rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10276

# **Abstract**

Although many countries have established investment promotion agencies over the past two decades, there is little evidence on what characteristics make them effective in attracting foreign direct investment into their home country. To provide new insight into this question, this paper brings together sectoral foreign direct investment data with survey data on investment promotion agency characteristics. Using a structural gravity model framework, it explores the effect of investment promotion agencies’ sectoral targeting on inward foreign direct investment stocks over 2013 to 2018, across a sample of 36 middle- and high-income countries. The study finds that investment promotion agency sectoral targeting provides a significant positive effect on the sector’s foreign direct investment stock in that country. Yet, a gravity model with country-interaction effects suggests that not all countries are equally effective at promoting investment. The results from the model are used to define two groups: high-performing investment promotion agencies (those with positive, significant effects in attracting foreign direct investment) and other investment promotion agencies (those with insignificant or negative significant effects). Using t-tests, the study considers which investment promotion agency characteristics significantly differ between the two groups. The findings suggest that effective investment promotion agencies are more likely to be private or semi-private agencies. Their mandate tends to be focused narrowly on foreign investment and exclude responsibilities for domestic investment promotion. Such investment promotion agencies are more likely to have a board of directors, and their staff tends to be better compensated. Finally, high-performing investment promotion agencies tend to provide more investor services, partly by engaging smart, sectoral analytics and adopting systems for identifying investor complaints or disputes.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at vsteenbergen
and does not capture acute deprivations in health or education, as the OPHI MPI does. For example, a third of those experiencing multi-dimensional poverty are not captured by the monetary headcount ratio (World Bank, 2020). Hence, it is not surprising that efforts are underway to combine the advantage of both measures like the World Bank’s Multi-Dimensional Poverty Measure (MPM; Nguyen et al., 2021).

In the case of FDPs, monetary and multi-dimensional measures of poverty are both indicated and, as it is often the case in other settings, they complement each other. In this respect, there is not much difference with other types of populations. However, the money metrics used for monetary indicators may be more challenging to estimate for FDPs than regular populations, and the composition of multi-dimensional indexes should be adapted to FDPs’ characteristics.

Moreover, especially in a context of very high deprivation, a poverty headcount might not be able to reveal the actual gravity of the situation and qualitative information should be sought to complement standard questionnaires and possibly inform the development of future questionnaires. Collection of socio-economic data is a passive process where respondents are asked pre-formulated questions. This constrains the respondents in sharing their own narratives and emphasizing what they feel is important. The implementation of household surveys can be seen as an opportunity to collect qualitative information and transform a one-sided narrative into one that provides voice to the poor. Pape (2020), for example, describes how short, voluntary video testimonials with informed consent from people living in South Sudan and Somalia<sup>9</sup> can be used to empower the poor in voicing their own needs. While this is a practice that can be developed in any context, it is particularly promising in contexts where measuring poverty is still relatively new and the specific challenges associated with this measurement are still little known, as in FDP contexts.

# Poverty metrics

Income, consumption or expenditure can be used as monetary metrics to estimate poverty. The correct measurement and classification of income can
# **1 Introduction**

The international fragmentation of production processes has challenged the capability of standard trade statistics to truly represent supply and demand linkages between economies. In general, bilateral exports differ from the portion of a country’s GDP related to the production of goods and services shipped to a certain foreign market. On the one hand, exports also embed imported intermediate inputs and on the other, the directly importing country often differs from the product’s ultimate destination. Whenever production is organized in sequential processing stages in different countries, trade statistics repeatedly double count the same value-added. The diffusion of global value chains (GVC) has therefore deepened the divergence between gross flows, as recorded by traditional trade statistics, and the data on production and final demand as accounted for in statistics based on value-added (above all GDP).

In this new context, we must reconsider how to track production-demand linkages, assess the spillovers of macroeconomic shocks, and evaluate the impact of trade policies. We also need to assess countries’ participation in the international sharing of production. New, specific empirical questions have also emerged such as: What part of a country’s exports can be ascribed to the value-added produced at home or abroad? How can we allocate this value-added across the different bilateral and sectoral trade flows? Which markets absorb this production as final demand? How important is GVC-oriented trade for a country’s exports and imports? To what extent is a country’s production affected by macroeconomic shocks emanating from other countries? How can it be affected by trade policies implemented in a given country on a given sector, and/or vis-à-vis certain partners? This is just a subset of all the key questions raised by the prevalence of GVCs.

