<br>Income|Disposable<br>Income|Consumable<br>Income|Total impact on<br>extreme<br>poverty|
|---|---|---|---|---|
||(1)|(2) =<br>(1) + pensions +<br>direct
y** _d_ ), assuming that the augmented vectors **y** _da_ = ( **y** _ds,_ **y** _d_ )<sup>_′_</sup> , composed by the survey and the census vectors of model responses, _d_ = 1 _, . . . , D_ , follow the nested error model (5.1). Hence, the CEB predictor of _δd_ is the EB predictor _δ_<sup>ˆ</sup> _d_<sup>_CEB_</sup> = _E_ **y** _d_ [ _δd|_ **y** _ds_ ; _θ_<sup>ˆ</sup> ] obtained under the nested error model for **y** _da_ , _d_ = 1 _, . . . , D_ . A general MC simulation procedure that approximates the CEB predictors is then adapted from that for EB predictors. This procedure is described next.

# **Monte Carlo simulation procedure for Census EB estimator:**

1. Fit the model (5.1) to the sample data ( **y** _s,_ **X** _s_ ), obtaining an estimator **_θ_**<sup>ˆ</sup> = ( **_β_**<sup>ˆ</sup><sup>_′_</sup> _,_ ˆ _σu_<sup>2</sup><sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2)</sup><sup>_′_of</sup><sup>**_θ_**= (</sup><sup>**_β_**</sup><sup>_′, σ_</sup> _u_<sup>2</sup><sup>_, σ_</sup> _e_<sup>
the notion that 70 percent of the world’s extreme poor are women.<sup>2</sup> Devarajan (2013) calls for urgent action to remedy such “statistical tragedy”. After all, research, policies and investments can only be as good and effective as the data and evidence informing them (Beegle et al., 2016; Jerven, 2016).

The topic of quality data and measurement has recently started to receive more attention, in the literature and in policy circles, especially for macroeconomic statistics (Jerven, 2013), literacy (UNESCO, 2015) and poverty (Beegle, et al., 2016). But the need to revisit common wisdom applies equally to agriculture, and in particular, African agriculture (Carletto, Joliffe and Banerjee, 2015). In part this is because the world in which African agriculture operates has been changing dramatically over the past two decades, following robust economic growth, rapid urbanization, and climate change. But the information base on African agriculture has been limited for a long time, often even lacking reliable statistics on basic metrics such as the country’s agricultural yields. More generally, translating economic concepts into numbers, such as the notions of productivity, seasonality, commercialization, or a household’s net food marketing position (net buyer/seller), remains intrinsically challenging,<sup>3</sup> often requiring special data that are not standardly collected at scale, forcing analysts to rely on outdated or case study evidence instead.

The household survey panel data collected under the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS‐ISA) Initiative provide a unique opportunity to take up this challenge. Over the period 2008‐2020, nationally representative surveys are to be conducted in 8 African countries, representing 45 percent of Sub‐Saharan Africa’s (SSA) population. In these countries, four or more waves of detailed information are collected on households’ economic activities, their income and well‐being,
|-0.33|1.19|2.83|1.67|1.41|1.76|
||Treated|0.032|0.041|0.016|0.038|0.057|0.086|
||Control|0.026|0.037|0.015|0.021|0.051|0.083|
|Women|Diff(p.p.)|0.006|0.003|0.001|0.016|0.006|0.003|
||Diff(%)|0.218|0.088|0.085|0.766|0.116|0.035|
||t-stat|1.28|0.72|0.32|1.99|1.12|0.22|

_Source:_ Authors’ tabulations using SEDLAC (CEDLAS and the World Bank). _Note:_ This table reports the labor force participation rate and unemployment rate of the treated group (those who received remittances) and the control group (similar individuals who did not receive remittances).

Youth in Latin America are vulnerable to unemployment and economic inactivity; research on the “ _ninis_ , _”_ from the Spanish acronym for “not in school and not working”, finds high rates in the countries of the Northern Triangle (de Hoyos, Rogers, and Szekely 2016). Considering only those between the ages of 18 and 25, Table 4 reports the likelihood of being neither in school nor employed for youth who receive remittances compared to similar youth who did not, by gender. With the exception of Honduran women in 2006, youth who received remittances were statistically more likely to be _ninis_ than similar youth who did not receive remittances. For women, the effect of receiving remittances on being a _nini_ are in line with the results reported above for participation rates for all women. In 2014,
# **What Explains Big Gender Disparities in India? Local Industrial Structures and Female Entrepreneurship**

Ejaz Ghani, William R. Kerr and Stephen D. O’Connell

**Keywords:** Female, gender, entrepreneurship, agglomeration, cluster, business networks, development, India, South Asia.

