rafmacalaba/data-use-mentions-extended huggingface dataset:
requires a decomposition into three components: pure backward participation, pure forward participation, and a two-sided engagement strategically positioned between the extremes, involving elements of both backward and forward participation. Furthermore, the paper also advocates for extending the assessment of GVC participation to include both trade-based and output-based metrics. This approach deviates significantly from conventional metrics that decompose participation in backward and forward linkages and focus exclusively on trade measures. These two improvements allow to capture indirect and non-exporting contributions to global production and align measures more closely with real-world production structures.

Applying this methodology to major ICIO datasets (EORA, ADB MRIOT, OECD TiVA, and WIOD), we construct a comprehensive set of indicators covering nearly 190 countries and up to 56 industries starting in 1990, and updated to the most recent year of available data, which at the time of writing (March 2025) goes to 2023. The new metrics show that previous approaches systematically underestimate the extent and complexity of GVC participation, especially for services, upstream manufacturing, and countries with low exportto-output ratios. They also reveal distinct patterns of engagement that matter for resilience, risk exposure, and by extension for economic development.

By bridging conceptual gaps in existing GVC measures, this paper equips policymakers and researchers with more accurate and actionable tools for understanding globalization’s evolving landscape and their implications for economic growth, exposure to shocks, and development. For example, the paper shows that the risks associated with a country’s involvement in GVCs
Zealanders either because of cultural proximity or self‐selection on these characteristics. Unfortunately, we do not survey New Zealanders and, in general, it is difficult to find representative surveys on preferences and beliefs. However, we have been able to find some evidence that suggest that Tongan PAC migrants are quite different in terms of preferences and beliefs than New Zealanders. For example, the representative 2011 World Values Survey (WVS) asks the same question that we include on general trust (Trusting Others in Table 3). While 70 percent of Tongan migrants in our survey report that most people can be trusted, only 57 percent of non‐migrant New Zealanders in the WVS feel the same way. Rieger et al. (2014) and Wang et al. (2016) survey students in 53 countries, including 91 in New Zealand. They ask both time preference and lottery questions similar to the ones we present in Table 2. In their most similar lottery (lottery #2 in their paper), 24 percent of New Zealand students are risk seekers and 5 percent have severe risk aversion versus only 6 percent of Tongan migrants being risk seekers and 13 percent having severe risk aversion. Similarly, while 75 percent of Tongan migrants are willing to wait one year to receive 1500 NZD instead of 1000 NZD at the current moment, only 45 percent of New Zealand students are willing to wait one month to receive 3800 USD instead of 3400 USD.

# **6.3 External Validity**

How much can we expect the results here to generalize to other migration settings? Our setting here considers a common form of international migration – a nuclear household moving from a poor to rich country permanently and leaving extended family behind. To the extent that it differs from other common migration types, it differs in ways which we might think would make it more likely that we would find changes in preferences or beliefs. That is, we would expect any effects to be smaller if migration was more temporary or if it was between countries
sessing whether the randomization process has been successful – are the two treatment groups fairly similar in terms of each of these characteristics? Comparing columns (2) and (3) of Table 1, the differences are quite small. As shown in column (4), only three are significantly different at the 10% level, or less. Participants in the threshold group payment treatment are on average 2 years older ( _p_ = 0 _._ 091), they are 10 percentage points more likely to be household head ( _p_ = 0 _._ 050), and they are also 10 percentage points more likely to own land ( _p_ = 0 _._ 086). Balance is thus decent, and this conclusion is reinforced when assessing the size of the normalized differences for each of the characteristics; see column (5) of Table 1. Normalized differences are generally preferred to _t_ -tests because they provide a scale-free comparison, and imbalances are typically identified as problematic if normalized differences exceed 0.25 (Imbens and Rubin, 2015; Abadie and Imbens, 2011). Most of the normalized differences are about 0.10 standard deviations or less, and none is larger than 0.22.<sup>8</sup>

# **2.4 Empirical approach**

We thus obtained endline tree survival data for 62 plots, 31 in each treatment arm. Our randomization procedure allows us to (i) treat the observed tree survival rates of the two treatments in the same block as matched pairs, and (ii) run regression models using forestblock fixed effects to capture any unobserved heterogeneity (such as the specific ecological circumstances) between blocks. Assuming an average survival rate of 30 percent and a standard deviation of the difference between the paired means of 20 percentage points, we have a 77% chance of detecting a minimum treatment difference of 10 percentage points (using a 5% significance criterion), or better.

Our study is thus adequately powered to provide reliable estimates of
The results are still useful in suggesting that in Africa the returns to MA may be up to 50% lower than in the US following Donaldson & Hornbeck (2016). A reason for this may be the prevalence of other market inefficiencies, often of a regulatory nature (Herrera Dappe et al., 2024). A prudent assumption is that in Africa MA gains translates into economic activity at a ratio of 4:1.