This paper develops a new toolkit of methodologies for value-added accounting of trade flows at the country-, bilateral- and sectoral-levels by using InterCountry Input-Output (ICIO) tables. The measures we propose can be used to properly address important empirical questions, such as those mentioned above,
the land transacted. These high costs are onerous for all, but are prohibitive for the rural poor, and explain in large measure why so many land holders do not hold effective title to their land. The risk of losing land through encroachment by others, and through lack of transparency in settlement and consolidation operations, is considerably higher where land holders lack clear title to their land. This is most likely to be true for the rural poor and other socially excluded groups. Measures to improve the efficiency of land records management and land registration generally focus on computerization. This is not a panacea, particularly if incentive structures within land management agencies are not simultaneously addressed. However, provided there is close coordination between the computerization of registration and of land records, it offers high potential for reducing transaction costs in obtaining title to land, and thereby helps facilitate access to institutional credit. Of utmost importance is the 'backwards integration' of land registration into land records management, so as to permit more or less simultaneous mutation of the official record.

- **_Critical reassessment of land administration agencies:_** Reforms in land administration will be ineffective if the incentive structures within which the relevant government officers work are not simultaneously addressed. Rent-seeking is widespread, and is usually a reflection of the fact that landholders are willing to pay a price to receive the kind of service they have a right to expect. Where this creates difficulties is that it tends to lead to the systematic exclusion of the rural poor from gaining more secure access to land, since the rural poor are least likely to be able to bear the high transaction costs involved in pressing for legal protection of their existing rights, or in acquiring new rights. Rent-seeking serves to ration access to government services. Day-to-day incentives and promotion opportunities for revenue inspectors and other low-level government officers are not performance-related. While reforms in land administration that aim to promote transparency and public access to

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expansion across Cox’s Bazaar since 2010. More recently, forests declined by nearly 20 percent from 2017 to 2020, while settlements expanded by 21 percent between 2017 and 2020.

Across the district of Cox’s Bazar, we use a stratified random sampling method to select 10,000 points and build a spatially explicit pixel-year panel data set that identifies the geodesic distance between each point and the nearest camp (in kilometers), as well as each point’s land cover class by year. Based on our initial descriptive analysis, we designate pixels 0-5 km from the nearest camp as part of the “treatment” group and pixels 10-15 km from the nearest camp as “control.” Using a difference-in-difference framework that includes year and pixel fixed effects, we find evidence of significant forest loss 0-5 kilometers from camp boundaries. All else equal, we find that pixels within this distance buffer were 7.6 percentage points more likely to become deforested relative to the control area after camp openings. Our heterogeneity analysis suggests that these forest losses are concentrated outside protected natural areas. Moreover, we find that settlement areas are expanding faster in the 0-5 km buffer area relative to control: pixels within the treatment buffer were 13 percentage points more likely to convert to settlement after camp openings.

# **2. Literature Review**

Why might refugee camps trigger forest cover losses? The arrival of thousands of displaced people constitutes a sudden and large increase in the local population count. Population growth has the potential to drive forest loss because of the resulting increase in the area needed for shelter and for agriculture, which (in the absence of appropriate technologies) requires an expansion in agricultural land use, often at the expense of forests.

There are other reasons why refugee camps can trigger forest cover loss. Evidence from Kakuma Camp in Kenya shows that camps can stimulate economic activity (Alix-Garcia et al., 2018). This is partly because refugees often participate in local markets. They expand the local supply of certain goods when they
regressors of interest and outcome variables, it could bias our results. In Table A3, we test whether firms included in the panel (and thus in our estimation sample) differ systematically from those in the cross-section. Specifically, we consider all firms in countries with at least two survey waves and for which geo-localization data are available. We then examine whether the likelihood of being interviewed a second time—i.e., of being included in the panel—is correlated with key variables used in the analysis: _Sales_ , _Total costs_ , _Profits_ , and _Conflict exposure_ (defined below in Section 2.2). Results in columns 1-5 show that the probability of inclusion in the estimation sample is not driven by these characteristics, allaying concerns about systematic differences between panel and non-panel firms.

Another potential concern with our data is that missing values may be correlated with conflict exposure, possibly biasing the estimation sample.<sup>10</sup> Results reported in Table A4 suggest that this is not the case. Column 1 shows that, in the full sample, our measure of conflict exposure (see Section 2.2) is not correlated with firms _not reporting_ sales. Column 2 confirms that this result also holds in the subsample of firms for which baseline control variables are available. Moreover, in the estimation sample, conflict exposure is not correlated with non-reporting of raw material and intermediate input costs (column 3) or labor costs (column 4). Taken together, these results indicate that, for the firm outcome variables collected in the WBES, non-reporting is not systematically associated with conflict exposure.

