rafmacalaba/data-use-mentions-extended huggingface dataset:
relationship between firm sales and firm-to-firm connections (Bernard et al., 2022; Bacilieri et al., 2023) as well as potential differences in how small firms organize their supply chains across space and sectors.<sup>21</sup> Table B1 shows that small firms within the same sector and county are less likely to directly source from manufacturing firms (upstream), but instead are more likely to source from retailers or wholesalers (downstream). Further, they are less likely to source from Nairobibased suppliers and more likely to source locally.

Third, the model incorporates a flexible network formation process such that the emergent degree distribution can follow a power law. The underlying dynamic network formation process gives rise to the substantial inequality in outdegrees across firms that has been widely documented in the literature (Bernard and Moxnes, 2018; Panigrahi, 2022; Bernard et al., 2022; Bacilieri et al., 2023). Figure B1 plots the outdegree and indegree distribution of the formal firms in our VAT data, revealing a very unequal degree distribution that resembles a power law. Our framework is flexible and allows us to estimate the share of firm-to-firm links formed via directed search (searching among the trade partners of existing suppliers) versus undirected search (often referred to as _random search_ in the networks literature (Jackson and Rogers, 2007)).

# **4.2 Model Setup**

Consider an economy with a set of firms denoted by _N_ . Each firm _i ∈ N_ is of a given type _θi ∈_ Θ where Θ is the set of all possible types. In our application, we specify firm types as unique sector-county-size combinations, i.e. all firms in the same sector, county, and size group are classified as the same type. Our aim is to predict a matrix _π_ that captures the number of links that exist between every possible pair of firm types.

The network formation process is as follows. In every period _t_ , a new buyer firm of type _θ_
# 3. Cross **Sectional Regressions**

We next test these hypotheses on a cross section of countries for which a consistent set of productivity and distortion variables are available. In the first set of regressions, we examine the determinants of self-employment as a share of the total work force. In the second set, we focus on two measures of turnover. Using deviations from the predicted values of these regressions, we construct somewhat speculative measures of the magnitude of distortions and of rigidities.

# **_3.1 Variables_**

The data sources and more detailed descriptions are listed in Appendix I.

# _3.1 Dependent Variables_

Share of Workforce in Self-employment: The OECD tabulates the share of non-agriculural workers in self-employment or as owners of firms. To the degree possible, the same variable was created from the employment and household surveys from Latin America. We focus on selfemployment rather than informality more generally for two reasons. First, we believe that it is the act of opening a business that is the central issue and that informality while important and often highly correlated is secondary.'<sup>3</sup> Second, data on informality in the OECD is largely unavailable and the LAC data sets differ in the variables available to use as proxies. Third, data on those employed in micro-enterprises is not available for the OECD. We assume that the total population employed in the self-employed sector, both as owners and workers is proportional to the share declaring themselves self-employed.

Mean Tenure in the Manufacturing Sector and Share of the Manufacturing Work Force with under Two Years of Tenure: These are two alternative variables available from the OECD and then generated from the LAC household and labor market surveys. We focus on manufacturing turnover because this is the best proxy for formal sector turnover that is available.

# _3.1.a Formal Sector Labor Productivity/Profitability (Z)_

Industrial Value Added (Indust. V.A.): The log of industrial value added per industrial worker is

> **13
, using long-term and aggregated data in order to provide sensible results.

In calculating the cost of extension provision in ATP 1, we use actual data available for technical assistance programs in INTA (Estrada-Rizo and Garcia, 1997). These estimates are presented in Table 8.

It appears from Table 8 that the annual average cost per extensionist in the technical assistance programs (ATPb and ATP 1) is $3612. Figure 6 suggests that about 70% of these costs
countries by level of income is based on the World Bank Atlas Method, which classifies every world economy as low income, middle income and high income depending on their gross national income. We have used the official WB thresholds for 1987 and divided the countries in our sample between North (with more than $6,000 per capita annually), which include high-income countries and South (less than $6,000 per capita annually), which include low- and middle-income countries.