**JEL Classification:** J16, L10, L26, L60, L80, M13, O10, R00, R10, R12

_Author institutions and contact details:_ <u>Ghani: World Bank,Eghani@worldbank.org; Kerr: Harvard University, Bank of Finland, and NBER,wkerr@hbs.edu; O’Connell: World Bank</u> and CUNY Graduate Center, <u>soconnell@gc.cuny.edu.</u> _Acknowledgments_ : We thank Muhamad Amin, Mehtabul Azam, Maria Correia, Meera Chatterjee, Rajeev Dehejia, Arti Grover, Debasree Das Gupta, Lakshmi Iyer, Henry Jewell, PratapBhanu Mehta, Reema Nanavaty, and Jennifer Solotaroff for helpful comments on this work. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. We are particularly indebted to Shanthi Nataraj for sharing her wisdom regarding the industrial survey data. Funding for this project was provided by World Bank and Multi-Donor Trade Trust Fund. The views expressed here are those of the authors and not of any institution they may be associated with.
- 17 -

used single-sided bookkeeping method (SES), and self-employed individuals who rely on double entry bookkeeping. Observations were collected until each stage of the decision tree includes at least 25 observations. In cases where it was impossible to have 25 observationc, data from other fiscal years, adjusted for inflation, were used. The probability of appeal at each stage is estimated as the percentage of taxpayers who appealed at each stage and the final increases in tax liabilities are the average differences between the declared tax of the taxpayer and the final asse *ment. The audit time at each stage is taken to be the time used by the tax administration to monitor its work-force.

The following table summarizes the return per day of auditing for each type of tax return. The highest return is for auditing tax returns of self employed individuals who use double entry bookkeeping while the lowest return is for auditing corporations. The low return for auditing corporations is unexpected. There are two contributing factors which may explain this outcome. Large corporations are audited in a separate center, hence they are not covered by our sample. Furthermore, in fiscal year 1982, the year of our sample, a new 'inflation-proof" corporate tax law was enacted to deal with taxation of capital income. Due to the high rate of inflation and the new and complicated tax law, accountants were apparently able to find "loopholes" in the tax law so that tax collection from corporations almost collapsed. Hence. the return for auditing a corporation that year reflected the ability of accountants to outperform tax in3pectors. Apparently, in a period of high inflation, investing time in auditing corporations does not yield the expected return and, therefore, in this period it is worth to divert some of the work-force to audit other files.
48.77|0.14|11.31|4.64|0.30|118.6|
|**1990**|36.61|0.55|4.79|0.35|43.07|0.15|10.05|3.84|0.58|139.4|
|**2000**|35.62|1.18|7.56|0.33|40.22|1.07|7.08|6.23|0.72|165.3|

_Source:_ Authors‘ calculations based on data described in text.

R&R: Raw and Remainder combined.

40
negative effects on growth; the positive one works through higher productivity and the negative one is caused by inefficient provision and distortionary effects of public taxation.

As noted by Bayraktar and Moreno-Delson (2010) in their literature review, conflicting results on the impact of public spending on economic growth still continue to be found in most recent studies (Schaltegger and Torgler 2006, Agell, Ohlsson, and Thoursie 2006, Benos 2009, Ghosh and Gregoriou 2006). A positive relationship between public spending and economic growth seems to be found only under specific circumstances. For instance, Gupta and al. (2005) show that government expenditure, especially its capital component, has a positive impact on growth for low-income countries when it is combined with a lower budget deficit. Baldacci and al. (2008) also find that education and health spending support higher growth in developing countries when controlled for governance. Overall, recent empirical findings remain largely consistent with Barro’s initial insights. At the lower end, public spending (when properly prioritized) provides the necessary public goods to protect various forms of economic and civil and political freedoms. Beyond a certain threshold, public spending distorts individual choices and could even undermine economic freedom itself.

# **3. Data**

## **3.1 Sources**

The concepts of economic freedom, civil and political rights, and entitlements rights are notoriously difficult to quantify and attempts to grasp such complex subjects in one summary index can only be deceptive. Each concept is wide in scope (both breadth and depth) and impossible to summarize in one all-encompassing indicator. The best that can be done is to approach each concept through a combination of measurable indicators and proxies. The data used in this paper includes the index of Economic Freedom of the World of the Fraser Institute and the indices of Civil Rights and Political Liberties published by the Freedom House (Annex 1). These are among the few available databases that cover those concepts for a large sample of countries and a relatively long period of
**<mark>Hypothesis 5</mark>** <mark>:</mark> _<mark>An increase in the benefit of being included in the emerging market corporate debt index</mark>_

