rafmacalaba/datause-encoder-data huggingface dataset:
fraction of manufacturing and public employment in the district. The latter in particular permits us to test for complementarities between individual and district characteristics.

We use four rounds of the NSS data – namely data from the “thick” rounds conducted in 1999-00, 2004-05, 2009-10 and 2010-11.<sup>1</sup> Since there has been a significant change in the number of districts during this period, we aggregate district boundaries corresponding to those in 1991.

# **2.2 Micro-Enterprise Survey**

We consider the survey on unincorporated non-agricultural enterprises to gauge the problems faced by micro-enterprises. This is a rich dataset covering enterprises which are not registered – under either the Factories Act of 1948, or the Companies Act of 1956 the two premier law statutes which govern corporate entities in India. The enterprises by definition thus operate in the informal sector. The operation of these enterprises are of particular interest as a third of the rural non-farm labor force during the period of our study report being “self-employed” (engaged in proprietorship), while a fourth report being employed as “casual workers”. This suggests that informal small and micro-enterprises are accounting for a substantial fraction of the rural non-farm employment observed in the data.

The micro-enterprise survey is also conducted by the NSS and covers all states and districts in India, accounting for over 50 million establishments, providing employment to 100 million individuals. The survey includes data on key measures of enterprise performance such as operating expenses, raw materials, fixed capital, workers, wage payments, revenues and gross-value addition. Qualitative measures of enterprise performance such as whether the establishment is expanding, shrinking or a recent entrant, in addition to outstanding credit (and its source) are also provided. Finally, the survey enquires enterprise owners regarding select problems faced by the enterprise. Our study exploits these qualitative questions to analyze how problems faced by enterprises affects enterprise performance, conditional on key enterprise parameters such as capital and labor.

# **2.3 Consumer Pyramids**

The last NSS employment-unemployment survey was conducted in 2011-12. In this regard, we use the four rounds of the NSS surveys between 1999 and 2011 to gauge the historical predictors of rural non-farm employment, and then use data from 2015 from the Consumer

> 1 The corresponding survey rounds are 55, 61, 66 and 68. The NSS conducts similar surveys in the intervening years but the sample sizes at the level of districts are too small to conduct any robust analysis. The surveys for each round begin on July 1, and continue to June 30 of the subsequent year.

8
# Do Performance Agreements Help Improve Service Delivery? The Experience of Brazilian States<sup>1</sup>

Lorena Viñuela and Laura Zoratto

**JEL Classification** : JEL: D02 Institutions: Design, Formation, Operations, and Impact, H11 Structure, Scope, and Performance of Government, and O54 Latin America • Caribbean. **Keywords** : Brazil, Performance-based management, variable pay, performance agreements, service delivery, education, health, and security.

1 The analysis builds on the World Bank’s extensive and innovative engagement on public sector reform at the state level in Brazil since 2007, notably including the work led by Deborah L. Wetzel, Roland Clarke, and Tarsila Velloso. This paper is directly linked to the various past and on-going state-level operations in Brazil. The authors especially acknowledge the contributions of Ana Mie H. Reis (LCSPS) provided valuable research assistance. William Dillinger, Philip Keefer, Barbara Nunberg, and Pedro Olinto offered important advice and insights for the research agenda as peer reviewers.
|R<sup>2</sup>|0.15|0.17|0.16|0.16|0.16|0.17|0.23|0.18|0.19|
|---|---|---|---|---|---|---|---|---|---|
|# obs.|6550|6550|6550|4334|4334|4334|2184|2184|2184|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. KCPE = Kenya Certificate of Primary Education.

