rafmacalaba/data-use-mentions-extended huggingface dataset:
female employees relatively higher wages than non-exporters, but female workers earn lower wages than male workers in both exporting and non-exporting firms. However, the ratio of female wages to total wages is lower in exporting firms than non-exporting firms. These differences are statistically significant (see the Appendix).

**Table 2: Size and wages of exporting vs. non-exporting firms**

||**All firms**|**Non-exporters**|**Exporters**|
|---|---|---|---|
|Average number of employed|28.7|19.7|82.8|
|Average number of female employed|15.4|11.9|32.1|
|Female employment share|0.48|0.51|0.35|
|Average wages|4,647|4,076|7,530|
|Average female wages|3,759|3,264|5,974|
|Female-to-male wage ratio|0.95|0.97|0.88|
|Average turnover|1,825,241|963,991|6,924,577|

Source: GEOSTAT Statistics Survey of Enterprises for 2006-2017. Notes: Exporters are firms that exported in at least 1 year of the sample.

Labor market outcomes in terms of employment and wages exist between firms that export to different destinations. Firms that export to the EU or high-income countries tend to be larger than other exporters, in terms of sales as well as number of employees (total and female). They also pay higher wages (total and female). However, the share of female workers in total workers as well as the female-to-male wage ratio is lower for firms that export to the EU or high-income countries than other exporters (see Table 3). These differences are statistically significant (see the Appendix).

**Table 3: Size and wages of exporting firms by destination**
records included expenses of the business (36 percent), income after expenses (34 percent), number of customers (31 percent), and debts or debtors (31 percent). For those businesses who offered credit to their customers, 71 percent kept a written record of owed payments.

Interestingly, 31 percent of small business owners said they relied solely on themselves for business information, with the most common other responses being family members, spouses, and friends. Relatively few cited other business owners or support organizations, and very few belonged to any business networking groups. Indeed, 75 percent of business owners were unaware of any organizations that gave support and advice to small business owners. In contrast, the owners of larger enterprises were more likely to seek support from such organizations, and have sophisticated business networks.

# **_Interest in financial education, however, appears to be widespread_**

The FinsScope surveys give cause for optimism with regard to the take-up of financial education programs in Sub-Saharan Africa, in spite (or because) of the low levels of financial literacy. For instance, in Tanzania, most people surveyed (80-85 percent) were interested in learning more about interest rates, savings, loans, insurance, and a range of other topics, suggesting a keen interest in financial education. The level of desired financial knowledge is also high in Nigeria. In the baseline survey of South African mineworkers described in Cole et al. (2010), about 90 percent of respondents felt they would benefit from a financial education course.

In Malawi, the majority of adults are interested in learning more about how credit and interest rates work, how to save and keep money safe, how to manage budget, and how to obtain property and life insurance. A near majority are also interested in learning about technologyrelated financial matters, such as transferring airtime on cell phones, using cell phones for banking, using ATMs to transfer or withdraw money, or using the internet for banking. Importantly, the majority of Malawians are
Downstream spillovers arising from policy reform and foreign participation in the services sectors are qualitatively different from those arising from foreign direct investment in manufacturing industries. Disruption in the provision of services can result in large delays in production and product delivery, high information costs and an inability to invest in potentially profitable new activities. There has not, however, been much empirical analysis of the downstream effects of services reform, and the few existing studies have focused on specific services sectors, usually banking.<sup>4</sup> Rajan and Zingales (1998) show that financial development increases growth. They weight industries by dependence on outside financing (as estimated from US data) and find that firms which are more dependent on external financing gain more from financial development than other firms. Bertrand, Scholar and Thesmar (2004) demonstrate that banking deregulation in France in 1985 led to improved productivity in manufacturing firms. Entry and exit rates increased following liberalization, suggesting that less productive firms had been protected by the easy access to credit allocated to large firms by the previously nationalized banking sector. Productivity effects were particularly strong in banking-dependent sectors. Aghion and Schankerman (1999) identify channels through which infrastructure and institutions affect entry and exit. They generate a Dixit-Stiglitz model to demonstrate that infrastructure investment increases the probability of entry by low cost firms and discourages entry by high cost firms. Thus, infrastructure development is likely to improve economic performance if it reduces transactions costs thereby increasing competition and fostering Schumpeterian “creative destruction.” The present paper is most closely related to Arnold, Javorcik and Mattoo (2011) which uses firm-level data to show that increased foreign participation in services provision led to improvement in manufacturing productivity in the Czech Republic in the period 1998-2003. The current paper studies the more complex and dynamic Indian context with new data on and measures of services reform. Furthermore, while the previous paper considered the services sector as a whole, in the present paper, by separating the liberalization measures into measures for banking, telecommunications, transport and insurance services
regions of risk. There is a debt to GDP ratio where the risk of a default is very low. At the same time, there is a debt to GDP ratio where default is very likely or imminent. Finally, there is a region between the no default and default thresholds where countries are vulnerable to exogenous shocks. These thresholds are useful from a policy dialogue perspective to identify sources of risks and policies to reduce those risks.

