rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Twenty-seven percent of the non-agricultural labor force in developing countries consists of self-employed business owners with no employees (Gindling and Newhouse, 2014), and 99 percent of the firms in many poor countries have 10 workers or fewer (McKenzie, 2017). These firms are an important source of income for the poor, and many policy interventions are designed to help people start and grow such firms. Yet there has been much less attention devoted to the death of such firms, with no systematic evidence available as to the rate of small firm death, which firms are more likely to die, and why they die. Almost two-thirds of published randomized experiments testing policy interventions for small firms in developing countries ignore firm death completely, neither reporting the death rate nor examining it as an outcome.<sup>1</sup> Understanding the prevalence, characteristics, and causes of firm death is important for poverty, productivity, and policy. Since self-employment is an important source of income for the poor, firms shutting down could mean a large loss in income for firm owners. A growing body of literature (e.g. Hsieh and Klenow, 2009) has noted large misallocation of resources in developing countries. Firm death can improve aggregate productivity if less productive firms die and reallocate resources and customers to more efficient competitors. The optimal policy response to firm death then depends on whether it involves large income losses for the poor, and whether it is efficiently selecting out the least productive firms or not.

This paper provides systematic evidence on firm death in small firms in developing countries by collating data on more than 14,000 small firms from 16 firm panel surveys in 12 countries, enabling estimation of the rate of firm death over horizons as short as 3 months, and as long as 17 years. This overcomes many of the limitations of the existing literature on firm death in developing countries, which have had to rely on a small number of time periods, usually just in a single country, often with small samples of micro firms, and high rates of attrition.<sup>2</sup> Moreover, we included detailed questions in nine of these surveys to measure cause of death, and most of our surveys also continue to track the firm owner after their firm has died to provide data on what they do once their firm dies.

> 1 Table A.1 provides details from 35 published randomized experiments testing different interventions for small firms in developing countries. Out of these 35 experiments, only 12 report the firm death rate and 13 examine firm death as an outcome.

> 2 We discuss how firm death has been measured in the existing literature in the next section, and in Appendix Figure A.2. and Table A.3, compare our estimates of death rates to those in existing studies.

2
Well-functioning financial sectors are critical in mobilizing resources, stimulating investment, and at the same time helping firms (and households) better manage their risks. As shown in Appendix 3C, the business services sector covers a variety of critical of services activities, ranging from software consulting and data processing to management consultancy, engineering and R&D services. Intensive use of these modern services can help manufacturing firms increase productivity, reduce the cost of doing business, expand their choices within a longer geographic distance, differentiate their products from those of their competitors,<sup>2</sup> strengthen their after-sale customer services, etc.<sup>3</sup> USITC (2013) shows that business services accounted for nearly half of all services purchased by manufacturing sectors in the U.S. in 2008.

Our first hypothesis is that, while the overall effect of services development on the performance of manufacturing sectors can be ambiguous, its effect is more likely to be positive for manufacturing sectors that use services inputs more intensively. Furthermore, we distinguish embodied domestic services inputs from embodied foreign services inputs. When domestic firms have access to foreign services, they may at least partially bypass their own inefficient services provision by relying more on imported services inputs. As our second hypothesis, we expect to see a more positive effect of access to foreign services inputs on manufacturing export performance in countries with lower levels of domestic services development.

We develop a methodology to quantify the indirect role of services in international trade in goods using a method developed by Koopman, Wang, and Wei (2014) and Wang, Wei, and Zhu (2013) that generalizes the vertical specialization measures proposed by Hummels, Ishii and Yi (2001). We use revealed comparative advantage (RCA) to measure the competitiveness of manufacturing sectors. Following Koopman, Wang, and Wei (2014) and Wang, Wei, and Zhu (2013), we improve on the traditional Balassa (1965) RCA and construct new measures of RCA based on domestic value added in gross exports by taking into account both domestic production sharing and international production sharing.

In our econometric analysis of the impact of services development on RCA of manufacturing sectors, the key explanatory variable is the interaction between a measure of the development of

> 2 To differentiate a product from others, firms need to invest more in R&D, quality-upgrading, and advertisement. The groups of manufacturing sectors with high embodied financial and business services as listed in Appendix 4 indeed produce more differentiated products than those sectors with low services input intensity. In addition, combining pure manufacturing and after-sale services is also a way to differentiate itself from competitors.

> 3 See the next section for discussion in the literature on how producer services may affect firms’ productivity.

