rafmacalaba/data-use-mentions-extended huggingface dataset:
are, in fact, physically separated by quite some distance.<sup>11</sup> More precisely, we use the GPS software to calculate linear distances between firms and find that the average distance from a treated firm to the nearest control firm in our sample is 1.160 kilometers (0.723 miles). Given that linear distances typically underestimate true travel time, it is highly unlikely that there were geographic spillovers from firms located more than a kilometer away.

Next, there is the related issue of general equilibrium effects. Specifically, did the extra profitability for treated firms come at the expense of the control group or other businesses in the economy? We again turn to the map of businesses in our sample and reemphasize that these businesses are spread throughout the Greater Cape Town area, which contains thousands of small businesses outside of our sample. Even in our sample selection process, we started with 10,000 firms that we identified over ten weeks. Our study sample of 852 firms represents less than 10% of this initial listing. Moreover, the actual number of similar businesses in the Cape Town area is significantly greater than 10,000. Hence, it is highly unlikely that treatment effects among the small number of firms in our sample, spread across sixteen industries, led to any significant market or general equilibrium effects.

One final concern is anticipation effects among the control group firms. Since these firms were promised a business training program after the end of the study period, they could have purposefully delayed certain improvement actions, waiting until the training to undertake them. While theoretically possible, the likelihood of such strategic withholding of investments and improvements is small for several reasons. First, these firms would have to wait and forego investment opportunities for eighteen months, which is the timeline they were provided, and for a small business owner that is a very long time to wait for enacting business improvements. Further, these firms were not informed about which type of training they would eventually receive, so it
starting values over the period 1992 to 2002. As mentioned previously, the food sector had the highest level of markup in the labor market while it had the lowest in the product market.

<!-- Start of picture text --> 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>Large Large<br>(a) (b)<br>2 5<br>1.8 4<br>1.6 3<br>Average Markup Average Markup<br>1.4 2<br>1.2 1<br><!-- End of picture text --> Figure 3: Trend in Markups Level, By Firm Size

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Markups level aggregated by firm sizes. Panel (a) shows markups computed on material input, while Panel (b) shows markups computed on labor.

Figure 3 displays the results of a similar exercise to that in fig. 2, but focusing on firm size instead of sector. Based on the cumulative distribution of the sample, the following size classification was adopted: small, 1-10 employees; medium, 11-50 employees; and, large, more than 50 employees. From panel (a) of fig. 3 it can be seen that both large and medium firms started at the same level of markup in 1992. Both categories of firm sizes registered some volatility in the markup level throughout the study period. While medium firms recorded the largest drop in markup, of 22 percent
5, where we documented how institutional quality and the practical implementation of laws and regulations seem to be significant determinants of firm performance, we now turn our attention to governance. We assign states to two groups based on their level of governance (see Table 6.1), and we are interested in assessing whether the pattern of factor misallocation over time differs between the two sub-samples. As discussed in Section 3.3 on data sources above, we distinguish between “high-” and “low-governance” states based on the governance rankings provided by Transparency International India and the Centre for Media Studies (2005).

In Tables 6.2 and 6.3, we confirm that the measure correlates well with firm perceptions on both business obstacles and corruption as captured by the latest wave of the Enterprise Survey of the World Bank.<sup>45</sup> When we group States according to our “quality of governance” classification, we observe in Table 6.2 that firms in States with lower governance are far more likely to report major or severe obstacles to operations along

> 42 Details can be found on the web portal of the Ministry of Electronics and Information Technology of the Government of India, cf. http://meity.gov.in/content/mission-mode-projects.

> 43 _eBiz_ has been developed as a PPP between Infosys Technologies Limited (Infosys) and the Department of Industrial Policy and Promotion (DIPP) of the Ministry of Commerce and Industry of the Government of India. Businesses can apply for 20 central government services (plus a number of state government services in Andhra Pradesh, Odisha and Delhi), to obtain licenses, approvals, clearances, no objection certificates, permits, and for filing of returns. The services available on the portal include those from Ministry of Corporate Affairs (MCA), Reserve Bank of India (RBI), Department of Industrial Policy and Promotion (DIPP), Central Board of Direct Taxes (CBDT), Directorate General of Foreign Trade (DGFT)
believed that a girl had to get married if she attracted a suitor, otherwise she might never marry, while currently child marriage occurs mainly for economic reasons.

