rafmacalaba/data-use-mentions-extended huggingface dataset:
initial year of the trade relationship t0, similarly to our industry characteristics (ExFj, CapIntj, HumIntj).

The ...nal database contains 71 countries exporting to the USA (see Appendix A). When controlling for all the variables of interest, our sample includes 191,078 observations (see Appendix C for the summary statistics).

# 4 Empirical Strategy

This paper investigates the disciplining forces of external debtholders and foreign product markets with regard to the long-term misallocation of resources. For this reason, we have opted for the empirical framework of survival analysis. This allows us to focus on the long-term sustainability of trade relationships, rather than examining the short-term year-to-year changes in export ‡ows. In our case, the duration of a trade relationship represents the number of years during which country c exports product k to the USA without interruption. In other words, it captures how long a product survives on the highly competitive US market. Ordinary Least Squares (OLS) estimation is not suitable for duration data as the survival times are restricted to be positive and thus have a skewed distribution. Survival analysis allows an examination of the relationship between the distribution of survival times and some covariates of interest. The survival function gives the probability that a trade relationship will survive past time t. Conversely, the hazard rate function, h(t), assesses the instantaneous risk of demise at time t, conditional on survival till that time. Formally, let T � 0, denote the survival time (length) of a trade relationship, with covariates X. The hazard rate, h(t), is thus given by: Alternatively, in discrete time: 14
and Ghazouani, 2007; Falahaty and Hook, 2013). For example, studying the rate of investment growth for 83 firms in five Arab countries during 1996-2001, Bolbol and Omran (2005) find no significant relation between equity issuance and investment growth in a given year. While they find debt growth to be associated with higher capital expenditure, there is no information as to the nature or type of this debt. No study has investigated how and which firms in the Arab world issue equity, bonds, or syndicated loans to raise finance, and how this is related to their growth performance.

So far, the literature has been mostly limited to the use of aggregate data to analyze the level of banking system or stock market development in the Arab region. This paper contributes to the literature by using a large transaction-level data set to present a detailed analysis of how Arab firms have used not only stock markets, but also corporate bond and syndicated loan markets to raise funds over a long period of time (1991-2014). Moreover, using a uniquely matched firm-level data set of real issuances and firm characteristics, we are able to examine how widespread the use of equity, bonds, and syndicated loans by firms in the Arab world is, and how firms that actually issue in these markets evolve compared to non-issuers. While some interesting findings have been documented for several countries (e.g. Didier and Schmukler, 2013; Didier, Levine, and Schmukler, 2015), none have specifically looked at firms in the Arab region.

The remainder of the paper is organized as follows. Section 2 describes the data. Section 3 describes the evolution and main characteristics of equity, bond, and syndicated loan markets in the Arab region. Section 4 examines which firms use these markets, focusing on firm characteristics such as size, growth, debt structure, and profitability. Section 5 studies the dynamics of ex ante and ex post firm performance and the evolution of FSD for issuing and non-issuing firms. Section 6 concludes.

5
How did the border closure affect price volatility across the country? Similarly to the analysis of the impact of the border closure on price differentials, we also examine the patterns of price volatility pre and post closure by geographic region. For this purpose, we construct a rolling measure of price volatility, that consists of the standard deviation of monthly price changes for a given commodity in a given city, over a window of five months (the graphs above, instead, show the standard deviation of monthly price changes for a given commodity in a given city over the whole pre-border closure period, and post-border closure period). Results from tabulating that measure of price volatility across the pre and post border closure periods and by region are presented in Table 3. They confirm that for sorghum, price volatility increased across the board after the border closure. In addition, they show that volatility was on average higher for northern states, although the post-border closure did not affect northern states any differently than it affected the rest of the country. For groundnuts and maize, for example, price volatility increased in northern states after the border closure, but it fell in the rest of the country.<sup>16</sup> For sesame, price volatility increased in northern states but remain unchanged in the rest of the country after the border closure, while for millet, volatility remained relatively stable in northern states and fell in the rest of the country after the border closure. For wheat flour, instead, volatility decreased in northern states, but remained stable in the rest of the country.

