rafmacalaba/datause-encoder-data huggingface dataset:
# **3.2 Measures of export dynamics and financial structures**

We are interested in the size and dynamics of the export sector. Among the measures for the size of the export sector, we consider the number of exporters (number of firms exporting in a given country-industry-year) and the average export sales per exporter. The dynamics of the export sector is captured by firms’ entry rate into export, firms’ exit rate from export, and the turnover rate of exporters. Firm entry rate into export is equal to the number of firms exporting in a given country-industry-year that were not exporting in the previous year over the number of exporters; firm exit rate equals the number of firms not exporting in a given country-industry-year that were exporting in the previous year over the number of exporters; the turnover rate is the sum of the entry and exit rates. In additional tests, we also consider measures of concentration of the export sector, namely the share of exports of the top 5% of exporters and the Herfindahl-Hirschman index of export sales.

We capture financial structures by considering measures of the relative importance of banks vis-`a-vis capital markets, and, within the banking sector, measures of the relative importance of domestic versus foreign banks. In the former set, we focus on the ratio of credit granted by banks to the private sector over the sum of stock market capitalization and bank credit, but also experiment with the ratio of bank credit over the sum of bond market capitalization and bank credit (for bond markets data are sparser than for stock markets). In the latter set, we focus on the share of foreign bank assets among total bank assets in the country.

As in prior studies, we use total credit granted by all financial institutions relative to the GDP to control for the financial development of a country and to account for the overall size of the financial institutions sector. Further, when studying the role of foreign banks, we control for the degree of competition in the banking sector using an index of banking sector concentration (the Herfindahl-Hirschman index of bank assets in the country).

The identification strategy relies on the approach originally proposed by Rajan and Zingales (1998), which exploits exogenous variation in the financial dependence of industries due to the technological features of production processes. As pointed out by Rajan and Zingales (1998), what matters is the ranking of the financial dependence of the industries. This ranking can be expected to largely reflect technological features, while financial dependence does not vary substantially across firms within an industry. The measure of industry-level degree of external financial dependence is constructed using data from Maskus et al. (2012) and Klapper et al. (2006).<sup>7</sup> Similar to Rajan and Zingales (1998), external financial dependence is defined as the median of the ratio of capital expenditures minus cash flow from operations divided by capital expenditures across U.S. firms in a given industry over the

> 7Industries are defined at the 2-digit level in ISIC Rev.3.

8
WPS3285

# **Gender Equality and Economic Development**

**The Role for Information and Communication Technologies**

## **Derek H. C. Chen** *

The Knowledge for Development Program The World Bank Washington DC 20433

## **Abstract**

This paper focuses on the role that information and communication technologies (ICTs) can play in improving gender equality, so as to enhance long-term economic growth. Employing OLS and IV panel regressions with country fixed-effects, the paper shows that increases in the level of ICT infrastructure tend to improve gender equality in education and employment. In addition, it shows that education among the general population is important for improving gender equality. The results provide evidence indicating that gender equality in education is an important contributor to gender equality in employment. Lastly, the results show that economic development itself tends to lead to some improvements in gender equality in the labor market. Hence, the use of ICTs to improve gender equality in education and employment may initiate a continuous cycle of positive reinforcing feedback effects between gender equality in employment and economic development, leading to further improvements in both.

### World Bank Policy Research Working Paper 3285, April 2004

_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._

*The Knowledge for Development Program of the World Bank Institute.  I am grateful to Carl Dahlman for suggesting this research topic.
933 sels, Belgium, July 2024. URL `https://www.bruegel.org/analysis/`

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- 945

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- 949 S. Dietz and F. Venmans. Cumulative carbon emissions and economic policy: In search of general 950 principles. _Journal of Environmental Economics and Management_ , 96:108–129, July 2019. ISSN 951 00950696. doi: 10.1016/j.jeem.2019.04.003. URL `https://linkinghub.elsevier.com/retrieve/` 952 `pii/S0095069618302122` .

- 953 S. Dietz, J. Rising, T. Stoerk, and G. Wagner. Economic impacts of tipping points in the climate 954 system. _Proceedings of the National Academy of Sciences_ , 118(34):e2103081118, Aug. 2021. ISSN 955 0027-8424, 1091-6490. doi: 10.1073/pnas.2103081118. URL `https://pnas.org/doi/full/10.1073/` 956 `pnas.2103081118` .

