rafmacalaba/data-use-mentions-extended huggingface dataset:
of switching countries that since 2000 have changed their governing framework to either the conditional model or the limited model.

Although it’s hard to resolve any endogeneity concern that may arise due to the fact that countries with stronger bilateral trade ties may therefore choose a certain data model, a panel data series would be a first step in solving a potential bias due to reverse causality. In doing so, equation (3) replicates the baseline equation from the previous section to which now a time dimension is added: D o 1GRAVo 2OP o 3CTo 4LTo 5C ∗intlo o o o (3) oo oo oo oo oo oo DD = exp [θθ + θθ + θθ + θθ + θθ + δδ + γγ ] ∗εε In equation (3), all variables stay the same for D o and the two vectors of GRAVo and C ∗intlo but now vary over time, as all the dummy variables as part of the vectors denoting the oo oo three models (i.e. OP o , CTo and LTo ). Note that the equation applies exporter-year and DD oo importer year fixed effects, denoted by o and o . Finally, o is the residual term. Dyadic fixed oo oo oo effects are not applied in the panel setting as that appears to be too demanding for the available oo δδ γγ εε dataset. As previously said, note that not all countries explicitly state when their regulations are implemented, and if a country does not explicitly allow for a framework at all, the default option of the open data model has been assigned to the country. The time period for regressions is 2005-2015 given that TiVA does not provide any trade data before nor after this time span.

Before turning to the results, some basic regressions are first performed without the three data models due to reasons for measuring the trade impact by their proper coefficient sizes, as explained 32
958|0.895|
||Log(value added)|61|9.183|8.819|9.174|9.505|0.616|7.059|11.440|
|Repair of machinery|Value added growth|<br>90|0.157|-0.015|0.169|0.368|0.418|-0.858|2.179|
||Log(value added)|94|8.836|8.085|8.736|9.375|0.960|7.113|11.524|

_Notes:_ Statistics by subsector are reported for subsectors where at least 50 observations are available.

# **3. Modeling firm growth dynamics in the machine building industry**

## **3.1 Gibrat's law: Firm size dynamics and convergence trends**

In his seminal contribution, Gibrat (1931) argues that firm growth is independent of firm size and, consequently, firm size follows a random walk. In the empirical firm growth literature this hypothesis is referred to as _Gibrat's law of proportionate growth_ . Formally, this implies that the 7
We calculate input tariffs using India’s Input-Output Transactions Table, following the method suggested by Amiti and Konings (2007). For example, if the footwear industry derives 80% of its inputs from the leather industry and 20% from the textile industry, then the input tariff for the footwear industry is 0.8 times the final goods tariff for the leather industry plus 0.2 times the final goods tariff for the textile industry. In our baseline measure of input tariffs, we use both traded and non-traded inputs, assigning tariff rates of zero to non-traded inputs.<sup>2</sup> One potential concern with including input tariffs in our empirical analysis is that final goods and input tariffs may be highly correlated, thus leading to multicollinearity problems in estimation. Panel (d) of Figure 1 shows the relationship between the change in final goods tariffs and the change in input tariffs for a given industry. Though the two measures are related, there are a number of industries that received relatively large reductions in final goods tariffs but relatively small reductions in input tariffs, and vice versa. Moreover, the overall correlation coefficient between final goods and input tariffs (across years and industries) is 0.7. Within years, the correlation coefficient is even lower (less than 0.5 in all years), which suggests that multicollinearity is not likely to be a significant problem.

We also consider two other policy changes that occurred during this period: the removal of industrial licensing requirements and the allowance of FDI into most industries without case-by-case approval. Until the 1980s, India’s “license raj” required every firm with more than 50 employees (100 employees without power) and a certain amount of assets to obtain an operating license. The license specified, among other things, the amount of output a firm could produce, the types of goods it could make, and its location. In 1985, approximately one-third of industries were “delicensed” (the requirement for a license was dropped);
adjusted z-score (internally standardized) obtained for child i on the � developmental domain j at time t. �� is the magnitude of the gradient for _k_ -th quintile ���, expressed as the size of the difference in mean z-scores obtained by wealth quintile ��� relative to the bottom quintile ( _k_ =1, the excluded category). The error term includes unobserved child and household characteristics clustered at the village level, which is our primary sample unit. The base specification is performed on the pooled sample and includes demographics (age and gender) and the full set of wealth quintile dummies (and urban and regional dummies). The evolution of the gap over time is modeled by adding a full set of interactions between quintile indicators and age � dummies, thereby allowing �� to flexibly vary with age.

