rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The minimum wage is an increasingly popular policy tool globally. Studies on countries of various income levels and stages of development find that minimum wages boost labor income by raising wages in the formal sector, thereby lessening deprivation (de Janvry and Sadoulet, 1996; Devereux, 2005; Bird and Manning, 2008; Gindling and Terrell, 2010; Lustig and McLeod, 1997; Saget, 2001). Such labor wage regulations are intended to improve workers’ welfare, but a rise in labor costs can reduce the number of employees firms have the capacity to hire and retain, and can squeeze profit margins to such a degree that firms’ survival may be at risk (Draca, Machin, and van Reenen, 2011). The minimum-wage literature on firm-level responses has been growing in high-income countries in recent years, but it remains quite sparse for developing countries.<sup>1</sup> In addition, due to data limitations, firm-level outcomes are typically analyzed in isolation from worker-level outcomes. The issue warrants greater attention; firm-level responses and welfare effects of the minimum wage may be different in developing-country settings, given prevailing labor-market characteristics, such as weak allocative efficiency, low firm productivity, and high informality.

This paper addresses this gap by studying the effects of raising the minimum wage on formal firm destruction, and the accompanying impacts on informal and informal employment in the context of a developing economy. We analyze the repercussions of this increase by using an extremely rich, employer-employee-match dataset that covers the universe of registered private firms and formal workers in Turkey.<sup>2</sup> This enables us to look jointly at the impacts of the minimum wage on firms and workers. We supplement this with data from a nationally representative survey on labor-market outcomes of the Turkish population; these data include representative samples of those who are employed (in formal and informal work), and those who are unemployed or out of the labor force. We estimate the effects of the minimum wage increase by exploiting a sharp rise by 33 percent in nominal terms that took effect in 2016. Though we mainly focus on firm destruction, our findings also shed light on impacts on workers’ employment in formal and informal firms, and unemployment in the wake of the wage increase. Thus, the paper also provides insights on the broader employment and welfare

> 1 In high-income countries, primarily in the US and the UK, the literature has looked at the effect of the minimum wage on firm profitability, stock market value, productivity, location, entries and exits, and consumer prices (Aaronson, 2001; Aaronson and French, 2007; Aaronson et al., 2018; Allegretto and Reich, 2018; Bell and Machin, 2018; Draca, Machin, and van Reenen, 2011; Riley and Rosazza-Bondibene, 2017; Rohlin, 2011). 2 In contrast, the vast majority of studies looking at firm responses to the minimum wage, including in high-income countries, rely on a sample of firms above a given size threshold and in specific sectors of the economy.

2
financial technologies (Sahay et al., 2020). While we cannot test this directly because we lack a measure of regulation of DFS, particularly digital lending, for a wide enough sample of countries, we know that banks are the most heavily regulated and supervised financial institutions in most countries, including low- and middle-income countries (hereafter referred to as LMICs; Anginer et al., 2022; World Bank 2019). We also know that banks, when permitted, have become increasingly involved in the provision of DFS, sometimes partnering with Fintech companies (Pazarbasioglu et al., 2020; Sahay et al., 2020). We therefore test a hypothesis related to the idea that light regulation of digital lending may foster instability, namely that banking sector development is associated with development of DFS, and that (overly) restrictive regulations and supervisory practices regarding the range of activities that banks may engage in can impede DFS development. In our regressions, we control for banking assets (as a share of GDP), which should be positively linked to DFS, and an index of restrictions on banks’ activities from the BRSS, which should be negatively linked to DFS indicators (as described above).

_Infrastructurei,2019_ represents two variables that describe the quality of digital infrastructure and the level of connectivity in a country, namely the share of the population with access to electricity and the share that uses the internet. We also control for the number of mobile cellular subscriptions (per 100 residents) since DFS are increasingly used via mobile phones. We expect that digital payment usage is positively linked to all three of these connectivity variables (access to electricity, usage of internet, and prevalence of mobile cellular subscriptions).