- 957 G. Dolphin, M. G. Pollitt, and D. M. Newbery. The political economy of carbon pricing: a 958 panel analysis. _Oxford Economic Papers_ , page gpz042, July 2019. ISSN 0030-7653, 1464959 3812. doi: 10.1093/oep/gpz042. URL `https://academic.oup.com/oep/advance-article/doi/` 960 `10.1093/oep/gpz042/5530742` .

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- Commitment", _Journal of Money, Credit, and Banking,_ Vol.30, No.1 (February), pp.<sup>1 0 2</sup> -<sup>1</sup> 18.

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Stiglitz, J., and A. Weiss (1981), " Credit Rationing in Markets with Imperfect 31
service _j_ (either electricity or water) as the difference between the cost of supplying the household with the service and the amount it paid for the service: Here _Si_ , _j_ = subsidy received (in USD/month) by household _i_ for the service _j_ , where _j_ = _e_ (electricity) or _j_ = _w_ (water); _Qi_ , _j_ = household _i_ ’s monthly use of service _j_ (kWh/month or m<sup>3</sup> /month); AC _j_ = total average cost of providing service _j_ in Addis Ababa ($/kWh or $/m<sup>3</sup> ); and EXP _i_ , _j_ = household _i_ ’s expenditure for service _j_ ($/month).

Since it is possible in theory for the cost of providing utility service to be either more or less than household _i_ ’s expenditure, it follows that the household could (respectively) either receive a subsidy or make a payment in excess of the actual cost of service. For every household we analyzed, however, the monthly cost of service (AC _j_ × _Qi_ , _j_ ) was greater than its monthly expenditure for both electricity and water service.

To determine the incidence of the subsidies received by the 366 households, we needed a wealth or income indicator for each of them. We used respondents’ answers to our household survey instrument’s questions about asset ownership and housing characteristics to construct a wealth index via principal components analysis (Filmer and Pritchett 2001; Filmer and Scott 2008).<sup>19</sup> Using this wealth index score for each of the 366 households, we grouped them into wealth quintiles and report the average electricity and water subsidies received by the households in each quintile. We also

> 19 Details on the variables included in the wealth index, along with the principal component scores, are given in Cardenas and Whittington (2019).

12
<mark>weight is retained. The resulting file is exported to .csv format. We upload the .csv file to the FIES Shiny App: https://fies.shinyapps.io/ExtendedApp/, which was developed by the Food and Agriculture Organization of the United Nations (FAO), the custodian agency for FIES</mark><sup>_34_</sup> <mark>.</mark> <mark>3. In the App, under the “Item and Raw Score Stat” tab, we check the Infit statistic for each component. If it is not within the bounds 0.7 and 1.3, it is dropped from the analysis. At least six questions should be retained in this step.</mark> <mark>4. We equate the included items to the global standard. It is sometimes the case that some items may diverge from the global standard to be included in the calibration of the country scale to the global standard. The rule then is to exclude items that differ by > 0.35. This is accomplished in the “Equating” tab. If there were multiple items differing by > 0.35 in our sample, we iterate on which set of items to exclude. At least five items must be retained for a robust estimation. The items that are excluded from the equating procedure are not dropped entirely from the construction of the FIES indicators but are simply ignored when equating the country scores to the global standard.</mark> <mark>5. Under the “Additional Information” tab, we next “download respondent-level model-based variables”. This provides a dataset with as many rows as the .csv file that was uploaded into the Shiny App in step #2. This downloaded dataset includes the estimates of prevalence of (i) moderate or severe and (ii) severe food insecurity among adult individuals in accordance with the “raw score” for each household (that is, the count of “yes” answers across the eight questions).</mark> <mark>6. We import the data downloaded in step #5 into Stata and using “raw score” as the linking variable, merge it with other Stata formatted data files that are defined at the householdlevel.</mark>