3
|Punjab State Road Sector Project|Elena Y. Chesheva|
|---|---|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|Punjab State Road Sector Project|Elena Y. Chesheva|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|IN Punjab Rural Water Supplyand Sanitation|Srinivasa Rao Podipireddy|
|NG-IDF StrengtheningNational System for Nigeria's MDG M&E|Foluso Okunmadewa|
|NG-IDF StrengtheningNational System for Nigeria's MDG M&E|Foluso Okunmadewa|
|NG-IDF StrengtheningNational System for Nigeria's MDG M&E|Foluso Okunmadewa|
|Supreme Audit Institution|Osval Rocha Andrade Romao|
|Supreme Audit Institution|Osval Rocha Andrade Romao|
|Supreme Audit Institution|Osval Rocha Andrade Romao|
|StrengtheningM&E of the PovertyReduction Strategy|Noro Aina Andriamihaja|
|StrengtheningM&E of the PovertyReduction Strategy|Noro Aina Andriamihaja|
|StrengtheningM&E of the PovertyReduction Strategy|Noro Aina Andriamihaja|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
|Second Multisectoral STI/HIV/AIDS Preventionproject|Jumana N.Qamruddin|
through key informant interviews, which are conducted in meetings with local crisis committee representatives, humanitarian and social organizations, community and tribal representatives, representatives of displaced groups, and other representatives from Baladiya<sup>7</sup> offices (e.g., Social Affairs, Muhalla Affairs) at a target location or a remote location. The choice of location is determined according to its accessibility and safety for interviewers and interviewees and its general security situation. When direct interviews were not possible, remote interviews were conducted. In practice, the three most common nationalities at the FMP during the week of the assessment, the three most popular destinations for the migrants at the FMP during the week of the assessment, and the percentage of migrants at the FMP within each length-of-stay category (e.g., less than two weeks, two weeks to three months) in the location during the week of the assessment are recorded. For those arriving or departing, information is collected on nationality and planned destination using the same questions.

Given how the questionnaire is constructed, the unit of observation in the data is the FMP. This allows the mapping and tracking of the FMPs’ evolving characteristics in terms of the relative presence of groups of individuals of given nationalities, preferred destination countries, and the like. Yet, the structure of the data also poses some severe limits to the analysis. For instance, it is not possible to link nationalities to preferred destination countries in each FMP because information is collected as aggregates (the three most common nationalities and three most preferred destination countries).

The pool of migrants observed at the IOM FMPs in 2017 and 2018 constitutes the population under study. We use data from 95 FMPs that are constant across rounds of data collection, which allows us to track the movement of migrants in a consistent way and to compare flows across time in the same area. Seventeen additional FMPs have been included during the period of analysis to allow data collection to cover new points that more migrants were crossing through for security reasons.

The locations of the FMPs are shown in figure 2. Although there is at least one FMP in each province, distribution across provinces is not homogenous; Almargeb, Aljafara, Murzuq, Tobruk, and Tripoli each have five FMPs, and Wadi Ashishati, Ghat, and Nalut each have only one.

Our analysis is performed by aggregating data at the provincial level.<sup>8</sup> The map of the provinces is shown in figure 3. In what follows, unless differently stated, each metric is obtained by averaging the observations recorded in a province within a year (2017 and 2018).

> 7 Second-level administrative subdivision of Libya.

> 8 By aggregating FMPs data at the provincial level, we use all information available yet avoiding the problem of missing data due to the absence of an FMP in a specific round.

8
In sum, the results presented in this paper highlight the critical role played by the level of migration in determining remittance flows. The education level of migrants, the size of the domestic economy, the level of economic development of recipient countries, and financial sector development, though a smaller extent, also explain some of the variations observed in remittance flows across the countries. The results also underline the importance of controlling for the endogeneity bias in estimating the determinants of remittances.

# **5. Conclusion and Policy Implications**

The paper has examined empirically the significance of some of the key factors in determining remittance flows based on the cross-country data. The results show that the level of migration is the main driver of remittances even after controlling for the endogeneity bias through the IV estimation. Among the other variables we have examined, the education level of migrants, the size of the domestic economy, the level of economic development of recipient countries are found to play a role in determining the flow of remittances. Financial sector development seems to have a positive impact on remittances, but its significance is found to be less clear. Our findings also illustrate the importance of accounting for the potential endogeneity bias in the estimation.

