rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Determinants of export growth at the extensive and intensive margins: Evidence from product and firm-level data for Pakistan**

# **1. Introduction**

Trade and export performance of Pakistan in the last 15-20 years has not been positive, as documented in several reports (Planning Commission, 2011, World Bank 2007, Manes 2010, Lahore University 2009). Given that trade is a key driver of growth and development – most, if not all, success stories in growth accelerations worldwide have an outward-looking strategy; analyzing in detail and understanding the reason for this performance is an integral part of the growth and development agenda for Pakistan.

This paper presents a comprehensive and detailed analysis of trade and specially exports performance in Pakistan in the last ten years. The analysis of trade outcomes is intended to guide a systematic generation of hypotheses about Pakistan’s export performance, prospects, and challenges. Following Reis and Farole (2012), the analysis uses the decomposition of the margins of trade growth as a framework for exploring trade competitiveness. Specifically, in this note we will review four main dimensions on which Pakistan’s country’s trade competitiveness performance can be determined: 1.) the **_level, growth, and market share_** performance of existing exports (the “intensive margin”) of exports as well as _market share_ performance; 2.) **_diversification_** of products and markets (the “extensive margin”); 3.) the **_quality and sophistication_** of exports (the “quality margin”); 4) dimension – the **_entry and survival_** of new exporters (the “sustainability margin”).

The principal source for the analysis is the UN COMTRADE database available through the World Integrated Trade Solutions (WITS) platform<sup>2</sup> . For the entry and survival analysis (sustainability margin) we use firm-level data, based on a customs transactions database for the period 2001-2010. Throughout this note,
and the estimated hazard rate retrieved from the first stage. The two stages can also (and more efficiently) be estimated simultaneously by maximum-likelihood.<sup>11</sup> The key issue is of course to find good instruments. We follow the approach suggested by Besley and Case (2001) and use political instruments taken from the World Bank’s worldwide governance database. Specifically, we use Kaufman’s governance index (the WB- Worldwide Governance Indicators (WGI) project<sup>12</sup> ) and its square, based on the idea that very corrupt and very honest governments would be reluctant to use inspection programs, the first because it would reduce corruption opportunities and the second because inspection would not be needed. This suggests that the probability of using an inspection program (the dependent variable in the first-stage probit) is a concave function of governance, something which can be captured by a second-degree polynomial. In some specifications, we also use IMF structural adjustment programs that were adopted simultaneously with PSI, as well as the official amount of OECD aid per country per year.

When no good instruments are available, the alternative route consists of using the Generalized Method of Moments (GMM), and in particular Blundell and Bond’s system-GMM (Blundell and Bond 1998). The system-GMM estimator runs two simultaneous equations, one in levels and one in first differences, in which endogenous RHS variables in levels are instrumented by their lagged first differences in the level equation, and by their lagged levels in the differenced equation. Suitable instruments can of course be added in the levels equation if available. The number of lags used as instruments is the experimenter’s choice, and it has been argued that this latitude sometimes makes results unstable. There is however a rule of thumb which states that the number of lags should not be larger than the number of individuals in the panel. In our case, this rule of thumb is of course not binding because the number of lags is anyway severely
where people are not being paid or are not being paid regularly. At the same time, the second question specifically asks about unpaid work. Even so, many women appear to not be reporting activities in response to this question. While these two questions could similarly affect responses from men who work in agriculture without regular income, it does not. Men are overall more confident in defining their daily or even irregular tasks as economic activity compared to women. This reflects the first explanation behind the discrepancy found in this study: the importance of gendered division of work spheres in reporting activities.

Second, both survey questions that measure employment explicitly exclude “housework”. To the extent that women’s household tasks in rural areas include contributions to agricultural production, including this phrase suggests to the respondent a limited scope of what `work’ means to the interviewer, preventing female respondents from reporting activities that could otherwise be seen as `work’. There is significant overlap between the tasks that are understood to be in the domestic sphere by the communities and as “employment activities” by the statistical definitions. The explicit exclusion of household tasks seems to limit women from reporting the multiple economic activities in which they engage at home, even if they receive payment for them directly or indirectly.

Third, part-time or irregular activities are underreported by women even though the Honduran household survey asks about any activity which required an hour or more in the last week. Even so, many women who worked half a day or a day per week in market activities (including activities directly remunerated like laundry services and selling berries to intermediaries) did not consider that to be sufficient time to count as employment. Gender differences in willingness to report these types of irregular activities as 21
communities and their host neighbors warrants a multidimensional lens on the experience of poverty, as a complement to a monetary poverty analysis.

