rafmacalaba/data-use-mentions-extended huggingface dataset:
adversely affect them compared to domestic firms, which likely have better ongoing relationships with the local governments in charge of various permits.

# **Further analysis of how foreign-owned firms and domestic firms respond to the BE**

We further gauge how foreign-owned firms (“Foreign”) and domestic firms respond to the BE differently by linking a firm’s _individual_ answers in the Enterprise Survey to the _country-level_ indicators of the business environment based on either the Global Competitiveness or _Doing Business_ surveys. This exercise, when combined with our earlier findings on the links between the business environment and firm performance, allows us to understand whether foreignowned and domestic firms respond differently to the BE due to differences in their endowment and/or returns. To this end, we allow this link to differ by whether the firm is foreign owned (“Foreign”). That is, Responsei i = α1 ⋅BEk + α2 ⋅BE × Foreign + X β1 + Wi β2 + θ + η + εi i . (2) We do this one aspect at a time to avoid multicollinearity. For instance, we regress a firm’s ii ii ii ii ii ii response to the Enterprise Survey indicator of Corruption Obstacle to the Global Competitiveness indicator on Corporate Ethics, conditioning on country and firm controls, industry and year fixed effects. The results are presented in table 8.

_Government Protection–corruption_ . Foreign-owned and domestic firms are similarly hampered by a low level of the Corporate Ethics measure, as shown by the similar magnitudes of the coefficient of the Global Competitiveness Corporate Ethics indicator in explaining the Enterprise Survey’s Corruption Obstacle indicator (0.151 versus 0.122). This is consistent with our earlier finding that foreign firms do not differ significantly from domestic firms in the effect of corruption on their firm performance.

_Government Protection–safety_ . Foreign-owned firms appear to be much more sensitive to physical safety. When the Global Competitiveness Police System score is lower—that is, when the host country’s physical safety is worse—
on property ownership in India is either not publicly available or not collected (Agarwal, Anthwal, and Mahesh 2020; Lahoti and Swaminathan 2016). This complicates an assessment of the level of property ownership among females. An extraction and analysis of raw data on home ownership from the National Family Health Survey (NFHS 2015–16)<sup>5</sup> shows that levels of home ownership overall are high across several states; see Table 1. (NFHS defines women property ownership as follows: Women owning a house and/or land, alone or jointly with others.) The proportions of female ownership are considerably lower, however, suggesting most homes are owned solely by males. Swaminathan (2016), using India Human Development Survey II (IHDS, 2011–12) data, reports that female land ownership in India constitutes about 3.9 percent of total land ownership, and female house ownership constitutes about 6.5 percent of total house ownership.

Table 1 shows that outright female ownership makes up only 13.98 percent of ownership in India as a whole, though this varies by state; in Delhi, for example, it is 24.5 percent. Table 1 also shows that when females own homes, they mostly own them outright, rather than jointly with others. For instance, in the

> 5. Raw data for the 2019–20 NFHS round has not yet been released.

3
Tables 2a and b show that movement into self-employment is always associated with a substantial and significant _rise_ in per hour after-tax remuneration from every other sector, reaching over 25% from the fornal and informal salaried sectors. However, the joint NUES/MES panel (table 3) shows that after modest adjustments for capital costs, and much larger ones for unpaid labor,"' both contract work and self-employment share simrilar premia with respect to formal salaried and informal salaried work, and the differential between them is insignificant. Contract work appears similar to selfemployment and may therefore share the same composition of voluntary and involuntary entrants.

Salaried informal employment, however, suffers a discount relative to all other sectors. After adjusting the differentials for standard firm size effects observed in the U.S.,"<sup>2</sup> moving to an informal sector firm from a comparably-sized formal sector firm now yields a 12-15% rise in the "microenterprise" sample, comparable to those above, but only a slight rise in the "unprotected" sample. Further, the large asymmetries in both cases suggest that informal salaried workers gain far more entering salaried formal work than they lose leaving, as would be predicted if they were queuing for 11 A return of 10% was imputed. Most micro-enterprises who save in commercial accounts or cajas de ahorro received 3% real. Implicitly we are assuming 7% for depreciation. The low level of capital employed results in the overall differential being relatively insensitive to the cost of capital value chosen. The more detailed treatment of taxation in the MS allows dropping the previous assumption of complete avoidance by the self-employed and induces a slight moderation in the after-tax differential. The adjustment for unpaid labor may be overstated if Balan et al are correc **t** hat "Since in most cases these family members would not otherwise be employed outside the household, their contribution to family finances is a 'net' one..."(p.218) or if the unpaid labor was in training
Combes (2000) investigated a number of possible localization and urbanization economies in French “ _zones d’emploi_ ”, including diversity and specialization discussed in the previous paragraph, in an attempt to examine the relative importance of urbanization and localization economies. The other variables he used to test for urbanization and localization agglomeration economies were competition, average firm size and employment density. Competition is the extent of within‐sector competition, measured by the Herfindahl index of the shares of employment of firms in the same industry and locality, which is a localization economy. Combes (2000) also used average firm size as a measure of within‐firm scale economies. He argues that if scale economies are external to firms then only city size should matter and not the average size of plants in the industry‐district. The final agglomeration economy examined in this paper is the most obvious and most widely used test of urbanization economies ‐ the effect of employment density in the locality. We include all these measures in our empirical analysis of agglomeration economies in Ghana.

