rafmacalaba/datause-encoder-data huggingface dataset:
Contrary to what is generally found in developing countries and to what Clay et al. (1990) have found in Rwanda, the EICV data suggest that the non-farm sector participation in rural areas is small. Clay et al. (1990) find that approximately half (47%) of farm households in Rwanda engage in some form of off-farm employment. This offfarm work is mostly carried out as agricultural wage laborers (31%) on the farms of their neighbors, while the remaining 69% of household members’ time in off-farm activities is spent in the rural non-farm sector, notably as artisans, laborers or in commerce -- generally in small businesses. Over five percent of off-farm employment is held by government functionaries. We find that in 2001 2.4 percent of women with a primary occupation (and 3.4 percent of women with a secondary occupation) are active in the non-farm sector. For men, the proportions are 7.4 of men with a primary occupation and 8.2 percent of men with a secondary occupation.. These numbers are modest, especially for women and in comparison to what Clay et al. (1990) reported in their paper at the beginning of the 1990s. Moreover, income from non-farm sector activities on average accounts for 20 percent of household income — one of the lowest shares among developing and transition countries (Ahituv and Kimhi, 2002).

**Table 1: Proportion of adult rural population by status and education (%)** **<u>and average hourly wages by education (Rwf)</u>**

||**No**<br>**education**|**Primary**|**Post-**<br>**primary**|**Secondary**|**Further**<br>**education**|_No. of Obs._|
|---|---|---|---|---|---|---|
|Proportion of adult ruralpopulation(%)|||||||
|No occupation|3.7|3.6|1.9|2.3|0.0||
|Agricultural sector|88.3|77.1|58.7|21.3|0.0||
|Non-farm enterprise|5.5|6.7|15.0|2.4|0.0||
|Non-farm wage worker|2.4|4.9|17.5|17.1|38.5||
|At school|0.0|7.7|6.9|57.0|61.5||
|_Number of observations_|_3965_|_8158_|_361_|_740_|_16_||
|Mean Wage for non-farm wage workers(Rwf)|||||||
|Primaryoccupation|52.1|78.9|159.8|193.8|646.3|_1523_|
|Secondary occupation|75.0|107.9|272.1|217.5|2674.7|_296_|
|Mean Wage for non-farm self-employed(Rwf)|||||||
|Primaryoccupation|106.8|269.1|335.7|555.1|1630.6|_632_|
|Secondaryoccupation|112.8|187.8|296.4|940.6|164.8|_412_|

Generally, the presence of barriers to entry in specific sectors as well as limited overall economic activity may lead to a concentration of employment in only a few areas. In Rwanda, for instance, wage workers with their primary occupation in the non-farm sector are mostly professionals in public and social services, while those who are self-employed mainly work in commercial and non-agricultural manual occupations.<sup>4</sup> Consistent with the literature (e.g., Clay et al., 1990), individuals with higher educational levels have greater access to the non-farm sector and derive higher wages (Table 1). In addition,

> 4 32.5 percent of non-farm wage workers are professionals and 45.8 percent are in public and para-public. 57.4 percent of non-farm self-employed are in commerce and an additional 32.6 percent are nonagricultural manual workers.

4
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costed at Medical Stores prices for equivalent drugs. Vaccine costs were estimated on the basis of quantities used and price CIF Malawi and added in.

The 1987 quantity of X-ray film used by film size was noted from departmental records and its cost allocated to the wards and OPD on the basis of an analysis of the origin of patients X-rayed and film size used (origin and film size is routinely noted in the X-ray department ledger).

Salary costs. Salaries are paid centrally and it was possible only to calculate actual district salary costs for two broad staff groupings; senior staff and junior staff. Since the cost apportionment process required more detailed data, salary costs were estimated based on staff numbers by type and grade and average salaries (mid-point of the salary scale). Total actual costs were used to cross-check this estimation procedure. 10% was added to the annual salary cost of pensionable staff to :allow for employment benefits.