The trade agreement dummies we create are effectively an interaction between a general FTA dummy and a dummy for whether the signatories are Southern, Northern or a combination. These dummies capture all kinds of observable and unobservable country characteristics. With reference to the theoretical discussion in Section 2, these include those features that make countries more natural traders and that influence the nature of the trade agreement struck. While our need to distinguish between types of FTAs is in part motivated by the theoretical discussion, this specification is not intended to test any claim directly.

We include time-and-country ( _it_ & _jt_ ) as well as bilateral ( _ij_ ) dummies. The time-and-country dummies are primarily included to control for multilateral resistance. An alternative would be to construct MR terms from observables usually included in gravity models (as in Adam & Cobham, 2008), but dummies offer a number of advantages. Construction of MR terms is subject to measurement error, particularly the internal distance measure (Baier & Bergstrand, 2009). In most cases, for example distance, the variation over time is due only to variation in the GDP share terms, which leads to multicolinearity. Furthermore, dummies also capture any other time-varying exporter- and importer-specific effects like GDP and unobserved characteristics. For this reason, we use the time-and-country dummies.<sup>13</sup> Identification of the FTAs as a causal effect must be mindful of unobserved characteristics that affect both trade and FTAs, some of which were noted in Section 2. Further, Baier & Bergstrand (2007) provide evidence
countries 49% of the firms have a bank loan, while 26% of recent investment is through banks. Surprisingly, the use of external finance is higher in the candidate countries, where 60% of the firms have a bank loan. Firm leverage seems to be the lowest in the EU neighborhood countries where only 39 percent of the firms have a bank loan.

Is the leverage of firms in Emerging Europe particularly low compared to firms in more advanced European economies? Recent evidence suggests that the use of bank finance is actually quite similar in emerging and advanced Europe. Using data from the 2005 wave of BEEPS, Brown et al. (2011a) show that the share of firms which have a loan in Emerging Europe is only slightly lower than in a selection of Western European countries (Ireland, Portugal, Spain, Greece, Germany).

**Table 4.1. Enterprise Credit by Country**

||Bank loan(%)|Overdraft(%)|Leverage|
|---|---|---|---|
|_EU members_|_49_|_42_|_26_|
|Bulgaria|39|28|28|
|Czech Rep|48|58|17|
|Estonia|50|41|23|
|Hungary|42|47|33|
|Latvia|48|19|28|
|Lithuania|52|13|31|
|Poland|50|52|23|
|Romania|43|44|21|
|Slovak Rep|43|53|21|
|Slovenia|74|64|34|
|_EU (potential ) candidates_|_61_|_60_|_28_|
|Albania|49|84|15|
|Bosnia|65|51|27|
|Croatia|66|67|33|
|Macedonia|62|21|29|
|Serbia|67|66|29|
|Turkey|56|68|38|
|_EU neighborhood_|_39_|_37_|_22_
_T_ 4), and (4) the effect of autonomy in the use of the grant ( _T_ 3 vs _T_ 2). Comparisons (1), (2) and (4) are, to the best of our knowledge, new in the literature and we briefly review their rationale below.

# **Hypothesis 1 (H1): Extension services paired with tailored recommendations increases practice adoption relative to control farmers (** _T_ 4 **vs** _C_ **)**

The extant research on providing farmers with tailored soil-analysis recommendations (e.g Harou et al., 2018) suggests limited adoption. However, such recommendations appear to be difficult to interpret and implement in isolation. We provide detailed soil analyses and recommendations (more detailed for instance than those provided by SoilDoc) and pair them with high quality extension services. The hypothesis is that the joint provision increases adoption.

Since control farmers received the recommendations at the start of the 2016 growing season, by the 2017 end-line survey, _T_ 4 and _C_ differ only in that _T_ 4 received extension services (in 2015) and had tailored recommendations for a year longer than control farmers. We hypothesize that in 2017, extension services paired with tailored recommendations increase adoption more than tailored recommendations alone.