# Testing for Agglomeration Economies

The possibility of agglomeration economies is most often tested for by examining the effect of agglomeration on productivity, either by using firm‐level data, such as value added per worker or average earnings in the firm, or individual level data, usually earnings. But the effects of agglomeration on employment growth are often studied, and this is what we undertake below, following a substantial literature reviewed in Combes and Gobillion (2015).

# The Modifiable Area Unit Problem

Whilst the literature testing for agglomeration economies initially used data from cities, with Glaeser et al (1992) being the path‐breaking contribution, there is also a substantial literature examining possible agglomeration economies for entire countries or several countries together (Combes and Gobillion, 2015), although the trend has generally been towards smaller spatial units or using individual firm or worker data. In this paper we use 110 districts, corresponding to administrative boundaries, covering the entirety of Ghana. Using data aggregated from firms for
Government|6.6%|0|0|0.0%|
|Municipal Government|1.6%|0|0|0.0%|
|Federal Government|0.9%|0|0|0.0%|

Note: CV estimates are calculated using NAICS code level weighted averages of the estimated JA-tariff equivalents. All figures are in 2016 dollars; per capita estimates use PR’s population in 2016. All calculations assume zero tariff equivalents for NAICS codes 3251, 3254 and 3390, the pharmaceutical sector.

Using the same approach, but considering the removal of 2016 U.S. MFN tariffs for all goods arriving from ROW partners that are not members of a preferential trade agreement with the U.S., we calculate the CV of removing such tariffs to be U.S.$150 million (about 0.1 percent of expenditures).<sup>45</sup> MFN tariffs on Puerto Rican households’ imports of non-PTA partner goods costs them approximately $94 million, or $28 per person per year. The implication is that the JA costs Puerto 45The tariffs we remove are trade-weighted averages of 2016 MFN tariffs imposed on non-U.S. PTA partners, assuming a value of zero for PTA partners.

39
that the calculations do _not_ represent normative assessments of GHG mitigation policies in the countries named; they are _only_ illustrative numerical examples.

We base our calculations on the Enerdata data set MAC, which was set up to project mitigation implications of carbon taxes introduced at particular future dates.<sup>10</sup> Our calculations are based on the “Enerbase” simulations created by Enerdata, where it is assumed that no climate policy is applied up to 2025 in these countries under business as usual (and where LULUCF emissions are ignored).<sup>11</sup> In our application of Enerdata, we consider two different carbon taxes in 2025, namely $10 and $20 per ton of CO2 e. We phase in the carbon tax linearly over time: in the $10 case, for example, the tax is zero in 2020, $2 in 2021, $4 in 2022, etc.

The Enerdata framework is somewhat broader than the simple static framework considered in Sections 2-4. The most important difference is that with the Enerdata approach, the quantity of mitigation resulting from doubling the carbon tax is less than double that from the initially chosen carbon tax. This reflects a presumption that a significant amount of “low-hanging fruit” can be harvested as mitigation outcomes at relatively low carbon taxes. In the model framework laid out in Sections 2-4, by contrast, mitigation outcomes are proportional to the implemented carbon tax. Similarly, in the conceptual model in Sections 2-4, support payments tend to increase with the square of the carbon tax rate, since a higher carbon tax leads to a proportionally higher mitigation rate. Since mitigation in the Enerdata simulations increases by less than the carbon tax, support payments grow more slowly than the square of the tax rate.

Table 2 gives figures for mitigation effected in the five sample countries, due to imposing a carbon tax which increases linearly by year, from zero today up to either $10 or $20 per ton CO2e by 2025. Column 5 gives mitigation in
both job and housing. Thus, inside and outside the _bantustans,_ rural areas entered South Africa's transition with a legacy of squandered assets and inappropriate production and investment strategies. The contraction of the economy and the erosion of the rural economic base through population growth in the absence of productive infrastructure implied that households previously dependent upon a cash income were left with neither the income nor the assets from which they could generate an adequate income. The resulting combination of lack of assets, absence of economic opportunities, and high levels of poverty threatens to persist long after South Africa's political transition.