An important category of employment benefit in Malawi is subsidised housing. The cost of housing was treated as a capital cost (see below) rather than a staff-related cost, but the significance of this employment benefit is discussed later in terms of the additional salary it represents.

Other recurrent costs. Specialists from the two central .nospitais made (tairly) regular visits to district hospitals. These visits were ignored, on the grounds that they were fairly similar for most hospitals, it was difficult to get an accurate 3
I POLICY RESEARCH WORKING PAPER 1479

# Summary findings

Varangis, Crossley, and Braga estimate the potential commercial benefits that tropical timber producing countries could enjoy by adopting timber certification schemes. Such benefits are crucial for encouraging the supply of certified timber.

Timber certification is a reality: various countries and organizations have launched initiatives for it. The initial response among producing countries was less than positive, but some have come to realize its potential benefits and have begun to adopt timber certification schemes.

Tropical timber trade accounts for only a small fraction of tropical timber production, and most of that trade is concentrated among developing countries in Asia and Japan - markets where demand for certified timber is currently weak. Only a small part of the trade reaches the eco-sensitive markets of Europe and the United States, where there is demand for certified timber.

Developing countries can benefit commercially from timber certification in two ways: through the "green premium" (consumers' willingness to pay a premium for certified timber) and by averting losses of market share in the tropical timber market from not having timber certified.

Based on surveys, on discussions with nongovernmental organizations, on market participants and analysts, and on estimates of price elasticity, Varangis, Crossley, and Braga develop a scenario for estimating the potential commercial benefits from adopting timber certification. Under this scenario, benefits would not exceed US$500 million a year (roughly 4 percent of all tropical-timber-related revenues earned by developing countries).

Timber certification is not expected to provide significant commercial benefits to developing countries in the near future. But timber certification could provide significant rents to individual firms that develop market niche strategies. And producing countries that pursue certification may enjoy longer-term social, economic, and environmental benefits by adopting the better forest management practices required for timber certification.

- - This paper a product of the Commodity Policy and Analysis Unit, International Economics Department is part of a larger effort in the department to analyze issues related to trade and the environment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contactJeanJacobson, room R2-075, extension 33710 (34 pages). June 1995.

**_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 used and cited accordingly. The findings, interpretations, and conclusions are the authors' own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._

Produced by the Policy Research Dissemination Center
through two-part payment terms, such as early payment discounts to incentivize buyers to pay early (e.g., Ng et al., 1999).

In sum then, we see three important factors driving trade credit: 1) As a way for suppliers with cheaper access to credit to finance buyers; 2) As a means for the buyer to exercise market power and obtain favorable price discrimination; and 3) As a warranty assuring buyers of product quality. Given these three factors, terms may be influenced by the supplier‘s need to contain financial risks.

Our data set is unique in that we know some characteristics of the parties on either side of the contract. So we can take a closer look at these rationales for trade credit, focusing on how contract terms vary with characteristics of the parties to the contract. The limitations of our data set are that we do not have detailed characteristics on the firms, and we have no way of getting more data. Therefore our tests are reduced-form in nature, allowing us to document associations but not identify causality. Nevertheless, what we can tease out is intriguing.

# **3. Data, Summary Statistics, and Variance Decomposition Analysis**

We use a novel database of trade credit contracts for nearly the universe of suppliers of 56 large buyers.<sup>5</sup> The data are provided by PrimeRevenue, an online network that links large, global companies, their suppliers, and third-party financial institutions, via the Internet. PrimeRevenue provides software and an IT platform for buyers to post their invoices directly. Suppliers choose whether to be paid at the maturity of the contract or to have the contract ―factored out‖ and be paid immediately at a discount. PrimeRevenue is a leading provider of such ―open platform supply-chain finance (SCF)‖ solutions, allowing multiple banks to participate directly in a buyer‘s SCF program.

