rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5780

# **Abstract**

Using a cross-section of more than 40,000 manufacturing and services firms in 79 developing countries from the World Bank’s Enterprise Surveys Database, this paper assesses how firm location determines the likelihood and extent of exporting in developing countries. Descriptive statistics confirm higher export participation (but not intensity) for firms in core versus non-core regions, despite the finding that firms in the core assess many aspects of the investment climate more negatively.

Results from a probit model show that, in addition to firm-specific characteristics, both regional investment climate and agglomeration factors have a significant impact on export participation. Specifically, customs clearance and electricity quality matter for export participation for manufacturing firms. Although localization economies and export spillovers are associated with increased exporting, the opposite is found for urbanization economies for both manufacturing and services firms. The analysis finds that firm-level determinants of exporting matter more for firms located in non-core regions, while regional determinants and agglomeration economies play a larger role in core regions.

The findings point to the presence of congestion costs in the core, and suggest that policy interventions to target export participation are likely to have a greater impact if they are focused on core regions over non-core regions, where firm-specific factors predominate. Moreover, the importance of export spillovers and localization economies highlights the potential value of efforts to remove barriers to natural agglomeration both in core and non-core regions, for example through investments in infrastructure, the provision of social services, and regional integration arrangements.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. 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 tfarole@worldbank.org and dwinkler2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in
# **1. Introduction**

Poor households build livelihood structures that include coping mechanisms to mitigate the many risks they face. Included are informal forms of insurance based on a willingness to help extended family members and neighbors in time of need with the expectation of receiving assistance when needed. In the absence of formal insurance and risk markets, informal mechanisms are crucial and can work well when risk events are idiosyncratic. However, informal insurance systems can fail when needed most in the face of a systemic risk event affecting all members of the informal insurance network. Governments can and do help prop up informal systems by responding to crises and by building all‐purpose safety nets.

In rural areas, households often choose to produce much of their own food. This livelihood choice provides a measure of protection against a loss of other sources of income and can be especially effective in the face of rising food prices. However, the protection comes at a cost, since the strategy can obligate families to devote limited land and labor resources to activities and production technologies that are less profitable under normal circumstances. In turn, this makes it harder for families to generate higher incomes, accumulate wealth and human capital, and escape reinforcing poverty traps.

In this essay we focus on surging food prices, a cardinal risk that can undermine the capacity of poor households to meet minimal nutritional needs. Specifically, we examine potentially permanent effects of the food price crisis of 2007‐08 on child nutrition intakes, distinguishing between food producers and non‐producers. To bypass measurement problems inherent in consumption data, we use standardized child anthropometric nutrition‐status measures such as the height‐for‐age and weight‐for‐age z scores, and we calculate local food price indices to take into account spatial differences in transmission of global price shocks and diet compositions. The analysis uses Indonesian household panel data collected in 2007 and 2010. The first round of 2007 was fielded from the second to third quarter, immediately before the initial food price run‐up
10 Figure 1: A process for estimating the political risk associated with institutional reforms <!-- Start of picture text --> Evaluation of the potential winners<br>and losers from the reforms<br>Identification of the various reform<br>performance levels<br>Identification of means by which the various parties<br>could influence the level of achievement of various reforms<br>Application of the Delphi process<br>Reporting<br>Probabilities Repetition of the Delphi process as necessary<br><!-- End of picture text -->

# POLITICAL ECONOMY OF THE WATER AND DRAINAGE SECTOR IN PAKISTAN

Because data was available for the NDP project in Pakistan (World Bank, 1997), we applied the analytical framework from the previous section to estimate the political risk of institutional reforms in the water and drainage sector in Pakistan.

## THE AGRARIAN ECONOMY

An understanding of the country's agrarian economy is essential to the proper realization of the political economy of the water and drainage sector in Pakistan, at least among the various segments of the fanning community. Naved et al. (1991) and Nabi et al. (1986) provide an excellent background on the agrarian sector of Pakistan. The agrarian sector of Pakistan is characterized
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firm competitiveness). In spite of this, if current plans are put into practice, the growth of private sector minimum wages will continue to accelerate at least until 2018. At the same time, productivity growth has lagged behind. Unless productivity growth picks up markedly over the coming years, it will further decouple from the pace of growth in statutory minimum wages.<sup>3</sup>

**Figure 4 – Index for strictness of EPL legislation**

<!-- Start of picture text --> Turkey<br>Mexico<br>Spain<br>Indonesia<br>Greece<br>Vietnam (2014)<br>Portugal<br>France<br>China<br>Norway<br>Luxembourg Protection of<br>Belgium permanent workers<br>Italy<br>Iceland against (individual)<br>dismissal<br>Germany<br>Specific requirements<br>Philippines<br>for collective dismissal<br>Austria<br>Poland<br>Vietnam (2010)<br>Cambodia Regulation on<br>Lao PDR temporary forms of<br>Thailand employment<br>Finland<br>Netherlands<br>Sweden<br>Hungary<br>Czech Republic<br>Slovak Republic<br>Denmark<br>Korea<br>Mongolia<br>Switzerland<br>Japan<br>Brunei Darussalam<br>Australia<br>Ireland<br>New Zealand<br>Malaysia<br>United Kingdom<br>Canada<br>Singapore<br>United States<br>0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00<br><!-- End of picture text -->

