rafmacalaba/data-use-mentions-extended huggingface dataset:
our new database.

**Why focus on the World Bank IDS?** The World Bank’s International Debt Statistics is uniquely suited for the purpose of our measurement approach. First, it is the most widely used source for developing and emerging market country external debt data and frequently cited by researchers, investors, rating agencies and market commentators. Reporting is mandatory for all countries with outstanding liabilities to the World Bank and debtor countries that violate their reporting obligations risk losing eligibility for financial support (World Bank, 2017). These conditions ensure high coverage across countries and time.

Second, the IDS has numerous desirable features that facilitate the interpretation of debt data revisions as a measure of hidden debt:

- The International Debt Statistics and its predecessor publications are based on direct debtor reporting. While the World Bank compiles the annual statistical report and performs consistency checks, all underlying data on debt instruments are reported by national debt management offices. Omissions and revisions of debt data can therefore be traced back to the reporting decisions of sovereign debtor countries.<sup>13</sup> An upward

> 13As explained in greater detail in Appendix Section A.2, the World Bank occasionally provides debt estimates if a reporting country does not fulfill its reporting obligations, but we exclude these estimates from our analysis.

8
_- -_ 45

# Methodological Note

# Regional country groupings are consistent with that of the World Bank. Accordingly, reference to:

Af*ica encompasses only sub-Saharan Africa.

_Asia_ encompasses South East Asia, excluding Japan. Note that Korea. although waiting for ratification of ts membership in the OECD, is included in Asia for the purpose of this analysis.

_ECA_ Region encompasses nations of Eastern Europe and Central Asia. Note that the Czech Republic. Hungary. Poland and Turkey. aithough members of the OECD. are included in ECA for the ouroose of this analysis.

_Laffn America_ **_and The_** Canibbean encompasses the following nations of North, Central and South America. Note that Mexico. although a member of the OECD. for the purpose of this analysis is included in the Latin America and the Caribbean grouping.

_Middle East and North_ Africa region encompasses selected Arab countries of North Africa and the Near East, in which the bank is active.

**_OECD 0egion_** encompasses member countries of the Organization for Economic Cooperation and Development, except for Turkey (included, for this analysis, in the ECA region), Mexico (included, likewise, in the LAC region), Luxembourg and Iceland (for which the requisite data are not available). Specifically, it includes: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.

# Sources of Data

_Governments:_ through statistical yearbooks, yearly budget documents, personnel ministries or agencies, inquiries to embassy personnel.

The World Bank alone has undertaken 86 lending operations with civil service/public administration components, in the context of stand-alone operations, as well as studies and analyses for various other types of Bank documents, such as Public Expenditure Reviews and Country Economic Memoranda. Data have also been collected by various Resident Missions, at the request of the authors.

The Intemational Monetary
|
|**Country FE**|Yes|Yes|Yes|Yes|
|**Year FE**|Yes|Yes|Yes|Yes|
|**Observations**|8,972|8,119|8,972|8,119|
|**Number of countries**|104|104|104|104|

Note: This table reports the results of the estimation of Equation (1) and (2). The dependent variable is the innovation score, as described in Table 1. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

We run several robustness checks to test the general validity of the results. To mitigate reverse causality issues,<sup>7</sup> we follow Caggese (2019) and restrict the analysis to firms that are not likely to be financially distressed. Because data in the Enterprise Survey is limited, we can consider just firms that are above the median value of real annual sales scaled by the total number of employees. Results in Table 6 shows that funding by NBFI remains weakly related to both the extent of firm innovation and a firm decision to innovate, allaying concerns that the overall strength of the relationship is related to the success of firms to get funding in the first place. Results for firm innovation adoption are also in line with the estimates in Tables 3 and 4.

7 Debt usage may correlate with future investment in innovation because firms may borrow in anticipation of investment in innovation, rather than borrowing to invest in current innovative projects.

