rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3921

**Does access to credit improve productivity? Evidence from Bulgarian firms**<sup>∗</sup>

Roberta Gatti Development Research Group The World Bank and CEPR Inessa Love Development Research Group The World Bank

# **Abstract**

Although it is widely accepted that financial development is associated with higher growth, the evidence on the channels through which credit affects growth on the micro-level is scant. Using data from a cross section of Bulgarian firms, we estimate the impact of access to credit (as proxied by indicators of whether firms have access to a credit or overdraft facility) on productivity. To overcome potential omitted variable bias of OLS estimates, we use information on firms’ past growth to instrument for access to credit. We find credit to be positively and strongly associated with TFP. These results are robust to a wide range of robustness checks.

# World Bank Policy Research Working Paper 3921, May 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ We thank Mary Hallward-Driemeier for useful discussions. Address correspondence to rgatti@worldbank.org.
B. Neiman (2013): “The Global Decline of the Labor Share,” _The Quarterly Journal of Economics_ , 129, 61–103.

- Korea Labor Institute (2019): “Korean Labor & Income Panel Study, KLIPS,” Korea Labor Institute.

- Ministerio de Desarrollo Social y Familia (2017): “Encuesta de Caracterizaci ́on Socioecon ́omica Nacional, CASEN,” Ministerio de Desarrollo Social y Familia, Chile.

- National Statistical Office (2018-2019): “Periodic Labour Force Survey,” Ministry of Statistics and Programme Implementation, National Statistical Office, Government of India.

- Official Statistics of Japan (2019): “Labour Force Survey,” Ministry of Internal Affairs and Communication.

22
22 corresponding to the observed demographics in 2013 are used to solve the diet cost problem from 2013 through 2015, and then the household composition and corresponding nutrient requirements observed in 2016/17 are used to solve the diet cost problem from January 2016 forward. We scale the nutrient requirements for any partial meal-taking in order to accurately draw comparisons with observed food spending which was collected for the previous seven days and therefore reflects the consumption of those who ate in the household in the last seven days (Fiedler and Mwangi 2016). For every household, 55 CoNA indices (36 observations from 20132015 and 19 observations from January 2016 to July 2017) are thus obtained, at both upper and lower bounds (based on shared and individualized diets, respectively).

We focus on two primary results from the linear modeling: feasibility of a solution and cost. We use the binary outcome of a solution or no solution to summarize the extent to which there is a feasible diet given the items for which prices are recorded and their known nutrient composition. Under the individualized diets scenario, we consider the household to have a leastcost diet solution only if there is a solution for all members. Where the market price list does not have an observation, it might reflect seasonal unavailability or an item that is never present in that market, or it could be measurement error as missing prices are common in agricultural price data even where items are known to be available (Kaiyatsa et al. 2021; Pauw et al. 2018).

Confidence that no solution to the least-cost diet problem mostly reflects realistic infeasibility to compile a diet that meets the specified nutrient requirements in that market and month rests on three observations. First, patterns observable in Figure 1 show the majority of unobserved prices in the data are for items that are perishable and thus reasonably understood to be only available during their production season. Further, they show that even when an item has
security is low compared to the liquidity of its ADR, funds are expected to hold a higher fraction of their investment in that firm in the form of ADRs (see H3). To examine the impact of these liquidity measures on the funds’ investment allocation between the ADR and the domestic security we estimate a model of the following form:

- _ADR Differential_ = β 0 + β 1( _Log Market Cap_ ) + β 2 ( _Number of Analysts_ ) + β 3 ( _Accounting Quality_ ) + β 4 ( _Liquidity_ − _Underlying_ ) + β 5 ( _Liquidity_ - _Relative_ ) + β 6 ( _GDP Per Capita_ ) + β 7 ( _MarketCap GDP_ ) + β 8 ( _Turnover Country_ )+ β 9 ( _Transaction Efficiency_ ) + β 10 ( _Settlement Efficiency_ ) + β 11( _FX Volatility_ ) + β 12 ( _French_ ) + β 13 ( _German_ )+ β 14 ( _Transition_ ) (5)

Equation 5 is based on the specification used to measure the impact of country level factors on the _ADR_Differential_ (equation 4). In addition to the country characteristics we now include the following variables:

- _Liquidity-Underlying:_ This is the ratio of the daily average volume of underlying traded (for 2001) to average number of underlying outstanding (simple average of 2000 and 2001 reported numbers). The data is obtained from Datastream.