The buyers in our dataset are PrimeRevenue‘s clients. The data on the suppliers is collected from the buyers, who hold extensive information on their suppliers, including detailed information on their trade credit contracts. The suppliers sell mostly final goods (only 1% of

> 5 Because purchasing history is proprietary information, we do not know the identity of buyers in our sample. However, as discussed in this section, PrimeRevenue provided us with buyer characteristics (such as size, sector, and location) and informed us that almost all buyers in our sample are Global Fortune 500 companies.

6
23

# 8.  Conclusion

This paper has used a new, 2005/06 nationally-representative household survey to analyze the impact of internal and international remittances on poverty and inequality in Ghana. Three key findings emerge.

First, with respect to methodology, this paper develops counterfactual expenditure estimates for migrant and non-migrant households by using econometric estimations to predict the expenditures of households with and without remittances. Since this method is problematic in the presence of selection bias, the paper uses a two-stage multinomial logit model to test for selection bias in the household receipt of remittances. To ensure the exogeneity of variables, the selection model is estimated using an instrumental variables approach focusing on variations in migration networks and remittances among various ethno-religious groups in Ghana. We find that controlling for the observable characteristics of the households, the observable characteristics of the ethno-religious groups, and regional and ethnic dummies, that selection in unobservable characteristics is not a problem for households receiving internal or international remittances in Ghana.

Second, using the expenditure results of the two-stage model to estimate predicted and counterfactual expenditures for households with and without remittances, the paper finds that both internal and international remittances reduce the level, depth and severity of poverty in Ghana. However, the size of the poverty reduction depends on the type of remittances being received. In general, international remittances have a greater impact on reducing poverty than internal remittances. For example, comparing the predicted and the counterfactual values for the poverty headcount measure shows that the inclusion of
ments. Nevertheless, to date there is no conclusive evidence on whether such policies are successful. Some studies find non-negligible positive effects on employment (Kramarz and Philippon, 2001; Egebark and Kaunitz, 2018; Saez, Schoefer and Seim, 2019), while others find little evidence for employment effects (Boockmann, Zwick, Ammerm ̈uller and Maier, 2012; Huttunen, Pirttil ̈a and Uusitalo, 2013). Our main contribution to this literature is that we focus on heterogeneity across firm types and offer a potential explanation for the inconsistencies found in the literature. Our heterogeneity results are not without antecedents in the literature, although our data and institutional setting make it possible to provide a more comprehensive overview on the differing impacts of payroll tax cuts by job and firm types. In line with our results, Albanese and Cockx (2019) find that a tax cut in Belgium targeting employees above age 58 increased employment at firms with high shares of low-wage workers, and at small firms.

Our paper also relates to the literature studying the impact of tax policies in the presence of search frictions (see e.g. Pissarides, 1985; Smith, 1994) or imperfect competition (see e.g. Berger, Herkenhoff and Mongey, 2022). Most of the literature makes theoretical arguments or presents calibrations to discuss the effect of tax policies on the allocation of labor and on the composition of jobs. For instance, Breda, Haywood and Wang (2019) study the heterogeneous impact of payroll taxes by calibrating a search and matching model in the French context. Here we instead provide direct empirical evidence on the heterogeneous impact of tax policies, which is a key prediction of these frameworks.

Finally, our paper is also related to the recent rent sharing literature that studies the impact of various firm-level and market-level shocks on wages and employment (see Card, Cardoso, Heining and Kline, 2018 and more recently Kline, Petkova, Williams and Zidar, 2019; J ̈ager, Schoefer, Young and Zweim ̈uller, 2020; **?** ; Lamadon, Mogstad and Setzler, 2022). The documented interaction between worker and firm heterogeneity in pass-through rates has not been fully appreciated in this recent literature.

The remainder of this paper proceeds as follows. Section 2 introduces a search model with heterogeneous firms to illustrate the effect of payroll taxes in the presence of rents across firms. In Section 3, we provide background on the payroll tax reform we study and describe the Hungarian administrative data used for our empirical analysis. We present our employment results in Section 4 and wage results in Section 5. We discuss welfare effects in Section 6. In Section 7 we provide results for younger workers and women excluded from our main analyses. Section 8 concludes.