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# **REGULATION AND MACROECONOMIC PERFORMANCE**<sup>*****</sup>

Norman V. Loayza Ana María Oviedo Luis Servén _The World Bank University of Maryland The World Bank_

## September 2004

### **Abstract**

Regulation is purportedly enacted to serve specific social purposes. In reality, however, it follows a more complex political economy process, where legitimate social goals are mixed with the objectives of particular interest groups. Whatever its justification and objectives, regulation can have potentially significant macroeconomic consequences, by helping or hampering the dynamics of economic restructuring and resource reallocation that underlie the growth process. This paper provides an empirical analysis of the macroeconomic impact of regulation. It first characterizes the stylized facts on regulation across the world, using a set of newly constructed, comprehensive indicators of regulation in a large number of countries in the 1990s. Using these indicators, the paper studies the effects of regulation on economic growth and macroeconomic volatility employing cross-country regression analysis. In particular, the paper considers whether the effects of regulation are affected by the country's level of institutional development. Finally, the analysis controls for the likely endogeneity of regulation with respect to macroeconomic performance. The paper concludes that a heavier regulatory burden reduces growth and increases volatility, although these effects are smaller the higher the quality of the overall institutional framework.

**_JEL_** _classification:_ H11, K20, K30, E32, O40.

_Keywords:_ Regulation, government structure, volatility, economic growth.

> * This research has been supported by the World Bank’s Latin America Regional Studies Program and the Poverty Reduction and Economic Management Network.  We are very grateful to Raphael Bergoeing, Caroline Freund, John Haltiwanger, Patricia Macchi, Andrea Repetto, Roberto Zagha, and participants in the EGDI-WIDER Conference on the Informal Sector (Helsinki, 2004) for useful comments and data.  The views expressed herein are those of the authors and do not necessarily represent those of the institutions to which they are affiliated.
# **_1. Introduction_**

Opening to trade can boost productivity growth through numerous channels. The literature identifies two main groups of mechanisms through which trade can affect productivity: i) those that induce changes _within firms_ and hence impact firm level productivity; and ii) mechanisms that trigger _intra-industry reallocation_ of resources towards more productive firms, thereby increasing aggregate productivity. Improvements in firm level productivity caused by within firm changes can be triggered by exposure to competition stemming from output tariff reduction (the so-called import competition channel) and are associated with (observable) actions, for example, as investment in new technologies, increase in R&D, and entry in export markets.<sup>2</sup> Within firm improvements in productivity can be also triggered by reduction of input tariffs - which allows firms to access better quality, higher variety and lower priced inputs (the so-called input channel) - or by reallocation of firm product mix towards more profitable products.<sup>3</sup> Trade liberalization can affect aggregate productivity as well, as trade shocks are expected to reshuffle market shares towards more efficient firms,<sup>4</sup> in this case, the magnitude of this reallocation process depends on how dispersed productivity was prior to trade liberalization shocks.

Empirical evidence for Brazil has shown that the unilateral trade liberalization episode of the late 1980s - early 1990s has brought positive payoffs to productivity. Using sector level data for the 1985/97 period, Rossi Jr and Cavalcanti Ferreira (1999) show that a 10 percent reduction of import tariffs is associated with an increase in labor productivity growth of 0.88 percent per year and of TFP growth by 3.3 percent per year at the sectoral level. Muendler (2004) uses firm level data for the manufacturing industry in the 1986-1998 period to analyze how the reduction of inward trade barriers between 1990 and 1993 affected productivity and finds evidence that foreign competition pressures firms to raise productivity markedly, whereas the use of foreign inputs plays a minor
firm-level data on inputs, outputs, and their patents registered from Japan, the USA, and European countries, Aldieri et al. (2020) show investment in environmental or clean technologies or knowledge, measured in terms of registered patents, affect firms’ productivity significantly and positively. Using firm-level panel data of Dutch manufacturing firms over the 2000-2008 period, van Leeuwen and Mohnen (2017) find a positive

> 9 Generally, product innovation includes new products, process innovation includes new technologies and organizational innovation includes new business practices (OECD, 2005).

