rafmacalaba/data-use-mentions-extended huggingface dataset:
classic Harris-Todaro model) is misleading. In fact, according to LFS 2002, the number of paid jobs located in the rural areas is just 50 percent of number of economically active residents, while this ratio is 95 percent in Riga and 79 percent in other cities (see also Appendix Table 8). In other words, employment possibilities are positively rather than negatively correlated with wages.<sup>4</sup> In Riga, where employment prospects are the best, the LFS-based unemployment rate is below average, and the registered unemployment rate is lower than elsewhere (see Figure 13 and Appendix Table 9).

Commuting helps to reduce disparities (Hazans 2004a). Commuting is the way in which the population of rural areas and small cities deals with geographical disparities in wages and unemployment. Appendix Table 10 describes commuting flows between rural and urban areas in Latvia and Lithuania in year 2000. One-third of full-time employees living in Latvian countryside had their jobs in cities (this proportion increased to 40 percent in 2002). More than 40 percent of employees living in small cities near Riga, and about 10 percent of those living in other cities, worked in Riga. Commuters enjoy significant earnings gains. According to calculations using the 2000 LFS, a rural resident commuting a distance of 50 km obtains a 53 percent ceteris paribus gain compared to non-commuters. A resident of a city outside Riga obtains a 35 percent gain (see Hazans 2003a, 2004a).

By integrating local labor markets, commuting works to reduce the overall wage gap and disparities in employment prospects between Riga and rural areas, as well as between Riga and other cities. By shifting labor from the countryside to cities, commuting, at least in the short run, increases national output (see Hazans 2004a).

> 4  Consistent with the _wage curve_ model (Blanchflower and Oswald (1994)), especially relevant in the context of inter-regional (rather than urban-rural) disparities; see section 4 for estimates of Latvian wage curve.

17
|23|7|
|Estonia|17,595|14,540|79|88|26|32|17|22|
|Slovak Rep.|15,827|17,883|67|60|36|78|36|49|
|Croatia|14,480|13,539|59|62|56|75|124|89|
|Hungary|13,629|13,933|73|61|38|63|66|54|

1: Percentile Rank (0 lowest rank, 100 highest rank). Source: The World Bank; World Development Indicators, Worldwide Governance Indicators, Global Competitiveness Dataset, Doing Business Reports.

_<u>Poor business environment, large productivity dispersion.</u>_ Another structural factor explains why GPS’s business environments need to be improved for growth: they have a large share of small, low productivity firms. The average size of firms is _smaller_ than in other European countries. Over 90% of firms have less than 9 employees (micro firms) and more than half of the workers are employed in firms with fewer than 50 employees (comparable numbers are 23% of workers in Germany, and 32% in France). The smaller firms are less productive and more labor intensive; moreover, more than 85% of total exports in European countries are from the largest 5,000 companies (European Economy, 2015).<sup>77</sup> Obstacles, such

> 76 Note that some of these indicators being “subjective” partly reflects worsening of perceptions than worsening on the ground.

> 77 See also Freund (2016) for the relationship between firm size and exports.

35
consistency between the micro data and the CGE results; however, by combining the two, it maps aggregate results from the CGE to the detailed information available in the household survey and provides a much more nuanced and useful analysis of poverty impact. This approach also allows decomposing the total effect on poverty into an aggregate income growth component and a redistribution component.

A useful insight from this analysis is that even if the total impact on poverty is not too large, its dispersion across households – due to their heterogeneity of factor endowments, inputs use, commodity production, and consumption preferences – is significant and this should help in designing compensatory policies. Additionally, our growth and redistribution decomposition shows that, at least in the short to medium run, redistribution can be as important as growth.

The paper is structured as follows: section 2 presents the CGE model, the micro module and the relevant data. The first part of section 3 describes the general equilibrium results of the trade policy shocks, and the second part the poverty implications. The final section presents some conclusions.

