rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 Key Characteristics and Productivity Drivers of Household Enterprises**

## **3.1 Characteristics of Household Enterprise Operators**

21. **Demographic profile** . Owing to high fertility and therefore high rates of population growth, the majority of HE operators are young, though they tend to be slightly older relative to the country‟s labor force in general (Tables A1.7 and A1.8). Over half falls in the age group 1534 years. Between 2001 and 2006, however, the share of HE operators in the age group 15-24 declined by three percentage points, while those in the 25-34 age group rose by about 3 percentage points. As will be noted later, this youngest group is also associated with low levels of education, most likely because they left school early. Urban areas have a high density of labor force in the age range 25-34 because of migration of this age group from rural areas. In Tanzania, women and men are equally likely to start an HE.

22. **Education and training profile.** The level of education of HE operators is similar to that of the labor force in general (Table A1.9). Over 60 percent of them completed primary education only. Consistent with an overall low level of education in rural areas, a higher share of HE operators in the rural areas had no education (24 percent), or completed primary education only (17 percent), compared to Dar es Salaam with 10 percent and 11 percent, respectively (Table A1.7).

23. ILFS data show that more than 80 percent of HEs have never had any training of any kind, with a mere 4 percent having attended on-the-job (onsite) training. Vocational and apprenticeship training is the main form of training for about 12 percent of HEs. The proportion of HEs without training increased, but only in the rural areas, even as the share of those who received on-the-job training rose between 2000 and 2006.
employment does not include paramilitary personnel in the people's militia (5,000 part-time force with police duties), or the Presidential Guard (one infantry battalion). State-owned Enterprise employment is taken from IMF Staff country Report No. 96/69 of August 1996. It states that from the 240,000 jobs which state-owned enterprises provided in the 1980's, 41,000 had been retrenched by 1993 and additional jobs were being targeted by the Bank-funded PSAC in 1996-97. Therefore the figure of 1993 corresponds to the 240,000 less the 41,000.

Wages and salaries for Consolidated Central Government are from IMF Government Finance Statistics, 1995 and relate to 1993. Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1991.

# Guinea

Military employment includes 7,500 conscripts, but does not include paramilitary forces such as the people's militia (7,000), the gendarmerie (1,000) or the Republican Guard (1,600).

## Guinea Bissau

Unemployment estimate is from WB Country economist and relate to 1995.

Data on Central Government come from Marcelo Andrade (AF5CO) based on his February 1996 mission, and refers to the end of 1995. Data on Education and Health are from IMF estimations. Source: Esteban Garcia de Motiloa (AF5CO). Local Government employment is a staff estimate (AF5CO).
These examples share a common theme—the interventions studied aim to change the way spouses interact around the production and allocation of goods, to encourage them towards greater intrahousehold cooperation in areas that Doss and Meinzen-Dick (2015) might identify as “collective action problems.” Doss and Meinzen-Dick engage with the natural resource management (NRM) literature to rethink what enables household members to reach cooperative outcomes (not necessarily efficient ones), and how interventions can strengthen collective action at the intrahousehold level for shared gains around household public goods. This perspective leads to the consideration of how interventions might affect the _processes_ of intrahousehold cooperation, in addition to considering how they affect the resources that each household member brings to intrahousehold negotiations. In other words, in what ways do NSA interventions that target both spouses affect _how_ household decisions are made?

Studies on targeting that focus on asymmetric information within households foreground the relative information resources that spouses bring to intrahousehold negotiations and decisions. This study adds to the literature by providing a complementary explanation of why and how the targeting of NSA extension programs matters. Drawing on female and male farmers’ explanations of why they value dual viewership and building on Doss and Meinzen-Dick’s (2015) focus on collective action, our findings suggest a need for greater attention to how interventions affect processes of intrahousehold collaboration for achieving improved nutrition outcomes.

