rafmacalaba/data-use-mentions-extended huggingface dataset:
demonstrate the causal impact of FDI penetration on manufacturing TFP in Chile. Rather than using a services reform indicator, the authors exploit the issue of whether the extent to which Chile’s accumulated net real FDI inflows in services, i.e. stock, had any significant impact on domestic firm performance. Their multiplicative form of FDI stock and Chile’s input coefficients relies on a firm‐level dependence indicator of services input as opposed to using input coefficients from input‐output tables as done in other firm‐level studies. They find a positive impact of services FDI on TFP for Chilean manufacturing industries, which in fact can explain 7 percent of its contribution to total TFP increase in Chile. Using Indonesian firm‐level data, Duggan _et al_ . (2013) provide evidence that services reform using the OECD’s regulatory restrictiveness index in FDI for Indonesia showed a positive contribution to manufacturing firm TFP. In this study the authors construct an input reliance variable using Indonesian input‐output tables.

Most of these studies have been conducted using country‐specific firm‐level data for mainly developing economies, while no study to date has taken up a multiple country approach or used a developing economy, not to mention the European Union (EU) as a central point.<sup>4</sup> Of course, a focus on developing countries is logical if one thinks of the fact that many of these economies still have restrictive services policies in place which make their sectors still relatively locked. From this perspective, concentrating on the potential welfare gains for poorer countries is therefore important. Yet, it is somewhat surprising that although much debate on potential productivity gains in Europe has been allocated to its divergent services sector performance, which implies that levels of restrictions significantly differ across European countries as can be seen in Figure 2, no comprehensive study on the EU’s services sector and its effect on the wider downstream economy has until now been undertaken. This paper therefore performs a first step to fill that gap and
# **4 Research Design**

## **4.1 Overview**

Figure 1 provides a graphical summary of the research design. Our intervention is designed as an information provision experiment and administered through a phone survey with our partner bank’s credit card customers. In addition to survey data, we observe pre-treatment and post-treatment administrative data covering the universe of credit card transactions for all participants of our study.

The intervention proceeds in the following four steps. First, all respondents are asked a set of standard questions on demographics and their general economic situation. Second, a survey module elicits macroeconomic expectations and provides randomly selected subsets of respondents with information about inflation, the exchange rate, or both. Third, we measure posterior beliefs to assess whether our information treatments affect respondents’ expectations. Finally, we combine survey responses with administrative data on credit card spending to test whether the information provided affects subsequent consumption in the manner predicted by economic theory. We provide additional details on each of these steps in the following sections.

## **4.2 Sample Population**

To construct the sample frame for our experiment, we first requested a list of credit card customers from our partner bank. We specified that this list should be restricted to customers who opened their accounts within the previous three years. We received a random sample of 33,000 credit card customers and invited these customers to participate in a phone survey, which included our information experiment.

The survey was conducted by a team of 11 call center operators who were trained to administer a short phone survey and supervised in person by a member of the research team. At the beginning of each workday, the operators were provided with a randomly selected list of credit card customers to call. The operators introduced themselves as surveyors working on behalf of researchers from UCLA and asked participants if they were willing to participate in a short survey about their economic expectations. If operators were unable to reach a respondent on the first attempt, they were instructed to
Firms actively use these markets to obtain financing. On average, firms raise capital between two and three times per decade, with the average issuance accounting for 19 percent of a firm’s total assets.

Third, firms that raise funds in capital markets tend to grow at a faster pace than non-issuing listed firms, and at a faster pace than GDP (Table 1, Panel B). Over the period 2009-16, the median firm raising capital through equities or bonds grew 4.3 percentage points faster in terms of assets than the median non-issuing firm in that period. This growth differential is consistently positive over time and across different measures of firm performance. For example, over the same period, the annual growth rate of employment by issuing firms was 2.8 percent, whereas it was 0.2 percent in the case of non-issuing firms. As comparison, the average growth rate over time of the median listed firm in the median country is similar to the average GDP growth rate of the median country. For example, for the median listed firm the average growth of sales was 2.8 percent over the period 1991-99, 11.7 percent over 2000-07, and 0.4 percent over 2009-16. This is similar to the average GDP growth rate for the median country, which was 2.4 percent, 8.3 percent, and 0.5 percent, respectively, over the same periods.

# **3. Capital Market Financing and Firm Performance**

To assess the difference in performance between issuing and non-issuing firms, we employ an event study approach, where the dependent variable is a measure of firm growth around a five-year issuance window. We use a difference-in-differences regression strategy that evaluates the growth of issuing and non-issuing firms before, during, and after issuances. We focus on five measures of firm growth: the growth rate of total assets, sales, tangible fixed assets (PPE), intangible assets, and the number of employees. Whereas total assets will increase after a firm issues
# **4. Methodology**

# _4.1. Defining a Control Group_

In the absence of experimental data which would allow to identify the impact of the ECT, we implement a quasi-experimental approach which consists in defining a control group using the LPMT targeting tool and an Inverse Probability Weight (IPW) estimator. This method allows us to identify a potential impact of the ECT.