# **2 Measuring the effects of trade reforms on poverty: linking a CGE model to household surveys**

This section describes the main features of the CGE model and household survey micro-simulation module.

## **_<u>The Nicaragua general equilibrium model and its data</u>_**

A 2000 Social Accounting Matrix (SAM) represents the initial benchmark equilibrium for the CGE model. This SAM, which includes 39 sectors, 39 commodities, 3 factors (skilled and unskilled labor and one composite capital), an aggregate household account, and other accounts (government, savings and investment, and the Rest of the World), has been assembled from various sources incorporating data from the 2000 Input-Output Table and the 2001 Living Standards Measurement Survey (LSMS). As 3
number of potential beneficiaries is 22,546. In the sample of interest, we observe a total of 294 municipalities. Of these, 124 are only observed in one round, 26 in two, 77 in three, and 67 in all four rounds. Most of the municipalities that are only observed once appear in 2011, while only one municipality that is sampled in 2012 is not observed in 2013 and vice-versa. The total number of municipalities sampled each year is 117 in 2010 (of which only 15 are never repeated in the following years), 218 in 2011, and 170 in both 2012 and 2013.

The treatment variable is binary, indicating whether or not any member in the household receives the Colombia Mayor benefit. The ENCV captures information on the receipt of government benefits only among households. This makes it impossible to determine who among the various potential beneficiaries in the same household receives the benefit. The instrumental variables (IVs) are described below. Table 8 illustrates the full set of control variables. In addition, all specifications include yearand municipality-specific fixed effects, which are omitted from the table. The first five variables (other urban, rural, age, Sisben score, and woman) are included in all specifications, while the remainder are additional control variables. The former directly determine treatment eligibility; their omission would thus result in a clear omitted variable bias.

**Table 8: List of Included Control Variables**

|_Control variable_|_Description_|
|---|---|
|Other urban<br>Rural|Categorical variables indicating two of the three geographical areas that<br>define eligibility for the Colombia Mayor benefit. The omitted category is<br>residence in one of the 14 major cities.|
|Age|Age inyears|
|Sisben|The household’s Sisben III score|
|Woman|A binary variable indicating gender. It is omitted from estimations by<br>gender.|
|Widowed<br>Divorced<br>Single|Categorical
Figure 3: Share of household heads working in agriculture across urban versus rural areas <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of household heads working in agriculture (%) Share of household heads working in agriculture (%) Share of household heads working in agriculture (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the share of household heads working in agriculture over different geographic areas in 16 SSA countries. WorldPop 250m is used for the DOU and DB methods.

poverty line considered ($2.15 or $3.65) or the method of urban delineation (official, DOU or DB) used. When using the extreme poverty line of $2.15 (Panel A), urban poverty rates are generally lower than rural poverty rates when urban areas are defined based on each country’s official definition. With the DOU approach, poverty rates are lowest in urban centers, followed by urban clusters and rural areas. In the case of the DB approach, poverty rates are lowest in urban cores, followed by suburbs, towns, and other rural areas. These patterns hold even when a higher poverty line is used (Panel B). This negative correlation between density and poverty is observed in almost all countries, as shown in Figure A4 in Appendix A.

It is interesting to note that poverty rates in denser urban areas, such as urban centers in the DOU approach and urban cores in the DB approach, are significantly lower than in other types of area. On the other hand, less dense urban areas, like urban clusters in the DOU
Do the parties rely on legal mechanisms to enforce their contracts or on alternative enforcement devices? If a contract dispute arises, does the contract stipulate an arbitration mechanism? Does the contract require "hostages" for deterring opportunistic behavior, such as collateral, and down payments? What devices do the parties involved in the exchange employ to economize on transaction costs? Are organizational forms and the scope of the firm affected by transaction costs considerations-for instance, because of contract enforcement problem, are family firms over-represented? Does the firm become more vertically-integrated to reduce contracting costs?