Sporadic efforts have been made by local governments across the country to reduce child marriage, locally known as PUP/ _Pendewasaan Usia Pernikahan_ or age maturation for marriage (Muh Bahrul Ulum, 2016). For example, in West Nusa Tenggara, the governor issued Circular Letter No. 150/1138/Kun on PUP that recommended the minimum marital age for males and females as 21 years old. Another example is from Gunung Kidul District, Yogyakarta. The regent regulated the minimum marital age at 20 years. In the district of Kebumen, there are eight villages where children must avoid early marriage and the communities are not allowed to recommend early marriage. The Commission for the Protection of Indonesian Children (KPAI) has been supportive with a positive response to PUP efforts.

3.3.2. School Violence While girls’ education is more negatively impacted by early marriage than boys’, studies on school violence have found that boys experience greater levels of violence in any form at school than do girls (PLAN, 2015). Indonesia participated in the 2015 Global Student-based School Health Survey, which found that 24 percent of males and 18 percent of females had experienced bullying in school, with even higher numbers for having experienced violence in school (39 percent for males and 21 percent for females).<sup>30</sup> More recent data of the 2018 National Survey of Children and Teenagers’ (13-24 years old) Life Experience show 33 percent of males were victims of physical violence, while 20 percent of females reported the same experience.<sup>31</sup> The survey also explored other types of violence, including sexual-based violence and emotional/psychological violence. These categories show higher prevalence for females. About 6 percent of males and 9 percent of females reported they had experienced sexual-based violence. Higher numbers are recorded for emotional/psychological violence
from BIS banks to nonbanks.

Table 3.4e shows the consolidated liabilities to BIS-reporting banks. The consolidated data take into account local lending by the domestic affiliates of foreign-owned banks, while netting out any cross-border loans by the foreign affiliates of domestically-owned banks. Taken in tandem with the locational data reported in Table 3.4f, these data show that local lending by foreign-owned affiliates are a substantial component of the total exposure of foreign banks in these countries. As indicated in Section 2, a high level of foreign ownership in the banking 13
The 2006 MICS compiled data on children using modules from the MICS3 questionnaire as well as data on young people from the questionnaire used for the PAPFAM. The goal was to provide indicators and information facilitating the implementation, evaluation, and reorientation of programs and services for children and young people. Initially, a sample size of 8,094 households was planned to ensure representativeness across the country in each area of residence (urban, rural) and even at the level of certain regions. An average size of 25 households per cluster was also targeted. Among the households selected, 7,931 were actually interviewed, yielding a response rate of 98 percent. Data were compiled between October 30, 2006 and December 30, 2006.

The MOH conducted the 2011 ENPSF with technical assistance from PAPFAM. Two questionnaires—designed and adapted based on the PAPFAM model questionnaires—were used to compile data; one household questionnaire and one individual questionnaire targeting ever-married women between the ages of 15 and 49 from the households surveyed. The number of households actually interviewed as part of the 2011 ENPSF was 15,343. The number of women actually interviewed stood at 11,069. Data were compiled between November 8, 2010 and March 7, 2011.

The ONDH survey undertook, for the first time in Morocco, a household panel survey to track the human development situation in the country and contribute to the assessment of public policies on human development. The 2012 data comprised the baseline round of the survey. The sample size was set at 8,000 households to have representative results at the national level and at the urban and rural levels throughout the country. Actual interviews of 7,854 households comprising 37,444 individuals were conducted. Of these individuals, 3,395 were children under the age of five. Field data for the 2012 group, the reference year of the panel, were collected between March and July 2012.

# _2.2 Measures of ECD_

ECD is a complex and multidimensional process that starts even before children are born.
and education—played a major role, but equally important was the fact that they were jumping onto a moving train, to wit, that of globalization and new technology.

We can see the great divergence in growth among emerging economies in the most recent numbers. Table 2 provides growth data for the BRICS countries. These countries have never seen such disparities in performance before. Again, there are, no doubt, proximate drivers, such as the fall in oil and commodity prices adversely impacting Russia and Brazil for instance, but the story does not end with that. China and, more recently India, which are growing well in this cluster, have been able to take advantage of the globalization, albeit in different ways. I shall briefly describe the India story in the next section.

**Table 2. GDP growth in the “BRICS”**

||Average<br>2001‐2008|2009|Average<br>2010‐2013|2014|2015 (est.)|
|---|---|---|---|---|---|
|_Brazil_|3.7|‐0.2|4|0.1|‐3.7|
|_China_|10.7|9.2|8.9|7.3|6.4|
|_India_|7.1|8.5|7.2|7.3|7.3|
|_Russian Federation_|6.6|‐7.8|3.4|0.6|‐3.8|
|_South Africa_|4.2|‐1.5|2.7|1.5|1.3|

_Source: World Development Indicators, and Global Economic Prospects (for 2015 estimates)._ A similar growth divergence in the Sub‐Saharan African experience is captured well by recent data. Contrary to the more‐uniform stagnation experienced before, there are
through the Act’s mechanisms of using local leaders to resolve land disputes and allocate land, women can face practical difficulties in securing rights (Deininger et. al, 2017b) and having input in decision-making over the management of agricultural parcels (Andersson Djurfeldt et al., 2018).

