rafmacalaba/datause-encoder-data huggingface dataset:
where _yi,t_ is the outcome variable, alternatively, the number of employees ( _Employment_ ), the labor productivity ( _Productivity_ ), and the firm-level wage rate ( _Wage_ ). _Xi,t−_ 1 is a vector of structural controls for firms’ size, age, exporting behavior, and sector. _γi_ and _λt_ are, respectively, firm and time fixed effects.

Table 3 shows the results for regression model **??** reporting in each column one of our labor market outcomes of interest. Estimates in column 1 indicate that employment is positively correlated with access to finance. That having access to bank credit has a positive and significant effect on the (log) number of employees confirms how external sources of funding may have a critical role in affecting firms’ employment growth. This effect goes beyond structural controls for the age of the firm, the exporting activity of the firm, operating sector of the company, common time components that capture cyclical factors (time fixed effects), as well as firm-specific fixed effects absorbing any time-invariant characteristic (observable or not). Results also indicate evidence of a stronger job creation for younger and export-oriented companies. Noticeably, the positive effect of loan availability is not limited to the raw number of employees but extends to labor productivity (as proxied by sales per worker) and the average salary paid.

Table 3: Access to finance and jobs

|Dependent variable:|Employment<br>(1)|Productivity<br>(2)|Wage<br>(3)|
|---|---|---|---|
|Loan availability|0.132***|0.256***|0.155**|
||[0.0392]|[0.0838]|[0.0672]|
|Age|-10.49***|-6.827|-0.776|
||[2.821]|[5.066]|[3.855]|
|Export|0.208***<br>[0.0565]|0.114<br>[0.110]|0.155*<br>[0.0898]|
|Manufacturing|0.139<br>[0.0969]|0.352**<br>[0.152]|0.246**<br>[0.122]|
|Retail|-0.131*<br>[0.0682]|0.221*<br>[0.134]|0.203**<br>[0.0995]|
|Model|OLS|OLS|OLS|
|Time FE|yes|yes|yes|
|Firm FE|yes|yes|yes|
|R2|0.0445|0.117|0.107|
|Observations|12605|11159|11581|

_Notes:_ within estimator with firm and time fixed effects. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

# **3.3 Finance and informality**

Next, we explore the determinants of firms’ access to finance and how this is related to competition from informal firms. We begin by looking at the characteristics of firms having access to a loan or credit-line from a bank. Then, we focus on the determinants of the firm’s decision to apply for a loan.

Our baseline specification reads as follows: 8
<!-- Start of picture text --> 1000<br>800<br>600<br>400 \<br>200 /“\N c~/ \<br>-77 SYNo \\/ _o><br>0 V<br>1995 2000 2005 2010 2015<br>— DI Equity<br>——- DI Debt<br><!-- End of picture text --> <!-- Start of picture text --> 1000 ir<br>/\<br>500 7 _/ S \ ’<br>ana eranNaeVe \‘\ 1siian oa<br>t) ve v7 \ D \ 7=<br>W/ ro\yY<br>Wi<br>ty<br>-500 i]<br>uv<br>1995 2000 2005 2010 2018<br>—— DI Debt ——- Portfolio Debt<br>---- Other Investment Debt<br><!-- End of picture text --> <!-- Start of picture text --> 400<br>300<br>200<br>A<br>100 2UN\-7roo,<br>0 eer<br>1995 2000 2005 2010 2015<br>— DI Equity<br>——-— DI Debt<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>\<br>150 i} \<br>100 11:<br>, i q va1 \<br>“ H wi \<br>50 , Ly *<br>A Z Vy _”TM,<br>=~ Lo ler<br>0 weyTet em NY<br>1995 2000 2005 2010 2016<br>—— DI Debt ——- Portfolio Debt<br>- --- Other Investment Debt<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>15<br>10<br>r<br>5 fH TA awA \ SN.<br>0 ~— NLU<br>1995 2000 2005 2010 2015<br>—— DI Equity<br>——- DI Debt<br><!-- End of picture text --> <!-- Start of picture text --> oa Haa<br>/\ iN<br>4 rani i! Q Ds,<br>\\ HF \\viMyn oN<br>2 \1 ( - /<br>1 /; Vil<br>of --H He 7<br>. 7 ~L —~y<br>SS<br>2<br>1995 2000 2005 2010 2016<br>—— DI Debt ——-— Portfolio Debt<br>---- Other Investment Debt<br><!-- End of picture text -->
detailed dataset, we cannot directly control for potentially important variables such as demand and price differences. In principle, such endogeneity problems could be remedied by means of instrumental variable estimation but, unfortunately, credible instruments are not available in our data. However, using the same Ethiopian dataset as considered in this paper, but focusing only on small manufacturing firms Loening et al. (2008) argue that endogeneity bias is likely to be small. Using precipitation based indicators of local agricultural performance as a proxy for unobserved local demand, they find that while better local agricultural performance is strongly correlated with firm sales, their parameter estimates are not very sensitive to controlling for this variable, indicating that endogeneity bias is small. Moreover, firms do not frequently adjust factor inputs, despite facing frequent shocks, another reason why the impact of endogeneity bias may be limited.

