rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Descriptive statistics: Documented versus undocumented return migrants

||Docu|mented|Undoc|umented||
|---|---|---|---|---|---|
||mig|rants|mig|rants||
||(1)<br>Mean|(2)<br>St. Dev.|(3)<br>Mean|(4)<br>St. Dev.|(5)<br>Diference|
|_Individual characteristics_||||||
|Age|42.332|9.736|42.652|10.260|-0.321|
|Ever-married|0.950|0.219|0.947|0.225|0.003|
|No educational degree|0.169|0.375|0.234|0.424|-0.065**|
|Primary or preparatory education|0.149|0.356|0.131|0.338|0.018|
|Secondary education|0.462|0.499|0.518|0.501|-0.055|
|Above secondary education|0.220|0.415|0.117|0.322|0.103***|
|Rural|0.600|0.491|0.699|0.460|-0.099***|
|_Geographical regions_||||||
|Cairo|0.103|0.304|0.043|0.202|0.060***|
|Alexandria and Canal cities|0.050|0.219|0.046|0.210|0.004|
|Urban Lower Egypt|0.124|0.329|0.131|0.338|-0.008|
|Urban Upper Egypt|0.128|0.335|0.082|0.274|0.047*|
|Rural Lower Egypt|0.304|0.461|0.511|0.501|-0.206***|
|Rural Upper Egypt|0.291|0.455|0.188|0.391|0.103***|
|_Current job characteristics_||||||
|Job tenure|11.847|8.909|13.904|9.743|-2.058***|
|Incidence of work contract|0.501|0.501|0.468|0.500|0.033|
|_Sector of employment_||||||
|Public sector|0.435|0.496|0.436|0.497|-0.001|
|Private sector|0.551|0.498|0.550|0.498|0.002|
|Other sector|0.014|0.117|0.014|0.118|0.000|
|_Economic activities_||||||
|Agriculture, Forestry, Fishing|0.121|0.327|0.113|0.318|0.008|
|Manufacturing, Mining, Quarrying|0.128|0.335|0.131|0.338|-0.003|
|Construction|0.181|0.385|0.191|0.394|-0.011|
|Wholesale, retail trade and transportation|0.162|0.369|0.156|0.364|0.006|
|Professional and administrative activities|0.023|0.150|0.025|0.156|-0.002|
|Other activities|0.384|0.487|0.383|0.487|0.001|
|Observations|4|37|2|82||

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

_Notes_ : Column 5 is t-test for whether the difference in means between the two groups is statistically significant.

37
# **_Market regulation_**

This is an unweighted average of the credit market and labor market regulation indexes provided by the Economic Freedom of the World’s Annual Report.<sup>16</sup> The credit market regulation captures issues such as the percentage of deposits held in privately owned banks, the degree of competition from foreign banks, the extension of credit to private sector and the interest rate controls. The labor market regulation index comprises the impact of minimum wages, hiring and firing practices, union coverage and the effect of unemployment benefits on the incentives to work. Please keep in mind that the Economic Freedom of the World indexes are constructed from hard data and survey data (such as the Global competitiveness Report). Hence there is room for subjective interpretations.

# **_Quality of the legal system_**

This variable results from the multiplication of the World Bank’s objective information on the efficiency of the courts in enforcing contracts and the subjective evaluation of the 2003 Economic Freedom of the World of the independence from the government, impartiality and integrity of the judicial system.

The World Bank’s database “Doing Business” has information on issues related to enforcing contracts derived from questionnaires answered by attorneys at private law firms. The questionnaires cover the step-by-step evolution of a debt recovery case before local courts in the country’s most populous city. It covers issues such as the number of procedures required, duration and cost of process as well as an index of the procedural complexity of contract enforcement. We have summarized the four dimensions related to contract enforcement (procedures, duration, cost and complexity) in a unique index following the same methodology as before, that is, assigning a 10 to the best country, a 0 to the worst and then taking the average.

# **_Size of the government_**

The size of government is approximated by the output supplied by State-operatedEnterprises and government investment as a percentage of GDP. The hard data come from the Economic Freedom of the World 2003 annual report.

