rafmacalaba/data-use-mentions-extended huggingface dataset:
this is the first empirical study of SSCBT and group trade. The survey data used cover eight different border crossing locations along the Eastern DRC border with Burundi and Rwanda and are, thus, not specific to the institutional and locational conditions at any particular border post. The data were collected from almost 600 traders through face-to-face interviews, which assured a high response rate and the minimization of non-response bias.

The econometric results reveal that operating a registered business, being a member in a traders’ association, and having access to motorized transport are significant determinants of traders’ participation in group trade. Other personal traits, such as gender and trading experience, logistics features, like transport distance, or product characteristics, such as perishability or trade protection, do not play a decisive role. These findings suggest that it is the more prosperous, politically and institutionally well-established traders that have engaged in group trade. Other SSCBT operators have been left to resort to other, presumably less advantageous strategies to cope with the COVID-19 sanitary measures. Group trade, thus, appears to have increased the intra-trader income gap within the border communities.

Another important finding is that those group traders who report increased incomes from the cooperative trading arrangement have achieved this success by being able to reach new clients and obtain higher prices for their products. In contrast, cost savings do not play a decisive role. Hence, it seems that better integration of small-scale traders into cross-border regional supply chains is key for the long-term viability of group trade once the COVID-19-related sanitary measures are phased out.

These core findings suggest that policy support for group trade might well be justified to increase marketing efficiency and enhance trader incomes, but such assistance is unlikely to advance poverty reduction objectives. The less prosperous segments of the trader population 5
personal income taxes and the planned introduction of the GST would alter these distributional impacts. With the reduction in PIT, direct taxes have become slightly more progressive; however, the inequalityreducing impact is slightly dampened than before. The GST as a broad-based tax likely leads to an increase in indirect taxes paid by households across the distribution, including those in the bottom 40 percent. At the same time, the GST is much less progressive than the existing sales tax. While the GST is povertyincreasing as a result, the poverty rate reverts to levels similar to the baseline in 2017.

The rest of the paper is organized as follows. Section 2 presents an overview of taxes and social spending in Bhutan. This is followed by a description of data sources and general methodology in section 3. It will elaborate and further expand on the standard methodology to account for an important limitation in the available data set, i.e., the lack of information on household income. The paper introduces a model which combines a consumption-based model with Mincer-type earnings to derive estimates of incomes. The incidence of taxes and social spending are subsequently presented in section 4, followed by simulations of personal income tax and GST in section 5. Section 6 concludes.

# 2. Overview of Taxes and Social Spending in Bhutan

Fiscal revenues in Bhutan rely on a mix of tax and non-tax revenues. Total revenue collection amounted to 18.6 percent of GDP in the fiscal year 2016-17,<sup>2</sup> out of which roughly two-thirds were from tax receipts. Among tax revenues, direct taxes have a slightly larger contribution than indirect taxes. Corporate income taxes account for a large share of direct taxes while the Personal Income Tax only contributed 0.6 percent of GDP.<sup>3</sup> Sales tax and excise tax accounted for a combined 80 percent of indirect taxes (table 1). Among the different types of taxes whose incidence can be estimated from the household survey, PIT and import duties are particularly overestimated compared
from the BFL model to the national figure using ENCEL 1997, the total cost of the program is estimated to be around 6.08 billion pesos, which is very close to the actual program cost of 6.06 billion pesos in January 2002 from the official program data (Table 5).

**Table 5: Estimated and Observed** **_PROGRESA_ 's Cost**

||_BFL Simulation_<sup>_1,2_</sup>|_January 2002_<sup>_1,3_</sup>|
|---|---|---|
|Number of Families with Children Receiving Benefits|4,567|1,681,254|
|Average Monthly Transfer for Families with Children|$301|$300|
|Estimated Total Annual Transfer|$16,517,580|$6,061,221,243|
|Scaling Up Total Transfer: Simulated average times<br>families in 2002|$6,080,632,364|n.a.|

_Note_ :

|1: November 1999 prices|
|---|
|2: Estimated by BFL model using Baseline survey of 1997, Rounds 1-4|
|3: National official numbers from:|
|http://www.oportunidades.gob.mx/indicadores_gestion/ene_feb_02/indice.htm|

Thus the BFL model applied to the _PROGRESA_ program in Mexico appears to have strong predictive power for the impact of the program on outcome indicators (i.e. enrollment rate, child labor and poverty) as well as the cost of the program, when compared against relevant benchmarks – namely AIT impacts estimated by _ex post_ evaluations using experimental methods and actual or observed program cost. Since _ex post_ AIT estimates represent a lower bound of the impact on households that actually _received_ treatment (for reasons explained earlier), the _ex ante_ estimates of program impact from BFL should also be seen as a _lower bound_ for the actual impacts of the program on
# **3. Small area estimation methodology**