The robustness of the results is assessed by controlling for a large set of childhood and parental characteristics ���. First, the longitudinal dimension of the panel is exploited and lagged child

> 5. Items were adapted to what was appropriate for a mostly poor and rural Malagasy context based on input from a local clinical psychologist.

> 6. Results are robust to using wealth rankings using the contemporaneous wealth indicators. These results are not presented but are available upon request.
, over the period from 1960 to 1990. Castelló and Doménech (2002) and Morrisson and Murtin (2007) also examine inequality in years of schooling across a large number of countries.

Interesting though those comparisons were, there is widespread agreement that a year of schooling is a problematic unit with which to measure “education”. Does a student learn the same amount in 6<sup>th</sup> grade in Zambia as in Finland? Is the value of one year of schooling the same even across different schools in a single country or city? The growing availability of data on student performance in comparable tests has confirmed what one already suspected: that the answer to these questions is generally ‘no’. The quality – and hence the ultimate value – of education varies considerably, both within and across countries.

Over the last decade, different projects have compiled school-based surveys that administer identical cognitive achievement tests to samples of students across a number of countries, as well as collecting (reasonably) comparable information about the students’ families and the schools they attend. The OECD’s _Program of International Student Assessment_ (PISA) and the International Association for the Evaluation of Educational Achievement’s _Trends in International Mathematics and Science Study_ (TIMSS) are perhaps the best known, but the 2
this context, understanding what role urban agglomeration plays in firms’ undertaking product and process innovations as well as investing in R&D is very relevant.

The remainder of this paper is organized as follows. The next section introduces the data. Section 3 describes the spatial distribution of innovation activities across developing Asian countries. Section 4 describes the empirical approach taken and presents the main results regarding agglomeration effects and firms’ innovation-related activities, followed by robustness tests and various extensions. Section 5 investigates the colocation of top university and firms as a channel through which agglomeration effects may be working. The final section concludes.

# **2. Data**

This paper’s analysis is based on a unique dataset that maps over 20,000 firms to nearly 500 cities across 25 developing economies in Asia. The firm data comes from the WBES conducted from 2012 to 2016.<sup>7</sup> The city data was developed using nighttime lights (NTL) satellite imagery, LandScan grid population data and other data sources.

# **2.1. Data on Firm Innovation**

WBES are firm-level surveys conducted in developing economies. Each survey consists of a cross-sectional representative sample of private firms mainly from an economy’s formal sector. Those in the agriculture sector and all state-owned enterprises are excluded. Surveyed firms are selected through stratified random sampling, based on the sector of activity and firm size. Since 2002, the data have been collected from face-to-face interviews with high-level managers or company owners with measures taken to ensure high quality of surveys.<sup>8</sup> The surveys cover a wide range of topics, with some of them country specific. A harmonized version of the WBES data that extracts and standardizes common information from each country is used. Apart from basic information on sample firms —such as number of employees, age, sector, and foreign direct investment (FDI) share—the surveys collect information about firms’ innovation-related activities through the following questions: (i) During
.7%|33.8%|58.9%|
|**Guatemala**|47.5%|0.0%|50.0%|
|**El Salvador**|63.2%|55.0%|72.4%|
|**Honduras**|33.9%|24.1%|36.4%|
|**Nicaragua**<br>Source:Authors cal|70.8%<br>culations base|66.7%<br>d onseveralhouse|74.1%<br>hold surveys circa|

Of the relatively small share of the poor who enroll in tertiary education, a surprisingly large proportion is enrolled in private institutions, even though public universities offer free education. Data for Nicaragua show that about two-thirds of those from the first quintile attend private institutions. In El Salvador about 55 percent of students in tertiary education attend private institutions Amongst students from quintiles 1 to 3, the private enrollment share is about 40 percent, while in the richest quintile, this share <mark>Source: Authors calculations based on several household surveys circa 2009</mark> rises to 60 percent (not shown in table) In Costa Rica, about 34 percent of students from quintile 1, and 40 percent form quintiles 2 and 3 attend private institutions (not shown in table). The equity objectives of public financing of tertiary education are drawn into question when a

> 15 Tertiary education enrollment defined as population attending any education level with 13 years of education of more. For Nicaragua is 12 years of education or more.

> 16 Household surveys in Panama do not distinguish between enrollment in public and private tertiary institutions.