Table 3: Price Volatility: Before and After the Border Closure, For Northern and Southern <u>States</u>

|Volatility|Broad<br>Beans|Groundnuts|Maize|Millet|Sesame|Sorghum|Wheat<br>Flour|
|---|---|---|---|---|---|---|---|
|
comparative advantage that mothers have in childcare-specific human capital, we investigate how the gap in the allocation of leave to both parents, that is, the difference between the number of days allocated to mothers versus fathers, relates to women’s labor market outcomes. We look at this primarily in terms of aggregate participation in the labor force, but also consider the gender wage gap, women’s propensity to be employed in managerial positions and in the formal sector, labor force participation rates for women of different education levels, and attitudes to women’s work.

# 3. Data and descriptive findings

# _3.1 Leave data_

The leave data we use in our analysis have been collected and compiled by the World Bank’s _Women, Business and the Law_ (WBL) project. The WBL index measures the legal inequalities that a woman faces as she navigates her career, from the time she enters the workforce through to her retirement. Eight
colony|||-0.543||||||-0.0850|
||||(2.178)||||||(5.575)|
|French colony|||0.281||||||-6.921|
||||(1.438)||||||(4.266)|
|Non-colony|||0.449||||||-1.578|
||||(1.199)||||||(1.957)|
|Landlocked|||-1.294||||||-6.117|
||||(1.635)||||||(7.199)|
|Latitude|||-0.0281||||||0.0248|
||||(0.0370)||||||(0.0910)|
|Observations|350|341|298|350|341|302|350|341|298|
|Countries|105|104|82|105|104|86|105|104|82|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported
this pattern displays noticeable regional variation: While estimated survey rates are fairly aligned with administrative figures in Latin America and the Caribbean (LAC), they systematically exceed administrative reports by large margins in Sub-Saharan Africa.

> 15 Most notably, monotonicity implies that negative reinforcement should not _increase_ the propensity of respondents to report being vaccinated and positive reinforcement should not _decrease_ their propensity. Furthermore, bounding assumes that the “true” survey-based estimate of the vaccination rate, i.e., the estimate absent any demand/social desirability effects, lies within the bounds provided by the estimated vaccination rate under negative and positive reinforcement. This also has implications for how to choose the framing: Framing should be chosen such that the induced demand effect is _just_ stronger (positively as well as negatively) than what is hypothesized to be the case in the original questionnaire design. Inducing a much stronger demand effect leads to demand-robust confidence intervals that are needlessly conservative. In other words: Conditional on inducing a demand effect that is stronger than in the original questionnaire, choosing the question framing that evokes the smallest demand effect is optimal as it leads to the narrowest demand-robust confidence interval within which we expect an experimenter-demand-free survey estimate would lie.

> 16 We use the end of the phone survey data collection period as the time of comparison, which allows for some time lag in administrative data reporting.

16
fixed effects. xx We expect the coefficient of interest, , to be negative if the shock led to a greater drop in reliance on αα θθ σσ ββ 7 See Table A1 in Annex for the list of countries in the sample.

8 We use product level bilateral trade data from WITS (UN Comtrade) reported at the 6-digit level in the HS 1988/92 classification for the period 2004-2018.

9 Results are robust to alternative selection methods. See Table A2 in Annex for results for the auto industry based on exports of final auto and selection based on imports of intermediates.

9
noted earlier, theory predicts that the domestic value-added content of foreign goods plays a different role inside versus outside reciprocal agreements, and accordingly we examine this prediction in the data. Together, these strategies constitute a new approach to examining bilateral trade policy data, which can be used to address many trade policy questions beyond this paper.

Summarizing our results, we first find that higher domestic value added in foreign final goods results in lower applied bilateral tariffs. This result holds across alternative specifications that control for confounding factors using both observable proxies and fixed effects. Consistent with the theory, this liberalizing effect of domestic value added holds for tariffs set under non-reciprocal preference programs, but not for reciprocal tariff preferences. Moreover, the estimated influence of domestic value added on tariffs becomes stronger when we instrument for domestic value-added content and correct for censoring. Second, we find that higher foreign value added in domestic final goods results in lower applied bilateral tariffs. This effect again strengthens when we correct for censoring and holds most strongly inside reciprocal trade agreements, where reciprocity does not neutralize the domestic-price externality.

Finally, we show that bilateral TTB coverage ratios respond to value-added content in much the same way as bilateral applied tariffs. These results both corroborate our findings for tariffs and extend our analysis to include these increasingly important discretionary trade policy instruments. Furthermore, we find the role of domestic value added in foreign production to be strongest for TTB-use against China, where antidumping and other TTBs were most actively deployed during the 1995-2009 period.