Finally, this approach only focuses on the direct impact of gender and the investment climate on firm productivity. However, perhaps the most important gender and investment climate effects are _indirect._ For example, the investment climate may also impact on allocative efficiency (see, e.g. Mengistae and Honorati, 2009), investment and growth, a possibility which is investigated in Appendix F, which shows that, prima facie, gender differences in growth and investment performance are small. It could also induce women to sort into small-scale low productivity sectors. In short, we have to be cautious when interpreting the results from these models.

# **(c) Results**

The regressions are estimated separately for each country. Results are presented in Table 5. The dependent variable is the log of sales in US dollars. We present five different models. The first model only includes a gender dummy while the second model adds a control for firm size. Subsequent models add controls for sector (model 3), factor usage and differences in technology across sectors (model 4) as well as other firm, human capital and investment climate characteristics (model 5).<sup>20</sup> 18
_Table 1. Descriptive statistics_

|**Variable**|**SDI**|**GEO**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|---|---|
|General Diagnostic accuracy|x||65.739|20.055|0|100|2578|
|Children and mothers’ cases diagnostic accuracy|x||67.471|19.984|0|100|2575|
|%Medicine Availability|x||44.686|23.547|0|94.737|2578|
|%Vaccine Availability|x||89.641|25.608|0|100|2023|
|ANC tests available forpregnant women|x||2.08|1.595|0|4|2230|
|Access to electricity|x||0.893|0.31|0|1|2578|
|Stable Access to electricity|x||0.555|0.497|0|1|2301|
|Unstable Access to electricity|x||0.445|0.497|0|1|2301|
|Access to improved sources of water|x||0.888|0.315|0|1|2578|
|Access to improved sources of sanitation|x||0.718|0.45|0|1|2578|
|Having a working fridge|x||0.925|0.264|0|1|1826|
|Having basic equipment|x||0.831|0.375|0|1|2578|
|Use ICT for supply management|x||0.147|0.354|0|1|2569|
|Access to 2G network||x|0.877|0.328|0|1|2578|
|Access to 3G network||x|0.173|0.379|0|1|2578|
|Catchment population area (x 100,000 inhabitants)||x|7.089|12.482|0|62.601|2577|
|Distance to at least “fair” quality Primary Road or||x|1.982|5.415|0|95.213|2578|
|SecondaryRoad(Km)||||||||
|Public|x||0.63|0.483|0|1|2578|
|Rural|x||0.766|0.423|0|1|2578|
|Deprivation score||x|38.179|17.378|1.388|92.915|2549|

# 3.3. Methodology

To analyze the relationship between quality of health services and infrastructure variables, we use linear regressions controlling for characteristics at the facility-level. Fixed effects at the county level are included in the specifications as Kenya devolved health services to its 47 semiautonomous counties in 2013. Equation 1 describes the model used in the analysis: Where Qualityfac are the health service quality variables at facility level (i.e., provider's knowledge or medicines/inputs availability), and INFfac are the infrastructure access variables (i.e., electricity, distance to a fair/good quality road, access to internet/cellphone coverage, or ICT use). Controlsfac are control variables at the facility level, including rural/urban, public/private, facility-level (hospital, health center, or dispensary), and a proxy for poverty small-area estimations. Finally, αcounty are county fix effects and εi is the random error term. In general, the large set of controls at the facility level that measure surrounding their characteristics allow us to clean our correlations between quality of health service delivery and infrastructure variables to some extent.<sup>16</sup>

# 3.4. Limitations of the Study

This section describes the limitations of the proposed approach for the analysis and each data source used and the discrepancies found between different sources.