_Digital Regulationi,2019_ represents the two indexes compiled from the World Bank Global Data Regulation Diagnostic described above – the index of enablers that facilitate use/reuse of data and the index of safeguards that protect the rights of market players in the data economy. We expect that at least the latter indicator will be positively associated with digital payment usage 13
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|---|---|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Micro,Small and Medium Enterprise Project|Ismail Radwan|
|Coal Mine Closure Project|Adriana Maria Eftimie|
|Coal Mine Closure Project|Adriana Maria Eftimie|
|Coal Mine Closure Project|Adriana Maria Eftimie|
|Coal Mine Closure Project|Adriana Maria Eftimie|
|Coal Mine Closure Project|Adriana Maria Eftimie|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|RAIL TRADE AND TRANSPORT FACILITATION|Antoine Avedis Kunth|
|First East-West HighwayImprovement|Jean-Francois Marteau|
|First East-West HighwayImprovement|Jean-Francois Marteau|
|First East-West HighwayImprovement|Jean-Francois Marteau|
|First East-West HighwayImprovement|Jean-Francois Marteau|
endogenous variables. It is important to remember that EViews will treat the first variable encountered in the specification of an equation as the endogenous variable for that equation.

The third step involves calibration and initialization of model variables. All variables can be declared at once using the FOR LOOP controlled by a string variable. Most variables are initialized on the basis of values contained in the SAM. We find it more convenient to set up the calibration process as a separate model whereby structural values of the parameters are computed as a function of observed values of relevant variables. The fact that EViews solves the model for each observation in the workfile means that sensitivity analysis can be built in and performed with a single SOLVE statement. The final step performs simulations starting with the baseline solution. The SCENARIO procedure is a very useful tool in this context as it allows one to keep in the same workfile solution values associated with different assumptions about some exogenous variables. The empirical examples studied in this paper, based on artificial data and a macroeconomic SAM for Indonesia in 2002, replicate the welfare and structural effects of shocks and policies as predicted by the underlying conceptual framework. They also reveal the key role played by structural parameters such as the elasticity of transformation and that of substitution in determining the extent of structural adjustment to shocks and the relevance of the policy response.

28
Policy Research Working Paper 8075

# **Abstract**

This paper studies the performance of alternative prediction models for conflict. The analysis contrasts the performance of conventional approaches based on predicted probabilities generated by binary response regressions and random forests with two unconventional classification algorithms. The unconventional algorithms are calibrated specifically to minimize a prediction loss function penalizing Type 1 and Type 2 errors: (1) an algorithm that selects linear combinations of correlates of conflict to minimize the prediction loss function, and (2) an algorithm that chooses a set of thresholds for the same variables, together with the number of breaches of thresholds that constitute a prediction of conflict, that minimize the prediction loss function. The paper evaluates the predictive power of these approaches in a set of conflict and non-conflict episodes constructed from a large country-year panel of developing countries since 1977, and finds substantial differences in the in-sample and out-of-sample predictive performance of these alternative algorithms. The threshold classifier has the best overall predictive performance, and moreover has advantages in simplicity and transparency that make it well suited for policy-making purposes. The paper explores the implications of these findings for the World Bank’s classification of fragile and conflict-affected states.

This paper is a product of the Fragility, Conflict and Violence Cross Cutting Solution Area, and the Macroeconomics and Growth Team, Development Research 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 at bceliku@worldbank.org or akraay@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 of the World Bank or the governments they represent._ Produced by the Research Support Team
7

# Table (1.1) ECONOMIC STRUCTURE AND PERFORMANCE IN SUB-SAHARAN AFRICA

||1970-81|1982-85|1986-89|
|---|---|---|---|
|Real growth rates||||
|GDP|3.7|1.4|2.0|
|Exports|3.6|1.1|3.3|
|Share in GDP||||
|Agriculture|39.3|36.7|36.3|
|Manufacturing|20.1|22.0|21.7|
|Services|37.9|41.3|41.2|
|Share in Labor Force||||
|Agriculture|78.5|||
|Manufacturing|7.8|||
|Services|13.7|...||
|Rural population (% of total population)|79.6|74.4|71.2|
|Source: World Bank Data Bank (BESD)||||
Figure 3: Growth in Capital per worker, Human Capital per worker, and TFP <!-- Start of picture text --> 0.04<br>0.03<br>0.02<br>0.0<br>-0.010<br>-0.02<br>1971-73 1974-76 1977-79 1980-82 1983-84 1987-88 1989-91 1992-94 1995-97<br>| Capital per worker * Human K per worker 1 TFPG I<br><!-- End of picture text -->

Figure 4: Contribution of Capital per worker, Human Capital per worker, and TFPG to growth in Output per worker <!-- Start of picture text -->
1.2<br>0.6<br>0.4<br>0.2<br>-0.2<br>-0.4 -<br>-0.6<br>1971-73 1974-76 1977-79 1980-82 1983-84 1987-88 1989-91 1992-94 1995-97<br>L<br>|CICapital per worker *Human K per worker OTFPG<br><!-- End of picture text -->