In addition to highlighting the role of global supply chains, our empirical results contribute to the existing trade policy literature in several other ways. Our evidence linking the domestic value-added content in foreign production to bilateral tariffs fits into an important literature documenting that terms-of-trade concerns matter for trade policy formulation [Broda, Lim ̃ao and Weinstein (2008), Bagwell and Staiger (2011), Ludema and Mayda (2013), Bown and Crowley (2013)]. To our knowledge, we are the first to demonstrate the relevance of
Policy Research Working Paper 9614

# **Abstract**

Why do more small firms in developing countries not use the market for professional business services like accounting, marketing, and human resource specialists? Two key reasons may be that firms lack information about the availability of these services, and that they struggle to distinguish the quality of good versus bad providers. A brand recognition exercise finds that most small firms are unaware of most providers in this market, and a survey of service providers reveals that they largely rely on word-of-mouth and informal reputation mechanisms for acquiring customers. This study set up a business services marketplace that contains information about the different providers present in the market and used mystery shopper visits to develop a quality ratings system. A randomized experiment with more than 1,000 firms provided access to this marketplace to the treatment group and randomized whether firms received just information or also quality ratings. The provision of quality ratings information shifts small firms’ preferences over which provider they would like to use, increasing the average quality rating of their preferred providers by 0.2 to 0.4 ratings points out of 5. However, neither the provision of information nor these quality ratings had any significant impact on the likelihood that small firms go on to hire a business service provider over the subsequent six months. The results suggest that alleviating information frictions alone is insufficient to increase usage of professional business services.

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 dmckenzie@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
those in Central Java.

Columns 3 and 4 include, as controls, household and village characteristics: the number of household members, household head’s age (years), household income (labor earnings in 2007, not including transfer incomes and non‐labor incomes), male head dummy, total asset values (residential and durable goods in 2007), income loss due to negative shocks during 2008‐2010,<sup>11</sup> asphalt inter‐village road indicator, distance to provincial capital (kilometers) and province dummies. Both household income and total assets are interacted with male dummy. Asphalt inter‐ village road indicator and distance to provincial capital are also interacted.

Both Columns 3 and 4 confirm a negative effect of the interaction of the non‐producer indicator and the price index.<sup>12</sup> The price index itself is again not significant. Both results in quantity and

> 11 There were 19 villages that reported crop failure. The effects of such incidents were included in this variable.

> 12 In this sense, the substitution effect seems to overweigh the income effect among food non‐producers, whereas the balance is not clear among food producers.

13
points.<sup>17</sup> These results are in line with Bloom and Canning (2004), which suggest an increase of 1 to 1.4 percentage points for growth of the working age population over total population.

As suggested by the descriptive statistics, the increase in the share of working age population was also followed by an increase in years of schooling. It might be the case that having a larger share of working age population with additional years of education leads to a higher impact on per capita GDP growth, given that these results are usually driven by younger and better educated cohorts. Table 6 shows that the interaction effect between changes in the share of working age population and years of schooling is positive for first-difference and panel fixed-effects. However, the result does not seem robust under the GMM specification.

**Table 6 Growth of the working-age share of the population can increase real GDP per capita**

||Fist-Differ|ence||Panel Fi|xed-effects||Generaliz|ed Method o|f Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|0.02|0.02|2.18***|‐0.30|‐0.27|1.99***|0.56|‐0.18|2.49***|
||‐0.93|‐0.93|‐0.52|‐0.98|‐0.98|‐0.50|‐1.50|‐1.34|‐0.89|
|||‐||‐<br>2.27*|‐|‐|||
- 8 -

RV-85 and RV-98 tables for males, to the RV-85 table for females. As a result, the rate of mortality improvement is essentially the same for both sexes.

Finally, the third table, referred to as RV-04, is a period table based on Chilean annuitant mortality data collected between 1995 and 2003. The RV-04 is more representative of the Chilean annuitant population than its two predecessors and has recently been adopted for all regulatory purposes. Among several of its positive features, both male and female tables were developed separately, and the representative version of the table passed all the standard consistency tests comfortably.<sup>5</sup> However, these recently constructed tables still have some shortcomings that need to be considered. Because there are fewer annuitants at older ages, data was included from the previous scheme. This implies that the mortality rates at older ages may not be as representative of annuitants as the earlier ages, and rather reflect the mortality of retirees under the old system. Rates were updated to 2004 using the national statistical agency’s assumed age-specific improvement rates for the population. The RV-04 table was selected for the computation of MWRs because it is the most representative of the current annuity population. The table was adjusted to the relevant year of issue of each annuity using the same approach adopted in the official table and the same rates.