> 16 Interactions between infrastructure variables (i.e., road connectivity and electricity access) were considered but the results did not explain the relationships better than the proposed model. Future research can explore this interdependencies more deeply.

10
very large and the estimated coefficients would otherwise not be very precise. As mentioned above, we use the first year a country introduced any of these reforms to create the ������ variable in our preferred specification.

As is the case with Rajan and Zingales’s (1998) measure of financial dependence, the measure of the sector’s ICT intensity should not be driven by the local demand and supply of Internet in the economy. Otherwise, this variable would be endogenous to the introduction of telecommunications reforms. Instead, the measure of ICT intensity should be independent of each country’s conditions. We assume that there is a technological reason to explain why some industries are more likely to adopt the Internet than others. For instance, the type of work carried out in certain industries may be more suitable to benefit from the use of the Internet at work. We measure the ICT intensity of a sector using data from a relatively frictionless environment. We argue that ICT use by firms in the United States is a relatively pure measure of their demand for this technology, because it is one of the first countries to have implemented telecommunications reforms to improve access and because its friendlier businesses environment may create more competitive pressures for firms to innovate and adopt new technologies.<sup>2</sup> As the measure of ICT intensity, therefore, we use data on ICT capital services per hour worked from EUKLEMS for the United States in the year 2007 (see **Table 2** ).<sup>3</sup> We estimate the dependent variables related to the labor market using the EU Labour Force Survey (EU‐ LFS) for the reference years 1995–2013. The EU‐LFS is the largest European household survey, with 1.8 million interviews conducted each quarter throughout the participating countries. The survey collects labor and demographic information for residents living in private households in the 28 member states of

> 2 There is a large literature on the effects of competition on innovation and technology adoption. Examples include Aguion et al. 2009; Ayyagari et al. 2012; Macher et al. 2015; Seim et al. 2011.

> 3 In a previous version of this paper we used, as a proxy for ICT intensity, the fraction of workers using a computer at work by sector taken from the Survey of Adult Skills (from the Programme for the International Assessment of Adult Competencies, PIAAC) for the United States. The results were very similar and are available from the authors upon request.

9
|Food insecurity score (0-12)|1.49|0.96|1.23|0.53|0.26|-0.27|
|---|---|---|---|---|---|---|
||(0.35)|(0.28)|(0.17)|(0.44)|(0.38)|(0.33)|
|Received support from social programs (1=yes)|0.19|0.23|0.22|-0.03|-0.03|0.01|
||(0.05)|(0.06)|(0.03)|(0.08)|(0.06)|(0.07)|
|Total land size (in ha)|1.48|1.83|1.65|-0.35|-0.18|0.17|
||(0.15)|(0.30)|(0.11)|(0.33)|(0.19)|(0.31)|
|No. of TLU owned|3.78|4.68|4.22|-0.90|-0.43|0.47|
||(0.31)|(0.45)|(0.28)|(0.54)|(0.41)|(0.53)|
|No. of adopted quickwins (0-5)|2.66|2.71|2.63|-0.05|0.02|0.07|
||(0.21)|(0.17)|(0.10)|(0.27)|(0.23)|(0.20)|
|Grows main crop (1=yes)|0.99|1.00|1.00|-0.01|-0.01|0.00|
||(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|(0.00)|
|N|94|106|200|200|294|306|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Total no. of observations N = 400. FREG stands for ‘Farmer research and extension group’. For variable definitions see notes of Table 1. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

10
- 17 -

11. For each type of general training,<sup>there is likely to be a corresponding,</sup> but different, specific training required. Thus, trying<sup>out a worker in</sup> one of the 1-1 other jobs will entail additional<sup>investments in specific</sup> training, again adding to the costs of discovery.

12. In a private communication (4 Dec. 1989), Jacob<sup>Mincer points out that a</sup> trained worker has an incentive to convey information about his training to the recruiting firm, which could be verified after a relatively<sup>short</sup> period. The central question, however, is whether the<sup>trained worker</sup> would be believed, given that untrained workers<sup>may attempt to obtain a</sup> free ride by posing as trained.