# **2.2. Fourth Integrated Household Survey and Integrated Household Panel Survey: Respondent Selection and Overview of Data on Assets, with a Focus on Land**<sup>**9**</sup>

The Fourth Integrated Household Survey (IHS4) 2016/17 was a multi-topic, cross-sectional household survey that followed the approach of surveying the “most knowledgeable” household member(s) to provide information on household members’ ownership of and rights to selected physical and financial assets, namely dwelling (including the residential plot), agricultural parcels, and financial accounts. This approach corresponds to Treatment Arm 1 (“T1”) of MEXA. In line with the prevailing implementation protocols, the selection of the most knowledgeable household member(s) was a function of the adult individuals that were available at the time of the interview. This could have meant that the first choice for the most knowledgeable member was not interviewed if he/she was unavailable during the time that the field team was going to be in that enumeration area (EA). In the context of agricultural land specifically, a roster of all owned and/or cultivated agricultural parcels was created first, and the enumerator was instructed to interview the most knowledgeable household member for each parcel.

On the other hand, the Integrated Household Panel Survey (IHPS) 2016 was the third wave of a multi-topic, longitudinal household survey that attempted to carry out personal interviews of adult household members, inquiring about their personal ownership of and rights to assets in the aforementioned asset classes - corresponding to Treatment 5 (“T5”) of MEXA and leveraging the contextualized and improved versions of the MEXA T5 questionnaire instrument.<sup>10</sup
Two additional sources of data are used. The tariff information does not capture the advalorem equivalents of quotas such as those on apparel exports under the MFA. We use information on whether an exporting country and MFA category defined by OTEXA faced a quota in the US market in 1992-2004 from Brambilla et al. (2010).<sup>17</sup> To analyze potential trade diversion effects from GSP LDC and AGOA, we use a new EU database that we constructed along similar lines as the US database, relying on Eurostat’s COMEXT trade flow data and on European Commission’s TARIC tariff data and WITS data for other countries’ imports.

# **4. Trade preferences in the US: GSP and AGOA**

The US has provided developing countries with preferential access to its markets through the GSP program since 1975.<sup>18</sup> Until 1997, for eligible LDCs, 3,507 out of 10,184 tariff lines (at HS 8-digit level) faced a preferential zero rate of duty, and another 3,131 faced an MFN zero rate of duty (see Panel A of Table 1). In 1997, the US GSP expanded the benefits for LDCs by allowing duty-free entry for an additional 1,670 tariff lines.<sup>19</sup> For Sub-Saharan African (SSA) countries the US GSP program was supplemented by the AGOA non-reciprocal preferential trade agreement that entered into force in 2001. The number of SSA countries eligible for AGOA increased from 34 in 2001 to 49 in 2017.<sup>20</sup> Firstly, AGOA extended US GSP duty-free treatment for non-apparel products. It granted to non-LDCs duty-free access for 1,610 tariffs lines that were duty-free for LDCs under GSP LDC (see Panel A of Table

> 17 The quota information is available at http://faculty.som.yale.edu/peterschott/sub_international.htm. The 3-digit MFA categories defined by OTEXA are mapped to 10-digit US HS codes using
looking at reform in the communications-related sectors. The first study used bilateral data on communication costs and trade flows for 107 countries, finding that costs affect trade flows negatively, with the effect being larger for differentiated goods. The second used a panel of Asian countries and found that variations in communications-related infrastructure affected export performance in those countries.

The channel we examine in this paper, linking services sector reform with manufacturing firms’ productivity levels has been previously explored in the context of the Czech Republic, Chile and India, and for the Sub-Saharan African (SSA) region. Arnold, Javorcik and Mattoo (2007) used firm-level data for manufacturers in Czech Republic, combined with several indicators of service sector reform, and found sizable effects on productivity of increased foreign entry into services. Arnold, Mattoo and Narciso (2008) focused on Africa, using data from 1000 firms in 10 SSA countries, and found a positive effect of performance of communications, electricity, and financial sectors on manufacturing TFP. Fernandes and Paunov (2008) used firm-level data of Chilean manufacturers combined with FDI stocks in the services sector, and examined the impact of the latter stocks on manufacturing firms’ productivity, finding a positive effect of significance both economically and statistically. Interestingly, they also found that those manufacturing firms furthest from the technology frontier had most to gain in terms of productivity improvements as a result of service sector liberalization.