# **Hypothesis 2 (H2): Plot-level recommendations increases practice adoption relative to clusterlevel recommendations (** _T_ 1 **vs** _T_ 2 **)**

It seems reasonable that farmers are more likely to trust and therefore act on recommendations that are based on the soil quality of their own plots, especially if heterogeneity in soil quality is significant. In addition, previous research, albeit in a different setting (e.g. Madajewicz et al., 2007), suggests that individualized information is more effective. Cluster-level recommendations, however, are cheaper to produce and deliver, so if outcome differences between the two are small, then cluster averaged recommendations may be more cost-effective.

Given the substantial soil heterogeneity we
. Samsung is one of the largest foreign investors in Vietnam with $9 billion invested to date, and an additional $3 billion smartphone factory under development. Its existing investments include a smartphone production facility in the northern Bac Ninh province, a smartphone and tablet display assembly facility, an electromechanical assembly operation for camera modules, and the Samsung Vietnam Mobile R&D Center. The Bac Ninh

> 10 The TiVA data set defines industries using the International Standard Industrial Classification (ISIC) system, revision 3. The electronics category is titled “electronics and optical equipment” and includes manufacturing codes for office, accounting and computing machinery; electrical machinery and apparatus; radio, television and communication equipment and apparatus; and medical, precision and optical instruments, watches and clocks. Thus, the definition of the electronics industry used by TiVA differs from the definition we are using for the UN Comtrade data presented above. The two data sources have not been harmonized, and therefore should not be compared directly.

> 11 “Trade in value-added describes a statistical approach used to estimate the source(s) of value (by country and industry) that is added in producing goods and services for export (and import).” For more information, see http://www.oecd.org/sti/ind/TIVA_FAQ_Final.pdf.

14
We examine the role of variables derived from the political economy literature such as the degree of social fragmentation (Ilzetzki, 2011) or the absence of a level playing field, such as the power distribution among social groups, the distribution of resources within societies, and the level of ethnicreligious tensions (Table 4, columns 1 to 3). All these variables are considered as a proxy to the social polarization, to capture the impact of the “voracity effect” on the budget. Indeed, social polarization is mostly captured by ethnic/linguistic/religious polarization indicators. We find that more equal distribution of resources and more ethnic-religious stability are associated with less procyclicality.

One of the more critical determinants of procyclicality is whether the country is resource-dependent or resource-rich (Table 4, columns 4, 5 and 6).<sup>9</sup> In all cases, we find a strong procyclical bias in resourcesdependent and resources-rich countries, suggesting a procyclical resource curse, in line with the recent literature (Arezki and Bruckner, 2012; Konuki and Villafuerte, 2016). One of the more interesting results is that procyclicality is arising from resource-dependence increases over time.

We also found a more significant procyclicality bias during election years (Column 7 of Table 4), though the effect appears decreasing over time. This effect is more pronounced in AFR than in LAC or MENA. We find no impact of the debt level on the procyclicality l bias, contrary to findings by Frankel et al. (2013) but like Konuki and Villafuerte (2016) for Africa.

To examine the correlation of procyclicality and fiscal institutions, we include a measure of the rigidity of the budget and variables that describe fiscal rules. The rigidity of spending variable dampens the procyclical bias. The rigid component of public expenditure reduces the procyclical bias (Table 4, column 9). But the effect is asymmetric over the business cycle: the mitigation of procyclicality through spending rigidity is larger during the upturns of the cycle (column 2 of Table 5)
Table A7: Heterogeneous effects of payment delays on durable spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|A<br>Assets|bove median:<br>Patience|
|1-day delay<br>8-day delay|-254.340<br>(1059.041)<br>430.667<br>(1242.231)|1951.916<br>(1361.749)<br>1912.118<br>(1608.583)|496.637<br>(1273.910)<br>-773.758<br>(1101.532)|440.985<br>(1223.070)<br>1481.456<br>(1368.852)|-776.129<br>(1406.079)<br>394.681<br>(1668.041)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|6507.55|4572.04|6861.69|5256.23|6107.70|
|R-squared|0.137|0.278|0.214|0.152|0.167|
|P-value: 1-day=8-day|0.569|0.979|0.262|0.494|0.495|
|Panel B:|Female|No previous|Far from|B|elow median:|
||HH head|bank use|branch|Assets|Patience|
|1-day delay|2015.864<br>(1591.954)|120.
the total population size. We believe these two assumptions give good approximations of the brain drain rate, broadly consistent with anecdotal evidence.