35
**Table ‎3.4:  Earnings Regressions (OLS) Model for HE Operators, by Gender**

|**1.8**|**Males**|**Females**|
|---|---|---|
|Age|0.056***|0.028***|
||(0.007)|(0.007)|
|Age2|<br>0.065***|<br>0.034***|
||(0.009)|(0.008)|
|_Education (reference: no education)_<br>|||
|Incomplete Primary|0.070|-0.020|
||(0.074)|(0.066)|
|Completed Primary<br>|0.226***<br>(0.064)<br>|0.104*<br>(0.055)<br>|
|Incomplete Secondary|0.247*|0.220|
||(0.129)|(0.150)|
|Completed Ordinary Secondary|<br>0.245**|<br>0.315***|
||(0.097)|(0.117)|
|Some or completed Adv. Secondary and Tertiary|0.644***|0.459|
||(0.195)|(0.390)|
|Recent migrant|-0.011|0.071|
||(0.061)|(0.064)|
|Market within 30 min|-0.027|0.163**|
||(0.086)|(0.081)|
|# Months business operates|<br>-0.014*|<br>-0.003|
||(0.007)|(0.007)|
|_Training (Omitted category: no training)_<br>|||
|On job/informal apprenticeship|0.004|0.031|
||(0.068)|(0.098)|
|Other(vocational/college/diploma/other)|0.213**|0.138|
||(0.097)|(0.129)|
|_Industry (Omitted category: wholesale/retail trade)_<br>|||
|Manufacturing, Energy|0.334***|-0.129**|
||(0.070)|(0.059)|
|Mining, Construction|0.298***|0.276*|
||(0.088)|(0.144)|
|Hotels / restaurants|-0.314***|0.100**|
||(0.088)|(0.049)|
|_Transport storage real estate_|-0.007|-0.099|
|_, ,_|<br>(0.086)|<br>(0.807)|
|Public, social, personal services|0.159*|0.256*|
||(0.095)|(0.131)|
|_INTERACTIONS_|||
|Training*manufacturing|0.219**|0.114|
||(0.109)|(0.131)|
|Training*Construction mining|-0164|-|
|,|.<br>(0.143)|<br>-|
|Training*Hotels / restaurants|<br>0.522**|-|
||(0.257)|-|
|Training* Public, social, personal services|-|0.137|
||-|(0.253)|
|_Area of Residence (reference: rural)_|||
|Dar|0456*|1011*|
||.<br>(0.274)|.<br>(0.549)|
|Other urban|<br>3093***|<br>4957***|
||.<br>(0.315)|.<br>(0.593)|
|Constant|3.380***|3.366***|
||(0.310)|(0.563)|
|Observations|3526|3207|
|R-squared<br>_Notes: The regression takes includes (i) EA s and (ii) quarter of the survey_<br>_not reported. Robust standard errors reported in parentheses. Significanc_<br>_Source: ILFS, 2006._|0.28<br>_, as fixed effects, however thes_<br>_e level: * p<0.1 ** p<0.05_|0.32<br>_e coefficients are_<br>_*** p<0.01._|

19
only information that was available to the bank at the original time of application. Because we observe the performance of loans in our data set, we can pay participants performance incentives based on their decision and the actual outcome of the loan. Our sample of loans consists of unsecured small-business working capital loans with a ticket size of less than Rs 500,000 (US$ 10,000). Sales and origination channels for this class of loans are generally distinct, so that (analogous to low-documentation loans in the United States) loans are sourced by a bank’s sales agents who collect client information, which is then forwarded to a credit officer for approval. The task faced by the bank’s credit officers is to screen and make profitable lending decisions based on the information contained in an applicant’s loan file.

The applicant information contained in the loan files is “hard” in the sense that it can be transcribed. However, borrower information differs in its degree of verifiability, and ranges from audited financials to information that requires a significant degree of interpretation, such as trade reference reports or a description of the applicant’s business (see Petersen (2004) for a discussion). Although regulators require banks to collect an applicant’s tax record and audited income statement, this information is often unreliable and difficult to verify. New applicants typically lack a credit score or established record of formal borrowing, which rules out the use of predictive credit scoring, “scorecard lending” and other more systematic loan approval technologies. Because the ticket size of a representative small business loan is small, relative to the fixed cost of underwriting, risk-management occurs primarily through ex-ante screening, rather than prohibitively costly relationship lending or ex-post monitoring.

# **4.2 Loan Database**

As a basis for the experiment, we requested a random sample of loan applications from a large commercial lender in India (hereafter “the Lender”), and received 650 loan files. The loan files contain all information available to the Lender at the time the application was processed, as 12
# **_Table 4: Gender Disaggregated Impact of COVID-19 Related School Closures with/without Child Dependents in the Household in 2020_**