The extent of rural poverty is confirmed by data from the 1993 SALDRU survey. As Table I illustrates, approximately half of South Africa's total population are poor but the poverty headcount in rural areas is 71%, compared to 29% in urban areas (SALDRU 1994). Thus, most of South Africa's poor, about 13.7 million people, live in rural areas. More than three quarters (76%) of the total poverty gap is accounted for by rural households, although they make up only half of the total population. Table 2 shows the poverty gap and the percentage of the population categorized as non-urban by the 1996 census for each 6
7 Before turning to the data, an obvious question concerns the accuracy of the responses. The survey was sent to the principal contacts in each country of the Basel Committee on Bank Supervision. Even though these contacts should know the regulatory environment, the survey’s scope is such that for any country a number of people usually are involved in its completion, and some or all of the members of this group might change over time, raising the issue of differences in the interpretation of questions over time (in addition to changes in the wording noted above). In the first three surveys, we ourselves went back to or directed correspondence with authorities for clarification, where there were notable changes, as well as posting the survey responses on the web, so that the data could be challenged and inconsistencies resolved. As none of us were with the World Bank, we were not involved with this cleaning process for Survey IV, and in this paper use the data posted on the World Bank website. As always, there are instances of unclear responses from the authorities and we accordingly recommend ongoing efforts to clean (and update) the data. There are also some notable gaps at present, such as the absence of responses from Japan and Saudi Arabia in the latest survey. It might also be noted that some countries chose not to respond to any surveys, not to respond to some surveys but to others, and not to answer some questions but others, which raises the question as to whether this was a strategic decision or simply survey fatigue.

# **III. Bank Regulation and Supervision around the World** : **What the Data Say**

With four surveys over more than a decade, one can ask to what extent have there been changes in the regulatory environment in countries around the world. As Survey IV is just becoming available, analysis of these changes understandably is in an early stage, and we hope that with the data available on the web, more people will investigate the impact of
- For the families composed only of work-unable persons (children, adults with disabilities, persons aged above 75 years (62 for the aid for the cold period of the year)) – the income derived from agricultural activities;

For 2017 the GMMI was established in the amount of 961 MDL. The GMMI is subject to annual indexation based on the CPI index for the previous year.

# Other social assistance benefits

In 2017 this category spent 1.74 billion MDL. It covers a vast number of different benefits, including subsidized tickets for treatment in balneary stations, summer camp vacations for children from vulnerable families, pension supplements up to the level of the minimal pension, and others.

However, the dominant item is the “supplementary financial support to some beneficiaries of pensions and social allocations”. The annual amount spent was approximately 1 billion MDL in 2017. Total number of beneficiaries was roughly equal to 560,000 persons, with the average supplement slightly more than 140 MDL / month.

With an annual amount of 557 million MDL, the pensions supplements up to the level of the minimal pension ranks the second in the category of other social assistance benefits. Slightly more than 153,000 persons were entitled to receive the supplement, which averaged 300 MDL.

# Students’ scholarships and subsidies

Students from superior and VET education are entitled to education scholarships (stipends). There are three types of public scholarships: the general education scholarships, the merit scholarships and the social scholarship. Scholarships can be provided to up to 70% of the local students enrolled in public-funded programs in educational establishments. Within this limit, scholarships can be provided both to budget-funded students and to those paying tuition fees. Details on the general education scholarships for different educational levels are provided in the Table A9, A10, and A11.

# **Table A9. General education scholarships for the tertiary education – cycle 3 (PhD program), resident doctors, clinical secondary doctors and post-doctoral students.**

|Nr.<br>crt.
self-employed and employers are similarly challenged to survive and grow into job-creating firms. They believe there is little benefit from registration. Their heterogeneity is found in their business practices. Fifth, after controlling for individual and job characteristics, access to public services is weakly associated with the probability of working in an informal job, though access to mobile phones is high across all job types, suggesting that digital connectivity is a valuable policy focus for informal (and formal) workers.

# **2. Data and Definitions**

We are interested in understanding the nature of jobs, both in terms of the worker’s relationship to the firm owner and in terms of job benefits and earnings potential offered by the firm. For the former, we consider three groups: employees who work for others, selfemployed who do not have paid employees, and employers. For the latter, we use the firm organization concept put forth by Bandiera et al. (2022) and refer to informal enterprises as those that are less “organized” in terms of business practices, linkages to the organized economy and institutions, spillovers to the household, and value-added, as compared to more organized and higher value-added formal firms.<sup>12,13</sup> We use four data sets in the analysis. First, we use the Global Monitoring Database (GMD) to create a profile of urban informal workers in 26 SSA countries. The GMD is the <mark>World Bank’s repository of harmonized household surveys used for poverty monitoring.</mark> We limit the sample to the 26 countries that provide information on employment type, education level, and labor benefits for wage employees, to classify workers as formal or informal.<sup>14</sup> The countries included in the sample represent about 85 million urban workers, equivalent to 68 percent of the total urban workforce of Sub-Saharan Africa.<sup>15</sup> Table A1 in the Appendix lists the survey and year used for each country
Table S1.3: CORRELATIONS BETWEEN HAVING TRANSFERS FROM EMPLOYERS AND TERMS OF LABOR CONTRACTS