Bank surveys have collected a significant amount of data on these issues, especially in recent years. Four major surveys-RPED, ICS, Bosnia, and _Survey of Russian Enterprises-all_ gather detailed information on the type of relationships firms had and the devices used to regulate transactions. Together, these surveys yield a rich picture of the enviromnent in which firms operate. The following extracts from existing bank surveys may serve as nice recipes for future surveys on transaction environments:

- The nature of contract: Do contract specifications cover price, methods of adjusting price to rate of inflation, quantity, quality, delivery date, method of payment, warranty, penalty for late payment, penalties for breach of contract, and the method of resolution if a dispute arises? (RPED Survey and the survey of Russian Enterprises).

- The contractual relations with suppliers, with subcontractors, and with clients: ownership, payment method, frequency of purchase, relationship with the firm, whether the other party comes from the same ethnic group, whether there is a trade discount, whether there is subcontracting, the amount of credit purchase, interest, guarantee to supplier (none, physical collateral, third-party checks, witnesses, signed invoices, group guarantees); whether using written contracts more frequently with new trading partners than old trading partners; whether using written contracts more frequently with new private enterprises than other new trading partners? (The survey of Russian Enterprises).

- Informal lending: the amount, maturity, the
economic outcomes between the two surveys.

We explain this argument formally. In an ideal scenario in which we would have had data before the peace agreement, let σ denote the short-run impact of the demobilization program, that is, the one-year impact between the reception of the allowances and the QUIBB survey. Similarly, λ denotes the measure of the long-run impact of the demobilization program, that is, the five-year impact between the reception of the allowances and the 2010 panel survey. Unfortunately, we are not able to measure σ and λ separately. However, we can capture the difference η = λ − σ, which can be thought of as the long-run evolution of the economic outcomes of CNDD+ ex-combatants. Therefore, in equation (1), the coefficient η1 measures the evolution of outcomes for _returnee_ CNDD+ households that did not benefit from the DDR, and the coefficient η2 measures the differential evolution for CNDD+ _demobilized_ households.<sup>20</sup> We recommend caution when comparing the impacts of the DDR on FNL and CNDD+ demobilized

> 19Some of the returnees have benefited from the FBU 100,000 (US$ 91) allocated to “Gardiens de la Paix”, “Militants Combatants” and “Adultes Associés”. We do not have access to registers to verify which households benefited from these reduced grants. Our empirical analysis will therefore slightly underestimate the effect of the demobilization program.

> 20In table S.5 in appendix, we show descriptive statistics disaggregated by demobilization status and faction along with seven joint tests of equality of means between the nondemobilized households and the civilians, and the nondemobilized and demobilized within and between faction. The statistics related to the demobilized are computed considering the second definition in table 2.

15
more willingly adopt slum clearance policies. Data on slum shares are only available for the most recent years (United Nations, 2020b). If we use the cross-sectional version of the long-difference model (thus not controlling for slums c. 1960), we find smaller slum shares in MFGSERV and NRXGDP countries when not controlling for urban economic development ((7)). Consistent with the results on tall building construction, when MFGSERV or NRXGDP countries develop and urbanize, whether due to industrialization broadly defined or resource windfalls, their urban sector also develops, which leads to some forms of formalization. But if we control for urban economic development ((8)), we find no correlations between the slum share and the variables of interest. Therefore, the slum share is not higher in NRXGDP countries. As a result, if any crowding takes places

> 33These results are not shown, but are available upon request.

> 34From (United Nations, 2020c) we know the GDP share of construction over time. We then obtain GDP using data from Bolt and van Zanden (2014), Gollin et al. (2016) and World Bank (2021).
with innovation and adoption of other non-automation technologies in industries with high exposure to robots. In that case, our estimates would be biased.<sup>12</sup> To mitigate such concerns, we exploit 2006 census year information on R&D units, product and process innovation, use of computers and the Internet to construct an index of “innovation intensity”. Then we interact the index with year effects. To the extent that innovation activity and IT use are correlated to plants’ propensity to adopt non-automation technologies, this approach should allow us to purge the estimates from their impact.