||<br>|<br>|<br>|**202**<br>|**0**<br>||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||||Time Spent on<br>|(Hoursper day) <br>|...<br>||
||Busine<br>(1=Ye|ss closed<br>s; 0=No)|Paid|Work|Care an<br>Respo<br>(Self/|Domestic<br>nsibilities<br>Married)|Care an<br>Respo<br>(Spous|d Domestic<br>nsibilities<br>e/Married)|
||_Children_|_No Children_|_Children_|_No Children_|_Children_|_No Children_|_Children_|_No Children_|
|Female Respondent|0.002|0.011|-1.012***|-0.249*|1.662***|0.659***|-2.304***|-0.924***|
||[0.01]|[0.01]|[0.13]|[0.14]|[0.11]|[0.10]|[0.11]|[0.11]|
|School closure due to COVID-19|0.001|-0.003|0.297**|0.237|0.007|-0.024|0.444***|0.283**|
||[0.01]|[0.01]|[0.13]|[0.15]|[0.09]|[0.10]|[0.12]|[0.12]|
|School closure due to COVID-19*Female|0.032***|0.001|-0.194|-0.227|0.855***|0.073|-0.444***|-0.437***|
||[0.01]|[0.01]|[0.17]|[0.19]|[0.14]|[0.12]|[0.14]|[0.14]|
|Academic Break|0.008|-0.009|0.585***|0.579***|-0.091|-0.149|-0.008|0.135|
||[0.01]|[0.01]|[0.16]|[0.18]|[0.10]|[0.12]|[0.14]|[0.14]|
|Academic Break*Female|0.012|-0.004|-0.314*|-0.501**|0.567***|0.029|0.123|-0.274*|
||[0.01]|[0.01]|[0.18]|[0.20]|[0.15]|[0.14]|[0.15]|[0.15]|
|GDP per capita (Ln)|-0.016***|-0.018***|0.430***|0.061|0.029|-0.453***|-0.154***|-0.445***|
||[0.00]|[0.00]|[0.06]|[0.06]|[0.04]|[0.04]|[0.05]|[0.06]|
|Constant|0.251***|0.254***|4.153***|7.641***|2.290***|6.806***|6.109***|7.383***|
||[0.04]|[0.05]|[0.63]|[0.65]|[0.48]|[0.48]|[0.52]|[0.58]|
|Observations|44438|36676|25612|21246|35408|22451|26862|16853|
|Adjusted R-squared|0.08|0.08|0.07|0.03|0.14|0.08|0.14|0.13|
|SC+SC*FEMALE(Coeff)|0.03|-0.00|0.10|0.01|0.86|0.05|0.00|-0.15|
|<br>SC+SC*FEMALE(SE)|0.01|0.01|0.15|0.17|0.13|0.11|0.12|0.12|
|<br>SC+SC*FEMALE(P value)|0.00|0.89|0.50|0.96|0.00|0.65|1.00|0.22|
|<br>Mean Male|0.15|0.14|7.83|7.86|4.09|3.11|6.21|3.81|
|Notes:* significant at 10% level, ** significant at 5% lev<br>[1] Female is a dummy variable where 1 = female-led b|el, *** significant a<br>usiness and 0 if m|t 1% level.<br>ale-led business. Sc|hool Closure due|to COVID-19 is a du|mmy variable whe|re 1 = schools close|d in country at ti|me of survey wave.|

[2] OLS pooled regressions include Region of the world Fixed Effects and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents).

[3] Full sample is split among those business owners with any child dependents aged 18years or less in the household = 1 (Columns (1), (3), (5), and (7)) versus no children dependents aged 18years or less in the household (Columns (2), (4), (6), and (8)).

40
# **4 Results**

# **4.1 Predicting time use activities**

We present the results for the two utilized predictive models, SGB and Random Forest. We find that our SGB model results in a macro average F1-score of 76 percent. Our Random Forest model results in an F1-score of 68 percent. As a basis for comparison, a random guess would result in a prediction accuracy and an F1-score of merely 8.33 percent since there are 12 balanced classes.

From our findings, we can conclusively assert the following:

1. Machine learning effectively utilizes accelerometer data to forecast time use.

2. The SGB model outperforms the Random Forest model.

Consequently, we have selected the SGB model as our best performing model, and all subsequent model results presented will pertain exclusively to the SGB model. Detailed prediction scores for the SGB machine learning model can be found in **Table 2** .