# **_Corruption Perception Index_**

The Corruption Perception Index is constructed by Transparency International. The data refers to 2002 and ranks the countries according to the degree in which corruption is perceived to exist among public officials and politicians. It draws on 15 different polls and surveys from nine independent institutions.

# **_The tax burden_**

Tax revenue as percentage of GDP. It comes from the World Development Indicators of the World Bank.

> 16 The following set of countries is excluded from the Economic Freedom Index: Kyrgyz Republic, Serbia and Montenegro, Bosnia, Macedonia, Kazakhstan, Uzbekistan, Moldova, Belarus, Azerbaijan, Georgia and Armenia

27
Federalism serves as a laboratory for policy inventions (Oates, 1999). Looking at Switzerland, at the cantonal level, evaluating the impact of federalism on tax morale and the size of the shadow economy, Torgler, Schneider and Schaltegger (2010) provide evidence that a higher level of local autonomy is correlated with higher tax morale and a lower shadow economy.

_3. Evidence from Developing Countries: The Case of South Africa and Botswana_ Further insights that might be relevant for transition countries might be derived from comparative evidence in developing countries, under a relatively controlled environment. Cummings, Martinez-Vazquez, McKee and Torgler (2009) have compared Botswana and South Africa. Both countries have experienced strikingly different social histories, despite being neighbors. Thus, these countries offer a natural experiment for the investigation of the effects of tax morale stemming from perceptions of government. Analyses of data from surveys of public attitudes toward government show that perceptions of government fairness and efficacy are considerably higher in Botswana and self-reported tax compliance appears to be higher as well. Using controlled laboratory experiments Cummings et al. (2009) are able to confirm that these reported differences in tax attitudes can be explained by social norms. In the experiments, the same tax reporting and enforcement regimes were imposed in sessions conducted in both countries. Given the experimental control, the authors argue that observed differences in compliance behavior are the result of differing social norms. Similarities in the tax systems allow employment of the same jargon in each country, and since English is widely used in both countries, the authors were able to conduct the experiments in a common language. These facilitate comparisons of tax compliance behavior across these otherwise widely divergent countries. Their laboratory experiments were conducted in the fall of 1999 and the survey data were collected during 1999 – 2000.

We have seen in the previous part of the report that tax compliance not only depends on enforcement effort, but also on the inhibitors that are inherent in the individual-government relationships. For the personal income tax (PIT), the selfassessment and audit processes are similar in both countries although there are varying degrees of aggressiveness in enforcement. Both countries rely on some form of 25
36

# VI. REFERENCES

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entrepreneurship: while the treatment group was more likely to try setting up a project and succeed at some point as a result of trying more, most impacts are concentrated in the year after graduation and phase out after that. Overall, patterns of heterogeneity are also weak, and the lack of medium-term impact holds across sub-groups based on gender, family wealth, skills or social capital. Finally, among the changes in skills that were observed in the short-term, only a small impact on business knowledge remained four years later. No lasting impacts were found on other intermediary outcomes conducive to entrepreneurship. Overall, there are no effects on latent entrepreneurship either. Most students who had business ideas have abandoned them and participants of the entrepreneurship training do not have more business ideas than the control group. The increased optimism observed one year after graduation was also short lived.

The paper is organized as follows. Section 2 provides context and an overview of the intervention. Section 3 provides a description of the experimental design, study timeline and data.<sup>1</sup> Section 4 details the empirical identification strategy. Section 5 provides the main results of the intervention on self-employment in the short and medium runs. Section 6 presents results on the mechanisms and channels for impact on entrepreneurship. Section 7 presents results of the intervention on broader employment outcomes. Section 8 concludes.

# **2. Context and Intervention**

Tunisia has been going through substantial political, social and economic transformations in recent years. Citizens’ demands for quality jobs contributed to the onset of the so-called Arab Spring in 2011 and have not subsided since. More than half of the Tunisian working-age population is outside the labor force, the vast majority being women. A high share of workers are occupied in the informal sector.<sup>2</sup> Unemployment rates are particularly high among youth (aged 15-29) holding a tertiary degree (the majority of whom are female<sup>3</sup> ), reaching 62 percent in 2012

> 1 Sections 2 and 3 build on Premand et al. (2016) and are provided to ensure the paper is self-standing.