The treated households are identified as those matched to the administrative data who declare that they have received a transfer from NaCSA and are residing in one of the five districts in which the program was implemented (Bo, Bombali, Kenema, Port Loko and Western Area Urban). These restrictions eliminate the possibility of including households before they received the ECT (i.e., a household listed as a beneficiary in the administrative data that declares it hasn’t received a cash transfer in the CIMS) and capturing the effect of other cash transfers. The sample restriction to the districts of interest excludes households that migrated outside of these cities or were not captured in Round 2 because of attrition. Moreover, as described in section 2, for a household to be considered as a beneficiary, they must be deemed eligible by the LPMT.

The main challenge in the implementation of the quasi-experimental method is to identify a relevant counterfactual for the treated households. We exploit the fact that beneficiaries of the ECT were chosen based on LPMT but that the scope of the ECT program was not sufficient to cover all informal sector households in need in the five districts in which the program was implemented. We therefore identify the control group as households that passed the LPMT in the CIMS data but did not receive the transfer 12
more than 10 percentage points higher for women than men in Bulgaria, Poland, and Romania. High NEET rates may reflect weak labor market policies and lower spending in ECA countries compared to the EU. Participation in training (based on survey data from recent years) ranged from less than 2 percent of the population aged 25-64 years in Bulgaria to 6 percent in Hungary and Türkiye. This compares with an EU average of 11 percent (European Commission 2022).

Other major drivers of TFP growth also slowed in 2011-21. After a boost from EUaccession reforms, governance reform efforts have slowed in many of the new member states and backtracked in others, weakening the business environment and likely hindering competition and innovation. Pervasive corruption and large informal sectors in some countries are major constraints on the ability of private firms to invest, innovate, and close productivity gaps with the EU. In 2018, ECA countries continued to fall short of the EU average in the public institutions component of the Global Competitiveness Index, with already sizable gaps in ethics and corruption widening in some cases. The adverse effects of such poor governance tend to be magnified by the state’s outsized footprint on the economy (figure 9.A-D). Even in ECA’s EU countries, World Bank Enterprise Survey

> 11 Enterprise Survey data from the World Bank indicate that an inadequately educated workforce is one of the largest constraints on firms’ ability to grow in Bulgaria, Poland, and Romania—especially in Bulgaria and Romania, where nearly a quarter of firms identified education as a constraint (World Bank 2022c).
_Table 1: Internet access in favor of:_

||**2018**|**2019**|**2020**|**2021**|
|---|---|---|---|---|
|**Algeria**|male|male|male|male|
|**Bahrain**|male|female|female|female|
|**Egypt, Arab Rep. **|male|male|male|male|
|**Iran, Islamic Rep. **|female|male|equal|male|
|**Jordan**|male|male|female|male|
|**Kuwait**|equal|equal|male|male|
|**Lebanon**|male|male|male|male|
|**Morocco**|male|male|male|male|
|**Oman**|male|male|male|female|
|**Qatar**|male|male|male|male|
|**Saudi Arabia**|female|equal|equal|equal|
|**Tunisia**|male|male|male|male|
|**United Arab Emirates**|male|female|female|female|

Source: Constructed by the author using the gender gap access indicator of the Internet Inclusion Index constructed by the EIU.

_Note: The measure of gender gap in internet access is computed as (% male access - % female access / % male access). Positive values indicate that male access exceeds female access. A smaller or negative gap indicates greater female connectivity (The Economist Intelligence Unit, 2021a)._ _Figure 2: Gender Economic Gap and Gender Gap in internet access (2018-2021)_ Source: Constructed by the author using gender gap access indicator of the Internet Inclusion Index constructed by the EIU and female-to-male labor force ratio from the
, the study period between 2010 and 2016 incorporates both the launching and development of a large-scale rural road development program (URRAP) and historically severe droughts, which allows it to analyze the contribution of rural roads to resilience against disaster shocks.

The study finds that recently developed rural roads have brought significant welfare and economic impacts for rural Ethiopians who would have otherwise been physically and economically isolated. Amid the recent severe droughts, rural roads played a fundamental role in reducing poverty and enhancing resilience against shocks by providing rural populations access to markets.

Rural roads supported household welfare amid the recent severe drought shocks. The econometric analysis of this study suggests that recent rural road development contributed to household welfare, which is measured by household consumption. Rural roads increased household consumption by 3.8 percent each year between 2012 and 2016. In other words, without the rural roads, the average household consumption would have been 16.1 percent lower between 2012 and 2016. The estimated impacts mainly come from rural households who were affected by the recent severe droughts, which indicates the contribution of rural roads to reducing vulnerability and enhancing resilience against disaster shocks. Rural households in the most remote communities benefited most from rural road development. Importantly, poorer people gained more from recently developed rural roads: without rural roads, 10 percent more households would have been poor.