13. A discussion of the technical aspects<sup>of such sampling, accompanied</sup> by some numerical examples, is given in Appendix<sup>2.</sup> 14. It is assumed here that neither T nor V are affected<sup>by the number of</sup> workers trained.

15. For the purposes of this illustration it is assumed that as the informational asymmetry increases, ZN tends to zero.

16. This line of reasoning suggests firms would<sup>also try to reduce training</sup> visibility by refraining from writing references<sup>for employees wishing to</sup> move to another firm. Indeed, a recent survey has<sup>shown that 40 percent</sup> of companies surveyed in the US have a formal,<sup>written policy not to</sup> provide outside references--a finding in conformity with the predictions of our model (NACPR, 1989). Fear of lawsuits from disgruntled<sup>former</sup> employees who are turned down for a new job may<sup>also help to explain</sup> employers' reticence in this matter.
contrarian trading based on contemporaneous returns (akin to what we obtained with the contemporaneous Ferson and Khang, 2002, measure in Table 13). There is still significant evidence of lagged momentum trading for domestic equities and quotas of foreign investment and mutual funds, which highlights the robustness of the evidence of momentum strategies in these asset classes, and some evidence of lagged momentum trading for foreign fixed-income assets and foreign equities.

To analyze the degree of regulatory constraints faced by different fund types, we treat each combination of PFA and type of fund as a portfolio and compute the momentum statistics for each PFA-fund-type at each point in time. We decompose the variation of these measures in PFA, asset-class, and time fixed effects, restricting the various sets of fixed effects to have zero mean and represent, therefore, deviations from the overall mean of the measure. Because all five portfolios held by a PFA are probably correlated and to conduct proper inference, we decompose the variation of the fund-type statistics as: where _LM j_ , _l_ , _t_ is the momentum measure for PFA _j_ , portfolio _l_ , at time _t_ . The θ ’s are fixed effects in the dimension indicated by the sub-index, restricted to have zero mean within their dimension. Therefore, the θ _l_ parameters represent the deviations of the average momentum statistic in each portfolio type with respect to the overall mean. Table 9 presents results that were estimated similarly as in equation (14) after adding an assetclass fixed effect to the specification. The results without asset-class fixed effects are not presented but are available upon request. The differences across fund types resulting from equation (14) are unconditional and those resulting from Table 9 (adding asset-class fixed effects) are conditional on the asset-class combination of each portfolio type, which is important because of the differences in momentum across asset classes reported above.

50
Edenhofer, O., B. Knopf, M. Leimbach, N. Bauer (2010)., The economics of low stabilization, _Energy Journal_ 31 (Special Issue I) Edenhofer, O. K Lessmann, C Kemfert, M. Grubb and J. Köhler (2006) Induced Technological Change: Exploring its Implications for the Economics of Atmospheric Stabilization: Synthesis Report from the Innovation Modeling Comparison Project _The Energy Journal,_ vol. Endogenous Technological Change, issue Special Issue #1, 57-108 Emmerling, J., L. Drouet, L.A. Reis, M. Bevione, L. Berger, V. Bosetti, S. Carrara, E. De Cian, G. De Maere D'Aertrycke, T. Longden, et al. The WITCH 2016 Model—Documentation and Implementation of the Shared Socioeconomic Pathways Fricko, O. et al. (2016) Energy sector water use implications of a 2°C climate policy. _Environmental Research Letters_ , 11(3), 034011.

Fujimori, S., T. Masui, Y. Matsuoka (2012) AIM/CGE [Basic] Manual, Center for Social and Environmental Systems Research, NIES, Tsukuba, Japan Gillingham, K, W Nordhaus, D Anthoff, G Blanford, V Bosetti, P Christensen, H McJeon, andJ Reilly (2018), ‘Modeling Uncertainty in Integrated Assessment of Climate Change: A Multimodel Comparison’, Journal of the Association of Environmental and Resource Economists 5(4): 791-826.

<mark>Grubb, Michael, Claudia Wieners, Pu Yang (2021) Modeling myths: On DICE and dynamic realism in integrated assessment models of climate change mitigation https://doi.org/10.1002/wcc.698</mark> Grubler, A. et al., 2018: A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies. Nature Energy, 3(6), 515–527, doi:10.1038/s41560-018-0172-6.