**Sources: World Bank staff calculations partly based on World Bank (2014a) and OECD data.**

**Notes: Data are for 2010; higher
** includes indicators of whether a ffii YY firm needs and has access to water and electricity, and computer access.<sup>35</sup> The urban explanatory variables are available for firms in two countries. We estimate Equation (4) without the _Xfc_ and _Jfc_ terms and then add the firm owner and firm characteristic as additional regressors. We include a proxy for productivity in the _Jfc_ vector to control for

> 34 <mark>The GMD includes data on the aggregate consumption at the household level, derived from household surveys. We use the distribution at the country level and designate all households in the lower 40 percent as “vulnerable.”</mark>

> 35 <mark>We use a smaller set of infrastructure and services variables in comparison to the descriptive analysis due high correlations between them.</mark>

12
ˆ coefficients ( <sup>ˆ</sup> ,  ) and the disturbance terms, both of which affect undernutrition estimates and the level of their accuracy. In ELL, a simulated z-score is calculated for each census child by a predicted z-score as in (2): These simulations are repeated a large number of times (100 in the case of this application), with new values for  ,  , and _uih_ drawn from their distributions, in each repetition. The distributions of  ,  are obtained through GLS, while the distribution of _uih_ is obtained through a non-parametric approximation following Elbers and van der Weide [27]. All the estimation is automated within the PovMap program.

For any given location (such as a woreda), each simulation is used to calculate the share of children underweight or stunted (a z-score below negative two standard deviations). The mean across the simulations of an undernutrition statistic provides a point estimate of the statistic, and the standard deviation provides an estimate of the standard error.

In most applications of ELL, the error term _uih_ is decomposed into two independent components: _uih_  _c_   _ih_ , where  _c_ is a cluster-specific effect and  _ih_ an individual effect. In this application, the error term has not been split into a cluster and individual effect, as no cluster effect was found. The lack of a cluster effect is likely closely related to the sampling design of EMDHS, as it is designed with few EAs within each region and relatively large samples within each EA.

# **Application of SAE to Ethiopian data**

SAE estimates of stunting and underweight is done at woreda level. Woreda is the third administration layer after region and zone. Each woreda is also divided into kebeles, which are the smallest administrative units in Ethiopia. A woreda on average has around 12,000 children, with a standard deviation of 8,000, with around 2,000 children at the 5 percentile and 28,500 children at
more evident for lower-caste households. Behrman and Deolalikar (1990) find that females eat less when food is scarce and the marginal value of food is high, and vice versa.

Considerable evidence against the unitary framework (Strauss and Thomas, 1995; Haddad et al., 1997) has made collective framework (Chiappori, 1988, 1992; Bourguignon et al., 1993, 1994) attractive. Using the latter, Thomas (1990) finds a mother’s unearned income has greater impact on daughters’ anthropometric outcomes than that of sons, while a father’s unearned income has the opposite effect. Using household food expenditure data, he also finds that the estimated impact of women’s unearned income is about seven times that of men’s unearned income for (per capita) calorie and protein consumptions. Schultz (1990) finds that women’s unearned income has significantly different effect (i.e., reduces more) than men’s unearned income on women’s labor supply, and women’s but not men’s unearned income has a significant positive effect on fertility. Hallman (2003) uses maternal shares of current assets, premarital assets, and marriage payments as proxy measures for resource controls, and finds that a mother’s assets are generally more beneficial for girls and a father’s for boys as far as child morbidity is concerned, which is consistent with Thomas (1994). She further finds that a greater share of marriage payments to husbands reduce child morbidity, regardless of a child’s sex. This is consistent with Rao (1997), who shows that lower dowries increase wife-beating and reduce child calorie intake during marriage. Targeting mothers for cash-transfers seems to increase secondary school enrollment, particularly for girls, and has positive effects on a child’s health, nutrition, and food consumption (Skoufias and McClafferty, 2003; Adato et al., 2003). Pitt and Khandker (1998) find that household consumption and child nutrition and education are
26 Table 2: Global average price transmission elasticities, key agricultural products, 75 focus countries, 1985 to 2004 (weighted average using value of national production at undistorted prices as weights)

|Short-run elasticity|Long-run elasticity|
|---|---|
|Product||
|Rice<br>0.52|0.71|
|Wheat<br>0.47|0.60|
|Maize<br>0.57|0.70|
|Soybean<br>0.72|0.79|
|Sugar<br>0.31|0.49|
|Cotton<br>0.57|0.53|
|Cocoa<br>0.57|0.73|
|Coffee<br>0.71|0.87|
|Beef<br>0.68|0.53|
|Pork<br>0.49|0.87|
|Poultry<br>0.58|0.77|
|**Unweighted average,**||
|**11 products**<br>**0.54**|**0.69**|

Source: Authors’ estimates based on NRAs from Anderson and Valenzuela (2008).
. The initial rollout of the survey was conducted in 2003, with subsequent and more complete rollouts conducted within a three-to-four-year span. For this paper, I use the data set from 2006–2018 provided by the World Bank. This data set contains firm-level observations for 135 countries, and it includes sections on corruption and regulations, among many other variables traditionally used in firm-level analysis.<sup>9</sup> In particular, establishments are asked whether they have

> 7 The names of these approaches were first introduced by Restuccia and Rogerson (2013).