In the following, instead of computing exact returns, I determine the returns needed in order to break even. This will take the form of a reverse macroeconomic present discounted value (PDV) calculation. African GDP in 2022 was 2.81 trillion 2015 USD. The median growth rate in 2000-2022 was 4.1%. If we are pessimistic, let it be 3% going forward. I discount the future by 10%. This is a reasonable rate given that stock market returns are generally below this—the MSCI World has a long-term return rate of 8-9%. Emerging market bonds yield even lower returns, around 6%, according to the EMBI index by JP Morgan. So at the macroeconomic scale, it is very difficult to invest with more than 10% returns. I also assume that the horizon of policymakers is 30 years and disregard benefits accruing after 30 years. With these assumptions, I calculate the change in the macroeconomic growth rate of Africa needed to break even in PDV terms for each of the three scenarios proposed above as well as the full/consensus construction and upgrade scenarios considered in Sections 5.4 and 5.7. Table 8 reports these calculations.

Table 8: Break Even Changes in the Macroeconomic Growth Rate for Different Investments

|**Work Package**|**Cost**|**%**∆**MA**|**BEG** (4.1%)|**BEG** (3%)|
|---|---|---|
# Data and methodology

This exercise builds on a similar one conducted in 2020 to examine child poverty using the GMD (Silwal, Engilbertsdottir, Cuesta, Newhouse and Stewart, 2020). The analysis contains records from 10.4 million individuals from 147 countries, taken from the 2022 spring version of the GMD with 2019 as a base year. The GMD is a collection of globally harmonized household survey data compiled by the Data for Goals group of the World Bank’s Poverty and Equity Global Practice. Further details on the background of the GMD are given in Castañeda et al. (2016). A unique feature of the GMD is that the welfare aggregates are the same as those used to compute the poverty estimates published by PovcalNet and the World Development Indicators. These aggregates are based on household per capita income or consumption, depending on the specific concept that is used to measure national poverty in any given country.

For the effects of this exercise, the rescaling of welfare was performed using the Poverty and Inequality Platform (PIP) which contains data on poverty headcounts for each year (2013-2018). For 2020-2022, the data was provided by the Poverty and Shared Prosperity team. A rescaling of the weights was done using the UNDESA population estimates from 2013-2021. In other words, the number of children is estimated by applying the proportion of people that are children in the relevant survey to the total UNDESA national population projections for each country. This could lead to discrepancies with UNDESA estimates of the number of children in a country. For the 2022 estimates, UNDESA population prospects were used.

Poverty estimates for 2020, 2021 and 2022 are “nowcasted”—that is, gross domestic product (GDP) growth rates have been used to forecast household incomes, assuming all households experience equal consumption growth in percentage terms.

Trends from 2013 to 2018 are all based on back casted projections rather than repeated survey data. These are based on distributional neutral growth assumptions applied to one survey per country rather than changes observed in repeated cross-sections.
the subsequent evolution of the crisis, we perform two imputation exercises: one only for data from the first wave, and one for data for all waves. In the first case, mobility values are imputed for between 65 and 135 of the 311 total regions, depending on the Google mobility indicator. When using the data for all waves, we demean the data using each region’s mean, to isolate the variation over time, and impute the demeaned variables. For this case, mobility values are imputed for between 315 and 540 of the 1,096 total region-waves.

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# **5 Conclusions**

This paper studies the impact of the financial crisis on the credit availability for micro, small and medium enterprises in Azerbaijan using a unique dataset of 168,483 loans made between January 2006 and August 2009 by AccessBank Azerbaijan. The timeline of events in Azerbaijan allows us to study a pure supply side shock to the bank’s refinancing in the second and third quarters of 2008, while firms had not yet been affected by the economic crisis. Then, we can contrast its impact on credit availability for firms of different sizes and industries from the impact of the economic crisis shock that reached Azerbaijan in the fourth quarter of 2008. On top of this, the structure of the dataset allows us to analyze credit availability on an aggregate level as well as on an individual loan level, thereby identifying two major channels of credit constraints.

By analyzing aggregate numbers of loan applications and approvals, we derive the first channel of credit constraints. We observe that its tight refinancing in the second and third quarters of 2008 seems to lead the bank to discourage (potential) borrowers from applying for new or additional loans. The bank’s temporary liquidity squeeze therefore resulted in a slowdown of its credit portfolio so that not all the demand could be met during this limited period of time. At the individual loan level, for the funding shock we find that approval rates for those (potential) borrowers who actually apply for a loan are not negatively affected in any borrower group. For the real economy shock, by contrast, our findings suggest that credit availability for agro loan borrowers is hardly affected by this event while micro loans face a moderate and SME loans a considerable reduction in approval rates. Apart from that, our results show that bank relationships help increase credit availability in good as in bad times.