14
, as captured in the model of Heath and Tan (2016). Additionally, allowing for the husband to choose his labor supply could lead to insignificant impacts on total labor income, to the extent that husbands reduce their labor in response to increase in their wives’ labor. While the IHPS labor data are quite rich, they are not sufficiently exhaustive to directly test for the impacts of control over income on days of labor supplied across the number of labor categories observed in the data set.

# **6.  Concluding Comments**

This paper began by reviewing the theoretical and empirical developments in the women’s empowerment literature, and its influence on donor policy. Though Boserup was concerned with potential development pathways that could lead to both reduced women’s welfare as well as inefficient production and slower development, the emphasis in donor policy as well as empirical research has been overwhelmingly on promoting and evaluating women’s empowerment in the allocation of resources, with little attention to production inefficiencies and slower development. This emphasis makes sense for two reasons. First, the equitable access to resources inherent in the women’s empowerment strategies is an important basic human right to support in and of itself.

19
research efforts to assess the impact of digital technologies and AI on occupations, utilizing the US-based O*NET database (e.g., Autor and Handel 2013). The O*NET database has most often been used for analysis of the labor market of the United States and other high-income countries. Originally developed by the US Department of Labor, the database provides detailed information on a wide range of occupations, including job duties, required skills, educational qualifications and labor market trends. The primary goal of the database is to help individuals make informed decisions about career paths based on their skills and interests. It does this by providing a detailed description of current occupations and the tasks and skills required to perform them. It is updated regularly, and the job definitions have been used in research to provide insights into job trend analysis and workforce development.

One of the most prevalent indicators utilized in AI-related analysis is the "Artificial Intelligence Occupation Exposure" (AIOE) index developed by Felten, Raj, and Seanmans (2021). This index is predicated on the O*NET classification of occupations and defines AI exposure by linking the human tasks in an occupation with AI applications, continuing the work by Frey and Osborne (2017) who focused on computerization. Pizzinelli et al. (2023) further developed this index by incorporating additional information from the O*NET database. This included the social, ethical, and physical context of occupations, as well as the required skill level. Another similar research 5
In terms of poverty reduction, the effect of the fiscal system in Brazil is less favorable (Figure 18). Although it is above average, the reduction is low, and even other countries in the region, such as Argentina, Ecuador, Costa Rica, and the Dominican Republic, achieve better results. A common factor Brazil has with such countries is that poverty reduction comes from direct transfers. As mentioned above, Brazil has the highest amount of direct transfers as a percentage of GDP. When moving to consumable income and incorporating the effects of indirect taxes and subsidies, poverty rises in almost all of them, except Ecuador, where subsidies exceed the effect of indirect taxes.

**Figure 18. Poverty reduction, upper‐middle income countries (percentage points)**

<!-- Start of picture text -->
-8.0<br>-6.0<br>-4.0<br>Brazil (2019), -1.6<br>-2.0<br>0.0<br>2.0<br>4.0<br>6.0 Brazil (2009), 3.7<br>8.0<br>10.0<br>income  (p.p.)<br>Poverty reduction from Market Income plus pensions to consumble<br><!-- End of picture text -->

Source: CEQ Data center. Notes: international comparisons are made using the World Bank international poverty line of USD 5.5 per person per day in purchasing power parity (PPP) 2011.

The results presented here suggest that fiscal policies in Brazil have improved over time in terms of their impacts on poverty and inequality reduction. In 2009, the difference in Gini between MIPP and consumable income was 3.1 compared with 6.4 in 2019. Moreover, the impacts on poverty have gone from being welfare decreasing (i.e. fiscal policies increased poverty in
|0.183***|0.144*|0.084|0.112|0.083|
||(0.069)|(0.079)|(0.087)|(0.083)|(0.098)|
|_N_|9870|9870|9870|9870|9870|
|_R_<sup>2</sup>|0.818|0.803|0.679|0.657|0.529|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Robust standard errors in parenthesis. All regressions include household and zone-year fixed effects, and log rainfall. All dependent variables are measured as inverse hyperbolic sine (IHS) transformation of hours per year to deal with zero values. The share of labor allocated to coffee is defined as the ratio of labor supply on coffee fields to one _plus_ labor supply on all fields, to account for zero values. Male labor supply includes labor supply by all male members above 13 years of age (including the husband if any). Female labor supply includes labor supply by all female household members above 13 years of age (including the spouse if any). Children are defined as household members who are _≤_ 13 years of age. The labor supply measure includes the sum of planting and harvesting labor applied on a field. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Table 5: Off-farm labor supply and income