All of these measures are used jointly to predict defaults. We benefit from advances in the econometric literature and test several specifications. In general, the predictions from the models do a reasonable job in predicting both in and out-of-sample defaults. Interestingly, the count index has the weakest explanatory power in predicting defaults.

There are obvious caveats to this methodology. As with any model, the outputs depend on the quality of the inputs. In the exercise described above, three significant sources of risk are not entirely captured due to lack of data: the realization of both implicit or explicit guarantees, the uncovering of hidden debts, and the risks associated with systemic shocks that spill over to countries. In cases where these data are available, the methodology can be appended to revise the results.

It should also be emphasized that the outputs of the debt vulnerability methodology are intended to supplement and support country risk assessments and not replace them. They indicate rising or abating vulnerabilities and analyze where the vulnerability build-up occurs. Consequently, the outputs in this paper tie in with expert opinion using DSAs and 26
datasets. An average household is located 14.6 km away from a main road and 7.9 km away from an electric grid.

# **3.2 Demographic and Health Surveys (DHS)**

Demographic and Health Surveys are repeated, cross-section surveys that have been conducted in most developing and middle-income countries since the mid-1980s. These surveys are representative at the national and subnational levels. Information on demographic and socioeconomic status (e.g., age, education, occupation) is collected for all household members. In addition, selected women and men provide information on a wide range of other variables, including matters concerning health status, fertility history, migration, and children. The DHS Program routinely collects geo-reference data (longitude and latitude) in selected surveyed countries, making it possible to combine these surveys with a wide range of external data and information. DHS have been used in recent studies to analyze the economic and social impacts of infrastructure in developing countries (Okoye et al., 2019; Hjort and Poulsen, 2019; Moneke, 2020; Canning et al., 2020; Herrera Dappe and Lebrand, 2021; Lebrand, 2022).

We use data from 73 surveys that cover 29 countries in sub-Saharan Africa. The combined sample includes 900,229 women and 405,142 men.<sup>2</sup> We look at the DHS data in 20km-by-20km grill-cell panels. Table 2 summarizes the characteristics of the individuals in the sample.

We categorize employed individuals as being either high-skilled or low-skilled workers. We also distin-

> 1Details are displayed in Table 2.

> 2The number of observations is greater for women than for men because women are oversampled in the DHS.

9
.3 shows that the “first stage” relationship between our constructed expected dowry measure and actual dowry that the household pays, including state fixed effects, year-of-birth fixed effects, caste fixed effects and their interactions, is highly significant.<sup>23</sup> Figure A.4 shows the scatter plot overlaid with the linear prediction plot without any controls. Moreover, the correlation between expected and actual dowry is positive and significant for all four of our caste categories.

Recall bias in dowry data: Like most survey data, one may be concerned about the recall bias in the reported dowry variable, especially when the year of marriage is too far back in time. To examine the extent of the recall error, we utilize data from the 1999 round of REDS and compare average dowry paid by year of marriage for years that are available in both rounds. Note that the question about dowry was asked differently in both rounds. Nonetheless, Figure A.5 shows that, for the sample period of our savings analysis (1986-2007), the two rounds report similar dowry amounts. Thus, recall bias is unlikely to be a significant issue for our regression estimates. Given

> 20The same issue prevents us from using district-level variation—the average number of marriages in a district-caste-year cell is just four.

> 21Due to missing data, the bulk of which are for ST, we do not have all 1,496 cells.

> 22On average, 100 observations are used to construct expected dowry for each caste-state-year of birth cell.