To gain deeper insights into the differential effects of the two crisis shocks on loan approval, we study an exogenous geographic variation in the severity of the crisis shock by distinguishing between locations in which the oil
# **1 Motivation**

Empowerment allows individuals to reach their full potential, to improve their political and social participation, and to believe in their own capabilities. Gender empowerment also has important ramifications for the rest of the household; empowered women have fewer children and higher child survival rates (Rosenzweig and Schultz, 1982; Dyson and Moore, 1983), healthier and better-fed children (Lundberg, Pollak and Wales, 1997; Kanbur and Haddad, 1994), and a generally greater allocation of resources to children (Thomas, 1990; Handa, 1996). Development programs aim to empower women by increasing their control over contraceptive choices, by providing them access to credit, and through education.

Women’s empowerment is particularly hard to achieve within a generation because it is driven not only by information about choices, but also by the acceptability of these choices, and what Kabeer (1994) calls the “power within”, i.e. a woman’s intrinsic belief in her ability to control resources and to make decisions. Communities are often governed by strict social norms, which can both be driven by and drive the choices traditionally made by women in the village. If the social stigma associated with working outside the home or using contraceptives is prohibitive, then mere access to education or birth control may not change empowerment outcomes. Instead, giving a woman access to others who have made different choices can expand her information set and demonstrate the outcomes associated with these choices. As an alternative to targeting individual women, empowerment for women may be affected by combining learning and influence through community action and peer networks.

In this paper, we use primary data from rural north India to examine the impact of a program called _Mahila Samakhya_ on female empowerment outcomes. _Mahila Samakhya_ aims to empower women by educating them. The program provides literacy camps, adult education classes, and vocational training. The program also creates support groups on issues of social importance, such as domestic violence and alcoholism. These support groups strengthen networks
the following criteria: they needed to have more than 10 conflict events during 2012–14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database (Raleigh et al. 2010).

Conflict-affected areas were oversampled to create a sample of individuals who had experienced conflict events to shed light on the types of events that had occurred. A random sample of the zones might have presented an insufficient sample of conflict-affected households; this, therefore, would have restricted the analysis of the types of conflict events. Due to the oversampling, however, the sample drawn was not representative at the level of the geopolitical zone, as is the case in the GHS. Indeed, out of the 717 households that are spread across 99 LGAs, all conflict events were concentrated in 52 LGAs—that is, almost half (47) of the LGAs did not have any household conflict exposure. This suggests strong geographical clustering of conflict events.<sup>9</sup> Even though the conflict was widespread in these areas, the LSMS panel data set contains a low fraction of households that have migrated during the data collection. All households in the telephone survey had lived in their LGA of residence during the entire time of the LSMS panel.

To account for the biases arising from the sampling, we present our results using probability weights. This renders the estimates representative at the level of the geopolitical zone (North East, North Central, and South South), as is the case with the GHS panel. The weights used were constructed using the wave 3 GHS panel weights as a benchmark, adjusting for the probability of being in the sample. The weights correct for the biases arising from the oversampling of the high-intensity conflict LGAs, for nonresponse, and for the fact that a minority of the households surveyed in wave 3, visit 2, of the GHS did not have a phone or did not provide a phone number. The mobile phone penetration rates are close to 90 percent in all three regions, however.
this, we extrapolate in the time-series and cross-section dimensions the Summers-Heston PPP-based investment and **GDP** series, on the basis of available constantprice investment and per capita **GDP** data. We next construct initial capital stock estimates for those countries lacking them **by** regressing average capital-output ratios (for countries with capital stock data in Summers-Heston) on per-capita income, and using the estimated regression coefficients to project the initial (average) capital/output ratios of the remaining countries. Finally, the capital stock series is computed **by** accumulating the PPP-based real investment flows to the initial stock.

In view of the narrow scope of stock markets in developing countries, as well as in the majority of industrial countries, we value these capital stocks in the same manner as FDI stocks,

> **By** the very nature of unrecorded assets, it is impossible to know their composition **by** currency and type of financial instrument, so that in this case we do not attempt to introduce any valuation adjustment.

**15**
2017.

## **4.4.1. Customs**

**Simulation of customs duties followed these steps:**

- (i) Mapped customs duty rates to PECS 2016/17 consumption items (average duty for those goods mapped to different HS codes).

- (ii) Excluded non-taxable items from the PECS 2016/17 consumption dataset (self-production, gifts). Netted down to observed households’ expenditure.<sup>43</sup>

- (iii) Applied statutory customs duty rates (differentiated by region) to all taxable products.