||Worked|Number of|PSNP|Worked as|Number of|Laborer|
|---|---|---|---|
. Incentives for project team leaders are to select indicators which projects have a strong chance of achieving. Project documents often imply that projects will shape these outcomes (e.g., “CDD can help reduce fragility”) but teams, understandably, do not want to be held accountable for achieving these aims. But these imperatives have worked against our understanding of how external actors can support processes such as institution building which are key for development.

Moving forward, it is important to unpack these broad concepts and to map project designs to a theory of change with clearly defined outcomes. Project documents often implicitly claim they will affect social outcomes but do not measure these outcomes and are not always designed in ways that would logically allow them to be achieved. There is a need to be realistic about what projects can achieve and to map interventions more clearly to outcomes _._ In parallel, it is important for evaluators to pay attention to actual project investments and not to test performance against outcomes that projects have never been designed to achieve. By extension, it is also entirely plausible that unanticipated positive and negative outcomes may be associated a given CDD intervention, which qualitative approaches may be best suited to discerning (see Rao et al 2017).

It is equally important to identify measurable indicators for these outcomes and to use appropriate methods to measure the indicators. Some of these lend themselves to quantitative measures, but some may not (and, in most cases, the use of multiple methods will be suitable to triangulate and better understand survey data). Not all outcomes will have simple indicators that can be included in project results frameworks.

23
How much are poor customers penalized by fixed charges and block structures? As noted above, over 50 percent of the sample countries have incorporated fixed charges in their tariff schemes. Fixed charges in African countries range from US$0.43 cents to US$5.00 per month (figure 23a). Furthermore, these charges constitute a large portion of the aggregate monthly bill particularly at subsistence levels of consumption (figure 23b). At 50 kWh/month—subsistence consumption—the fixed charge represents more than 40 percent of the monthly bill in over 30 percent of the countries. At higher levels of consumption—75kWh/month and 100 kWh/month—the weight of the fixed charge is less prominent. This indicates the disproportionate weight of the fixed charge in the bills of households consuming at the subsistence level.

<!-- Start of picture text --> Figure 23. Fixed portion of residential tariffs<br>a. Residential fixed charge per month ($) b. Fixed charge as a share of monthly bill (%)<br>Niger<br>Nigeria 70%<br>Ghana<br>Côte d'Ivoire 60%<br>Ethiopia 50%<br>Malawi<br>Uganda 40%<br>Burkina Faso<br>30%<br>Zambia<br>Tanzania 20%<br>Botswana<br>Kenya 10%<br>Mozambique 0%<br>Madagascar<br>Cameroon 50 kWh/mo 75 kWh/mo 100 kWh/mo<br>Congo, Rep.<br>1%-20% 21%-40% >40%<br>- 1 2 3 4 5<br>Percentage of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

# **e. Are there any other kinds of hidden subsidies?**

Besides tariffs, there are also other less
consequently, spans between 2002 and 2010. Data from 2002 to 2005 comes from the _Encuesta Continua de Hogares_ and the _Gran Encuesta Integrada de Hogares_ from 2006 to 2010. Both surveys are comparable across time, but the later introduced improvements such as new questions and a sharp increase in the number of municipalities surveyed. From 2002 to 2005 we can only identify the exact location of workers located in the 13 main cities of Colombia (i.e., 13 municipalities), but beginning in 2006 we observe their locations in 609 municipalities.