# **2.5 Is the Plant-Level Approach Solid?**

This section presents several pieces of evidence on the robustness of the key assumption underlying equation (3), i.e. that secondary education is a good proxy for routine taskintensive employment. If that is the case, plants with a larger share of workers with secondary education should be more likely to adopt robots.

First, we check whether the industries with an initial large share of secondary education workers have adopted relatively more robots in subsequent years. Figure 3 shows that this is indeed the case, thus providing some initial support to our approach. In particular, the two industries with the highest penetration of robots, Motor vehicles and Rubber and plastics, are among the industries with the largest share of secondary education workers.

Next, we exploit the fact that in our manufacturing data plant level investment in machinery and equipment should also include investment in robots. Consistently with this assumption, Figure 4 shows that imports of industrial robots shipped to each manufacturing industry in a year are positively related to total investment in machinery and equipment in the same industry-year pair.<sup>13</sup> Having established a positive link between imports of robots and aggregate investment in the data, an important check of the validity of our plant level proxy for exposure to automation is whether _ETRf,t_ in (3) is positively correlated with machinery and equipment investments at
The map was<br>elaborated as part of the socio-economic ecological zoning exercise (ZSEE) in Mato<br>Grosso.|Map 6|
|Natural vegetation cover of<br>Mato Grosso|1995|=|1; 250,000|Derived from land use/<br>land cover map (own calculation)|Natural vegetation cover corresponds to the land cover (without any human activity as of<br>1995) of the SEPLAN land use/land cover map.|Map 7|
|High/low natural vegetation<br>cover in Mato Grosso|1995|=|1; 250,000|Derived from natural vegetation<br>cover map (own calculation)|Mean natural vegetation cover on a 25x25 pixel neighborhood (corresponding to a mean<br>property size in Mato Grosso of 625 hectareas) was calculated for each pixel on the 100<br>meter rasterized natural vegetation cover 1995. The resulting mean values were ove|Map 8|
|Elevation of Mato Grosso|2002-2003|2003|90 meter|US Geological Survey (USGS)|Digital elevation model for Mato Grosso from the Shuttle Radar Topography Mission (SRTM)|=|
|Slope of Mato Grosso|2002-2003|o|90 meter|Derived from elevation grid (own<br>calculation)|Slope was calculated from elevation grid.|Map 9|
|Roads of Mato Grosso|unknown|2003|1; 250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|Primary (paved) and secondary (unpaved) roads in Mato Grosso|=|
|Distance to roads in Mato<br>Grosso|unknown|=|1: 250,000|Derived from road map (own<br>calculation)|Bird distance to the nearest road calculated for each pixel of the 100 meter rasterized<br>road map.|Map 10|
|Agricultural suitability of<br>Mato Grosso|1995|2002|
**Figure 4: Impact of** **_Seguro Popular_ on Formality over Time:**

<!-- Start of picture text --> Regression Coefficients and 10% Confidence Intervals.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4a: Cross Section Estimation Figure 4b: Panel Estimation (Fixed Effects)<br>2.00 1.50<br>1.50<br>1.00<br>1.00<br>0.50<br>0.50<br>0.00<br>0.00<br>-0.50 -0.50<br>-1.00<br>-1.00<br>-1.50<br>-1.50<br><!-- End of picture text --> Source: Authors‘ estimates from _SP_ registry and household surveys.