**The PECS 2016/2017 does not have a variable to identify imported products, so the study assumes the “Law of One Price”—that is, the prices of domestic goods converge to prices of similar imported goods.** We also account for how tax systems vary by region by assuming that residents in the West Bank face Palestinian customs duty rates while residents in East Jerusalem face Israeli customs duty rates, and that Gaza residents pay no customs duties.

## **4.4.2. VAT**

## **VAT simulation followed these steps:**

> 42 The property valuation data provided by the Government for the year 2017 had significant variation across cities for the valuation amounts. As a result, for the analysis, an average property value per governorate is applied.

> 43 Netting-down for customs duties means that self-reported household expenditure in the PECS needs to be divided by (1+customs duty rate)*(1+VAT rate). This allows the analysis to recover the households’ pre-fiscal expenditure before any taxes.

29
; consequently, the resources of exiting firms will be reallocated towards more productive firms.

Whether trade liberalization does induce competition becomes an empirical question that needs to be investigated. Previous empirical studies of developing economies have focused on Latin American and Asian countries (Pavcnik, 2002; Amiti and Konings, 2007; Topalova and Khandelwal, 2011) with the exception of Harrison (1994) which studies Cote d’Ivoire. While trade and productivity linkages have dominated the existing literature on evaluation of the effect of trade openness, this article takes a different approach by analysing firms’ price-cost margins. Other articles preceding the present work include; Brandt et al. (2017) on China, De Loecker et al. (2014) on Belgium and De Loecker et al. (2016) on India.

In view of the above, this section provides a detailed description of the computation of markups and market imperfections parameters using firm-level production data. The theoretical framework follows the work of De Loecker and Warzynski (2012), which is an extension of the seminal work of Hall (1988) on price-cost margins.

## **3.1 Markups**

Firm _i_ produces output _Q_ at time _t_ according to the following production function: 7
br>area|2005 to 2019|RBI|
|Telecom Density (TEL)|_Telephone_(fixed-line plus mobile)<br>connections per 100 people|2005 to 2019|RBI|
|Per Capita Installed power<br>capacity (KW) (POWER)|Installed power generation capacity<br>per person|2005 to 2019|RBI|
|**_Social Infrastructure_**||||
|School Density (SCH)|School per 1000 people|2005 to 2019|CMIE|
|Teachers (TCH)|School teachers per 1000 people|2005 to 2019|CMIE|
|Hospital Beds (BED)|Hospital Beds per 1000 people|2005 to 2019|CMIE|
|Doctors (DOC)|Allopathic doctors registered with<br>the State Medical Councils/Medical<br>Council of India per 1000 people|2005 to 2019|Ministry of<br>Health & Family<br>Welfare|
|**_Financial Infrastructure_**||||
|Bank Accounts (AC)|Bank Accounts per 1000 people|2005 to 2019|RBI|
|Bank Branches (BB)|Bank Branches per million people|2005 to 2019|RBI|
|Post office (PO)|Post office per million people|2005 to 2019|Department of<br>Post|
|**_Control Variables_**||||

> 7 The selection of states and time period are based on consistent data availability.  The study is also fairly representative as it covers 18 major states that accounts for 88 percent of GDP and 93 percent of population in India.

> 8Although the roles of good institutions and favorable business climate play a critical role in promoting infrastructure development, we could not include them in this study due to unavailability of reliable and consistent data over the study period at the state level in India.

13
procedure. This latter step often involved artificial stamp shortages and stamp price hikes by the officials, and stamp forgery (causing government revenue leakages). Further impediments included hassle and harassment by officials and the need for middlemen at every step to navigate the bureaucratic maze of the registration process and side payments and bribes.

Due to the ICT led reform, the registration time has been reduced to 1 day, and one final visit to pick up the registration certificate. The payment of fees can be completed in 15 minutes through leveraging one of the country’s leading bank networks (BRAC bank). The applicant can access a transparent online system in which a business can check its status without the need to visit the agency. Overall, it seems safe to assume a reduced scope of corruption due to limited interactions with the

# **4 Intervention**

## **4.1 Information campaign**

We implemented an information experiment around this business registration reform to test if a simplified registration regime would encourage informal firms to register and become formal once they learn how to register and about the potential benefits of registration.

We extracted a sample of informal firms (3,000) from two waves of the IFCs quarterly Business Confidence Surveys, BCS (Q1 and Q2, 2009) and IFCs Informality Survey of 2010.<sup>2</sup> These are all representative surveys of the businesses operating in Bangladesh.

The data from these surveys form our baseline sample with information regarding business registration as well as several variables concerning business performance and composition. We then randomly assign these firms to a treatment and control group. The assignment is implemented at the firm level.

We then had members of our staff visit the “treated” firms and provide information on the process and potential benefits of registration. In practice the staff members presented a set of potential legal and economic benefits of the registration and the step-by-step registration process. They were equipped with two booklets containing all the information in easy to understand (Bangla) language