Finally, we explore potential issues related to attrition. In Table A5, we test whether conflict exposure is correlated with firms exiting our sample. As expected, the results show that firms are more likely to exit when they are exposed to a higher number of conflict events between two survey waves, and when their pre-exit sales levels are lower. This pattern suggests that, if anything, our estimates of the negative
sup> round from India excluded from its sampling frame the remote areas of Nagaland, and Andaman and Nicobar Islands. The population of Andaman and Nicobar Islands is 380,000, while that of Nagaland is 1.98 million. Given that India had a population of 1.247 billion in 2011, this makes the proportion of individuals excluded from the survey reach just 0.2 percent of the total population.

Sample sizes vary widely across household-level surveys used to measure poverty. Maldives surveys about 1,800 households, while India surveys over 100,000. The range of individuals covered by these household surveys varies from 11,500 in Maldives to over 464,000 in India. This translates to a wide span of sampling ratio – from 0.04 percent in Bangladesh to 7 percent in Bhutan.

# _Monetary welfare measure_

Countries in the region use broadly similar methods to measure per capita consumption aggregates. All countries in South Asia use consumption rather than income to measure poverty. To estimate international poverty, total household consumption is divided by the number of individuals in the household to get a per capita estimate. This matches the methodology used by all countries in the region to estimate national per capita consumption aggregates, except for Pakistan. Pakistan uses

> 10 The provinces of Helmand and Khost were included in the household survey but these are not used to estimate poverty as there are issues with the consumption data quality in these two provinces.

> 11 Central Statistics Organization (2017).

> 12 There is a long-standing debate in Bangladesh about the size of the slum population. The Census and Mapping of Slums collected by the Center for Urban Studies in 2015 estimated that 35.2 percent of the urban population in Bangladesh lived in slums, while the UN Habitat Global Report on Human Settlements (2013) estimated the proportion or urban slum dwellers at 61.6 percent for 2009.

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– after the<br>expansion of<br>employment<br>opportunities in tea<br>estates in Tamil<br>Nadu, India – have<br>a positive impact on<br>female children in<br>the household?|Survey of ~ 4000<br>female workers<br>from 2002-2003|Different<br>instrumental<br>variables are used to<br>instrument variation<br>in female income,<br>and estimate its’<br>causal impact on<br>child outcomes|Increase in female<br>income leads to an<br>increase in female<br>children’s schooling,<br>particularly for<br>historically<br>disadvantaged<br>groups|
|Qian (2008)|Do increases in<br>female agricultural<br>income (relative to<br>male income) cause<br>an improvement in<br>the survival<br>prospects and<br>educational<br>attainment of girls?|1990 Population<br>census for China,<br>and 1993<br>household-level<br>survey data from<br>Ministry of<br>Agriculture|Difference-in-<br>difference strategy<br>that compares the<br>gender gap in<br>survival and<br>education in the<br>Maoist era and post-<br>Mao era, between<br>counties that plant<br>tea and counties that<br>do not plant tea. A<br>county’s production<br>of tea is a<br>differentiating<br>factor because post-<br>Mao reforms led to<br>an increase in tea<br>prices, and an<br>increase in wages<br>for tea cultivators|Increases in female<br>income increases the<br>survival prospects<br
.028***|‐0.064***|‐0.057***|
|participation|(0.002)|(0.006)|(0.006)|(0.006)|(0.001)|(0.000)|(0.003)|(0.003)|(0.006)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|No|No|No|No|No|No|No|No|No|
|Observations|209150|3004|7465|20671|48001|12016|24032|77959|17504|
|R‐squared|0.015|0.031|0.058|0.05|0.001|0.002|0.029|0.025|0.023|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

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**Table 1. Trends of the Decentralized Reforms in the 1980s**

||_80_|_81_|_82_|_83_|_84_|_85_|_86_|_87_|_88_|_89_|
|---|---|---|---|---|---|---|---|---|---|---|
|Marginal retention rate|.11|.12|.11|.14|.17|.17|.19|.23|.26|.27|
|Autonomy of production decisions|.07|.08|.10|.14|.25|.35|.40|.53|.64|.67|
|Share of output under mandatory<br>plan of the govermnent||Not av|ailable||.64|.62|.60|.58|.57|.57|
|Share of finrs with wage discretion|.01|.01|.01|.02|_.05_|.09|.12|.20|.32|.35|
|Share of fims under Contract<br>Responsibility System|.00|.00|.00|.01|.02|.04|.08|.42|.83|.88|
|Ex ante firm-level pay sensitivity<br>for firms under CRS|To|o few ob|servatio|ns|.51|.39|.41|.46|.43|.42|
|Share offirms with management<br>turnover<br>I|.09<br>|.01|.06|.10|.16|.14|.07|.15|.10|.09|