Guivarch, C., R. Crassous, O. Sassi, and S. Hallegatte, 2011: The costs of climate policies in a second-best world with labour market imperfections. Climate Policy, 11(1), 768–788, Hänsel, Martin C. Moritz A. Drupp, Daniel J. A. Johansson, Frikk Nesje, Christian Azar, Mark C. Freeman, Ben Groom & Thomas Sterner (2020) Climate economics support for the UN climate targets, _Nature Climate Change_ volume 10, 781–789.

Hejazi, M. et al., 2014: Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technological Forecasting and Social Change, 81, 205–226, doi:10.1016/j.techfore.2013.05.006.

38
**Table 2: Heterogeneity by access to divorce**

||(I)<br>Physical<br>Violence|(II)<br>Sexual<br>Violence|(III)<br>Any<br>Violence|
|---|---|---|---|
|A: Equal access to divo|rce|||
|ln(Uf)|1.433***|1.526***|1.211***|
||(0.087)|(0.019)|(0.051)|
|ln(Um)|-0.789***|-0.815***|-0.662***|
||(0.045)|(0.009)|(0.023)|
|Observations|95040|35794|35736|
|X controls|Y|Y|Y|
|Countryand Year FE|Y|Y|Y|
|B: Women lack equal a|ccess to divorce|||
|Ln(Uf)|-0.517***|-0.290**|-0.631**|
||(0.107)|(0.131)|(0.243)|
|Ln(Um)|0.562***|0.200|0.597***|
||(0.077)|(0.121)|(0.191)|
|Observations|253125|232379|230781|
|X controls|Y|Y|Y|
|Countryand Year FE|Y|Y|Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. All estimates are of equation (1). The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (I), sexual (II) or either (III) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Details on the countries in samples in panel A and B, as well as the definition of equality in access to divorce, is given in Table S1.2. Differences in observations between columns (I) compared to (II) and (III) are due to information on sexual violence not being collected in all surveys (see Table S1.1).

29
An important focus of these works is how best to estimate the size of the informal sector. Methodologies include using demand for cash (Tanzi, 1980), electricity inputs (see Johnston et al., 1998), and the MIMIC method (see Schneider, 2002 and World Bank, 2007 for comprehensive reviews).

In parallel, many of these contributions investigate the determinants of informality. There is consensus that unevenly enforced and burdensome regulations (De Soto, 1989; Djankov et al., 2002; Loayza et al, 2005; Loayza and Rigolini, 2006), corruption<sup>5</sup> (Friedman et al., 2000), financial development and entry costs (Straub, 2005) are significantly associated with informality.<sup>6</sup> In parallel, Loayza and Rigolini (2006) provide convincing evidence that the informal sector – as proxied by the share of self-employed workers in the labor force – is countercyclical.

In this paper we focus on a specific definition of informality – tax compliance (or lack thereof) by registered, formal firms – and, amongst others, we provide evidence of how this variable correlates with countries characteristics. In this sense, our paper contributes to this literature by proposing an alternative measure, albeit partial, of the size of the informal sector.

Our main focus, however, is studying the link between informality and access to credit. In particular, we aim at providing systematic empirical content to the claims that informality affects productivity and its determinants in significant ways so as to inform on the channels through which these effects operate. This work is thus linked to the vast literature that identifies access to credit as an important engine for firm growth (see for example Claessens and Leaven, 2005 and Levine, 2005). In many of these models, access to credit affects productivity by alleviating information and transaction costs and making the longer-gestation, higher-return projects more attractive (see, for example, King and Levine, 1993; and Bencivenga et al., 1995). Using data for Bulgarian firms, Gatti and Love (2006) show that access to credit is robustly and significantly associated with TFP. In turn, Easterly and Levine (2001) provide macroeconomic evidence that total factor productivity (TFP) accounts for most of the variation in the cross-country differences in economic development and growth.

Finally, the recent availability of firm-level data has spurred new interesting empirical work that is closely related to ours. Using Mexican micro firm surveys, Fajnzylber et al. (2006a)

> 5 In a companion paper (Gatti and Honorati, 2007) we investigate the relationship between formality and corruption at the firm level. Preliminary findings indicate that tax compliance is consistently negatively associated with bribe paying.

> 6 A related literature addresses specific issues of tax compliance (see the review in Andreoni et al., 1998). However, because of data restrictions, empirical work on tax compliance focuses mostly on individuals.

5