- _Liquidity-Relative_ : This is calculated by dividing the average daily trading volume of ADRs (converted into the equivalent underlying by using the ADR to underlying ratio) to the average trading volume of the underlying. Thus a high value for Liquidity-Relative implies that the ADR security is more liquid than the underlying. The trading volume data is obtained from Bloomberg.

While intuitively straightforward, data collection on each of these liquidity variables posed some special challenges. The underlying security is not always the common stock. Issuers from Brazil and Mexico almost always have multiple classes of shares. For example, Brazilian firms issue ADRs that have preferred shares as the underlying security. The preferred share is the main security for
</mark>

<mark>The prices were grouped into six broad categories, each of which contained at least three but no more than six series in order to maintain a balanced representation. The rest of this Appendix lists the commodities used in the analysis, the groupings, and their main uses. A technical description is also given for each series. More details on sources and description can be found in</mark> World Bank (2020, <mark>pp. 77-82).</mark>

# <mark>A.1. Energy (3 series)</mark>

<mark>The energy group includes three series: Coal, crude oil, and natural gas.</mark> **Crude oil** is primarily used for transport and, to a lesser degree, petrochemicals while most **coal** and **natural gas** are used for electricity generation and less so for industrial purposes. The prices belonging to the energy group are defined as follows:

- **Coal** (Australia): Thermal, f.o.b. Newcastle, 6,000 kcal/kg, spot price.

- **Crude oil** : Average price of **Brent** (38° API), Dubai Fateh (32° API), and West Texas Intermediate (WTI, 40° API). Equally weighed.

- **Natural gas** (Index): Weights based on five-year consumption volumes for Europe, U.S., and Japan (Liquefied Natural Gas).

_— 33 —_
|8.8|$1.2|71.6|69.6|
|<br><br>Europe & Central Asia<br>2|41.6|41.6|10.4|34.3|49.0|37.4|
|LatinAmerica&Caribbean<br>6|37.0|35.0|11.0|24.6|53.7|42.1|
|<br><br>Middle East& North Africa<br>4|41.8|42.3|15.3|26.8|56.0|44.8|
|South Asia<br>2|63.7|63.7|11.9|55.3|72.1|72.2|
|Sub-SaharanAfrica<br>2|54.7|54.7|18.3|41.8|67.6|68.4|

Source: Global Financial Development Database, 2008-10 data. Note: OECD = Organisation for Economic Co-operation and Development. a. Weighted average by stock market capitalization.
with a technological investment decision. Moreover, these firms that import both intermediate inputs and capital equipment goods improve their productivity gains suggesting a complementarity between imported inputs and foreign technology in the production process.

Our empirical analysis is motivated by a simple model of heterogeneous firms, endogenous technology adoption and imported inputs that captures these main features of the Indian data. The aim of the theoretical model is to rationalize the channels through which input-trade liberalization affects firms’ decision to upgrade foreign technology embodied in imported capital goods. Input-trade liberalization reduces the costs of imported intermediate inputs and allows firms to decrease their marginal costs and increase their profitability. In the presence of fixed cost of technology adoption, heterogeneous firms and complementarity between imported inputs and high-foreign technology, the model yields two main testable implications. First, input tariff reductions increase the probability of importing capital goods. Second, the effect of input-trade liberalization on firms’ technology choice is heterogeneous across firms depending on their initial productivity level. Firms that will benefit from input-trade liberalization are those with a high productivity level using low-technology embodied in domestic capital goods before input tariff cuts.

We then test the model implications using the Indian firm-level dataset, Prowess, over the 1989-1997 period. This data was collected by the Centre for Monitoring the Indian Economy (CMIE). The Prowess dataset provides information on imports distinguished by type of goods (capital equipment, intermediate goods and final goods). To establish the causal link between the availability of imported intermediate goods and firms’ decision to import capital goods, we rely on the unilateral trade reform that took place in India at the beginning of the 1990s as a part of the ‘Eighth Five-Year Plan’.<sup>3</sup> We depart from previous studies of input-trade liberalization by distinguishing tariffs on variable inputs from tariffs on capital equipment products. The empirical identification strategy disentangles the direct effects of tariffs on capital goods and the indirect effects of tariffs on other variable intermediate goods on firms’ decision

> 3Section 5.2 describes the policy instruments applied by
**Figure 3. GDP Growth Forecast Errors by Geographical and Institution Type**