> 23We cannot include the firstborn girl interactions here because we do not know the sibling composition and birth order of individuals for whom actual dowry information is available. One may also worry that the significant relationship in Table A.3 is driven by outliers. To test if this is the case, we ‘residualized” the fixed effects included in Table A.3 from expected dowry as well as actual dowry and then regressed actual dowry on expected dowry after removing 6 outlier observations—the coefficient
>_∗∗_</sup>|0.317|
|(log)||||(0.139)|(0.198)|(0.248)|
|Metals&minerals||||0.470<sup>_∗∗∗_</sup>|0.0258|-0.0607|
|price (log)||||(0.123)|(0.177)|(0.221)|
|_cons|-2.688<sup>_∗∗∗_</sup>|-1.537<sup>_∗_</sup>|-2.076<sup>_∗∗_</sup>|-2.737<sup>_∗∗∗_</sup>|-1.608<sup>_∗_</sup>|-1.942<sup>_∗_</sup>|
||(0.668)|(0.851)|(1.057)|(0.588)|(0.841)|(1.053)|
|_N_|36|36|36|36|36|36|
|_R_<sup>2</sup>|0.926|0.883|0.815|0.942|0.885|0.816|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 8: VARX (in levels) with one lag of the endogenous variables. The Financial Openness measure is a 3-year moving average of the Chinn-Ito capital account openness index averaged over advanced and emerging countries.

44
restrictive gender attitudes and worse mental health outcomes among adolescents in Bangladesh and Ethiopia, and in Andrew et al. (2022) who find positive impacts of a norms program on mental health outcomes for girls in India after engaging the wider community.

Our paper makes three important contributions. The first contribution is in the development of measures that capture social influences and norms. This aspect of our work is novel because previous work have mostly investigated the effects of gender norms programs using self-reported survey data. A key concern in evaluating the effects of norms programs on self-reported attitudes is that they may reflect some social desirability bias among the treatment group rather than actual changes in attitudes. Using a lab-in-the-field experiment, we try to link responses to observed behaviors to address this issue.<sup>11</sup> While the reference group in our experiment is a randomly-assigned group of adolescent peers, future work could explore social dynamics with alternative reference groups.<sup>12</sup> 8In the experiment we randomly grouped participants into teams of five with different gender compositions (all boys, all girls, and mixed gender groups).

> 9In the experiment each adolescent was privately interviewed and asked if they agreed or disagreed with three gender attitudes statements and play two line-match games where participants were asked to match lines drawn on paper according to their length in private. This private interview was done with an enumerator asking questions that are typical of self-reported survey questions. We subsequently repeated the two exercises in their team setting. Participants were asked to publicly express their gender attitudes and the line choice in their teams, one after the other, where they could deviate from their private answers.

> 10In our analysis, we consider both the influence of responses in the positions before (position effects) and potential peer effects from teammates who attended the gender norms program ( _Choices_ peer effects).

> 11We also collect a short-form version of the Marlowe-Crowne social desirability scale to check our main results are robust to a social desirability bias correction as in Dhar
forest cover change map (Hansen et al., 2013). Tree cover loss can be associated with a habitat disturbance and ecosystem service disruptions, which can make human landscapes more fragile to other impacts (Sodhi et al., 2010). And it can also undermine the livelihoods of poor people highly dependent on forest timber and other resources (Angelsen et al., 2014).

**3) Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter‐annual mean trend of Normalized Difference Vegetation Index based on data from Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellite between 1982 and 2006, which is corrected for climate effects to only measure human‐induced degradation (Vu et al., 2014b). Soil fertility, agricultural productivity and ultimately food security of smallholder farmers can be largely compromised on degraded lands (Von Braun et al., 2013).

**4) Slope** is measured by the area weighted average of slope categories. This variable is calculated based on data from the Harmonized World Soil Database version 1.2 with eight slope classes: 1 for least steep (elevation of 0‐0.5 percent) and 8 for most steep slope (elevation greater than 45 percent).<sup>3</sup> Steep slopes are much more prone to surface water runoff and soil erosion ‐ particularly in areas affected by heavy rainfalls and tree cover loss (Sidle et al., 2006; Vezina et al., 2006). At the same time tropical cyclones can cause fatal landslides, which already result in significant loss of life in mountainous areas of South and East Asia (Petley, 2012, Petley, 2010).