While most studies note the varied socio-economic issues facing displaced populations, they still tend to discuss poverty largely in monetary terms (Chaaban et al 2013, Oruc 2015, Verwimp 2012, Zetter and Ruaudel 2014) or in terms of single or specific dimensions of nonmonetary well-being. However, a small number of recent studies have attempted an analysis of multidimensional poverty in these communities. For instance, Temgoua et al. (2020) analyze the relationship between internal displacement and poverty status according to the global MPI for Iraq. Loaiza Quintero et al. (2018) develop an MPI for Antioquia in Colombia to study the situation of internally displaced persons due to armed conflict. This paper builds on those studies by taking a cross-country MPI approach to explore patterns of multidimensional poverty among IDPs and refugees in five African countries.

# **2.2 The role of gender in poverty and forced displacement**

Gender plays a prominent role in contexts of forced displacement. Although men typically suffer higher mortality rates due to conflict, the demographics of internal displacement still reflect overall gender parity, with the global male-female ratio of IDPs remaining largely halfand-half over the last decade (UNHCR 2020). Moreover, this increased mortality of men and displacement of women can lead to a higher percentage of female-headed households among forcibly displaced populations (Buvinic et al 2013), and these households may be particularly vulnerable, especially in terms of income and social networks (Brück and Schindler 2009, Hanmer et al 2020). That women constitute half the IDP population but are nonetheless exposed to increased vulnerabilities and deprivations deserves serious attention.

Forcibly displaced and refugee women may suffer greater impacts on health and more instances of gender-based violence than their male counterparts (Kelly 2021, Kelly et al. 2021). Gendered challenges with food insecurity, lack of livelihood opportunities, and inaccessibility to land ownership, public services, and community structures also characterize poverty and forced
_8_ backup generators. Finally, extreme temperature changes could compromise the effective functioning of critical infrastructure, communications, and transportation systems, imposing additional disruptions to business activities.

# **III. Data**

To estimate the impacts of temperature deviations from the long-term average on firm productivity, we match two sets of data. The first data set is global firm-level data from the WBES.<sup>7</sup> This a nationally representative survey covering nonagricultural firms with five or more full-time permanent employees in low-, middle-, and high-income economies. The coverage is comprehensive, with more than 190,000 observations from 154 countries and spanning 2006–22 .<sup>8</sup> The data are collected using a standardized questionnaire, allowing comparability across countries. The WBES collects information on several firm-level variables, including annual sales, number of workers, various firm-level characteristics, and selfreported obstacles to business, such as licensing and power outages. The WBES data include estimates of TFP for a subset of the sample for which detailed data on labor, capital, and material inputs are available. The WBES also contains confidential geomasked information on firms’ longitude and latitude coordinates that allows us to match the WBES data with area-level climate data. Geomasked coordinates are available for 143,047 observations. Map 1 shows the countries covered by the WBES for which geomasked locations of the firms are available, where the dots represent the specific locations of firms in the sample.

The key outcome variable of interest is annual sales per worker, measured in 2009 US dollars, which is considered in the literature as a reasonable proxy for firm productivity. A limitation of this measure is that the price variation in sales may reflect both supply and demand factors including differences in market power, demand and quality (Cusolito and Maloney, 2018). Its attraction, however it its simplicity and direct interpretation compared to other relatively complicated productivity measures. We also use other measures of firm productivity, such as value added per worker and TFP
1|0.24|0.18 *|2.2|0.39|
|Floor_(notfinishedfloor)_|0.29|*|0.3|0.10|-0.05|0.1|0.00|
|<br>Sewage_(not sewage)_|0.25|*|0.8|0.20|0.26 *|0.2|0.04|
|<br>Number of Children|-0.09|*|<br>2.3|-0.20|-0.21 *|2.5|-0.52|
|Mill's Ratio|0.47|*|<br>2.2|1.04|0.38 *|3.3|1.25|
|Constant|-1.39|*||-1.39|-3.60 *||-3.60|
|Dependent Variable:||||||||
|<br>Log Nat Expenditure|||5.9|5.88||4.7|4.68|
|<br>Evaluated in pesos|||359.70|359.57||106.90|107.31|

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, * * Significant at 10%

**Table 3. Household Expenditure on Educational Private Service by Area**

|||**Urba**|**n**||**Rural**||
|---|---|---|---|---|---|---|
|**Explanatories variables**|**Coef.**|**Mean**|**M.W. to P.
.