||_W_|_omen’s labor con_|_tracts:_||_Men’s labor cont_|_racts:_|
|---|---|---|---|---|---|---|
||Hourly<br>wage|Range of<br>wage income|Range /<br>hourlywage|Hourly<br>wage|Range of<br>wage income|Range /<br>hourlywage|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Interlinked contract|-0.277***|-88.121*|-8.370|-0.705***|-101.017|-0.947|
||(0.099)|(47.645)|(8.051)|(0.154)|(74.337)|(8.341)|
|Observations|6358|6358|6358|7988|7984|7984|
|Mean level of outcome|5.743|1218.879|221.179|9.643|2271.537|252.602|
|Adjusted R-squared|0.217|0.113|0.067|0.268|0.074|0.073|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports baseline correlations between having received transfers from employers and contract terms (wage per hour and volatility (range) of monthly wage earnings). ‘Received transfers from an employer’ is a dummy variable equal to 1 if respondent is working for an employer who lives in the same community and who is also reported as a source of transfers for the respondent’
non-adopters in A villages, and households who expressed interest to adopt if the technology is introduced and who were uninterested in B villages) were selected<sup>12</sup> .

The survey questionnaire was administered to 5,541 individuals in 955 rural households in each round who were selected through this multi-stage sampling approach. The survey collected detailed information on demographic characteristics, agricultural production, other income-earning activities, expenditure patterns, time allocation, individual food intakes, health, morbidity, and education. It also collected information on family background, marriage history, assets at marriage, transfers at marriage, inheritance, women mobility, and empowerment.

IFPRI sampling required that the households were representative of adopters and non-adopter households in A villages and likely adopters and likely non-adopters in B villages, and not necessarily representatives of rural Bangladeshi households. Nonetheless, a comparison of IFPRI sample with that of 1995-96 National Household Expenditure Survey (HES) of Bangladesh Bureau of Statistics of the Government of Bangladesh indicates that the IFPRI sample is broadly comparable to the nationally representative HES rural sample of 5,020 households based on household size, per capita expenditure, landholding, and poverty rates (Table 1).

# **5.1 Variables and Descriptives**

Table 1 provides the descriptive statistics of the variables used in the econometric analysis, and the variables are described below.

**Survey round and site dummies** : Survey round dummies (with 4th round omitted) are included to control for any agricultural seasonality that may affect nutrient consumption as in lean seasons there may be lack of food availability and labor market activities that can affect household food expenditure and income. As there are two major rice cultivation seasons, there are also two lean seasons that reduce employment and income earning opportunities. The major lean season is from mid-September to mid-November preceding the Aman harvest, which falls in round 2. The other lean season falls in round 3, which is from mid-March to mid-April, prior to Boro harvest<sup>13</sup>
defined according to nuclear family relationships.<sup>7</sup> It is therefore possible that more than one family may reside in a single household. Partly because of this, we present three models of the PMT: Model 1 includes 36 predictors of consumption, inclusive of 9 household head variables. Model 2 excludes head characteristics, and therefore includes 27 predictors, while a more parsimonious Model 3 has only 18 predictors.<sup>8</sup> Models 2 and 3 exclude head characteristics because current targeting of the Samurdhi program is done at the family level, and there may be significant differences between household and family head information. Although including more variables predicts welfare slightly better, including more variables in the model makes data collection more costly and increases the chance of errors. PMT models used in other countries are usually similar to Model 1 and include characteristics of the household head. In fact, the results of the three models are not substantially different from each other, and all three lead to considerably better targeting than the existing selection method.

While income typically fluctuates as a result of both permanent and transitory income shocks, households typically smooth consumption over time, so we use the log of per capita household consumption as the measure of welfare. Consumption comprises monthly expenditure on both durables and non‐durables, as well as the reported rental value of owner‐occupied housing. The DCS has published official poverty lines for each district based on the 2016 HIES. We use these district poverty lines to construct a district price index, and deflate the consumption measure by this index.<sup>9</sup> This enables us to set a single cutoff score for households across the country. The PMT score for a household is created by multiplying its predicted log real per capita consumption by 100.

The household head characteristics included in Model 1 are age, gender, marital status, and employment status (government, semi‐government, private, or self‐employment). Household demographics include categorical