**<mark>Consumption quintiles:</mark>** <mark>To define pre-COVID-19 household per capita consumption quintiles, we rely on the pre-public-dissemination versions of the consumption aggregates. These are provided by the Living Standards Measurement Study (LSMS) team on an exceptional basis, with clearance from the respective NSOs, for the pre-COVID-19 LSMS-ISA-supported surveys that serve as a sampling frame for the high-frequency phone surveys on COVID-19. In the case of Ethiopia, Nigeria and Malawi, the consumption aggregates are computed by the respective NSO, with technical assistance from the LSMS. In the case of Uganda, the consumption aggregate is computed by and has been obtained from the Uganda Bureau of Statistics. In the specific case of Malawi, the consumption aggregate is for the IHPS 2016, as this information is unavailable for the IHPS 2019.</mark>

# **<mark>Econometric Analysis</mark>**

**<mark>Phone Survey Weights:</mark>** <mark>Since we rely on phone interviews with a sample of households that had been interviewed face-to-face prior to the COVID-19 pandemic as part of the LSMS-ISA-</mark>

17
I extend the model to allow banks to choose the level of distortion, where a higher level of distortion is associated with a lower cost. When financial integration is associated with a large drop in the price of funding, banks endogenously select a higher level of distortion. Intuitively, when funding is cheap, most projects should be implemented anyway; the returns to sustaining institutions that enable the differential contraction of productive and unproductive projects goes down. Interestingly, these endogenous institutional changes have a mixed effect on output and output volatility. While the output response to large contractions in the supply of funds is amplified, the deterioration in the financial system actually serves to increase and stabilize output during normal times. In the extreme case, the financial system implements all projects, regardless of small fluctuations in the price of funding, resulting in high and stable output. However, when the supply of funds contracts so much that the economy can no longer afford to run at full capacity, there is an efficiency loss resulting from the economy’s inability to absorb contractions through the discontinuation of the least productive projects.

The model’s predictions depend crucially on the assumption that the marginal return to funding is declining less steeply in more distorted economies. Formally, the key assumption is that the marginal return to funding is log supermodular in funding and the level of distortion. To assess the relevance of this assumption, one can ask (i) under which microfoundations it will hold and (ii) whether or not these microfoundations are themselves empirically relevant. I show that this assumption arises naturally in a wide variety of models in which the equilibrium is characterized by a stochastic matching between funding priorities and projects. Distortions create a mismatch, making better projects less likely to receive high priority, and relatively worse projects relatively more likely to receive a higher priority. I present empirical evidence for these properties using firm level data from emerging and developed economies. I find that in emerging economies, productive firms are relatively more sensitive to fluctuations in the aggregate supply of funds, and that the opposite is true for relatively less productive firms. I show that this type of mismatch can arise from many common sources of distortions, such as incomplete information, inefficient government intervention, collateral constraints and search frictions.

5
mean value of the independent variable is 0.085 - that is, on average women are close to 0.085 mines. Conditional on being close to a mine, the main independent variable is 1.33 - that is, a woman sampled close to a mine is close to 1.33 mines on average. Panel C of table A3 shows the effects on women’s labor market participation. We note that the estimates are similar in direction as before, where mines are positively associated with service and sales jobs and with cash earnings, but negatively associated with agriculture and professional jobs.

Panel D of table A3 shows the results if we drop the part of the sample that lives 20 km to 40 km away from a mine, and if we drop those that are sampled two years before mine opening. The rationale for this is to have a cleaner control group, since those that live just outside our 20 km cutoff distance may also be “treated” by the mine, and the investment phase of the mine that precedes initial production can generate substantial employment. Overall, the effects do not change much except making the cash earnings coefficient larger and more significant. The increase in cash earning opportunities is estimated at 7.5 to 7.8 percentage points compared with 5.4 in the baseline estimation.

# **6.6 Employment and wages using the GLSS**

The DHS data do not provide detailed information regarding how much an individual earns for work, or her wage rate, but the GLSS does collect such data. First, we try to replicate the results estimated with the DHS data. Panel A of table 11 indicates that agriculture becomes less important in mining communities for women (statistically insignificant), who mainly shift into services and sales (statistically insignificant, except for strategy 2). Men are more likely to work as miners (statistically significant across all strategies).

29
# **<u>4.2.1 Firm size and age</u>**

Many studies have discovered a positive relationship between start-up size and survival (see Audretsch and Mahmood, 1995; Mata, Portugal and Guimaraes, 1995; Agarval and Audretsch, 2001; for more controversial results, see Audretsch, Santarelli and Vivarelli, 1999a and 1999b). Since entry implies sunk costs (see Sutton, 1991) and generally occurs at a scale that is lower than the MES, a larger entry size is a signal of commitment and selfconfidence and makes both the occurrence of an entry mistake (see Section 3.2) and the risk of a failure due to diseconomies of scale less likely.