Some important policy implications can be drawn from these findings. Firstly, the negative influence of the difference in the education level of migrants and the population in home countries indicates that migrants from less well-off families seem to be remitting more to their families. Moreover, poorer countries are found to be receiving relatively more remittances. These findings seem to suggest some possible positive impact of remittances on the welfare of recipient families and of recipient countries, though a more detailed microeconomic analysis is obviously required to make any definitive conclusion. Finally, based on the findings that the migration level is one of the main determinants of remittance flows, migration seems to provide an important source of foreign exchange much needed for economic development of recipient countries. However, as the number 12
<!-- Start of picture text --> 0.03 —Creation of new firms ,! a ==<br>~ = =Formalizationae of informal: firms. | ;<br>’<br>e ¢<br>0.02 ’<br>7<br>o<br>¢ “<br>o<br>-* ‘<br>0.01 ’ ¢ rae<br>¢<br>-* °<br>ie<br>0<br>00.1 0.2 0.3 0.4<br><!-- End of picture text -->

<!-- Start of picture text --> 0.08 — Creationi of new firms _ ¢ ><br>= = =Formalization of informal firms . -<br>0.06 ue<br>¢<br>rae<br>e<br>¢<br>0.04. ae -<br>a c oe<br>0.02 -*<br>¢<br>- °<br>0<br>0 0.2 0.4 0.6 0.8<br><!-- End of picture text --> <!-- Start of picture text --> 0.35<br>—— Creation of new firms<br>0.3 } = = =Formalization of informal firms -7 - oc?<br>0.25 oc” -<br>02 . os<br>, ae<br>¢<br>0.15 ao<br>ae<br>0.1 o<br>/<br>0.05, -*<br>fe)<br>ie) 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> <!-- Start of picture text --> 0.1 cote<br>morc -<br>!<br>0.08<br>0.06 ' —— Creation of new firms<br>, 7— = =Formalization of informal firms<br>i<br>0.04 /<br>sae<br>0.02<br>0<br>0 0.1 0.2 0.3 0.4<br><!-- End of picture text --> <!-- Start of picture text --> 0.2<br>Creation. of new firms.<br>= = =Formalization of informal firms<br>0.15 orre .<br>-?<br>e - ¢<br>O17 ue ¢<br>a<br>on<br>0.05 ’<br>- aa<br>-"<br>ie) -<br>0 0.1 0.2 03 04 O05 06<br><!-- End of picture text --> <!-- Start of picture text --> 0.14<br>—— Creation of new firms<br>0.12 = = =Formalization of informal firms ae .-~<br>one<br>0.1 ¢<br>, ¢<br>0.08 ae i. ¢<br>0.06 oe<br>e ¢<br>cA<br>0.04 oc” ¢<br>0.0247"<br>(e)<br>0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text -->
# _ceteris paribus_ .

But it is important to appreciate the distinction between the true ability of a child _φ_ and a parent’s estimate of a child’s ability denoted as _φ_<sup> ̃</sup><sup>_j_</sup> . The investment choices of parents are determined by the estimated ability _φ_<sup> ̃</sup><sup>_j_</sup> , and given an investment level, the actual educational attainment is determined by the true ability of a child _φ_ . In societies where son preference is strong, it is likely that the parents would overestimate a son’s ability and underestimate a daughter’s ability, implying that _φ_<sup> ̃</sup><sup>_s_</sup> _> φ > φ_<sup> ̃</sup><sup>_d_</sup> .<sup>12</sup> The expected schooling (denoted as _H_<sup> ̃</sup><sup>_c_</sup> ) for a given level of financial investment can be written as: where _δ_<sup> ̃</sup> 1<sup>_j_=</sup> _δ_ 1<sup>0+</sup><sup>_γ_1</sup><sup>_qj_+</sup><sup>_γ_2</sup><sup>_φ_ ̃</sup><sup>_j_�</sup> and _δ_<sup> ̃</sup> 2<sup>_j_=</sup> _δ_ 2<sup>0</sup><sup>_−ω_1</sup><sup>_qj−ω_2</sup><sup>_φ_ ̃</sup><sup>_j_�</sup> In contrast to the true � � production function (3), ability is gender specific in equation (5), with _j_ = _s, d_ . The income function for the children is: The returns to education are gender specific; when return to education is lower for girls, we expect _R_<sup>_cs_</sup> _> R_<sup>_cd_</sup> . There is substantial evidence that returns to education in the labor market may be higher for women. In their extensive cross-country study, Psacharopoulos and Patrinos (2018) find that, in about 66 percent cases, returns to education in the labor market are higher for women. Higher returns for girls are also observed in India and China during the 1980s and 1990s (see, for example, the estimates reported by Bargain et al. (2009)).<sup>13</sup> Pitt et al. (2012), and Rosenzweig and Zhang (2013) suggest that the higher

> 12If gender bias in the household is strong enough to systematically discriminate against girls in food and medical care, especially in the early years of a child’s life, girls may end up with lower academic ability. There is evidence that the development of a child’s brain is significantly affected by socioeconomic conditions (Noble et al. (2015). In this case, the inequality above will reflect both the actual differences in cognitive ability generated by gender bias, and also biased estimate given a certain level of cognitive ability.