In this section we present a summary of the small area methodologies we use to estimate headcount poverty rates at the level of the target area in the five countries of interest. We use a version of the Empirical Best Predictor (EBP) (Battese et al., 1988; Jiang and Lahiri, 2006; Molina and Rao, 2010; Tzavidis et al., 2018) under the unit context nested error regression model with households as the unit of analysis and covariates defined by zonal statistics of geospatial variables at grid cell level (centroid of enumeration areas within target areas). Our methodology is similar to the one used by Masaki et al. (2022), which uses small area estimation to estimate non-monetary poverty indicators in Tanzania and Sri Lanka with geospatial covariates, and Newhouse et al. (2022), which applies similar techniques with geospatial covariates to estimate monetary poverty in Mexico. Van der Weide et al. (2022) also examines the performance of poverty estimates with geospatial covariates in Malawi but using a spatial error model with subarea-level estimates of poverty rates as the outcome, and geospatial zonal statistics as covariates.

As mentioned in previous sections, when the census and survey data are collected from around the same time, using household-level census covariates is generally preferred, because the census tends to contain richer auxiliary information than geospatial data. When census data are sufficiently old, however, using cluster-level covariate aggregates taken from the old census can generate more accurate estimates than using old census household-level covariates (Lange et al., 2021). None of these variations of covariate use, however, reflect any changes in the distribution of the census covariates since the last census. Because, except for Burkina Faso, the census data in the focus countries of this paper are not up to date, we explore the use of more current geospatial data as covariates instead of old census data.

We opt for a household-level model of welfare over a grid cell-level model of poverty rates
during a financial crisis. Moreover, welfare gains from policy may become negative. Empirically, I present evidence from Brazilian data that expectations of economic fundamentals respond to capital control announcements: first, the paper uses a highfrequency strategy to show that such announcements tend to affect daily forecasts of GDP collected by the Central Bank of Brazil; second, the paper constructs an econometric model to nowcast Brazilian GDP with real-time macroeconomic data, and shows that such announcements reduce the forecast errors of the model.

In the theoretical section, I incorporate learning from policy into a standard model researchers use to study capital controls in emerging market economies. In this twosector small open economy model, borrowing by domestic agent is limited by tradable and nontradable income through a collateral constraint. This environment features a pecuniary externality because individual agents do not internalize the effect of their borrowing decisions on the value of collaterals and hence on borrowing capacity. Capital controls are usually deployed by the government to mitigate the effect of overborrowing by agents arising from the pecuniary externality. I incorporate three novel features in this setting to model learning from policy: first, the government knows more about future economic fundamentals than the representative agent. Second, the agent knows that the government is levying capital control taxes according to a simple policy rule which reveals useful information previously unknown to her. Last but not least, the agent learns from policy by completing a Bayesian updating of her information set when she observes policy changes.

A version of my model is calibrated to the Brazilian economy and the quantitative model features unintended consequences of capital controls when people learn from policy. I choose the Brazilian economy between 2006 and 2013 because the country experienced large and volatile capital inflows during the period and actively used capital controls to manage such inflows with some success. I solve the calibrated model for the decentralized equilibrium<sup>2</sup> , the equilibrium with policy interventions but no learning from policy and most importantly, the equilibrium with both. When people learn from policy, the domestic economy borrows more from
while Adnot et al. (2017) confirm that these ‘quitters’ were replaced by higher-performers. In Wisconsin, a reform permitted approximately half of the state’s school districts to introduce flexible salary schemes that allow pay to vary with performance. In that setting, Biasi (2019) finds that high-value-added teachers were more likely to move to districts with flexible pay, and were less likely to quit, than their low-value-added counterparts. Our prospective, experimental study of pay-for-performance contributes to this literature methodologically but also substantively since the Rwandan labor market shares important features with high-income contexts.<sup>1</sup>

> 1Notably, there is no public sector pay premium in Rwanda, which is unusual for a low-income country and more typical of high-income countries (Finan et al., 2017). The 2017 Rwanda Labour Force Survey includes a small sample of recent Teacher Training College graduates (aged below age 30). Of these, 37 percent were in teaching jobs earning an average monthly salary of 43,431 RWF ,

4
(2008) emphasizes entrepreneurship for India‘s future, and reallocation can help close India‘s productivity gap (e.g., Hsieh and Klenow 2009).

While focusing on entrepreneurship, we recognize that large firms also play an important role in regional development. Audretsch and Fritsch (2002), for example, discuss the different paths to Germany‘s development. The giant firms of South Asia are becoming globally powerful and growing in efficiency, and they too will shape employment opportunities in the decades ahead. However, the history of regional development shows that big firms are not sufficient forever. An entrepreneurial foundation that provides for local growth and regeneration is essential for long-term success and prosperity. This is evident in the current struggles of Detroit, Michigan, and its car manufacturers. Jacobs (1970) highlights how Manchester, England, and its giant textile mills in the 1800s were a model of short-term efficiency and power but also were ultimately insufficient for long-term regional growth. Likewise, the very dynamic times ahead for South Asia require that entrepreneurship be embraced and supported at the regional level.