_<mark>causes some medium-sized firms, which previously would not have issued a sufficient amount of debt to gain inclusion in the index, to issue bonds large enough to gain inclusion in the index. The change in the probability of issuing large bonds should be greater for medium-sized firms than for firms in the upper and lower tails of the size distribution.</mark>_ _<mark>lower tails of the size distribution.</mark>_ <mark>Lastly, an increase in the size yield discount D has no effect on the cash holdings of sufficiently large firms, defined as those that would issue 500 or more in debt irrespective of the changes in the yield discount. In contrast, medium-sized firms that prior to the increase in D would have chosen to issue X in debt, respond to the increase in D by choosing to issue 500 in debt, rather than X < 500, and accumulate cash equal to (500 – X). Thus, within the group of firms that choose to issue 500 in bonds, firms of relatively small size will increase their cash holdings more than relatively large issuers of large bonds. We summarize this result in Hypothesis 6:</mark>

**<mark>Hypothesis 6</mark>** <mark>:</mark> _<mark>Within the group of large bond issuers, relatively small-sized firms will increase their cash holdings by more than relatively large-sized firms.</mark>_

# **<mark>3. Data</mark>**

<mark>We use data from different sources. The data on bond issuances come from the Thomson Reuters Security Data Corporation Platinum database (SDC Platinum). This database contains transaction-level information on new issuances of
significantly underperform relative to predicted growth and check for economic policy changes during this period.

# **6.5 Grouping of Variables**

With an increasing number of explanatory _x_ variables in the model, it becomes increasingly difficult to display the results in an easy-to-access fashion for several periods. The studies cited at the beginning of this section hence aggregate those variables into different groups. In the above illustration for Bangladesh, for example, one could group the growth contributions of inflation, the real exchange rate, financial crises, government consumption, and growth volatility into a “macro stabilization” category. Grouping choices will depend on the question or hypothesis one is interested in. For example, Araujo et al. (2016) were particularly interested in whether the solid growth performance in most Latin American and Caribbean countries in the early 2000s was due to good policies or a favorable external environment, which motivated their variable grouping into structural policies, stabilization policies, and external conditions.

# **6.6 Extending the Model to More Recent Periods**

Our data set ends with period 10, covering the years 2015-2019. If one wants to analyze a more recent period for an individual country, one does not necessarily need to update the whole data set (although we plan for regular updates in the GitHub repository). Remember that country-specific growth analysis is performed with changes of variables. One can hence rely on the parameter estimates from models like those in table 2 and apply them also to more recent changes in variables, even if they are not fully consistent with the level variables in the model. For example, one may not be able to re-calculate the full infrastructure index in our dataset for more recent periods. But one could alternatively create a similar index for the country under investigation with available variables that resembles the index in the dataset for the pre-2019 periods as good as possible and then take changes beyond 2019 for country-specific growth analysis.

# **6.7 Creation of Growth Scenarios and Benchmarking**

A likely question
different constraints that surround it, in the allocation of resources among sectors and in the formation and growth of

> 1We adopt the definition of these three sectors based on Herrendorf et al.’s (2014) survey of the literature on structural transformation. In particular, the agriculture sector includes cultivation and breeding of animals, plants, and fungi. The services sector refers to the tertiary sector: financial, business, distribution and personnel services. Finally, the manufacturing sector captures all production activities that fall outside agriculture and services.

2
.034)|(0.032)|(0.033)|(0.033)|
|<br>**Observations**|327|327|327|327|
|**R2**|0.217|0.223|0.217|0.226|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

The results from both estimations do not provide a clear direction in the relationship between public spending in agriculture and vulnerability. It is possible—in fact, desirable—that public spending allocations are guided by levels of past vulnerability. Hence, endogeneity may be biasing the results.<sup>10</sup> Some categories of public spending, such as infrastructure, could be 10 We investigate this relationship by regressing the impact of 2003 VAM levels on agricultural spending in 2007, at the municipal level. Annex 3 reports the results, which confirm that an endogenous relationship may well underlie 15
for 36% of Kenya’s GDP on average between 2015 and 2019 (Appendix Table A4). Excluding VAT-exempt sectors such as agriculture, non-market services and finance, the share increases to 67% of residual economic activity, implying an informal sector share of 33%. The share of informal firms and people employed in the informal sector is even higher. Of the 7.4 million businesses identified in the 2016 MSME report, only one fifth were licensed, and only 2.5% appeared in VAT data (KNBS, 2016). Similarly, VAT-registered private sector firms employed 5% of Kenya’s workforce in 2019.