As shown in Table 2, there are significant differences in the shape of the three mortality tables. Mortality rates in the RV-98 and RV-04 are systematically and substantially lower than those in the RV-85. Male mortality rates in the RV-04 are lower than those in RV98 for intermediate ages, but higher at some younger and older ages. Female mortality rates, however, are substantially lower in the RV-04. As noted above, the earlier (RV-85 and RV-98) tables for females are constructed more subjectively than the RV-04 tables for both sexes and the RV-98 table for males. Whilst the shape and level of the mortality tables is still a matter for some debate in Chile
s SQL server from which the inventory of loan disbursements were directly exported. For overdue repayments, scheduled and actual dates of

> 11 We acknowledge that loan officers are not the only influence on mission drift. But to the extent that they have little or no influence on the committee’s decisions, this biases our analysis against finding any significant results, and we find significant associations between loan officers’ backgrounds and the size of their loans in the regressions that follow.

11
## **Item Nonresponse**

Another challenge in estimating total factor productivity is dealing with item nonresponse: i.e., sampled firms do not answer specific questions of the survey. For example, respondents may answer the employment question but not sales. To reduce item non-response, the Enterprise Surveys team follows a strict quality control process that identifies non-responses and contacts firms to complete the data.

Despite this effort, like many other firm-level datasets, the ES also suffers from item nonresponse in variables needed to calculate TFPR. One way to handle the item nonresponse is through imputation. For example, in the U.S. Census Bureau’s 2007 manufacturing data 73% of observations have imputed data for at least one variable used to compute TFPR (White et al. 2018). While item non-response may be consequential for most analysis, we do not attempt to address it in the data. We do not employ any of the available imputation or re-weighting methods that assume that data “missingness” is not ignorable and is related to underlying firm characteristics (Little & Rubin 2019). Additionally, the survey (probability) weights included in the data are agnostic to item non-response and the missingness of productivity estimates: weights are not re-adjusted or scaled to account for this missingness.

We exclude observations missing any one of the main production function variables (i.e., sales, labor, capital, or materials). Additionally, note that in cases of negative value-added, logarithms cannot be defined, and thus these observations are not included. This leaves 50,754 observations in 134 countries .

# **3 Estimating Firm-Level Total Factor Productivity**

We begin with a Cobb-Douglas production function (1) for ease of exposition. Throughout this paper, all lowercase variables denote the natural logarithms of the corresponding uppercase variables, representing raw, level values.

Where firm-level value-added ( _vai_ ) is a function of inputs of capital ( _ki_ ), and labor ( _li_ ). Firms’ (logged) TFP is
# **– 4.1.2 PNAD Brazil**

The Brazilian data come from the Pesquisa Nacional por Amostra de Domic ́ılios (PNAD), an annual Brazilian household survey that samples households across (almost) the entire country.<sup>10</sup> It collects information on various household characteristics, as well as individual incomes and education levels. We use PNAD data for 1995 and 2007. For comparability, we use the same four variables as for the CPS: real hourly labor earnings, age, gender, and a categorical educational attainment variable. IBGE, the Brazilian Statistics Bureau that is responsible for running PNAD, started including the rural Northern region in the PNAD sample after 2004 but, for comparability across years, we do not use information on the rural North for 2007.

As for the US, we restrict the sample to individuals aged 16 to 65 that reported a positive labor income in the previous year. Individuals with missing values for income or any of our three covariates were excluded from the sample. The top and bottom 0.5% of the distribution of hourly wages in each year was trimmed, as in the CPS. Hourly wages are in Brazilian reais (BRL) of September 2007, which means that nominal values in 1995 were multiplied by 2.89 to be expressed in 2007 prices. The final sample contains a total of 275,749 observations. Summary statistics are presented in Table 2.

– Table 2: Summary Statistics Brazil

||Mean|S.D.|Min.|Max.|Observations|
|---|---|---|---|---|---|
|PNAD 1995||||||
|Hourly Work Earnings|5.565|7.312|0.300|61.317|119,770|
|Age|34.924|11.936|16|65|119,770|
|Male|0.