As statistical data may still be affected by biases or measurement error, we complement statistical yield data with field-level indices of vegetative health and soil water content for pixels with different crop rotations as alternative outcome variables. To do so, we consider the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at 250m and the Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Land Surface Water Index (LSWI) at 500m resolution from MODIS data.<sup>11</sup> To avoid unequal number of observations due to clouds or shadows, a median filter was applied to produce 16-day composites for each of the indices that were then summed over relevant crops’ vegetation periods. The crop classification map was then converted to the same spatial resolution as these indices and for each VC, crop, and index, the overlap

> 9 As budget-related factors precluded collection of enough training data in all years to distinguish wheat and barley in the imagery, we aggregate the two and note that, based on statistical data reported below, an increasing share of cereal is devoted to wheat. 10 The data used come from ‘form F29’. Detailed information on inputs and outputs is supposed to be collected from a sample of those farming fewer than 200 ha (or a threshold of value added) via ‘form F2’ and for the universe of farms above 200 ha via ‘form F50’. As these data are collected at the enterprise level and as even for those farming only one operational unit, fields are generally dispersed across several VCs, matching becomes much more difficult. See Deininger _et al._ (2018) for a more detailed description of the data. 11 As fields are relatively large and to ease computation burden and capitalize on higher temporal frequency of MODIS as compared to Sentinel, these indicators were produced at the field level by aggregating daily MODIS data.

8
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Figure 1 shows the backcasted trend in poverty headcount rates (Panel a) and number of poor (Panel b) at the US$1.90 poverty line for the period 2009-2019, assuming a pass-through rate of one, that is, the growth rate in sectoral GDP is assumed to be the same as the growth rate in household consumption. The backcasts suggest that poverty rates were considerably lower in 2009, 2010, and 2011 than the estimates obtained using the 2009/10 HNLSS directly. Specifically, the backcasted trend estimates a poverty rate of 46.3 percent in 2009, 42.8 percent in 2010, and 41.8 percent in 2011, corresponding to 71.4, 67.8 and 68.1 million poor respectively (see Table _7_ in the Appendix, for the full backcasted series). The 2009/10

> 16 This confirms that all values of the consumption vector are positive.

11
# **2 Gender indicators for the SDGs**

Although nearly all countries have agreed to report on the SDG indicators, major gaps exist in indicator availability since the SDG agenda’s inception in 2015 (Dang and Serajuddin 2020). Gender-related SDG indicators are no exception. There are 231 unique SDG indicators. Many of the indicators, even if not obviously related to gender, nonetheless have sub-indicators, such as, indicators by sex, age, or disability status. The UN global SDG indicators database provides access to the data compiled for tracking progress toward fulfilling the SDGs. We use this data source, rather than individual NSO websites, because data submitted to the UN Global SDG monitoring database goes through a standardized process including a certain level of quality control and documentation review.

We explore the coverage of the 50 gender-related SDG indicators out of the 231 unique indicators.<sup>1</sup> As noted earlier, gender-related SDG indicators are not limited to SDG 5 on gender equality, but rather span indicators across 10 out of 17 of the SDG goals. All 50 SDG-gender indicators are Tier 1 or 2 SDG indicators.<sup>2</sup> While most are related to sex disaggregation of data 1 These 50 indicators closely match the UN Women minimum set of 52 quantitative gender indicators from the SDGs (United Nations Economic and Social Council 2012), subsequently revised to be 51 quantitative indicators, with a few exceptions. These exceptions are: (i) indicators 4.7.1, 4.a.1, and 13.3.1 are in the UN Women minimum set but not here as in our view they are not gender-related or sex-disaggregated measures. (ii) indicator 1.1.1 includes the "working poor" (employed population below international poverty line) by sex component which is in Table 1 but not in the UN Women list. Relatedly, Open Data Watch (2019) refers to 32 SDG gender indicators and another 36 “additional” SDG gender
households in each. To select a starting point the enumerator used a code of the day<sup>16</sup> and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali.

To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census.

<!-- Start of picture text --> Figure 2: Ethnic composition of the Figure 3: Ethnic composition of<br>North, 2009 Census sample<br>Other<br>Other Arab Peulh/Foul ethnicities Arab<br>ethnicities 3% be 3% 5%<br>13%<br>7%<br>Peulh/Foulbe<br>7%<br>Tamashek<br>32% Tamashek<br>36%<br>Songhai<br>Songhai 49%<br>45%<br><!-- End of picture text --> Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north.