19
WPS3804

# Disinflation, Fiscal Sustainability, and Labor Market Adjustment in Turkey

∗ ∗∗ Pierre-Richard Agénor, Henning Tarp Jensen, Mathew Verghis,∗∗∗ and Erinc Yeldan∗∗∗∗

## World Bank Policy Research Working Paper 3804, January 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._ ∗University of Manchester, and Centre for Growth and Business Cycle Research; ∗∗University of Copenhagen; ∗∗∗World Bank; and ∗∗∗∗Bilkent University. We are grateful to Vandana Chandra, Koray Alper, participants from Treasury, the State Planning Organization, and the Central Bank of Turkey at presentations made at the General Directorate of Economic Research of the Undersecretary of Treasury in Turkey, participants at seminars at Bogazici University and the World Bank, Vikram Nehru, Edgardo Favaro, Santiago Herrera, and especially Peter Montiel, for helpful comments and discussions.
# Annex 1: Bibliography

Barron's online; _Crowed Skies: Will satellite phone systems come crashing down on investors' heads,_ June 15 1998 Brook Cowen, Penelope and Cowen, Tyler (1998); 'Deregulated private water supply: A policy option for Developing Countries', _The Cato Journal, Vol.18, No. I_ Chernow, R (1997); _The Death of the Banker: The decline and fall of the great financial dynasties and the triumph of the sncall investor,_ Vintage Books, Random House Inc., New York Commerce Commission (1996); _Report on the proposed acquisition of shares in Ansett Australia by Air New Zealand_ Crandall and Ellig (1997); _Economic Deregulation and Customer Choice: Lessons for the Electric Industry,_ The Centre of Market Processes Inc.

Crane, Randall (1994); article in _Water and Development announced in Voices from the City, the Peri-urban network newsletter,_ April 1994; cited in Solo and Snell (1998) de Boles, David and Evans, Lewis (1995); _The economic efficiency of telecommunications in a deregulated market: The case of New Zealand,_ Graduate School of Business Government and Management Wellington University Working Paper 8/95 De Soto, Hemando (1989); _The Other Path,_ New York, New York, Harper and Row; cited in Mauro (1995) Drangaert, Pili, Malgarejo, Silvio, Kemper, Karin and Bakalian Alex (1997); _Water Supply in Paraguay: The Role of Small Entrepreneurs,_ World Bank Economist, The, 12 August 1995; _Investing in Africa: A new scramble_ Economist, The, May 30 - June 5th 1998 p.<sup>3 5</sup> ESMAP (1991); _Republic of Yemen Household Energy Strategy Study, Phase I A Preliminary Study of Northern Governorates,_ Report No. 126/91 Energy Sector Management Assistance Program (ESMAP) ESMAP (1993); _Nigeria: Issues and Options in the Energy Sector,_ (No.1 1672-UNI) ESMAP (1997); _Institutional Development for OFFGRID Electrification in Laos (Draft),_ UNDP and World Bank Energy Sector Management Assistance Programme, March 1998 Financial Times Business Report: Technology (Q3:35) 13 February 1997 Frost, R. L (1991); _Alternating Currents: Nationalised Power in France, 1946-1970,_ Ithaca, Cornell University Press,
piped water or sanitary services, no electricity, low quality of fuel for cooking) and household assets (does not own a TV, blender, refrigerator, telephone, automobile, boiler).

The types of economic outcomes summarized by the variable _Y_ in equation (3) are limited by the information collected in the 2000 Population and Housing Census. We use the following outcomes: (i) whether the individual is an internal migrant in the sense that he/she was born in a different state than he/she is currently residing (1=yes, 0=no) ; (ii) whether the individual is receiving cash transfers from PROCAMPO and/or OPORTUNIDADES programs (1=yes, 0=no)<sup>4</sup> ; (iii) whether the individual is employed as an employee or laborer ( _empleado o obrero_ ); (iv) employed as a day laborer or unskilled worker ( _jornalero o peon_ ); and (v) selfemployed. We also used binary variables identifying some key sectors of employment among indigenous peoples, such as agriculture (code=11) and manufacturing (codes 31-33). For teenagers between 12 and 17 years of age, we also used the information of whether the child is attending school and working.