With data on the formal sector only capturing a proportion of overall economic activity, we now turn to the question on whether the inability to observe informal firms in the VAT data might result in distinct trade patterns that deviate from overall firm-to-firm trade.

# **3 Representativeness of the Formal Firm Network**

In this section, we establish two key stylized facts about the extent to which trade among formal firms might be representative of overall economic activity. First, we show that trade among formal firms is highly concentrated around urban centers and places greater emphasis on inter-county rather than within-county trade. This contrasts with other measures of economic activity that include less formalised activities and which we find to be more geographically dispersed. Second, we document that the incidence of informality varies systematically across geography, sectors, and positions in the supply chain, suggesting that informality is indeed not evenly distributed across the economy. As a result, we expect overall trade patterns to diverge from the ones we document for the formal sector.

## **Fact 1: Formal sector trade is more spatially concentrated than overall economic activity and distinctly centered around urban hubs.**

### **Formal sector data reveal a high degree of spatial concentration compared to overall economic activity.**

Our first observation is that formal sector trade flows are strongly concentrated around Kenya’s largest metropolitan areas Nairobi and Mombasa.
CLEMENS & TIONGSON

## the survey were exhausted (which occurred at 5 points from the cutoff).<sup>19</sup>

The survey was “blind” in two senses. First, no one at the survey firm knew which households were those whose member passed and which were those whose member failed. The firm was only given the name, permanent address, and phone number of the applicant. Second, the survey enumerators and respondents were told only that the study was a “follow-up survey on families of POEA job applicants”. Neither enumerators nor respondents were told that a goal of the study was to identify the effect of migration by a household member on the household.

Rough power calculations before the survey suggested a target sample size of roughly 900 households.<sup>20</sup> In the end, enumerators attempted to locate the permanent addresses of 2,053 EPS applicants, of which they successfully located and visited 1,532. Of these visits, 899 (59%) resulted in a completed survey. The rest were nonresponders— either because the residents declined to complete the survey (9%), no one was home or the residents were not the applicant’s family (15%), or neighbors indicated that the applicants’ family had once lived there but had moved away (17%) or died (0.1%). All 899 completed surveys represent the households of applicants who scored within 5 points of the cutoff.

# 5.2. Checking the discontinuity: Survey sample

Because only 59% of located addresses produced a completed survey, we must check for bias due to nonresponse. For example, if an important use of remittances were to purchase a new residence and move away, passing the test itself may affect the response rate. This could produce differences across the passing threshold not attributable to passing the test or migration.

> 19An alternative method might have led to a less representative sample. For instance, if we had provided
Snijders and Bosker, 1994; De Luca, Magnus, and Peracchi, 2018). As such, it is useful to examine imputation accuracy for different models.

> 15 For Viet Nam, house wall material is assigned numerical values using the following categories: 6 "cement", 5 "brick", 4 "iron/wood", 3 "earth/straw", 2 "bamboo/board", and 1 "others". Toilet type is assigned numerical values using the following categories: 6 "septic", 5 "suilabh", 4 "double septic", 3 "fish bridge", 2 "others", and 1 "none".

16
secondary education to stop working. Workers with tertiary education were on average 6.6 percentage points less likely to be out of work than those with less than high school education (Figure 7, Panel C and Appendix Table 3). Interestingly, differences between workers with tertiary education and those without decrease once we control for sector of employment: The relative advantage of tertiary educated workers to those with less than high school education declines to 3.7 percentage points (Appendix Table 3). Yet, the difference remains significant. suggesting that differences within sector associated with type of occupation, job formality/security, and the ability to work from home may be driving results. In addition, when looking at differences across quintiles, controlling for education, the coefficient on quintile 5 is reduced only slightly (from -0.131 to -0.126) and remains significant (Appendix Table 1, column 5).

A third hypothesis, though one that we are not able to test here, is that even within certain economic sectors, poorer workers are generally more likely to work in micro or small firms, which have been disproportionately affected to the pandemic (Freund and Pesme, 2021). Adopting digital technology such as digital platforms and digital solutions has been a common response among businesses to dealing with decreased sales during the pandemic. Findings from the Business Pulse Surveys suggest that digital adoption is associated with a smaller decline in sales. However, these surveys show that smaller businesses have been significantly less likely to adopt digital technology than larger ones (Freund and Pesme, 2021).

To note, in addition to differences in work stoppages across quintiles and by education levels, female workers were more likely to stop working once we control for education and sector of employment, according to pooled regression analyses (Appendix Table 2, 3). However, there is some variation across countries (Figure 8, Appendix Table 2). In places like Indonesia and the Philippines, women were less likely than men to return to work