Finally, Arnold, Javorcik, Lipscomb and Mattoo (2010) used data from Indian manufacturing firms and constructed indicators of service sector reform in the same country, and found that potential productivity gains appear to be greatest for reforming those service sectors most closely related to trade: transport, communications and finance. They found effects on foreign firms to be stronger.

# **3. The Service Sector in Indonesia: Some Background Information**

## **3.1 Trends**

In 2011, the services sector accounted for 53.8% of Indonesian GDP. It has been growing at an average rate of 7.
# **APPENDIX 2: THE MAMS DATABASE**

As noted in Section 3, the database for a typical MAMS application includes a wide range of data for its base-year – a social accounting matrix (SAM); stocks for production factors (including one or more types of labor and capital), population, and school enrollment; indicators for selected MDGs and the educational system – as well as a set of elasticities (for production, consumption, trade, and human development relationships), and projections into the future (for growth in GDP at factor cost and the evolution of disaggregated MDG and education indicators and determinants). We will here outline the contents of the current database and how it was used, emphasizing parameters related to the rural-urban disaggregation and the MDGs.

Like other CGE models, MAMS is calibrated so that its base-year solution exactly replicates the base-year SAM. MAMS is also calibrated dynamically to replicate projected GDP growth and, in this application, projections for a set of HD outcomes, conditional on the simulated evolution of variables influencing these outcomes. Much of the inputs required to construct such a database are available from national and international databases, which in recent years have become richer and more easily accessed in electronic form. However, in some areas, data and knowledge are incomplete or unsettled, requiring the analyst to apply judgment and consult with country and subject specialists. In this context, calibration to base-year and projected data (especially for HD outcomes) provides an important means of eliminating many sources of errors in data and model structure as well as of ensuring that simulation results are consistent with available evidence.

Table A2.1 shows the basic disaggregation of the current database. The main inputs into the construction of a LIC SAM with a matching disaggregation were (a) an existing detailed SAM for a LIC (Uganda); (b) a macro SAM for a median LIC during the period 2005-2009 – Table A2.2 shows the corresponding macro SAM for 2013; and (c) median data for LICs
32

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# **Table A17: Impacts of mental health on labor outcomes – HILDA survey**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores|-0.034***<br>(0.004)|-0.030***<br>(0.009)|
|Standardized mental health scores*Migrants|-0.005<br>(0.008)|-0.003<br>(0.021)|
|Dep. Mean|0.804|1.556|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|38,239|38,239|
|Numberof individuals|4,500|4,500|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Column (1) shows results of Logit-FE model (marginal effects). Column (2) shows results of FE model. Standard errors in parentheses are clustered at the individual level. Data is taken from HILDA survey (2001 – 2019). Control variables include age group dummy variables, marital status, education, and number of children. Mental health in the HILDA survey is measured using the five-item Mental Health Inventory (MHI-5) scale, with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

49
Table A8: Psychometric score predictiveness for repayment of uncollateralized loans

|||**Treatmen**|**t group only**||
|---|---|---|---|---|
||(1)<br>WEDP-x loan amount<br>(Level)|(2)<br>WEDP-x loan amount<br>(Level)|(3)<br>Loan repayment overdue<br>by at least 90 days<br>(Yes 1; No 0)|(4)<br>Loan repayment overdue<br>by at least 90 days<br>(Yes 1; No 0)|
|Psychometric score|-586.6||-0.0051*||
||(413.2)||(0.0027)||
|Psychometric score above median (0/1)<br>34||-23151.4<br>(14332.5)||-0.15<br>(0.10)|
|Average monthly proft from main business (100 ETB)|166.9***|166.1***|-0.00011|-0.00011|
||(39.0)|(39.7)|(0.00021)|(0.00021)|
|Observations|58|58|58|58|
|_R_<sup>2</sup>|0.283|0.288|0.0686|0.0454|

Notes: Table only shows results for the IE treatment group. Outcomes 1-2 are the uncollateralized (WEDP-x) loan amounts disbursed according to Wasasa’s administrative data. Outcomes 3-4 indicate if the entrepreneur was over 90 days late on their scheduled (WEDP-x) loan repayment according to Wasasa (0/1). Columns 1 and 3 present specifications with the continuous