The plan of this paper is as follows. The next section discusses our entrepreneurship data and spatial differences in entry across India. Section 3 reviews the determinants of spatial locations for firms and our metrics. Sections 4 and 5 provide our empirical results for unconditional and conditional estimations, respectively, and the last section concludes.<sup>2</sup>

# **Spatial Entrepreneurship Rates in India**

This section discusses our two primary datasets and describes the spatial variation on entrepreneurship in India. We first define how we will measure entrepreneurship.

## _Definitions of Entrepreneurship_

Defining entrepreneurship is hard and controversial, even in advanced countries.<sup>3</sup> One approach, dating back to Cantillion (1730), is to describe entrepreneurship as the number of people leading independent businesses. Evans and Jovanovic (1989), Blanchflower and Oswald (1998), and many others have accordingly used self-employment rates as
_Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors.

— = Not available.

*traced to needs.

# What else can be done?

Broadly speaking there are three alternative ways to close the infrastructure funding gap. The first is to raise additional finance. The second is to find most cost-effective ways of meeting the infrastructure targets. The third is to lengthen the time period needed to reach the targets, beyond the ten years assumed in this analysis.

Uganda’s prospects for raising the volumes of additional infrastructure funding needed look quite hopeful for a number of reasons. Once efficiency gains are fully captured there would likely be surplus spending in the power sector that could potentially be reallocated toward sectors with funding gaps. Looking ahead, Uganda’s new-found oil wealth will in due course boost fiscal revenues part of which could be allocated to close the infrastructure funding gap. This gives Uganda an important new source of funding, which – however – should not become a pretext for overlooking efficiency issues. Furthermore, Uganda has been one of the most successful African countries at attracting private finance to the infrastructure sectors, and not only into telecommunications. It is unclear whether this track record suggests that even more could be done, or whether it rather signals that all of the most relevant opportunities have already been tapped (figure 33).

**Figure 33. Numerous African countries capture more private investment than Uganda**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ Private Participation in Infrastructure Database, 2010 .

_Note
. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993.

Data on Central Government employment, Education and Health employment is from Vera Wilhelm after consultation with Statistical Office and relates to 1995. Central Govemment employment probably also includes local govemment employment. Data on military employment are taken from the International Institute for Strategic Studies: The Military Balance Survey of 1995-96, and include conscripts, but exclude personnel in paramilitary units, i.e., the Border Guard (4,300) and the Coast Guard.

GDP at market prices and wages and salaries are from Statistical Handbook 1995: States of the former USSR and relate to 1993. Average Government wages is a staff estimate based on figures from IMF Report No. SMI94/158. The IMF Report indicates average quarterly wage. The authors have taken the average of the four estimates for 1993 and multiplied it by 12 (months) to obtain average yearly wage. Data on wages in manufacturing are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# **Lithuania**

Unemployment rate is taken from reflects only official unemployment for 1994. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Central Government, Education and Health employment data are taken from the Department of Statistics of the Government of Lithuania (Vera Wilhelm EC4BS and Gediminas Dubauskas provided us with the data) and relates to 1995. Non Central Government employment is taken from the same source and relates to 1995 as well. Estimate includes personnel in municipalities, lower municipalities and police structure. Data on military employment include conscripts, but exclude personnel in paramilitary units, e.g., the Border Guard (4,000).

GDP at market prices is taken from Statistical Handbook 1995: States of the former USSR and relates to 1992. Wages and salaries are taken from IMF Government Finance Statistics and relate to 1992. Average Government
</sup> Relative employment patterns of men in the 55 to 59 age range follow a similar pattern: 68 and 67 percent of urban men in this age range in China and Korea, respectively, are employed, while 82 percent of urban Indonesia men of this age are still working.

**Mandatory Retirement Provisions and Retirement Patterns** . Incentives created by gender differences in the mandatory retirement age may encourage early exits from the labor force for women, particularly for women who face the prospect of job search in their 40s. Career changes and job changes later in working life can be difficult in developed and developing economies

> 11Note that this is the urban employment rate for women using the CHARLS sample, in the CHNS sample the rate is higher at 42 percent.

7
be self-employed and provide childcare for their children at the same time. In our sample, 50% of skilled workers were self-employed.