> 2 Angel-Urdinola et al. (2015) report that 64 percent of employed individuals in Tunisia are either informal wage earners or self-employed. Data from OECD (2015) show that a large proportion of youth is employed in informal jobs: share of youth who work: i) without a contract (24%); ii) in an unregistered firm (40%); iii) in a micro-business (five or fewer employees) (56%); and iv) without contributing to social security (53%). 3 Out of the pool of those unemployed, aged 15-29 and with a tertiary degree, 68 percent are female and 32 percent are male.

5
# **1. Introduction**

An old adage goes, “You are what you eat.” While the original saying was mainly intended to provoke a better diet, the spirit of this adage sits deep within how economists view the determinants of firm productivity. In particular, economists have long recognized that access to material inputs and their qualities can influence the performance and success of firms. Not surprisingly, the sourcing of inputs thus drives the location choices of many firms. Moreover, many discussions of the differences in productivity across countries highlight the weak input conditions in developing or emerging economies that limit firm performance.

This paper uses establishment-level manufacturing data for India to trace out one feature of this process. Over the last two decades, endogenous growth theory has emphasized a particular gain for differentiated input access over and above the basic qualities of available inputs to the firm. The source of this productivity advantage comes from the fact that differentiated inputs allow firms to better match the needs that they have with the tools and materials they utilize. For example, it is possible for a firm both to cut a board and open up a can with a saw, and it is likely also possible (with enough time) to do both tasks if the firm only possesses a can opener. Nevertheless, we would generally anticipate firms to be more productive if they have access to both tools. The essential gain that the literature emphasizes is that this gain is not due to the better quality of the saw or the can opener, but instead their joint use. By having a greater range of potential inputs, firms are best able to match needs with inputs and thus encounter less diminishing productivity in their materials usage.<sup>1</sup> We empirically quantify these patterns in the Indian data with a focus on raw material inputs into the firm. We consider the organized and unorganized manufacturing sectors in the year 2000, and we evaluate spatial dimensions and productivity connections to varied input use. A very attractive feature of Indian data is the direct collection of input records for all plants at the establishment level. This allows us to simultaneously compare inputs to the local or industry conditions of firms and to consider productivity implications.

> 1 Seminal theoretical contributions include Romer (1987, 1990), Rivera-Batiz and Romer (1991), and Grossman and Helpman (1991). Evidence at the firm level from the trade literature on these links includes Muendler (2004), Halpern, Koren, and Szeidl (2006), Amiti and Konings (2007), and Kasahara and Rodrigue (2008). Goldberg et al. (2010a,b) provide a detailed review of the sector- and product-level studies also undertaken.

2
34 Freidson, E. 1970. Profession of Medicine: A Study in the Sociology of ARplied KnowledRe. New York: Harper & Row.

Freidson, E. 1985. "The Reorganization of the Medical Profession." Medical Care Review 42:11-35.

- Frenk, J. 1983. Social OriRin, Professional Socialization, and Labor Market Dynamics: The Determinants of Career Preferences among Medical Interns in Mexico. Ann Arbor, MI: The University of Michigan, Ph.D. dissertation.

- Frenk, J. 1985. "Career Preferences under Conditions of Medical Unemployment: The Case of Interns in Mexico." Medical Care 23:320332.

- Frenk, J., J. L. Bobadilla, J. Sepulveda, J. Rosenthal, E. Ruelas, M. A. Gonzalez-Block, J. Urrusti. 1986. "An Innovative Model for Public Health Research: The Case of a New Center in Mexico." Journal of Health Administration Education 4: in press.

- Frenk, J., A. Donabedian. 1986. "State Intervention in Medical Care: Types, Trends, and Variables." Presented at the 114th Annual Meeting of the American Public Health Association, Las Vegas, October 1.