> 8 For a more exhaustive description of the resource misallocation literature, see Hopenhayn (2014), Jones (2011), Restuccia and Rogerson (2013), and references therein.

> 9 The WBES data on corruption and bribery have reliability problems, but they are the only source of such data available, to my knowledge. Due to their illegal nature, it is difficult to obtain data on corruption and bribery. Data on lobbying activity (a “legal

6
additional dimensions like sector and size using difference-in-differences (e.g., exploiting differences in the cross-industry variation across countries).

As will become clear in our discussion of the data in the next section, limitations in the data in different dimensions across countries and compromises that were made to generate ‘comparable’ data, may hamper analysis of certain questions and generally suggest caution in interpreting simple cross country differences. We now turn to a discussion of the data.

# **3. A new dataset of firm-level data from industrial and developing countries**

The dataset used in the study was collected in various stages. Most recently, the firm-level project organized by the World Bank collected indicators for 14 countries (Estonia, Hungary, Latvia, Romania, Slovenia; Argentina, Brazil, Chile, Colombia, Mexico, Venezuela, Indonesia, South Korea and Taiwan.(China)) An earlier OECD study collected indicators based on information on firms from: Canada, Denmark, Germany, Finland, France, Italy, the Netherlands, Portugal, United Kingdom and United States.

These projects made use of a common analytical framework and the data analysis and collection was conducted by active experts in each of the countries.<sup>5</sup> The framework involves the harmonization, to the extent possible, of key concepts (e.g. entry, exit, or the definition of the unit of measurement) as well as the definition of common methodologies for studying firm-level data. The methodology for collecting the country/industry/time panel dataset built up from underlying micro-level datasets has been referred to as ‘distributed micro-data analysis’ (Bartelsman 2004). A detailed technical description of the dataset may be found in Bartelsman, Haltiwanger and Scarpetta (2004).

The distributed micro-data analysis was conducted for two separate analytical themes. The first set of analyses gathered data relating to firm demographics, such as entry and exit, jobs flows, size distribution and firm survival. The second theme gathered indicators of movements of firms and
Table A7: Initial factor prices and other calibrated parameters

|Initial wage in South|||5.70||
|---|---|---|---|---|
|Initial wage in Other|||26.7||
|Initial wage in North|||39.5||
|Initial rental rate of robots|||59.0||
|_θ_|||5.00||
|_ν_|||9.00||
|||sector|X<br>sector Y|sector NT|
||South|0.48|0.31|0.00|
|_α_<sup>_m,i_</sup><br>_M_|Other|0.34|0.23|0.00|
||North|0.34|0.21|0.00|
||South|0.20|0.30|0.26|
|_α_<sup>_m,i_</sup><br>_T_|Other|0.38|0.45|0.41|
||North|0.36|0.49|0.40|
||South|0.20|0.47|0.33|
|_γ_<sup>_m,i_</sup>|Other|0.10|0.49|0.42|
||North|0.13|0.59|0.28|

_Notes_ : Initial wages are calibrated with data on per capita GDP and expressed in thousands of USD. The initial effective rental rate of robots is _<u>φ</u>_<sup>_i_</sup> _<u>R</u>_<sup>=59.</sup> The _φ_<sup>_i_</sup> _L_<sup>_wR_
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Figure 7: Average Job Quality Ratio over Time, by Country <!-- Start of picture text --> Argentina Bolivia Brazil Chile<br>.5 .3 .65 .5<br>.45 .6 .45<br>.25<br>.4 .55 .4<br>.35 .2 .5 .35<br>Colombia Costa Rica Dominican Republic Ecuador<br>.29 .445 .32 .45<br>.285.28 .44 .3 .4<br>.275 .435 .28 .35<br>.27 .43 .26 .3<br>El Salvador Guatemala Honduras Mexico<br>.4 .2 .28 .4<br>.35 .195.19 .26 .35<br>.3 .185 .24 .3<br>.25 .18 .22 .25<br>Panama Paraguay Peru Uruguay<br>.345 .4 .25 .7<br>.34<br>.335 .35 .2 .65<br>.33<br>.325 .3 .15 .6<br>2004 2006 2008 2010 2012 2000 2005 2010 2015 1995 2000 2005 2010 1995 2000 2005 2010<br>2008 2009 2010 2011 2012 20102010.5 20112011.5 2012 2000 2005 2010 2015 2000 2005 2010 2015<br>2004 2006 2008 2010 2012 2000 2005 2010 2006 2008 2010 2012 1995 2000 2005 2010<br>2006 2008 2010 2012 1995 2000 2005 2010 2004 2006 2008 2010 2012 2006 2008 2010 2012<br>Average Job Quality<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data.

not show the change in average job quality across time for the different employment sectors, the change in job quality does not seem to be associated with a substantial structural change in the type of work. With the exception of