6
# SMALL FIRMS’ FORMALIZATION THE STICK TREATMENT<sup>_∗_</sup>

Giacomo De Giorgi Matthew Ploenzke Federeal Reserve Bank of New York Federeal Reserve Bank of New York BREAD and CEPR Aminur Rahman World Bank Group Keywords: Firms, informality, development JEL Code: O1

> _∗_ We are grateful to PEDL initiative for partial funding under the SEED Grant #168. Thanks to seminar participants at the SME Initiative workshop at the MIT. We thank David McKenzie for useful comments, officials from Bangladesh National Board of Revenue, in particular Mr. Aminul Karim for partnering with us in this research project, and Najmul Hossain and his team from Data International for implementing the field work. Corresponding author: Giacomo De Giorgi (giacomo.degiorgi@gmail.com) Federal Reserve Bank of New York. Giacomo De Giorgi also acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2011-0075) and ECO2011-28822, the EU through the Marie Curie CIG grant FP7-631510.
**Table 4: Baseline Group Comparison on School Characteristics**

||WSD|Grant|Control|
|---|---|---|---|
|_Student Observations_||||
|Number of students|461|433|426|
||(59)|(41)|(45)|
|Student-teacher ratio|32|34|32|
||(0.89)|(0.97)|(1.14)|
|Double shift|0.33|0.49|0.41|
||(0.50)|(0.50)|(0.05)|
|Tap drinking water|0.23|0.20*|0.33|
||(0.04)|(0.04)|(0.05)|
|Student-latrine ratio|79|49|64|
||(15)|(4)|(9)|
|Has a library/storage for books|0.37|0.53|0.47|
||(0.05)|(0.05)|(0.05)|
|Received cash/in-kind from community|0.38<br>|0.31<br>|0.29<br>|
||(0.05)|(0.05)|(0.05)|
|Number of meetings with parents|4.39**|3.70|3.69|
||(0.27)|(0.24)|(0.25)|
|Has mentoring system|0.86|0.82|0.81|
||(0.04)|(0.04)|(0.04)|
|Written staff code of conduct|0.39|0.43|0.44|
||(0.05)|(0.05)|(0.05)|
|Pupils per class (2006 Administrative Data)|34|33|34|
||(0.10)|(0.10)|(0.11)|
|Adult literacy (2003 Census)|38%|39%|38%|
||(0.015)|(0.014)|(0.012)|
|Primary Education or more (2003 Census)|57%|55%|55%|
||(0.017)|(0.016)|(0.014)|
|Years Established|24|25|24|
||(1.6)|(1.8)|(1.9)|
|Number of observations|90|94|89|
|_Classroom Observations_||||
|Teacher has lesson notes|0.31|0.33|0.27|
||(0.04)|(0.04)|(0.03)|
|Percentage of pupils absent|0.25|0.21*|0.26|
||(0.06)|(0.02)|(0.02)|
|Hours/week English|3.67|3.57|3.81|
||(0.15)|(0.15)|(0.13)|
|Number of observations|175|180|173|

Notes: Standard errors are in parentheses. *** 1% Significance Level, **5% significance Level, *10% Significance Level. The mean comparison test contrasts each treatment group with the control group.