<!-- Start of picture text --> Panel (A) Panel (B)<br>Absolute Forecast Error Regressions Forecast Error Regressions<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1.0 -1.0<br>IMF Forecast Consensus/Focus Regional International IMF Forecast Consensus/Focus Regional International<br>Forecasters Forecasters Forecasters Forecasters Forecasters Forecasters<br>Institution Regression Geographic Regression Institution Regression Geographic Regression<br>Estimated coefficients Estimated coefficients<br><!-- End of picture text --> Source: Authors’ calculations based on the January Consensus/Focus Economics Forecasts, the International Monetary Fund’s January _World Economic Outlook_ (WEO) and the World Bank’s January _Global Economic Prospects_ (GEP). Note: This figure shows the results of two separate regressions for forecasters classified according to their institution type; and classified according to geography. In the institution regressions, the three categories are WB-GEP, IMF-WEO and Consensus/Focus Economics: WB-GEP is the omitted category. In the geographic regressions, the international, regional, and local categorization is based on the proximity of the forecaster’s headquarters to the country being forecasted: “Local Forecasters” is the omitted category. Same-year forecasts in January are used for WB-GEP Forecasts (2010-2020), IMF-WEO Forecasts (2010-2020) and Consensus/Focus Economics Forecasts (2015-2020) for a pooled sample of 132 countries. Bars represent 90 percent confidence intervals _._ 16.6 percent of forecasts in the sample are by local forecasters. 17 percent of forecasts in the sample are by regional forecasters. 66.4 percent of forecasts in the sample are by international forecasters. There are a total of 218 local forecasters, 93 regional forecasters, and 107 international forecasters
natural resources. Dasgupta (2010) concludes that GDP per capita and the Human Development Index (HDI) should not be regarded as comprehensive indicators of economic and social welfare, as they do not reflect the depletion of the natural capital stock, and that more sophisticated forms of wealth accounting that include produced, human and natural capital are more appropriate measures of inclusive and sustainable development. The World Bank has long been a major advocate for integrating the sustainable management of natural capital into growth strategies. Cleveland et al. (1996) analyzed the relationship between natural capital, ecosystem services and economic growth, highlighting that economic growth models tend not to account for either the degradation of critical, non-replaceable ecosystem services or the depletion of natural capital. The study concludes that these elements should be taken into account in future analyses of the link between natural capital and growth.

In recent years increased attention to the issue of income inequality has added a new dimension to the debate over the role of the depletion of natural capital in economic growth and development. Barbier (2014) argues that understanding the depletion of natural capital is crucial to assessing both the extent to which natural capital will

> 4 Natural capital includes both nonrenewable resources such as oil and minerals and renewable resources such as arable land, forests and fisheries, as well as environmental quality.

2
<mark>“housework” as a means of gaining access to work in public places while appearing to comply with the norm that their actual place is in the home or that the man is the family breadwinner (</mark> Franck and Olsson 2014).

# **Women’s labor force participation in rural Honduras**

Given the definition of employment in use by the Honduran official statistics, which includes work for own consumption,<sup>10</sup> and high reliance on small-scale farming, labor force participation among women in rural Honduras is surprisingly low. According to the 2015 Household Survey for Multiple Purposes (EPHPM), only 39 percent of women ages 15 and older in rural areas report being in the labor force, compared to 90 percent of men. This discrepancy in labor force participation between genders in rural areas is not limited to Honduras. Sousa and Muller (2018) find that rural labor force participation rates of women in most Latin American countries, including the six Central American countries, are below 50 percent. Peru and Bolivia, where these rates exceed 80 percent, are notable exceptions.<sup>11</sup> A priori, there are two possible explanations for these low participation rates: 1) the majority of women in rural areas are not participating in any activities that would be considered employment (including agriculture for own consumption); or 2) the participation of women in employment activities in rural areas is being undercounted. In the EPHPM 2015, when asked why they did not work or look for a job during the past month, over 90 percent of Honduran rural women not in the labor force reported that the main reason is that they were too busy with household tasks/chores. At first glance, this suggests support for the first explanation - the labor intensity of household tasks is too high to engage in economic activities. However, the survey questions used to identify participation specifically ask that respondents exclude “housework” when reporting their activities. Following
production and operative occupations, and routine cognitive tasks are most intensively used in clerical and sales occupations. We split the activity data accordingly into two samples and redo the baseline regressions for each sample, see Table 5. For activities with a high routine manual intensity, we indeed observe a major difference across advanced and developing economies. New specializations in routine manual activities strongly relate to proximity of the activities to current specialization. For developing economies, a 1 standard deviation increase in average proximity relates to a 10.9 percentage point increase in the probability of specializing in a new routine manual activity. In advanced economies the effect is only 2.9 percentage points. The impact of proximity on specialization in routine cognitive activities however is comparable to the impact for all activities found in the baseline (columns 4-6).