> 21 Not reported. Results available upon request. Recipient indicators for PSNP and other transfers are added as controls.

12
# Structural and Behavioral Barriers to Improving Development Outcomes: The Case of Maternal CVre in Haiti<sup>∗</sup>

Emilie Perge, Jimena Llopis Abella, and Anna Fruttero<sup>†</sup> JEL: D91, I12, I15, I18.

Keywords: Maternal health, behavioral biases, multilevel model, mixed methods, Haiti.

## **1 Introduction**

With 480 deaths per 100,000 live births in 2017, Haiti, was far from achieving the Sustainable Development Goal of having fewer than 70 maternal deaths per 100,000 live births by 2030 (UNICEF, 2022). In the Latin America and Caribbean region the average maternal mortality rate was 74 deaths per 100,000 live births. Pregnant women die as a result of complications such as preeclampsia, eclampsia, severe bleeding, and infections.

While some intrapartum complications cannot be reliably predicted or prevented, most of them can be successfully detected and treated with prompt diagnosis and care (Say et al., 2014). Adequate professional care before, during, and after childbirth has been proven to reduce death rates (UNICEF, 2014). Thus, all women should access antenatal care (ANC)

> ∗Acknowledgments: The authors thank the two peer reviewers, Jorge Luis Casta ̃neda and Nicolas Collin, for their detailed comments; Ondine Berland for excellent research assistance; Donald Antoine, Jamesson Vamblain, Mayerline Antoine, Manouchka Justin, Louise Estavien, Tania Mathurin and Fleurimonde Charles Joseph for their help collecting qualitative data in Haiti; Ingrid Dallmann for her help with the SPA data; and Lauren Manning and Chiara Broccolini for support with editing. All findings, interpretations, and errors belong to the authors.

> †Perge is affiliated with UN Sustainable Development Solutions Network, Llopis Abella is affiliated with Save the Children, and Fruttero is affiliated with the World Bank Group. Correspondence: Emilie Perge (emilie.perge@unsdsn.org), Jimena Llopis Abella (jimena.llopis@savethechildren.org and Anna
|
|Low skilled manual<br>services (LMS)<br>Agriculture|Street vendors and street service workers; personal services workers in<br>establishments; domestic service workers; watchmen and guards, drivers<br>of transportation powered by means of human or animal traction.<br>Workers in the agricultural sector|

Source: Own elaboration.

Table 5 presents the total income, average schooling and ISEI average of individuals between 30 and 64 years of age belonging to each occupational stratum, according to the income data from the ENIGH 2016.<sup>45</sup> Based on these data, it is possible to affirm that the proposed order is related

> 44 Occupational classifications commonly assign individuals to an occupational stratum through the combination of three variables: occupation, position in the occupation and size of the establishment. Unfortunately, ENCEL 2017 only contained information on the occupation for youths and providers, so the assignment of occupational strata was based on this variable. This restrains the construction of the variable, however, in the case of this sector of the national population the variability of the other two variables is limited, since most are in a wage-earning position and work at small establishments. We consider that, in the absence of the other variables, occupation provides a proper approach to the relevant stratum.

> 45 As previously mentioned, this age range is considered to include adults with a stable occupation and job position.

25
**Table 1: Ten PD Survey Indicators**

|**Principle**|**Indicators**|**How Measured**|
|---|---|---|
|Alignment|**Ind 3:**Aid flows are aligned with national<br>priorities (aid on budget)|Aid flows reported in the annual budget by<br>government/aid<br>flows<br>actually<br>disbursed<br>by<br>development partners (%)|
|Alignment|**Ind 4:**Strengthen capacity by coordinated<br>support (technicalassistance)|Amount of coordinated technical cooperation /<br>totaltechnicalcooperation(%)|
|Alignment|**Ind 5a: **Use ofcountryPFMsystems|Use of PFM/totalassistance (%)|
|Alignment|**Ind 5b:**Use of country procurement<br>systems|Use of procurement / total assistance (%)|
|Alignment|**Ind 6:**Strengthen capacity by avoiding<br>parallel<br>project<br>implementation<br>units<br>(PIUs)|Number of parallel PIUs|
|Alignment|**Ind 7:**Aid is more predictable|Actual<br>aid<br>flows<br>recorded<br>by<br>the<br>government/scheduled aid flows by development<br>partners (%)|
|Alignment|**Ind8:**Aid is untied|OECD-DAC data|
|Harmonization|**Ind 9:**Use of common arrangements or<br>procedures (program-based approaches—<br>PBAs)|Program-based approaches (budget support + non-<br>budget support PBAs)/total assistance (%)|