27
# _Table of Contents_

|Backgroun|d<br>.......................................<br>1|
|---|---|
|Part I: Intro|duction-Developments from 1989 to 1990 ...............<br>........................<br>2|
|A.|The Soviet Umbrella, or a Different Set of Rules for the Region in the<br>1980s? .............................<br>2|
|B.|Indicators of Relative Indebtedness in the Region<br>.4|
|C.|Summary of Funds Flowing to the Region Post-1989<br>.4|
|D.|Maturity Profile of Total External Debt<br>.8|
|E.|Direct Foreign Investment-Project Finance<br>.9|
|F.|Institutional Setup-How is the Borrowing Managed in Different<br>Countries? .11|
|G.|Access to Bond Finance and other Private MLT Financial Flows .12|
|Part II: Deta|ils by Country .................<br>14|
|Hun|gary .<br>14|
|<br>Cze|<br>choslovakia ...........<br>16|
|Pola|nd ...........<br>19|
|Bulg|aria ...........<br>21|
|Rom|ania ...........<br>23|
# **Figure 4. Breakdown of the Study Sample**

<!-- Start of picture text --> 4 out of 15 districts<br>selected in Balkh<br>Province<br>100 villages and 200<br>PRAs<br>selected in study<br>districts<br>26,957 households<br>included in wealth<br>ranking exercise<br>Ultra-poor: Poor: Better-off: Well-off:<br>11,859 (44%) 9,121 (34%) 4,394 (16%) 1,583 (6%)<br>Eligible after<br>PMT: 1,953<br>Eligible after<br>MISFA<br>verification:<br>1,235<br>Included in<br>lottery: 1,219<br>Not included in<br>households in<br>lottery: 16*<br>80 villages and<br>133 PRAs<br>Control: 728 Treatment: 491<br>Surveyed in<br>Surveyed in follow-up:<br>follow-up: 689<br>458 (93%)<br>(95%)<br><!-- End of picture text --> _Notes._ The figure depicts the selection process that led to the final sample sizes and response rates. 16 households were not included in the lottery and assigned to treatment (*) because there was only 1 household eligible for the program in the PRA.

14
between road infrastructure and labor outcomes often suffer from endogeneity and reverse causation problems. Road quality is usually not exogenous. Other than topographic characteristics, it is often driven by economic, social or political factors (Banerjee et al., 2012; Datta, 2012; Nguyen et al., 2011; Burgess et al., 2015). To address the endogeneity and reverse causation issues, this study uses a (historical) instrumental variable strategy. The instrumental variable has been obtained by georeferencing and digitizing a militarytopographic map of the Caucasus region printed by the Russian Empire in 1903. The study uses historical, primary military and post roads to instrument the existing road quality a century later.

The results show that a shorter distance to the nearest good quality road has statistically significant positive impact on overall non-agricultural employment, on skilled manual employment for rural men, and non-agricultural employment and cash earnings for rural women. People are more likely to work outside of their villages if they have access to good quality roads, and also tend to work for more hours. The analysis has been carried out on two different datasets, the Demographic and Health Survey and the Integrated Living Conditions Survey of Armenia, using different estimation methods. The results are similar and robust from both datasets.

This paper contributes to three groups of literature: (1) estimating impact of road infrastructure improvement, (2) examining rural employment and structural transformation, and (3) using historical setting for causal inference. There is a growing number of studies evaluating road construction or improvement programs in various countries, like in India (Aggarwal, 2018; Asher and Novosad, 2020; Bell and van Dillen, 2014; Duranton et al., 2014; Datta, 2012; Ghani et al., 2016; Herrera Dappe et al., 2021), Bangladesh (Khandker et al., 2009; Khandker and Koolwal, 2011), Papua New Guinea (Gibson and Rozelle, 2003; Wiegand et al., 2017), Ethiopia (Dercon et al., 2009), Indonesia (Gibson and Olivia, 2010; Gertler et al., 2022), Viet Nam (Mu and van de Walle, 2011), China (Banerjee et al., 2012; Wang et al., 2016; Fan and Chan-Kang, 2005; Faber, 2014), Georgia (Lokshin and Yemtsov, 2005), among others. Overall, improved connectivity reduces travel time and cost. The cost and time saving stimulates mobility, connecting rural areas to urban centers, people to markets and services. Easier mobility quite often is a key factor in trading, as a result reducing product prices (Donaldson, 2018; Andrabi and Kuehlwein, 3