17
1

# **I. Introduction**

Major progress has been made over the last 30 years in reducing the prevalence of malnutrition amongst children. Between 1970 and 2000 the proportion of malnourished children was reduced by 20 % in developing countries [1, 2]. Despite this, approximately 27% of children under the age of five in developing countries are still malnourished [1]. Malnutrition currently claims about the half of the 10 million deaths each year among under-five children in the developing world, and is the risk factor responsible for the greatest loss of DALYs'globally, accounting for 16% of total DALYs [3]. Furthermore, malnutrition is highly associated with poverty-levels of malnutrition are higher in poor countries than in better-off countries. In low income countries 36 % of children are malnourished compared with 12% and 1% in middle income countries and the United States, respectively [4]. There is also growing evidence (see below) that, _within countries,_ the poor suffer from higher rates of malnutrition than the nonpoor. This has led for calls for the focus to be on reducing levels of malnutrition _amongst the poor [5-_ 7]. And yet the goals and targets of international development and bilateral aid agencies continue to be couched in terms of improving population averages [8]. For example, the only nutritionfocused target of the OECD's Development Assistance Committee (DAC) is couched in terms of population averages-to reduce the proportion of children under-five who are underweight by one half between 1990 and 2015 [9].

Part of the reason for this gap between rhetoric and reality is lack of data-until recently, only patchy data existed on poor-nonpoor differences in malnutrition rates. This is now changing. Two recent studies of malnutrition in Africa [10, 11] documented the gaps in malnutrition across consumption groups in that continent, while a new dataset [121 allows comparisons to be made across 48 countries of malnutrition differentials across quintiles of "assets".

This paper contributes to this growing empirical literature on
ad valorem transportation rates in 2008 for commodities _Tc_ ,2008 are aggregated to an industry basis _Ti_ ,2008.

Finally, to generate a time series, yearly trucking industry price indices **P** trans, _t_ and manufacturing industry price indices **P** _i_ , _t_ from Statistics Canada’s klems database are used to project the ad valorem rates backwards and forwards in time, thereby creating an industry-specific ad valorem transportation rate time series: Observe that our measure (4) of transport costs is more direct and detailed than those usually used in the agglomeration literature.<sup>9</sup> Yet, it is by construction unlikely to be fully exogenous to industrial location patterns since it depends on price indices. We return to this important point later. Note, however, that we estimate transport costs for a ‘representative shipment’ by truck, holding distance fixed at 500 kilometers. Hence, variable shipping distances that result from optimal location choices of plants in an industry have a priori no direct Figure 1 depicts the year-on-year changes in the (unweighted) cross-industry average transport costs for a representative 500 kilometers shipment. As can be seen, transport costs are

> 7Time enters as trends through a spline with knots set to reflect changing trends in a trucking price index generated from the same file.

> 8While we do not directly control for the time costs of transportation they will be, at least partially, embedded in the transport prices (which would capture quality of service for time-dependent trips).

> 9One exception is the work by Combes and Lafourcade (2005), who provide detailed estimates of transport costs for France. Their costs are, however, not industry specific.

10
demand (fronthaul) cannot find return trips (backhaul)<sup>2</sup> . At the national level, the median country has a physical trade imbalance of about 50 percent, indicating that it exports twice as many kilograms of goods as it imports, or vice versa. The imbalance can grow larger when one focuses on only neighbor-level or lane-level trade. Second, search and matching frictions can generate empty miles as carriers are unable to find a job and would prefer to travel empty to another market in search of jobs. Empirical research points to cases where improved matching technology lowered empty trip shares by 4 to 5 percentage points, while also cautioning that the effect of matching on empty trips may not be monotonic. Third, regulatory or capital restrictions may create barriers that prevent certain carriers from serving particular products or lanes. In a World Bank survey, the majority of countries restrict foreign carriers from at least some types of jobs, with cabotage trips being the most commonly restricted. This translates into significant empty miles; in the European context, carriers from countries with cabotage rights have 20 percentage point lower rates of empty miles than carriers from countries without cabotage rights.

Diving deeper into the mechanisms, I build a simple model that captures the effect of the three causes on empty trips. Shippers search for carriers to carry fronthaul and backhaul jobs. Carriers search for a fronthaul job; if they find a job, they travel to the destination and search for a backhaul job. There are fewer backhaul shippers than fronthaul shippers, capturing the role of demand imbalance. Search frictions cause fewer shipper-carrier matches to be produced than the number of searching shippers and carriers. Some carriers are ineligible to search for or accept a backhaul job, reflecting the presence of regulatory barriers to cabotage. In equilibrium, all three forces contribute to depress backhaul probabilities and raise fronthaul prices. However, search frictions behave differently from demand imbalances and regulatory restrictions, as they also affect the number of fronthaul jobs. As a result, reducing search
the squared market share (in terms of assets) of each bank from<br>Bankscope.<br>Inflation calculated as the annual change in the GDP deflator from the<br>World Development Indicators (WDI) World Bank.<br>Domestic credit to the private sector (fraction of GDP) from WDI.<br>Depth of credit information index is a measure of the coverage, scope<br>and accessibility of credit information available through either a public<br>credit registry or a private credit bureau, both by law, and in practice.<br>(0-6) obtained from Doing Business Indicators.<br>Fraction of banking system's assets in banks that are 50% or more<br>government-owned from the World Bank Regulation and Supervision<br>Survey.|