- Garg, M. L., J. L. Mulligan, W. A. Gliebe. 1978. "Physicans' Specialty, Quality and Cost of In-patient Care." Social Science and Medicine 13c:187-90.

- Georgopoulos, B. S., F. C. Mann. 1962. The Community General Hospital. Toronto: Macmillan.

- Gray, J. L. 1974. "Matrix Organizational Design as a Vehicle for Effective Delivery of Public Health Care and Social Services." ManaRement International Review 14: 73-82.

- Harris, J. E. 1977. "The Internal Organization of Hospitals: Some Economic Implications." Bell Journal of Economics 8:467-82.

- Hasenfeld, Y. 1983. Human Service Organizations. Englewood Cliffs, NJ: Prentice-Hall.

- Howe, G. E. 1969. "Decentralization Aids Coordination of Patient Care Services." Hospitals 43:53-55.

- Jacobs, P. 1974. "A Survey of Economic Models of Hospitals." Inquiry 11:83-97.

- Katz, D. 1974. "Skills of an Effective Administrator." Harvard Business Review 52:90-120.

- Kimberly, _3._ R., P. Leatt, S. Shortell. 1983. "Organization Design." In Shortell, S., A. Kaluzny (eds). Health Care ManaRement: A Text in Organizational Theory and Behavior. New York: Wiley, pp. 291-332.
Figure A7: Specification Charts for Number Days with Rain <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.02 .02<br>.01 .01<br>0<br>0 -.01<br>* Weather Product * -.01 * Weather Product * -.02<br>TAMSATERA5 -.02 TAMSATERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.05 .02<br>.01<br>0 0<br>-.01<br>* Weather Product * -.05 * Weather Product * -.02<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.015 .01<br>.01 .005<br>.005 0<br>-.005<br>0 -.01<br>* Weather Product * -.005 * Weather Product * -.015<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

55
costs) from macroeconomic linkages. Roe _et al_ . (2005) provide a clear discussion of the issues, and an application of such a computable general equilibrium (CGE) model.

# **6. Discussion and Conclusions**

When looking for water in the agricultural water productivity and efficiency literature, it becomes apparent that many studies have examined the question of agricultural water productivity from various perspectives. The irrigation literature, on the one hand, is dominated by studies using single-factor productivity measures. They allow the incorporation of different measures of water use (such as water applied and water consumed) and various scales, ranging from the field to the basin and even global levels. They find large variations in agricultural water productivity, yet usually do not proceed to empirically investigate the factors that might explain the different findings. The use of such single-factor productivity measures, where all variations in output are attributed to the water input, is problematic— especially when they form the basis of policy recommendations for improving agricultural water productivity. These measures disregard the effects of linkages with other inputs (including environmental influences), do not incorporate prices or costs, and do not consider the different sources of productivity.

The agricultural economics literature on productivity and efficiency, on the other hand, has relied on inductive methods, such as TFP indices and frontier models. As multi-factor approaches, these methods avoid some of the key problems of single-factor productivity measures, but have their own shortcomings when it comes to incorporating water aspects and providing insights into how water could be used more productively. TFP studies at the national level tend to not include water as a separate input, often due to data problems. A few TFP studies at subnational levels, such as the district level, capture water aspects as dummy variables and may show, for example, that water availability (in connection with other factors) is an important input associated with TFP growth.

The frontier model studies examined here tend to be based on farm level data and focus mostly on technical efficiency. With a few exceptions, they include water aspects only in qualitative form as dummy variables. Most of the studies examine the extent of inefficiency as well as the significance and magnitude of the factors that may be causing the inefficiency. Depending on the particular case, the problem analyzed, and the approach used, they find that water aspects (such as water availability, irrigation infrastructure, farms’ location along a canal, or farmers’ water management arrangements) play different roles in terms of efficiency. Two frontier model studies, by McGuckin _et al_ . (1992) and Karagiannis _et al_ . (2003), stand out: they specifically examine irrigation water efficiency in economic terms, and try to estimate potential water savings. However, both studies are limited in that they only consider one measure of water use at the farm level, water applied, and assume that any reduction in this 33