# 3. Methods and context

This paper is based on qualitative data collected in two rounds in rural Ethiopia. Formative qualitative research was conducted in May 2019, which informed the design and analysis of the subsequent two rounds of research this paper is based on. The formative phase helped the research team gain a better understanding of how the agricultural extension system functions in practice. It included 34 interviews of officials involved in agricultural extension and 36 observations of extension activities. Round one of the primary qualitative data collection took place during August-September 2021, and round two took place during May-June 2022. Both rounds of qualitative data collection were embedded in an impact evaluation of a video-based NSA intervention that is part
The results in Table 1 confirm the general picture in Figure 1. The electoral parameter in columns (i), (iii) and (v) is positive and significart at the 5% level or better in all three specifications. This suggests that expenditures on both comrnunity-based projects and special projects were significantly higher than expected in the three months leading up to elections. When the three elections enter into the regression separately, the influence of elections on the timing of expenditures is most apparent for the special projects, especially for the October 1993 referendum and the April 1995 presidential election.<sup>7</sup> The empirical analysis above suggests that Fujimori boosted FONCODES expenditures when it mattered most. But there are obviously other factors at work. Consider, for example, the expenditure spike in September 1994 (month=35), the second-largest in 1he data set. In August 1994, Manuel Vara Ochoa replaced Manuel Estela as the executive director of FONCODES. Estela was a technocrat who was highly regarded at the World Bank and the Inter-American Development Bank. Prior to being appointed executive director of FONCODES, he had been the head of SUNAT, the govemrnment agency charged with the collection of taxes in Peru. Estela brought to FONCODES his advisors from SUNAT, and a working method which emphasized a rigorous screening of project proposals. This was not, however, to Fujimori's liking, and the president quickly became dissatisfied with Estela's unwillingness to meet "disbursement targets". Estela's replacement, Manuel Vara Ochoa, was a friend of President Fujimori's from their days together as professors at the Agrarian University in Lima, and was understood to be more sensitive to the political importance of a speedy approval process. The massive spike in expenditures in September 1994 may be a result of the wholesale approval of projects which had not been funded during the Estela period.

In conclusion, there is some evidence of a political expenditure cycle in FONCODES projects, but the electoral calendar is by no means the only determinant
approach to modeling relative employment changes will predominantly be identifying demand rather than supply effects.

# **3 Data and Descriptive Statistics**

# **3.1 Data and Construction of Key Explanatory Variables**

The Indonesian Manufacturing Census ( _Statistik Industri,_ or SI) contains longitudinal data on all Indonesian manufacturing establishments with more than 20 employees from 1993-2004. A number of features of this dataset render it very suitable for analyzing employer responses to crisis.<sup>10</sup> To start with, a major strength of the data is that it disaggregates firm-level employment into blue and white collar workers by gender. Thus, we can analyze the impact of the East Asian crisis on the employment prospects of men and women, allowing for further differentiation by the broad types of jobs held by men and women. Second and related, the data enable us to study _within-firm_ gender differences in employment controlling for crude occupational category (i.e. differentiating between white and blue-collar workers). Third, by virtue of being a census the data allow us to assess to what extent job losses are driven by exit and reduced entry, and to what extent they are the result of employment adjustment by incumbents. Fourth, the data contain information on the wage-bill for blue- and white-collar workers separately. Following Amiti and Cameron (2007) and Harrison and Scorse (2009), we use this information to construct proxies for firm-level average wages for blue-and white collar workers by dividing those wage bills by number of blue- and white-collar workers respectively. Although these firm-level wage data are not gender-disaggregated, they help us assess gender differences in wage adjustment: following Hellerstein and Neumark (1995, 1999) we focus on the relationship between an establishment‘s average wage in an occupation and the share of female employees in that occupation.<sup>11</sup> Last but not least, the SI data contain information on a rich number of firm characteristics such as their sector, material input usage, output, capital
infrastructure and business services and the use of tools such as professional market research findings, advisory services, marketing assistance and training for SME growth. Business development services have also been identified as the most needed competitiveness improving services by SMEs in developing countries.<sup>16</sup> Given the recognized importance of these practices, in our analysis, we use a combination of business development and financial planning measures to create two indices as proxies for business success for SMEs in our sample.

**Access to finance has been identified as a key element for SMEs to succeed.** Financial resources are crucial for SMEs in their drive to build productive capacity, to compete, to create jobs, and to contribute to poverty alleviation in developing countries.<sup>17</sup> Finance has been identified in many business surveys as the most important factor determining the survival and growth of SMEs in both developing and developed countries. Access to finance allows businesses to undertake investments to expand their operations and improve their competitiveness. In this paper, we proceed to use access to finance as another indicator to measure business development of the SMEs in our study.

**Adoption of formal HRM policies is linked to improvement of business development outcomes and to the creation**

**of an enabling business environment.** It has been shown that the adoption of formal family-friendly policies promotes business development, and improves productivity, job satisfaction, and employee retention (Bin Bae and Yang, 2017 and Born, 2015). A UNICEF report (2019) presents evidence that manufacturing workers in Bangladesh and Vietnam are more likely to be retained in workplaces that implement family-friendly policies and shows results from a study in China which found that HRM policies were an important factor that increased trust in management and overall employee retention—by reducing the cost of staff absenteeism and turnover, they would increase business performance by increasing productivity (IFC, 2019). At a global level, it has been shown that the adoption of HRM policies is positively
# **A.2 Details on deposit insurance schemes for each country**

In this section we provide details on the deposit insurance schemes in each country along with sources and the relevant laws and names of the governing institutions wherever available. The sources referenced here are listed with details below in section A.3.