> 16 The code of the day is the sum of the two figures of the date, i.e. if it’s the 25th of August the code of the day is 2+5=7. The
of women aged 15-29 having suffered genital mutilation (UN 2019).

Lastly, while the gender gap in both gross primary and secondary school enrollment has reversed in recent years, more than half of Burkinabè women aged 15-24 are illiterate, and girls have lower competency in math, especially in late primary school (PASEC 2015). These gender gaps in harmonized test scores have not decreased since 2017. In conjunction with prevailing social norms and women’s lower access to market information, these challenges contribute to the fact that women in Burkina Faso concentrate in lower-paying sectors, as employees, as entrepreneurs and as farmers. Our calculations from the 2018/2019 EHCVM show a large degree of occupational segregation across sectors, with male-dominated industries being more lucrative. For example, the average monthly earnings in construction are 1,390,000 CFA, while the average earnings in the food sector are 262,000 CFA. Women also make up most family workers and are underrepresented both in middle and senior management. In agriculture, virtually all export crops are grown by men.

Three main policy priorities emerge from our findings. First, increasing women’s skills and engagement in higher-value sectors (in entrepreneurship, but also in agriculture). Second, increasing women’s access to

> 13 This survey was conducted as part of the endline for the World Bank’s ‘Travaux à Haute Intensité de Main d’œuvre’ (THIMO) project in Burkina Faso.

16
their village.<sup>10</sup> Experience from qualitative fieldwork would suggest that this problem is a real one. In many cases, authority figures would fail to mention significant conflicts that were only later discovered after the number and type of informants was increased. Further, lack of disclosure may have a moral as well as rational basis. Conflict, in Indonesia as elsewhere, is often seen as a sign of failure, with harmony in village life particularly valued.<sup>11</sup> As such, it is not surprising that many village heads might be economical with the truth when describing unrest in their villages.<sup>12</sup> What are the implications for how we interpret PODES and to what extent does it limit the dataset’s utility in mapping conflict and assessing its causes? The first clear implication is that levels of conflict may in fact be significantly higher than the PODES data would suggest. This conclusion is backed up by other case study research – using qualitative and secondary data 10 Under the New Order, the village head was appointed by, and accountable to, the district government. While this fundamentally changed under the regional autonomy law (Law 22/1999), with village heads elected and accountable to the elected village council (BPD), in reality changes have been slow coming, with the new system only recently implemented in some districts, and still awaited in others (Antlov 2001).

11 Many anthropologists of Indonesian life stress the value local communities place upon harmony and social cohesion. See, for example, Tule (2000) who emphasises the importance in Flores of the ‘house’ as a provider of harmony: “for the Keo of Flores and other tribes in NTT [the house] is a cultural force which plays a centripetal role in uniting and incorporating its members into a social unit ... [a] system has been built up which supports the harmonious relationship among house members, both Christians and Muslims.” While
Using three rounds of Demographic and Health Surveys (DHS) conducted in Nepal over the last decade linked to data on precipitation from an expansive network of rainfall stations, we find that a 10% increase in rainfall from historic norms during the most recently completed monsoon season is associated with a 0.15 standard deviation increase in weight-for-age across all children ages 0-36 months. However, a similar rainfall shock during the _current_ monsoon leads to a 0.02 standard deviation _reduction_ in weight-for-age. We can thus infer that the total impact of the past monsoon rainfall consists of a negative “disease environment effect” of no more than 0.02 standard deviations and a positive “income effect” of no less than 0.15 standard deviations, but as high as 0.17 standard deviations.

Our evidence also indicates that excess monsoon rainfall experienced in the _second_ year of life (though not in the first year, as found by Maccini and Yang, 2009) has a significantly positive impact on both male and female stature (not just on female stature as in Maccini and Yang). This timing pattern suggests heightened vulnerability immediately following the transition to the family diet. Importantly, however, these nutritional shocks appear to be largely transitory. By the time a child reaches age five, the negative height impact of drought experienced at age two completely dissipates.

The rest of the paper is organized as follows. Section 2 lays out the conceptual framework. Section 3 describes the anthropometric and rainfall data. Sections 4 and 5 present the empirical specification and estimation results, respectively. The final section recaps and discusses implications for policy.

# **2. Conceptual Framework**

A standard human capital production function specifies a child’s nutritional status at the end of period _t_ ( _Ht_ ) as a function _f_ of her nutritional status at the end of the previous period ( _Ht-1_ ), all of the relevant nutritional and health inputs she received during period _t_ ( _It_ ), and her period _t_ disease exposure ( _Et_ ). Thus