Note. All numbers come from the author's computation based on _A Survey of State Enterprises: 1980-89._
$6,917.88|-$27,133.43|-$34,051.31|-1.80|5.00|-1.40|1.00|
|6|$6,341.39|$-|-$6,341.39|-|-|-|-|
|7|$1,729.47|-$2,876.68|-$4,606.15|-1.00|2.00|0.00|0.00|
|8|$4,611.92|-$4,646.32|-$9,258.24|-1.00|3.00|1.00|0.00|

The above economic estimates (derived from the enhanced CBA APP) are consistent with the findings of our regression analyses, both in strength and direction, where there were positive estimated benefits of the PIP intervention for children with 1 or 2 siblings and 27
|_Yes_|_Yes_|
|Other controls (HHsize, marital status, education,<br>occupation, income source)|_No_|_No_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|
|Household fixed effects|_No_|_No_|_No_|

To do so, we use the variables capturing whether an individual is a wage worker, self-employed, an unpaid or a family worker.<sup>27</sup> We decompose the previous results from Table 11 into these employment categories by interacting each of the variables with the refugee index.

Table 12: Decomposition of the OLS effects of refugee presence on labor market outcomes, with analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009-2012)

||(1)|(2)|(3)|
|---|---|---|---|
||<br>Labor force parti<br>|<br>cipation (12 mo<br>|<br>nths)<br>|
|Log of refugees (50km)|-0.00738|-0.00802|-0.01587**|
||(0.00681)|(0.00669)|(0.00781)|
|Log of refugees(50km)#Wage workers|0.01975***|0.01959***|0.01368***|
||(0.00610)|(0.00607)|(0.00444)|
|Log of refugees(50km)#Self-employed|0.01836***|0.01794***|0.01296***|
||(0.00584)|(0.00568)|(0.00447)|
|Log of refugees(50km)#Unpaid workers|0.00980*|
and final goods as follows: intermediate goods are primary goods, semi-finished goods, parts and components; and final goods are consumption goods and capital goods.

We use two disaggregations. In the first, trade flows are divided into capital goods, intermediate inputs and consumption. In the second, we divide trade flows into intermediate inputs, final goods and primary goods. We construct index for trade proximity (TP) as follows. For given aggregate bilateral trade flows (total, intermediate input or final goods) between a country _i_ and a country _j_ , we define: where _Tj→j_ is the export value from country _j_ to country _i_ .

**Measure of GVC participation based on Value Added.** One of the main purposes in this paper is to identify production linkages between countries. Over the past decade, several accounting and theoretical framework have been developed to measure GVC participation. In this paper, we use the recent GVC indicators from Borin and Mancini (2019). They offer a new toolkit for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels that can be used to investigate a broad set of empirical questions—including in this case an assessment of the global inflation synchronization.

**Product Stickiness.** Another related feature of GVCs is their relational aspect. Some products are associated with a higher degree of rigidity in the buyer–seller relationships, which is sometimes associated with a high degree of product customization to integrate within the same production process. Martin et al. (2018) propose a measure of business relationships’ stickiness based on the duration of firm-to-firm trade and use firm level data in France to measure a “stickiness” index for more than 4,000 HS6 products. We use their classification to separate trade flow into their “sticky” and “non-sticky” parts, based on the products that are traded.

**Financial Proximity.** Financial interconnection could impact GDP comovement in a way that is not related to international trade. We use
to be made to undertake any assessment. The appropriate definition of progressivity will depend on the underlying question being asked, and will potentially impact the way in which the degree of progressivity can be measured. Contingent upon the definition chosen, there are several approaches that can then be adopted to measure the degree of progressivity of a tax, each with benefits and drawbacks that must be weighed in determining the most appropriate approach. Finally, measurement of tax progressivity – particularly when based on microdata – can be extremely resource intensive, and this must be taken into account, particularly in the current context where the progressivity of a large number of countries needs to be assessed.

The first impact that data limitations have is on the range of taxes that can be examined. Ideally, policy makers should be interested in assessing the progressivity of the entire tax system (and indeed, more broadly, in the progressivity of the entire fiscal system). However, data limitations make it virtually impossible to undertake a detailed analysis of the progressivity of the entire tax system, yet alone the entire fiscal system – although the in-depth CEQ analyses undertaken by the CEQ Institute and the World Bank go a long way towards this by assessing the distributional impacts of a number of major components of tax, transfer and expenditure systems (see Commitment to Equity Institute Data Center on Fiscal Redistribution, 2022; Lustig, 2018).

> 1 For more information on IDA, see: https://ida.worldbank.org/en/home.

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