**Albania.** ( _Albanian Deposit Insurance Agency, Law No. 8873, Law on the Insurance of the Deposits_ ) The explicit deposit insurance scheme (EDIS) of Albania was established in March 2002. It has an official administration by the Deposit Insurance Agency. The membership for the fund is compulsory and is contributed by both the state and the banks, where the premiums are not risk-adjusted and they are 0.5% of the average insured deposits. The deposits of up to 350,000 lek are fully insured, and 85% of 350,000 to 700,000 lek is insured.

_Sources_ : Bank of Albania. (2002), IADI Member Profiles: Albanian Deposit Insurance Agency.

**Algeria.** ( _Bank of_ Algeria) The deposit insurance mechanism was established in December 1997. The maximum coverage is 600,000 Algerian dinars per depositor per institution and it has not changed since establishment as of 2004. The system was introduced in response to the expansion of the banking sector with the start up of many private national and international banks. Prior to this date, all the deposit banks were owned by the state (the treasury) with an implicit blanket guarantee. _Source_ : Own survey of deposit insurers.

**Argentina.** ( _SEDESA, Law 24, 485_ ) Before 1979 deposits were unconditionally guaranteed by the Argentinean government. In 1979 an explicit system of deposit insurance scheme was established by the military government. The scheme was optional for private banks and required the insured banks to make contributions to the fund. The central bank provided full coverage for the first million pesos (about $640) and ninety percent thereafter. Later in 1991 the
First, we analyze the pattern of US investment over time through an event study approach that investigates whether the coefficients on distance, border, NATO, and same country variables have changed. We find limited evidence of nearshoring or friend-shoring as the coefficients of the dummy variables for border and NATO appear relatively stable over time. However, the data suggest an intensification in reshoring activities among US companies. Specifically, the same country dummy, which captures how much companies headquartered in the US are investing domestically, became positive and statistically significantly different in 2020 and further increased in magnitude in 2023.

Second, we implement a triple-difference approach comparing US investments to EU investments over time to identify differences in their patterns following the beginning of the US-China trade war, the Covid-19 pandemic, and the Russian invasion of Ukraine. This approach allows us to control for all source country-time, destination country-time, and country-pair unobservable characteristics. The results confirm that US companies intensified their reshoring activities. In addition, we find that, compared to EU firms, US companies also increased nearshoring following the Covid-19 shock. The estimates indicate that US investment in neighboring countries and within the US states doubled during the post Russia’s invasion of Ukraine period, with slightly larger effects for US investment in the US.

Finally, we exploit the richness of the fDi Markets database to assess the impact of the shocks on direct job creation associated with greenfield investment announcements. This allows us to determine whether the increase in investment announcements is associated with job creation in the US or if these investments are likely leading to jobless growth in the manufacturing sector with highly automated factories. The results suggest that recent investments are associated with job creation in both the US and neighboring countries. For example, following the invasion of Ukraine, the number of direct jobs in greenfield investment announcements were more than double in the US and its neighbors. These results combined with the ones for investment, may suggest that more labor-intensive projects are being directed to Mexico.

4
containers in six randomly selected households per village, on average. Testing was done concurrently with the household interviews. We used the Aquagenx Compartment Bag Test E. Coli +Total Coliform Most Probable Number (MPN) Kit. This measured the MPN of fecal indicator bacteria [28].

To measure subjective performance of local WASH institutions, we used survey questions about: fairness of selection of water governance entity, perception of fair treatment, confidence in the entity’s management of money, confidence in the entity’s response to infrastructure breakdowns, confidence in management, and overall satisfaction. The data collection team also directly observed water point functionality. Length of breakdowns was reported via a water committee and village leader survey.

# Statistical analyses

The number of village clusters in the study was determined by the program budget and proximity of villages to one another (details above). Sample size calculations were used to determine how many households in each village should be interviewed. Based on the primary outcome of diarrhea prevalence, with a minimum detectable effect of 8 percentage points (pp), 32%
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 39 Productivity

- Lee, B; Gordon, P (2011). Urban structure: its role in urban growth, net new business formation and industrial churn, _Région et Developpement_ , 33: 137-159.