# **Table 2**

SGB model prediction scores and test set characteristics for the twelve main activities.

|**Activity**|**Set size**|<sup>**Participant**</sup><br>**s**|**Precisio**<br>**n**|**Recall**|**F1-score**|
|---|---|---|---|---|---|
|1. Social or religious activities and hobbies|2036|333|68 %|54 %|61 %|
|2. Farming|2036|199|75 %|77 %|76 %|
|3. Cook orprepare food or drinks|2036|239|56 %|69 %|62 %|
|4. Travellingand commuting|2036|316|80 %|55 %|65 %|
|5. Eatingand drinking|2036|380|68 %|55 %|61 %|
|6. Resting|2036|234|77 %|75 %|76 %|
|7. Hunt orgather foodstuffs|2036|149|79 %|86 %|82 %|
|8. Personal care|2036|345|91 %|67 %|77 %|
|9. Clean the house,wash,or iron|2036|170|79 %|93 %|86 %|
|10. Fetch water|2036|179|78 %|93 %|85 %|
|11. Work for a wage or salary|2036|37|89 %|99 %|94 %|
|12. Running or working in household<br>business|2036|40|85 %|99 %|92 %|
|Accuracy|||||77 %|
|Macro average F1-score|||||76 %|

The prediction scores demonstrate that our model accurately classifies the reported time use activities in over three-quarters of the cases, using the data from the physical activity sensor signal. To further validate the model's predictive power, we conducted a sensitivity test by varying the dataset size. As the dataset size increases, the prediction scores exhibit a positive trajectory, indicating the model's capacity to improve its performance with growing datasets. This evidence substantiates that time use activities can be reliably predicted using sensor data, effectively addressing our main research question.

For the trained eye **Table 2** also illustrates variations in prediction scores among different activities. Interestingly, despite having balanced train and test sets, the activities less frequently reported prior to the creation of the balanced dataset for analyses (see **Figure 1** ) exhibit higher prediction scores. One plausible explanation for these variations is that the more frequently reported activities also originate 9
(SDG target 3.2), quality-adjusted years of schooling (SDG target 4.1), adult survival rate (SDG target 3.4), and stunting (SDG target 2.2).

- _The State of Social Safety Nets._ The WBG’s _State of Social Safety Nets_ tracks progress toward SDG target 1.3, on social protection coverage, and target 1.5, on the vulnerability of the poor to economic, social, and climate-related shocks and natural disasters (e.g., World Bank 2018c).

- _Health Equity and Financial Protection Indicators._ Through its Health Equity and Financial Protection Indicators database, the WBG tracks progress toward SDG 2 (on hunger and nutrition) and SDG target 3.8 (on universal health coverage; World Bank 2018d).

- _WASH Poverty Diagnostic._ The Water, Sanitation, and Hygiene Poverty Diagnostic tracks progress toward sustainable water and sanitation (SDG 6) in 18 countries (World Bank 2017b).

- _WBG flagship reports._ The _World Development Report_ (WDR) investigates a different development issue and outcome each year. Recent reports have examined the changing nature of work with key insights on SDG 3 (health) and SDG 4 (education) (World Bank 2019a); the issue of learning to realize education’s promise (World Bank 2018e); and the impact of governance and law on development (World Bank 2017c).<sup>15</sup> The _Global Economic Prospects_ (GEP) regularly assesses the economic growth of developing countries and the associated topical policy issues affecting the SDGs, such as fiscal space, fiscal policy, and structural policies  (World Bank 2015b and 2019b); potential growth and investment prospects (World Bank 2017d and 2018f); the prevalence of the informal economy (World Bank 2019c); and challenges of low-income countries (World Bank 2019b).

- _Joint monitoring reports._ In collaboration with other agencies, the WBG has produced reports to capture the global and country-level progress toward reaching specific SDGs. A joint report with the International Energy Agency and several other international agencies monitors progress towards the SDG 7 on affordable and clean energy (IEA, IRENA, UNSD, World Bank, and WHO 2019). Since 2015, a joint biennial report with the WHO tracks universal health coverage under SDG 3 (WHO and World Bank 2015, 2017). The WBG contributed to the UN’s global monitoring report on gender equality (i.e., SDG 5; UN Women 2018).

- _Inter-Agency Expert Group on the SDG Indicators._ The WBG participates in the _InterAgency Expert Group on the SDG Indicators_ , a group of UN member states and international agencies. This group identifies the specific metrics by which progress toward the SDGs is measured. The WBG leads the methodology and data collection for SDG 1 (on poverty reduction) within the group. Through partnerships with the UN agencies and other international institutions, the WBG collaborates on the development of tools, capacity building, and strategies to achieve the SDGs.

> 15 During the era of the MDGs, the WBG’s _Global Monitoring Report_ provided a global update on progress toward the MDGs.