The qualitative results presented in Tables 3, 4 and 5 appear robust in a battery of additional tests, as shown in Appendix D. Taken together, the regression results show a strong commonality in the way activity export baskets develop over time, especially in earlier stages of development. Proximity to the initial product basket plays a strong role in the development of new product specializations. Low-income countries initially specialize across the extensive margin, exporting new activities carried out by workers of the same occupational grouping but in different industries. Proximity to the initial specialization basket is particularly important for the development of new routine manual activities in this phase. As income levels rise, specialization along the intensive margins becomes more prevalent, and workers shift towards different activities in the same industries. Proximity to the initial basket becomes less important for new specializations in this later phase of development.

21
Policy Research Working Paper 7658

# **Abstract**

This paper examines the effect of air pollution on labor supply in Lima, Peru. It focuses on fine particulate matter (PM2.5), an important pollutant for health according to the medical literature, and shows that moderate levels of pollution reduce hours worked for working adults. The research design takes advantage of rich household panel data in labor outcomes to address omitted variables and allows investigation of whether the response to air pollution is non-linear. The analysis finds that the effect of moderate pollution levels on hours worked is concentrated among households with susceptible dependents, that is small children and elderly adults, while the highest concentrations affect all households. This suggests that caregiving is likely a mechanism linking air pollution to labor supply at moderate levels. Further evidence of this mechanism is provided using DHS data on children morbidity for the same time period. Finally, no evidence is found of intra-household attenuation behavior. For instance, there is no re-allocation of labor across household members, and earnings decrease with air pollution.

This paper is a product of the Environment and Natural Resources Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jjmiranda@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
**Figure 9. Fiscal Policy’s Impact on Inequality, 2015**

<!-- Start of picture text --> 0.33 0.325<br>0.322<br>0.32<br>0.307<br>0.31<br>0.299<br>0.30<br>0.29<br>0.281<br>0.28<br>0.27<br>0.26<br>0.25<br>Market (incl. Net market Disposable Consumable Final<br>pensions)<br>Gini coefficient<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget figures.

The redistributive effect<sup>42</sup> of fiscal policy in Egypt is about mid-range when compared to other countries in the region or those with similar levels of development (Figure 10).<sup>43</sup> This is due primarily to a relatively low impact of direct transfer spending on inequality. Egypt’s main cash transfer program, the food ration program, is approximately a universal transfer, so richer households receive the transfer approximately as frequently as poorer households; untargeted transfers often do not generate large impacts on inequality. Figure 10 also demonstrates that prefiscal inequality in Egypt is quite low.<sup>44</sup> Fiscal policy is also poverty reducing. As discussed in Lustig (2018), to measure the impact of fiscal policy on poverty, the indicator of choice—usually the poverty headcount rate or the poverty gap—is calculated for market income, disposable income, and consumable income. Poverty statistics are not typically measured for final income, which includes the government-cost value of health and education services (see Figure 7).

Figure 11 indicates that in the absence of the taxes and expenditures in this CEQ assessment, the poverty headcount rate at prefiscal income (market
after the tapering talk episode.<sup>16</sup> Reserve accumulation has picked up again since 2014, and as of end April 2015, reserves were at an all-time high of about $ 350 billion.

**Figure 12: Monthly percent change in Reserves**

<!-- Start of picture text --> 30<br>Large monthly percent changes<br>25 in reserves were consistent<br>Monthly changes in reserves were<br>20 with the objective to seek a stable more modest than before, consistent with<br>exchange rate<br>15 increased flexibility of the exchange rate<br>10<br>5<br>0<br>‐5 Reserves were neither<br>‐10 accumulated nor used<br>avtively to modulate<br>‐15<br>exchange rate volatility<br>‐20 during this period<br>% Chg. m-o-m<br>199011 199108 199205 199302 199311 199408 199505 199602 199611 199708 199805 199902 199911 200008 200105 200202 200211 200308 200405 200502 200511 200608 200705 200802 200811 200908 201005 201102 201111 201208 201305 201402 201411<br><!-- End of picture text --> Note: calculated using data from Bloomberg.

How reserve management affects money supply, interest rates and the real economy depends on whether they are sterilized or not. Sterilized intervention by keeping money supply unchanged is unlikely to have an impact on interest rates, investment and growth; but imposes the cost of sterilization on the central bank or the government—known as the quasi fiscal deficit in some parlance. Non-sterilized intervention on the other hand increases money supply, drives the interest rates lower and may have a positive impact on investment on one hand and generate the tendency for the economy to overheat on the other. India has for most part in the past partially, but not fully, sterilized its reserve accumulation.

Looking broadly at the decomposition of reserve accumulation into sterilized and non-sterilized components since 1991, the RBI partially