# 2.3 Speci...c assumptions in release 1.0

Obtaining Census and Register data from all OECD countries is a long process involving numerous exchanges of information. Before data are available in the ...nal desired format, adequate estimates can be provided on the basis of Survey data. This is especially the case for European countries where the Censuses for 2000 or 2001 are sometimes not yet available. In these cases, estimates can be obtained by crossing European Council, Register or Census data on the origin of immigrants, with Labor Force Survey data on education and age structures.

The last column in Table 1 describes the speci...c assumptions in release 1.0. Labor Force Survey data are used for 12 EU countries in 2000 and 1990 (Austria, Belgium, Denmark, Finland, Germany, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden and the United Kingdom) and immigrants’ educational attainment in Norway is temporarily extrapolated on the basis of the available OECD countries. In 2000, while Census data will ...nally capture 55.974 million OECD immigrants (95.7% of the 2000 OECD stock), Release 1.0 uses Survey data for characterizing 13.626 million of them (23.3% of the 2000 stock). In 1990, Release 1.0 uses Survey data for identifying 8.315 million (20.9% of the 1990 OECD stock) out of 36.862 million immigrants for which Census data will be available (92.5% of the 1990 stock).

Such temporary assumptions<sup>11</sup> induce two major shortcomings. First, European Council data must be used for 10 countries. These data are based on the concept of citizenship (rather than country of birth). In all cases, information on the birth country will be
4 1.4 1.2 .9<br>1.3 1.3 1.1 .8<br>1.21.1 1.2 1 .7<br>1 1.1 .9 .6<br>Wage Premium, Skilled-Unskilled<br>1<br>1.3 1.1 .9<br>1.2 .9 .8<br>1.1 1 .8<br>1 .7 .7<br>.9 .9 .6 .6<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

48
effects of the quake unrelated to the exemption, for example due to the fact that only younger cohorts responded to the limited income transfers assigned to sampled treated towns. The increase in the high school

> 22The 1991 census does not provide information on family background. We thus recover the information from the 1981 population census as follows. For each town and cohort, we select all households living in the town in 1981 with a male belonging to the relevant cohort and compute the employment rates and educational achievement of parents. In 1981 the males in the relevant sample were 16 to 23 years old, thus largely still living in their parents’ household (see Manacorda and Moretti (2006) for a discussion of the motivations of Italian youths to live in the parental household). Unfortunately, the 1981 census does not collect information on income or wealth.
that assumption for identification purposes can be questioned.

# 7 **Conclusions**

We have assessed whether bank branch placement in Bangladesh is consistent with the pursuit of unrealized gains to the poor from non-farm rural development. The main explanatory variable we have tested is our own estimate of the gains to switching from the farm to non-farm sector, based on independent household survey data. Our estimation methods allowed for heterogeneity in the prospects of being successful in non-farm activities, and included tests for whether or not a process of endogenous self-selection was already at work.

We find that typical farm households in Bangladesh are both poor, and poorly endowed in c1haracteristics conducive to success in more profitable non-farm activities. Thus, comparing average living standards overstates the potential gains to switching from the farm to non-farm sector. Nevertheless, we find that unexploited gains from switching exist, even after controlling for a wide range of household attributes, including education levels, landholding, demographics and location. Average consumption for a farm household would be about 6% higher in the nonfarm sector, and the proportionate gains tend to be largest for the poorest farm households. There are striking geographic differences in the gains. Across districts of Bangladesh, the average gain (controlling for heterogeneity) varies from -4% to 24% of consumption. The highest average gains from switching to the non-farm sector are not, however, in the poorest rural areas.