On the other hand, a vast number of papers have found (conditional on survival), a negative relationship between start-up size and post-entry growth, thus rejecting Gibrat’s Law (see Gibrat, 1931; Hall, 1987; Hart and Oulton, 1996; Sutton, 1997; Goddard, Wilson and Blandon, 2002; Bottazzi and Secchi, 2006; Lotti, Santarelli and Vivarelli, 2003 and 2009). This evidence means that smaller entrants with a sub-optimal entry size and with a higher risk of early failure (see above) must grow in order to survive and reach the MES as soon as possible<sup>20</sup> .

Consistently, a firm’s age turns out to be positively correlated with survival and negatively with growth (see Evans, 1987; Dunne and Hughes, 1994; Yasuda, 2005; Calvo 2006). This is not surprising and is consistent with the learning theories discussed in the previous Section 4.1: experienced, mature firms are more able to deal with market dynamics and so more likely to survive; however, having already reached (or being very close to) the MES, they do not have to grow very fast.

While all the studies cited so far concern developed countries, the evidence from DCs is similar. For instance, Das (1995), dealing with the Indian computer industry, found a significant negative relationship between firm growth and initial firm size; McPherson (1996), in a study on five southern African countries, detected a significant negative link between firm growth and both the firm’s size and age; Goedhuys and Sleuwaegen (2000)

> 20 However, initial size may display a non-linear impact on post-entry growth; for instance, Stam, and Wennberg (2009) find an overall negative relationship between start-up size and firm’s growth, however shifting to positive once the top 10% fastest-growing firms are considered. By the same token, micro-startups, which display a size well below a minimum threshold, either early exit the industry or grow significantly less than the average (see Bonaccorsi and Giannangeli, 2010; Stam _et. al._ , 2012, pp. 98-99).

19
Table 2: Bilateral Tariffs and Domestic Value Added in Foreign Production

|Panel A: Importer-Industry-Year-Decile & Ex|porter-Ind|ustry-Year|Fixed Efe|cts|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup><br>|-0.92***<br>(0.27)|-0.46***<br>(0.16)|||
|Log DVA Outside RTAs: [1_−RTAijt_]_×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup><br>|||-0.55***<br>(0.19)|-0.66**<br>(0.32)|
|Log DVA Inside RTAs: _RTAijt×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|||0.26<br>(0.42)||
|Reciprocal Trade Agreement: _RTAijt_||-3.68***<br>(0.82)|-7.86**<br>(3.28)|-7.00***<br>(2.07)|
|Observations|8,853|8,853|8,853|8,853|
|R-Squared|0.988|0.990|0.991|0.991|
|Panel B: Importer-Industry-Year & Expor|ter-Industr|y-Year Fix|ed Efects||
||(5)|(6)|(7)|(8)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|-1.32***<br>(0.35)|-0.61***<br>(0.21)|||
|Log DVA Outside RTAs: [1_−RTAijt_]_×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|||-0.69***<br>(0.22)|-0.64***<br>(0.24)|
|Log DVA Inside RTAs: _RTAijt×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|||-0.12<br>(0.46)||
|Reciprocal Trade Agreement: _RTAijt_||-4.53***<br>(0.91)|-7.39***<br>(2.80)|-7.84***<br>(1.71)|
|Observations|8,853|8,853|8,853|8,853|
|R-Squared|0.967|0.974|0.974|0.974|

Note: The regression specification is based on Equation (16). The dependent variable in all columns is the applied bilateral tariff of country _i_ in industry _x_ against exporter _j_ at time _t_ : _t_<sup>_i_</sup> _xjt_<sup>.LogDVA(ln(</sup><sup>_DV Aj_</sup> _ijt_<sup>))</sup> is domestic value added from the importing country( _i_ ) embodied in final production in industry _x_ in the exporting country ( _j_ ). Reciprocal Trade Agreement ( _RTAijt_ ) is an indicator that takes the value one if _i_ and _j_ have a RTA in force in year _t_ . Standard errors (in parentheses) are clustered by importer-exporter pair. Significance levels: * _p < ._ 1 , ** _p < ._ 05, *** _p < ._ 01.

38