Based on the specification of equation (3), the existence of social networks can be demonstrated by positive estimates of the parameter α ( α > 0 ) that are significantly different from zero. These estimates provide evidence on the degree to which of social networks or social ties play a significant explanatory role in the different social and economic outcomes after accounting for the role of other individual socioeconomic characteristics, household assets, access to infrastructure, the type of indigenous language spoken and the municipality of residence. It is important to keep in mind that this empirical approach provides is very

> 4 Oportunidades has a particularly heavy presence among the indigenous. As shown by Ramirez (2006),  in 2002 nearly 70 percent of the poorest fifth of the population in indigenous municipalities received Oportunidades, compared to only 42 percent in non-indigenous ones".

8
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||**DOMAIN**||**INDICATOR OF FORMS OF EMP**|**OWERMENT**|
|---|---|---|---|---|
||**Sub-domain**|**National**|**Intermediary**|**Local**|
|||•Score of satisfaction with<br>national executive<br>administration (key line<br>ministries)<br>•Score of effectiveness of<br>regional executive<br>administration (key line<br>ministries) compared with<br>other social groups|•Score of satisfaction with<br>regional executive administration<br>•Score of effectiveness of regional<br>executive administration<br>compared with other social<br>groups|•No. of publicly provided services available<br>locally (4.33)<br>•Percentage able to access public services (4.34;<br>4.37)<br>•Number of public services used (4.35)<br>•Score of quality of public services used (4.36)<br>•Percentage individuals that have complained<br>about public service delivery (4.38)<br>•Percentage of households that have complained<br>about public service delivery (4.39)<br>•Frequency of complaints (4.40)<br>•Score of satisfaction with outcome of complaint<br>(4.41)<br>•Score of equitability in addressing needs and<br>concerns (4.42)<br>•Score of influence of social characteristics on the<br>authorities treatment of people (4.43)|
|**Market**|**Credit**<br>**Labor**|•Score of civil society<br>advocacy activity for pro-poor<br>credit provision<br>•Percentage of credit provision<br>by formal institutions<br>according to<br>social/ethnic/religious group<br>•Diversity of national credit<br>providing institutions|•Score of consultation levels by<br>credit providing agencies with<br>clients<br>•Number of partnerships in credit<br>system design and delivery<br>•Diversity of local formal credit<br>sources<br>•Diversity of local informal credit<br>sources|•Percentage needing to borrow money or goods in<br>past year (4.44)<br>•Percentage borrowing money or goods in past<br>year (4.45)<br>•Score of awareness of formal/ informal credit<br>services (4.46)<br>•Score of accessibility to formal credit-providing<br>institutions (4.47-4.50)<br>•Score of control over loans and savings (4.51-<br>4.52)|

76
The main specification measures IPV as dummy variables, taking the value of one if the woman has ever faced i) physical, ii) sexual or iii) any form of violence from her partner. As an alternative, the study aggregates information from the underlying questions on specific acts of IPV into summary indices, since this aggregation can improve statistical power to detect effects that go in the same direction within a domain (Kling et al. 2007). The study firsts convert answers to individual physical violence and sexual violence questions to z-scores by subtracting the country-specific mean and dividing by the country-specific standard deviation, for each of the individual questions on physical and sexual violence. The study then averages over these z-scores to create an index for i) physical violence, ii) sexual violence, as in Erten and Keskin (2018). Finally, it creates an index for iii) any violence, which is the arithmetic mean of the physical and sexual violence indices. Results in Table S1.5 show that the results are robust to using indices of violence, and gain statistical significance when considering sexual violence (Panel B), which now becomes statistically significant. Overall, the results are qualitatively similar and confirm the main analysis.

Finally, the study investigated the association of domestic violence with the (potentially endogenous) _individual_ unemployment status of the woman and her partner, using questions in the DHS that ask whether the individual worked in the last 12 months. This acts as a consistency check on the main analysis. A caveat is that male unemployment is measured poorly in the DHS data – it is collected in only 18 of our 31 countries<sup>34</sup> , and even where it is collected, it is missing in around 67% of cases. Still, the pattern of results for individual unemployment mirrors the pattern for unemployment rates (Table S1.6). Holding constant the unemployment status of their spouse, female unemployment in the last 12 months is associated with lower incidence of spousal violence. Male

> 34 This discrepancy in availability across countries is due to the fact that women are not asked about the employment status of their partners. Information on spousal unemployment is only available for surveys in which a men’s survey is collected, which is not the case in all countries.

17