Most importantly, we find a strong effect of childcare attendance on women’s wageearning jobs. Childcare attendance increases the probability of having a wage-earning job by 0.41. Sending a child to childcare can free up time for women and allow them to participate in the labor market. Childcare attendance has no effect on overall employment but a positive effect on wage-paying employment. This means that women switch from self-employed work to wage-earning work. Table 3 shows the negative effect of childcare on self-employed farm work.

There is also a significant positive effect of childcare attendance on women’s formal jobs. Having a child in childcare increases the probability of women having a formal job by 0.257. Formal jobs are medium-term employment with a social insurance contribution. Thus, sending children to childcare helps women not only to participate in the labor market but also to find a medium-term, stable job.

<mark>We measure the effect of having children in childcare on the wages of wage earners. The effect of childcare attendance on monthly wages is positive but not statistically significant. However, there is a significant effect on annual wages. The increase in total wages may be due to an increase in the number of women in formal jobs as well as an increase in productivity.</mark> <mark>In Panel A of Table 3, childcare attendance and maternal employment are measured in the current year. An important question is whether putting children in childcare has an ongoing or medium-term effect on maternal employment. To examine the medium-term effect, we regress the employment variables of women measured in one VHLSS on childcare attendance recorded in the previous VHLSS, using panel data from two consecutive surveys.</mark><sup>16</sup> <mark>This approach aims to measure the 2-year lagged effect of childcare attendance on parental education. We use the same model specification as the model
# Figure 5. Evolution of potential growth

<!-- Start of picture text --> A. Potential growth B. Potential growth<br>Percent Potential growth Percent Potential growth<br>10 10<br>Actual growth Actual growth<br>8 8<br>2000-2021 potential growth 2000-2021 potential growth<br>6 6<br>4 4<br>2 2<br>0 0<br>World AEs EMDEs G7 EM7 LICs<br>C. Potential growth of per capita output D. Potential growth of per capita output<br>Percent Potential growth Percent Potential growth<br>6 Actual growth 8 Actual growth<br>2000-2021 potential growth 2000-2021 potential growth<br>4 6<br>4<br>2<br>2<br>0<br>0<br>G7 EM7 LICs<br>World AEs EMDEs<br>Sources: Penn World Tables, World Bank.<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Note: AEs = advanced economies; EMDEs = emerging market and developing economies; EM7 = Brazil, China, India, Indonesia, Mexico, and Turkey; G7 = Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States; LICs = four low-income countries; Based on potential growth derived using production function approach. GDP-weighted average.

A.C. Sample includes 29 advanced and 53 emerging market and developing economies.
Urban Agglomeration and Firm Innovation: Evidence from Asia Liming Chen, Rana Hasan, and Yi Jiang<sup>1</sup> _Keywords:_ agglomeration economies; firm innovation; R&D; knowledge spillovers _JEL codes:_ O3, R11, O1 1 Liming Chen: Young Professional in the South Asia Department, Asian Development Bank (ADB), Mandaluyong City 1550, Metro Manila, Philippines. E-mail: limingchen@adb.org; Rana Hasan: Regional Economic Advisor in the South Asia Department, ADB, Mandaluyong City 1550, Metro Manila, Philippines. E-mail: rhasan@adb.org; Yi Jiang: Principal Economist in the Economic Research and Regional Cooperation Department, ADB, Mandaluyong City 1550, Metro Manila, Philippines. E-mail: yijiang@adb.org. The authors thank Gilles Duranton for detailed comments and suggestions on an earlier draft, and participants at the 2019 SMU Conference on Urban and Regional Economics, ADB Economics Seminar Series, and ADB Economists’ Forum for constructive feedback. We are grateful to the Enterprise Analysis Unit of the World Bank for providing geomasked GPS coordinates for the firms in the World Bank Enterprise Survey data used in in our analysis. Patricia Thea Basilio, Ma. Adelle Gia Toledo Arbo, Marjorie Villanueva Remolador, and Renz Adrian T. Calub provided excellent research assistance. The views expressed in this paper are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this paper, and accepts no responsibility for any consequence of their use.
!-- Start of picture text --> Informal channel Formal channel<br>(personal networks) (digital/paper job postings)<br>88%<br>73%<br>27%<br>12%<br>Treatment group Control group<br><!-- End of picture text --> _Source:_ Original figure for this publication.

_Note:_ The difference is statistically significant at the 5 percent level. The number of observations with data on job search channels is 73 for the treatment group and 49 for the control group.

# Impact on Quality of Employment

## **Wage Levels**

Next, we examine the impact of RPL on wage levels among those who are in wage employment. In the labor economics literature, wage functions usually take academic qualification or years of schooling as one of the main influencing factors. However, that is generally limited to verifiable formal education qualifications. Another primary variable is years of work experience (or age as an alternative), usually

> 9 Graduate tracer studies in Bangladesh consistently find that wages for those who found jobs through informal channels are lower than wages for those who found work through formal channels.

17