> 13The estimated coefficients for the higher secondary schooling in 2002-2004 from the Mincer equation are: 1.09 (women, India), 0.55 (men, India), 0.51 (women, China), and 0.26 (men, China) (see Table A3 in Bargain et al. (2009)). They use CHIP data from China and NSS data for India. Their estimates are at the national level, they do not present separate estimates for rural and urban areas. We discuss the

8
# **1. Background**

Jharkhand is one of the poorest states in India. A nationwide survey of the situation of agricultural households (SASH) in 2019 found that farmers in Jharkhand have the lowest average farm incomes in the country (SASH, 2021). The average farmer in Jharkhand earned only Rs. 8,000 in 2018-19 from all crops, compared to the national average farm income of Rs. 37,000. The average total income of agricultural households in Jharkhand is the lowest in India (Rs. 3,993/month, compared to the national average of Rs. 8,337/month) and the share of crop income in the total household income is also the lowest (17%) after Jammu & Kashmir and Kerala ( _ibid_ ). The most recent NSSO Consumption Expenditure Survey, carried out in 2011-12, found that more than 45 percent of farmer households in Jharkhand lived in poverty – the highest in India. Income from agriculture in Jharkhand is also highly vulnerable to droughts, dry spells, and other weather shocks. In 201819 for example, 75 percent of all paddy growers and 96 percent of farmers growing black matpe ( _urad_ ) in Jharkhand reported losses due to droughts or inadequate rains.

Poor access to irrigation is a major reason why agriculture is a high-risk, low-return endeavor in Jharkhand. An analysis carried out in 2016 found that 18 districts in Jharkhand were among the most irrigation deprived in India, and that high energy costs rather than a physical scarcity of water was the main reason for poor access to irrigation in the state (Shah et al., 2016). According to the most recent Minor Irrigation Census, 94 percent of small irrigation structures in Jharkhand were powered by diesel or muscle in 2013-14, and both were significantly more expensive than grid electricity as a source of energy for irrigation. While 65 percent of farmers in the state irrigated at least a part of their land in the _Rabi_ (winter) season, only 19 percent of the cropped area was irrigated, compared to 58 percent at the all-India level (SASH, 2021). The state government’s Jharkhand Vision & Action Plan 2021 sought to increase the gross irrigated area from 0.5 million hectares (ha) in 2016-17 to 0.8 million ha by 2020-21 as an element of its larger objective of doubling farmers’ incomes. Resources from the Pradhan Mantri Krishi Sinchai Yojana (PMKSY) and other government programs were to be utilized for this purpose.

The Jharkhand Opportunities for Harnessing Rural Growth (JOHAR) project was launched in 2017 with support from the World Bank and implemented by an agency established by the state Ministry of Rural Development titled the Jharkhand State Livelihoods Promotion Society (JSLPS). JOHAR’s development objective is “to enhance and diversify the incomes of targeted rural 2
could be because many of the informal savings and credit arrangements revolve around social activities (meetings) that all household members know about.

Although the head of household and the full enumeration tend to yield very similar usage rates, there is one exception, _insurance_ . One would expect that the full enumeration would provide the most complete information and thus produce the highest usage levels. And yet, the percentage of households that have insurance is 11.3% when information is provided by the head of household and only 7.9% when a full enumeration of individual usage is collected. It is conceivable that the head of household has purchased insurance for other household members of which those members are not aware. Another issue, which we turn to in more detail below, is that the institution-based question is a poor method of collecting information on insurance use, and thus none of the estimates for that indicator are reliable in Table 6.<sup>9</sup> Comparisons between usage rates calculated from product- versus institutionbased questions also reveal stark differences across indicators. To calculate productbased questions, we relied on a series of questions that are similar to those used in FinScope surveys. For example, a respondent was considered _banked_ if she responded yes to any of the following questions:

1. Do you currently have an ATM card?

2. Do you currently have a debit card?

3. Do you currently have a Savings Plus account?<sup>10</sup> 4. Do you currently have a current account (checking)?

5. Do you currently have a savings account at a bank?

6. Do you currently have a PostBank account or a post office savings account?

> 9 Recall that the head of household is only asked about his or her own personal use of insurance products in the full enumerations, and thus it is possible that the full enumeration could yield a smaller average usage rate than when the head responds on behalf of the household for the reason mentioned. However, having observed the field training, our sense is that institution-based question is simply not a good method for collecting reliable information about insurance use.

> 10 This is the brand name of a specialized savings account offered by some Ghanaian banks with additional features such as limited checking.

20