**5) Rainfall variability** is defined as the 1981‐2010 standard deviation of monthly rainfall levels. The variable is constructed from the global CRU TS3.21 dataset from the University
Policy Research Working Paper 9998

# **Abstract**

To date, the impact of foreign direct investment on market power and consumer welfare in developing countries has been relatively understudied. Utilizing a firm survey dataset from Vietnam, this paper first calculates firm-level markups for manufacturing firms and then analyzes the impact of foreign direct investment and foreign ownership on firm markups. Overall, the findings show that increases in the presence of foreign firms in a given industry are associated with decreases in markups in that industry, despite foreign firms individually charging higher markups on average than their domestic competitors. The findings further show that while the markups of both foreign- and domestic-owned private firms tend to decrease with greater foreign direct investment, state-owned enterprises may be relatively insulated from foreign direct investment driven competitive pressures. These results are robust to the inclusion or exclusion of potential outliers and the potential non-random selection of firms acquired by foreign investors.

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 authors may be contacted at rkuo@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 if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
i ’s job is a success in dimension d , and wd are the weights assigned to each dimension.

The proposed JQM sums the successes across dimensions and assumes that dimensions are weighted equally with unity weights to highlight the difference with deprivation indicators. Measuring job quality in this way will reach a job quality score of 0 if a worker has a job that does not meet any of the criteria of success in all dimensions. In contrast, a worker with a job that has successes in all four defined dimensions receives the maximum job quality score of 4.

Whether each dimension should be equally weighted is debatable. In the case of linear aggregation indicators whereby the success within each dimension is based on indicator functions as in the AlkireFoster method, weights take on the sole role of normatively determining the relative importance of each dimension.<sup>17</sup> The crucial issue is how to identify the appropriate weights. There are various weighting options for multidimensional indexes, of which the equal weights approach is the standard practice in the field (Decancq and Lugo 2013; World Bank 2018). Equal weighting can be justified if there are no compelling reasons to consider some dimensions more important than others or if the intention is to minimize intervention (Alkire and Foster 2011; Brandolini and D’Alessio 1998; Mayer and Jencks 1989). In the case of job quality, however, it can be argued that remuneration is the most important dimension. The analysis in this paper therefore conducts sensitivity analysis giving a higher weight to the earnings dimension. Other approaches to the selection of weights are those based on the revealed preferences of the population. Although these data-driven methods would reduce arbitrariness, it would not allow for

> 17 In contrast, other multidimensional indexes such as the Human Development Index do not use indicator functions, and, in addition to weights, the marginal rate of substitution among dimensions may be nonconstant.

11
the national level, for urban and rural areas in the 10 departments of the country, and for the urban area of Port-au-Prince. Although the DHS surveys do not include income or consumption information, it is possible to use the asset data to construct a wealth index, which we employ as a measure of socioeconomic status.

We complement the DHS with information from the 2003 School Census, which contains school-level information for all preschool, primary, and secondary schools in the country and includes information about whether each school is privately or publicly operated, the year the school was opened, and the fees charged.<sup>5</sup> An infrastructure module contains data on access to public services and school facilities. Finally, the census includes characteristics of the teaching personnel: the number of principal and secondary teachers in each grade and the age, gender, and contract status of each teacher. To assess the relationship between school supply and attendance, data on the local availability of schools from the census is matched with household information from the DHS.<sup>6</sup> Households are matched with the aggregated indices of school supply at the level of geographic units defined by urban areas within each _commune_ and (separately) rural areas within each _commune_ .

# **3. Historical Evolution of the Education Sector in Haiti** .

The dominant story in the twentieth century for Haitian education has been the anemic growth of the public school system and the explosive growth of the private school system beginning in the 1960s. Figure 1 shows the population of public and private schools by year from 1930 onwards (based on schools that existed at the time of the 2003 school census), along with estimates of the national population since 1960.<sup>7</sup> Until the early 1960s, most schools in Haiti were public. Thereafter, the share of public schools among all schools declined dramatically, falling to 21 percent in 1980 and just 8 percent in 2003. The number of private schools grew 27fold
|−0.360|−0.401|−0.633|
||(0.002)|(0.002)|(0.002)|(0.001)|
|**Illegal and informal**|−0.120|0.023|−0.162|−0.184|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Observations|2,368,471|2,537,348|2,655,551|3,371,272|
|R-squared|0.091|0.056|0.072|0.072|

_Source:_ Author’s computations based on Economic Census data _. Note:_ Standard errors are in brackets. All coefficients are significant at the 99 percent level.

# **2.4. Formal and Informal Firms Co-exist in the Same Sectors**

At times the reference to formal and informal sectors suggests that firms in each sector produce very different goods. This is not the case in Mexico. Formal and informal firms co-exist in the same six-digit sector, and in fact the overlap between firms in the same sector has been increasing, in parallel to the increase in misallocation. In 1998 informal firms (legal and illegal) were the 11