19
56

|**Country**|**Types of fuel andprices**|**Source**|
|---|---|---|
|Sri Lanka|90 RON gasoline, LPG in 12.5-kg cylinders in<br>Colombo. For figure 10, super diesel with 0.05 percent<br>sulfur. For pass-through coefficients, auto diesel with<br>0.3 percent sulfur.|www.ceypetco.gov.lk.<br>litrogas.com for LPG.|
|Syrian Arab Rep.|90 RON gasoline. LPG price averaged over the month<br>of Jan 2012.|News articles.|
|Tajikistan|92 RON gasoline|World Food Program price survey.|
|Tanzania|Maximum retail prices in Dar es Salaam.|www.ewura.com|
|Thailand|91 RON gasoline, diesel with mandatory 5 percent<br>biodiesel in Jan 2012, LPG for residential users.<br>Weighted-average monthly prices in Bangkok for all<br>fuels.|www.eppo.go.th/info/index_prices.<br>html.|
|Togo|LPG in 12.5-kg cylinders. All official prices.|World Bank office in Lomé.|
|Tunisia|95 RON gasoline, diesel with 0.2 percent in figure 10,<br>diesel with 0.005 percent sulfur (Euro 3) for pass-<br>through calculations, LPG in 13-kg cylinders. Prices in<br>Jan 2009 are those revised on Jan 16.|Announcements by the Ministry of<br>Industry and Technology.|
|Turkey|95 RON gasoline, LPG in 12-kg cylinders. Prices are in<br>Ankara, averaged over the month over all retailers<br>reported.|http://gm.poas.com.tr/pompafiyat/p<br>ompafiyatgrid.aspx.<br>http://lpg
contributor to economic growth and poverty reduction. However, little consideration has been given to possible discrepancies arising from differences in the survey method used.

A growing body of evidence indicates that survey methods matter for the labor statistics they generate. This is well illustrated for OECD countries, especially for the US (see Bound et al 2001), and recent evidence confirms this for developing countries (see Bardasi et al 2011).<sup>4</sup> Survey methods may impact estimates of structural parameters if measurement error varies with survey design in a systematic way. Non‐random measurement error in a continuous left hand side variable would normally not bias OLS point estimates (although it may reduce precision).<sup>5</sup> However, it may lead to bias if the measurement error comes from respondents strategically trying to guess the true value of a variable on which they have imperfect information, and making a systematic error in this judgment. Response by proxy may result in this type of error as it is potentially prone to strategic guessing. A common example is that husbands may be systematically under‐reporting earnings of their wives (or vice versa), for instance because they are not aware of all activities of the latter, and the earnings related to them.<sup>6</sup> Similarly, the wording and detail of questions may affect how respondents categorize themselves, and thus the labor statistics generated from the data. These differences, for instance in labor force participation and occupational distribution, may result in distinct subsamples on which estimations are carried out. This is especially relevant for returns to schooling, which are typically estimated for wage workers alone. Yet little is known how factors of survey design affect estimated returns to education.

To address this gap, this study implemented a field experiment that creates variation in two key dimensions of survey design: the level of detail of the questions ‐ whether including or excluding employment screening questions ‐ and the respondent selected: self or proxy – and
Policy Research Working Paper 5745

# **Abstract**

This paper examines how Nepal’s 1996–2006 civil conflict affected women’s decisions to engage in employment. Using three waves of the Nepal Demographic and Health Survey, the authors employ a difference-in-difference approach to identify the impact of war on women’s employment decisions. The results indicate that as a result of the Maoist-led insurgency, women’s employment probabilities were substantially higher in 2001 and 2006 relative to the outbreak of war in 1996. These employment results also hold for selfemployment decisions, and they hold for smaller subsamples that condition on husband’s migration status and women’s status as widows or household heads. Numerous robustness checks of the difference-in-difference estimates based on alternative empirical methods provide compelling evidence that women’s likelihood of employment increased as a consequence of the conflict.

This paper is a product of the Gender and Development Unit, Poverty Reduction and Economic Management Network, with generous funding from the Government of Norway. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nmenon@brandeis.edu and yrodgers@rci.rutgers.edu .

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team