> 6 <u>The Paris Declaration on Aid Effectiveness</u>

> 7 Appendix 1 has list and brief explanations of
Policy Research Working Paper 9275

# **Abstract**

Studies of female business leaders and economic performance are rarely conducted with worldwide observational data, and with considerations on the underlying cultural, institutional, and business environment. This paper uses worldwide, firm-level data from more than 100 countries to study how female-headed firms differ from male-headed firms in productivity level and growth, and whether the female leader performance disparity hinges on the underlying environment. Female-headed firms account for about 11 percent of firms and are more prevalent in countries with better rule of law, gender equality, and stronger individualistic culture. On average, female-headed firms have 9 to 16 percent lower productivity and 1.6 percentage points lower labor productivity growth, compared with male-headed firms. The disadvantage is mainly in manufacturing firms, largely nonexistent in service firms, and present in relatively small firms. Although the female leader performance disadvantage is surprisingly not related to gender equality, it is smaller where there is less emphasis on personal networks (better rule of law, lower trade credit linkages, lower usage of bank credit, and more equalizing internet), less competition, and the culture is more collective. The study does not find that the female leader disadvantage is amplified in corrupt environments. Africa differs significantly in that it features lower female disadvantage, stronger female advantage in services relative to manufacturing, and stronger sensitivity of female business leaders to electricity provision and bank credit access.

This paper is a product of the Development Research Group, Development Economics. 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 authors may be contacted at lxu1@worldbank.org.

_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
surveys. We do nonetheless examine these simulations in part as a point of comparison with existing literature and, also as a point of contrast with our main simulation.

For the second simulation, we assume that the HHS - NAS gap is mainly a result of the incomes and consumption of better-off households being inadequately captured in survey data (primarily in terms of item nonresponse and underreporting). This simulation draws on the empirical evidence discussed in Section 3, that measurement error in surveys disproportionately comes from underestimating the top tail of the distribution. Our approach is informed by the work of Lakner and Milanovic (2016) and closely follows Chandy and Seidel (2017a, 2017b) who adjust the top tail of each survey distribution, in proportion to the HHS - NAS gap. Both fit a Pareto distribution to allocate the HHS - NAS gap to the richest household of the survey distribution. Their methods build on an approach suggested by Atkinson (2007) who uses a Pareto imputation to “elongate” the upper part of the distribution. Lakner and Milanovic add the full HHS - NAS gap to the top decile of the distribution in their data (though with an upper bound). We follow the approach of Chandy and Seidel (2017b), who add a top segment to the Lorenz curve from the survey distribution, with income corresponding to _half_ the HHS - NAS gap, fitted with the pareto distribution from the top survey decile. In this method, the elongation of the distribution is a function of both the size of the gap and the observed inequality of the top survey decile.

# **_Poverty_**

Several researchers have proposed uniformly scaling up survey data to match national accounts. Bhalla (2002) scales up survey means to match HFCE from national accounts to estimate global poverty and inequality. Bourguignon and Morrisson (2002), Sala-i-Martin (2006), Sala-i-Martin and Pinkovskiy take a similar approach but scale up to match per capita GDP. Chen and Ravallion (2010) offer a more measured approach to scaling up survey distributions
# **1   Introduction**

Sectoral linkages – the extent to which goods from different sectors are utilized as intermediates in the production of sectoral goods, bear a systematic relationship with GDP per capita. The share of services in intermediate expenditure is higher in the developed countries. While this holds in the production of all sectoral goods, the gap in the services share across countries is starker in the production of services itself (Sposi, 2018). An interpretation that helps reconcile the cross-country variation is that distortions exist in the intermediate markets.

Nevertheless, such analyses typically employ a production technology with a CobbDouglas (CD) association across sectoral inputs. Hence, linkages do not respond to changes in relative prices. On the other hand, a well-documented fact in the development literature is that the relative price of services rises with GDP per capita.<sup>2</sup> If intermediates from different sectors enter production as complements, then the higher relative price of services in developed countries will lead to the sector commanding a larger share of the aggregate intermediate expenditure seen in the data.<sup>3</sup> The objective of this note is to examine if the elasticity of substitution across sectoral inputs differs from unity, making the price channel a factor in accounting for the cross-country variation in linkages. Using panel data on input-output intensities and expenditure prices from 28 countries, I find the elasticity to be less than one in each of the three sectors. Intermediates are most complementary in the production of services, while it is easiest to substitute across intermediates in the production of agricultural goods.

**2   Theoretical Framework and Data** The gross output of a sector is produced using the intermediate bundle and a composite aggregated from all other factors. The production technology is CD in and . In jj jj QQ jj XX addition to price-effects, I also allow for scale-effects in production technology, i.e., the jj jj jj ZZ XX ZZ intermediate expenditure shares react to changes in . The scale-effect establishes an