We estimate a linear probability model for the probability of being employed in these sectors on IDP inflows including fixed effects by year, municipality, month (when the survey was collected), individuals covariates (such as gender, marital status, education level, and household size), and municipal controls (including all the controls included in our previous estimates). The results are presented in Table XII and largely suggest that the probability of begin employed in a highly informal sector increases with higher IDP inflows. Additionally, the effects of IDP inflows on the employment probability within highly formal sectors are not significant and even has a negative sign. Particularly, our estimates in Panel C and column 2 suggest that when the share of IDP increases in 1 percent, the probability of being employed in a highly informal sector increases by 0.2%.

Considering that the labor force surveys are only available between 2002 and 2010 and that they only include data for a handful of municipalities, we also re-estimate all of our previous analysis restricting the sample in the manufacturing surveys in two ways. First, by restricting the sample period (to either 1995-2010 and 2002-2010) and second, by restricting the municipalities to only include those available in the manufacturing census and the labor force surveys. Our results are presented in Appendix II and are consistent with our previous results.

# **VII Discussion**

This paper investigates the effects of inflows of IDP on the behavior of firms in an economy segmented into a normal-sized
<0.05,**p|rformance<br><0.01, *** p<0.001|0.161|‐0.925|1.086**|<br>0.40|0.007|56.4|17.8|
|60.0|**Figure 1:**<br>**Ja**|**Differenc**<br>**lanidhi a**|**es in Pe**<br>**nd KW**|**rcentile**<br>**A-BM S**|**Ranks**<br>**cheme**|**between**<br>**s**|||
|40.0<br>50.0|35.8|38|.8|||354|38.6||
|‐40.0<br>‐30.0<br>‐20.0<br>‐10.0<br>0.0<br>10.0<br>20.0<br>30.0<br><br>Difference in Percentile Rank|Availability &<br>Reliability Index|Household<br>Satisfaction In|dex<br>House<br>Serv|‐12.0<br>hold Cost of<br>ice Index|Operati<br>Maintenan|.<br>on &<br>ce Index<br>Perf|Overall<br>ormance Index||

PSM comparisons of Jalanidhi and other community managed schemes show no significant differences in performances in terms of availability and Reliability index, household satisfaction index, and household cost of service index (Table 14). The Jalanidhi schemes perform significantly better than the other community managed schemes in the O&M index, institutional sustainability index and overall performance index
and one agent can now act on his/her fertility preferences. In four of our five study countries, men desire more children than women (Appendix Table A1). We could therefore expect that an increase in female profits could translate into a boost in her bargaining power, thereby allowing her to act on her preferences and reduce fertility.

Using microdata from 48 developing countries, Vogl (2016) finds substantial variation in the relationship between economic status (proxied through asset ownership) and cumulative fertility across regions and over time. Though the relationship tends to be negatively-sloped in later periods, for African women in earlier periods—circa 1990—he finds either an increasing or hump-shaped relationship. Chatterjee and Vogl (2018) expand this analysis to study the interaction between fertility and growth since 1950, combining microeconomic data on the birth histories of 2.3 million women from 255 household surveys with macroeconomic data on economic growth. Their findings indicate that fertility responses to economic fluctuations vary depending on the duration of the economic shift and the life cycle stage at which it occurs. Specifically, fertility is procyclical with annual fluctuations, particularly among women aged 20-34 and those with lower education levels. Short-term fertility elasticity seems to be driven by economic downturns, suggesting the incapacity of poor households to smooth their income and execute their fertility plan. However, when there is sustained economic growth spanning over two decades, fertility declines and the timing of childbirth is delayed. Consistently, prior research indicates that economic crises typically correlate with a decline in fertility rates in developing countries (Council et al., 1993; Tapinos et al., 1997; Lindstrom and Berhanu, 1999; Adsera and Menendez, 2011).

Lastly, the extensive literature on cash transfers in developing countries has found either no impact or a negative impact on fertility (Baird et al., 2011; McQueston et al., 2013; Rosenberg et al., 2015; Khan et al., 2016; Palermo and Hjelm, 2016; Khan et al., 2016;