The geographic placement of bank branches is strongly influenced by the potential gains from switching to rural non-farm activities. But there is a marked difference in the way the (private, nonprofit) Grameen Bank responds to the potential gains versus the traditional, 20
of the economic and fiscal questions for policy makers considering reforms in stamp duties, especially stamp tax reductions.<sup>6</sup> In particular, we examine stamp duties and registration fees, referred to for simplicity as “stamp duties”. The administration of the stamp duties will be briefly examined, but our main focus will be on two other issues. First, how distorting are the stamp duties in their effects on resource allocation? Second, what would be the effects, especially the revenue effects, of previously suggested reforms in stamp duties that lead generally to lower tax rates? In this analysis, the ways in which

> 3 . Department of Economics, Andrew Young School of Policy Studies, Georgia State University; World Bank; and Ministry of Finance and Company Affairs, Government of India, respectively.  The authors are grateful to a number of individuals with whom they met for many helpful discussions and much useful information: Mr. Govinda Rao, Mr. Om Prakash Mathur, and Mr. Tapas Sen, the National Institute of Public Finance and Policy; Mr. P.S.S. Thomas, Planning Commission; Mr. Vinor Tewari, the National Institute of Urban Affairs; Ms. Renuka Viswanathan, Tariff Commission; Dr. Vijay Kelkar, Advisor to the Ministry of Finance and Company Affairs; Mr. N. John Kurian Planning Commission; Mr. G.C. Srivastava, Finance Commission.  Ms. Kathryn Owens and Mr. Jerry Kalarickal at the World Bank provided valuable research assistance for this paper. The authors are grateful to Mr. K. G. Krishnamurthy of the Housing Development Finance Corporation for providing data on market values of real estate in selected Indian cities.

> 4  See the National Institute of Public Finance and Policy (1995), _Economic Reforms and the Stamp Act_ (New Delhi, India).

> 5  See Reserve Bank of India (2003), _State Finances – A Study of Budgets 2002-03_ (New Delhi, India). 6 A number of
9 definitions<sup>6</sup> with the other HEFPI bed net use data points rendered meaningful comparisons impossible.

The trend checks, dropping of bed net use data points from the second MICS round and additional ad hoc discovery of surveys which did not fulfill HEPFI requirements<sup>7</sup> led us to exclude a total of 338 data points from the 2019 HEFPI database which were included in the 2018 version.

# 3.2. <u>Changes in data points</u>

# 3.2.1. Changes in indicator values due to changes in sources

The HEFPI 2019 database changed the underlying data sources of 161 data points that were already included in the 2018 version.

## _From multiple to single sources_

For the 2018 database, we allowed multiple data sources for data points of a given year and indicator. In these cases, the HEFPI data point’s value was computed as the unweighted mean of the indicator values from each source. For instance, the HEFPI 2018 stunting rate for Rwanda in 2000 – 46.2% – was computed as the mean over the data points from the 2000 DHS and MICS surveys.

In the 2019 database we no longer use such averaging and instead rely on a single source per data point. When data is available from multiple sources, we select the point to include based on four criteria: (1) Consistency in data sources over time (e.g. prefer the Rwanda 2000 DHS stunting point

> 6 The other HEPFI insecticide-treated bed net points consider a bed net treated if it is a) a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pretreated net which was soaked in insecticides less than 12 months ago. MICS 2 data points by contrast consider bed nets treated if they were _ever_ treated.

> 7 For example, we dropped two STEPS surveys as their samples did not represent populations meaningful for HEFPI’s purposes
unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf. See also the IMF</u> (2018) for an interesting discussion on some potential differences between their macroeconomic databases. 16 A salient case is the U.S., where the inconsistency between different surveys is well documented in the literature. For example, Abraham _et al._ (2013) examine the differences between employment data between the Current Population Surveys (CPS) and employer-reported administrative data. Bavier (2014) finds spending and income poverty in the Consumer Expenditure Survey (CES) to be an outlier compared with those in other surveys including the Panel Study of Income Dynamics (PSID).

17