- Li, X; Zhou, Y (2017). Urban mapping using DMSP/OLS stable night-time light: a review. _International Journal of Remote Sensing_ , 0(0): 1–17. https://doi.org/10.1080/01431161. 2016.1274451

- Lo, CP (2001). Modeling the Population of China Using DMSP Operational Linescan System Nighttime Data. _Photogrammetric Engineering and Remote Sensing_ , 67(9): 1037–1047.

- Lowe, M (2014). The Privatization of African Rail. Working Paper, Massachusetts Institute of Technology, Boston MA.

- Ma, L; Wu, J; Li, W; Peng, J; Liu, H (2014). Evaluating saturation correction methods for DMSP/OLS nighttime light data: A case study from China’s cities. _Remote Sensing_ , 6(10): 9853– 9872. https://doi.org/10.3390/rs6109853

- Meijers, E; Burger, M (2010). Spatial structure and productivity in US metropolitan areas, _Environment and planning A_ , 42: 1383-1402.

- Melo, P; Graham, D; Levinson, D; Aarabi, S (2017). Agglomeration, accessibility and productivity: Evidence for large metropolitan areas in the US. _Urban Studies_ , 54(1): 179-195.

- Michalopoulos, S; Papaioannou, E (2013). Pre-Colonial Ethnic Institutions and Contemporary African Development. _Econometrica_ , 81(1):113-152.

- Nunn, N; Puga, D (2012). Ruggedness: the blessing of bad geography in Africa. _The Review of Economics and
provide, highlighting policy questions which have received less attention. Private employment firms often provide a range of services related to job intermediation, including worker profiling, labor market information, CV and interview preparation, job search assistance, and job matching. When outsourcing intermediation to the private sector, both the relative impact of different services, and in particular, the efforts made in providing these services across a heterogeneous pool of job seekers are important to consider when designing incentive schemes.

This paper concentrates on a pilot program in BiH that was narrow in scope and facilitated comparison of employment outcomes across two levels of service provided by a private provider. Further, access to information on counseling sessions and job counselor assessments make it possible to examine both the classification of jobseekers and counselor decisions. Under the pilot, which was rolled out in three calls between August 2020 and April 2021, self-selected participants were randomly allocated to one of two schemes administered by a private sector provider. A base scheme includes many features of interventions highlighted in recent research, including assistance with CVs, providing information about potential occupations (e.g., Abebe et al, 2021; Beam, 2016; Jensen, 2012), and developing job search plans (e.g., Abel, 2019). The second, more intensive scheme, included all features of the base scheme, with additional effort to match jobseekers with potential employers.

Access to job matching relative to only job counseling led to significantly higher rates of employment in the short-run, with smaller effects in the medium-run. Analysis of administrative data reveals that 3 to 6 months after the program, jobseekers assigned to both counseling and job matching services were 5.8 percentage points, or 25.2 percent, more likely to be formally employed than those assigned to only counseling services. In the medium term, 21 to 24 months after the program, this effect is 4.1 percentage points or a 22.6 percent greater likelihood of being formally employed. Moreover, from examining heterogeneity in the
proposed by Imai and Ratkovic (2014).<sup>41</sup> This strategy enables the analysis of a pseudo-sample of youths in which the characteristics of their households of origin, prior to the intervention of the program, are not associated to the different intervention levels to which they were exposed (Ridgeway et al. 2015).<sup>42</sup> Appendix B shows the variables used in the construction of the weights and the favorable balance achieved through their application.

The weights obtained were used in the construction of the mobility tables for the groups with low and high treatment intensity, and for the purpose of estimating the effect of the program on both absolute occupational mobility (through the mobility rates) and on relative mobility (through the use of log-linear models).

# D. _Methods and variables for the study of intergenerational occupational mobility_

This section describes the methods and variables used to answer the first set of questions posed. This requires the analysis of both the absolute mobility and the relative mobility of the youths of the sample and the relevant subgroups. First, we present the scheme of the occupational strata used, followed by the mobility tables and log-linear models.

# _D.1 Occupational strata scheme_

For the analysis of intergenerational occupational mobility, we used an approach to occupational stratification that seeks to establish a relation between intergenerational mobility and the improvement of the socioeconomic status of the beneficiary youths of PROSPERA. This scheme was used by Yaschine (2015) for the analysis of intergenerational occupational mobility with data of ENCEL 2007 and is based on the scheme proposed by Solís and Cortés (2009) for Mexico, which, in turn, used elements of the CASMIN classification.<sup>43</sup> An ordinal scheme is proposed, which assumes that a position in the highest occupational stratum grants greater access to life opportunities and, therefore, allows the identification of upward and downward movements of the youths. In table 4 our approach to occupational stratification is presented, which is ordered

> 41