11
gender ratios in entrepreneurship (e.g., Minniti 2010, Minniti and Naudé 2010), and we analyze very local determinants. Second, our paper contributes to a growing set of spatial studies on entrepreneurship in India (e.g., Khanna 2008, Ghani et al. 2011a,c, Mukim 2011) and a broader set of work on India’s industrial organization and economic performance.<sup>1</sup> These contributions constitute an important input to the growing body of work on entrepreneurship and economic advancement in developing countries (e.g., Ardagna and Lusardi 2008, Schoar 2009, Klapper et al. 2009).

Beyond the few papers on the spatial determinants of entrepreneurship in India, the closest paper to our work is Rosenthal and Strange (2011). Rosenthal and Strange (2011) document within-metropolitan area sorting for female entrepreneurs in the United States. They show that a spatial mismatch exists for female entrepreneurs similar to that found earlier on racial lines. Our work has several key differences. First, Rosenthal and Strange (2011) focus on spatial differences within metropolitan areas (e.g., using one-mile rings around a business). Our Indian data only identify districts for firms, which are often larger than U.S.metropolitan areas. Thus, we do not study sorting at the same spatial level. Second, and more generally, the contextual differences of the two countries are very stark. Rosenthal and Strange (2011), for example, consider automobile commuting patterns for U.S. entrepreneurs, whereas our focus will be on whether basic infrastructure like paved roads and electricity exist. Nonetheless, the two studies are connected in that they describe a link of past female business ownership in a spatial area with subsequent female entry. We hope that further research continues to refine our understanding of this interface between incumbent conditions and gender ratios of new entrants.

The plan of this paper is as follows. Section 2 discusses our entrepreneurship data and spatial differences in femalebusiness ownershipacross India. Section 3 reviews the spatial determinants of entrepreneurshipand our metrics.Section 4 quantifies the spatial determinants of female entrepreneurship in manufacturing and services. Section 5 presents our instrumental variable specifications that use lagged incumbent conditions from 1994. The final section concludes.

# **Female Entrepreneurship Rates in India**

We employ cross-sectional establishment-level surveys of manufacturing and services enterprises carried out by the Government of India. Our manufacturing data are taken from

> 1 For example, Lall et al. (2004), Lall and Mengistae (2005),Nataraj (2009), Kathuria et al. (2010), Hasan and Jandoc (2010), Dehejia and Panagariya (2010), Fernandes and Pakes (2010), Ghani (2010b), Fernandes and Sharma (2011), and Mukim and Nunnenkamp (2011).

4
final good in region j is then: where _ηk_ (<sup>�</sup> _k_<sup>_η_</sup> _k_<sup>= 1)representsaconstantCobb-Douglascostshareofcomponentk,</sup><sup>_β_repre-</sup> _−_ sents the share of labor in assembly, and 1 _β_ the share of components in the cost of assembly. We assume the technology of assembly is the same across all commodities and regions. The price in region i of the final good, _Pi_<sup>_F_(</sup><sup>_ω_),istheminimumdeliveredpriceaccountingfortrade</sup> costs _τ_ when the good is imported. We describe the calibration of this model in section 5.5.

# **5 Quantitative analysis**

## **5.1 Data**

Our main sources of data are the Asian input-output tables developed by IDE-JETRO. These tables provide information on gross output, value-added, and (most importantly) input purchases by product, parent industry (downstream industry), source country and destination country. For instance, the data report the amount of metals purchased from China by the auto industry in Japan. These 4-dimensional input-output tables, are, as far as we know, the only tables that track international transactions directly, rather than imputing them from trade flows. This is an exceptional data set for investigating the organization and evolution of international production fragmentation in a region of the world where fragmentation is an important feature of international trading relationships.

The dataset covers 9 Asian countries and the US.<sup>28</sup> Our analysis mostly focuses on the year 2000 (the most recent year available), but we also compare our results to IDE-JETRO data from 1975 and 1990. This period marks a time in which the region began to emerge as an important location for internationally fragmented production (see Baldwin and Lopez-Gonzalez (2012) for example).

Information on input purchases and production is disaggregated at the 76-sector level in 2000. For the sake of comparison to previous input-output tables (1975 and 1990), we construct a more aggregated 46-sector classification to obtain harmonized product categories across years. The sector classification is far more detailed for manufacturing goods and commodities than services (among the 46 sectors, only 5 of them are service industries). We thus mostly restrict our attention to tradable goods: commodities and manufacturing goods.

> 28The countries in the data base are the US, Japan, China, Taiwan, Korea, Singapore, Malaysia, Thailand, Indonesia and the Philippines.

34