# _Intra-household dynamics_

We next explore if, within households, individuals have different incentives to become informal given the availability of _SP_ depending on their position in the household. These different dynamics could arise, for example, because coverage of SS is extended to eligible dependents of a contributor. Given the current design of SS, eligible dependents of a contributor who also contribute obtain exactly the same benefits as if they do not. Therefore, SS offers few incentives to contribute as a second earner. The availability of _SP_ may make switching to informality more attractive for a second earner if the switcher and her family can get coverage _by SP in addition to_ coverage by SS, thus maximizing access to different means of protection. In some municipalities, _SP_ may provide closer access to clinics or hospitals even if of lower quality, which make it convenient for families to have both and chose depending on needs. Another benefit of _SP_ is that it extends health insurance coverage to other members of the household, such as elderly
**Annex Table 1.2. Bayesian Model Averaging**

|Dependent variable: Investment growth||
|---|---|
|VARIABLES|(1)|
|Lagged real GDP growth (percent)|0.558|
||[1.00]|
|Change in FDI inflows (percentage points of GDP)|0.703<br>[1.00]|
|Political stability|0.126<br>[0.83]|
|Lagged credit to GDP ratio (percent of GDP)|-0.067<br>[0.81]|
|Terms of trade growth (percent)|0.154<br>[0.99]|
|Large reform spurt|3.017<br>[0.53]|
|Sudden stop dummy|-3.777|
||[0.84]|
|Constant|-1.116|
||[1.00]|
|Observations|1,098|

Note: Estimation results are based on Bayesian model averaging. The sample is the same as in Annex Table 1.1. Probability of inclusion of each variable are in brackets.

# **Annex 2. Definitions and methodology**

**A. Investment‐less credit booms** . Data for the broadest definition of credit are provided by the Bank for International Settlements for 14 EMDEs from 1980 to 2015 (Argentina, Brazil, China, Hungary, India, Indonesia, Malaysia, Mexico, Poland, Russia, Saudi Arabia, South Africa, Thailand, and Turkey).

For other EMDEs, where credit from the domestic banking system remains the main source of credit (Ohnsorge and Yu 2016), annual data on claims by banks on the private sector, provided by the IMF’s International Financial Statistics, are used as proxies for credit to the nonfinancial private sector. This broadens the sample by another 41 countries, mainly from 2000 onwards
Figure 5: Level of data policy restrictiveness and innovation score in digital services (2018) <!-- Start of picture text --> Data policy index & Digital innovation<br>CHN<br>MNG<br>PHL<br>MYS MMR IDN<br>LAO<br>KHM<br>VNM<br>THA<br>0 .2 .4 .6 .8 1<br>Restrictiveness index (0-1)<br>.5<br>.4<br>.3<br>.2<br>.1<br>Indicator of innovation in digital sector<br>0<br><!-- End of picture text --> Source: ECIPE and World Bank Enterprise Survey Database. Note: Latest year taken for 2019. The indicator of innovation represents the “h5” question in the World Bank Enterprise Survey Database, which asks whether the establishment introduced new or significant improved or introduced new process of organizational or management structures over the last 3 years. For this figure, only the sectors of Computer and related services; Publishing, printing and recorded media; and Post and Telecommunication (ISIC Rev. 4) have been selected and averaged by country. The trend line is plotted excluding China.

22
Policy Research Working Paper 9337

# **Abstract**

This paper studies whether there is a connection between finance and growth at the firm level. It employs a new dataset of 150,165 equity and bond issuances around the world, matched with income and balance sheet data for 62,653 listed firms in 65 countries over 1990–2016. Three main patterns emerge from the analyses. First, firms that choose to issue in capital markets use the funds raised to grow by enhancing their productive capabilities, increasing their tangible and intangible capital and the number of employees.

Growth accelerates as firms raise funds. Second, the faster growth is more pronounced among firms that are more likely to face tighter financing constraints, namely, small, young, and high-R&D firms. Third, capital market issuances are associated with faster growth among firms located in countries with more developed capital markets relative to banks. Capital markets are also comparatively effective at allowing financially constrained firms to raise capital.

This paper is joint product of the Chief Economist Office, Equitable Growth, Finance and Institutions Vice Presidency, the Development Research Group, Development Economics; and the Office of the Global Director, 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 tdidier@worldbank.org, rosslevine@berkeley.edu, rllovetmontanes@worldbank.org, and sschmukler@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