rafmacalaba/datause-encoder-data huggingface dataset:
Minimum ratios, on the other hand, are designed to stimulate the volume of training and may vary from occupation to occupation in the same country. In India, for example, the Apprenticeship Act of 1961, as amended in 1975, prescribes a minimum ratio of 1:2 for tool and die makers and 1:7 for carpenters and bricklayers.

In some countries the national authority regulates the wages paid to apprentices without contributing to them. The usual format is a prescribed ratio of apprentice wages to experienced worker wages. The usual intention is to prevent the exploitation of the apprentice, but the measure can have an inhibiting effect on the provision of apprentice places if apprentice wages, especially initial apprentice wages, are set too close to experienced worker wages. In Panama, for example, "the lack of formal apprenticeship training is not out of choice. The Labor Code's equal pay for equal work provision and universal minimum wage have prevented the development of such systems... the universal minimum wage of the code does not allow employers to pay apprentices on a different scale as they learn. This.. .makes it almost impossible to carry out apprenticeship training programs. Most employers believe that personnel in training have lower performance and productivity levels than do regular workers and that trainees should be compensated by lower rates of pay." (Cuervo, 1985).

# Measures aimed at reducing mobility

Private sector enterprises often cite the high mobility of the labor force as a major deterrent to the provision of training, and this would certainlv seem to be che case in Korea. It follows that training provision might be promoted by measures which have the effect of reducing turnover, obvious examples being job-security and minimum-wage legislation. Usually such measures are introduced with other objectives in mind, but in Indonesia the expectation of encouraging training by reducing job-hopping was a factor behind the establishment of minimum wage legislation (Ichimura, 1983, p. 89). However, a heavy-handed implementation of such measures carries the risk of widening the gulf between the regulated sector where they are enforced (usually larger enterprises in urban areas, especially those which are foreign-owned) and the unregulated sector which flouts them, encouraging the adoption of even more capital-intensive technology in the regulated sector and diminishing its capacity for disseminating skill development via the outflow of its workers.

# 5.7 The scope for government intervention in the traditional sector

Awareness of the existence of informal apprenticeship schemes and other sources of skill-development in the traditional sector has stimulated an interest in exploring the scope for promoting them as a recognized component of the national training system. For example, a recent study of apprenticeship in Asia and the Pacific (APSDEP, 1981), noting that the regulated apprenticeship system was a superimposed colonial model, stated that one of its purposes was to "identify, in a number of key occupational areas which include both small and larger undertakings and in which both formal and

- 48 -
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Hahn, R. W. 1984. “Market Power and Transferable Property Rights.” _Quarterly Journal of Economics_ 99 (4): 753–65.

23
# **1. Introduction**

A country’s education level is of huge importance to its economic success. Indeed, the economic literature suggests that differences in human capital endowments among countries are largely responsible for economic development gaps observed between industrialized nations and developing countries. For a long time, most authors explained growth differences using quantitative indicators such as years of schooling or enrollment rates in primary and secondary schools (Barro 1991; Mankiw et al. 1992). However, recent evidence has shown a quite different pattern: It is not the time spent in school that matters most, but rather what is effectively learned. Thus, qualitative skills acquired during schooling play a decisive role in influencing a country’s growth (Hanushek and Woessmann 2008).

This new insight comes at the same time as a large increase in availability of international student achievement tests. These tests, carried out by institutions such as the OECD and the International Association for the Evaluation of Educational Achievement (IEA), measure student cognitive skills around the world. Several econometric studies show that qualitative indicators measured by these international achievement tests explain growth patterns significantly more than quantitative indicators, such as school enrollment (Hanushek and Woessmann 2008). Moreover, recent analyses reveal a direct and persistent association between cognitive skills and economic growth even controlling for unobserved country differences. Indeed, Hanushek and Woessmann (2009a) use an instrumental variables methodology to demonstrate a causal chain between a nation’s stock of cognitive skills and its economic growth.

This evidence motivates the identification of factors that enhance the stock of cognitive skills - a key input in driving country growth. The most common tool in such analyses is the estimation of education production functions which include a host of input factors, such as individual characteristics, family background, school inputs (e.g. class size), and systemic elements (e.g. accountability). These input factors drive an output, for example, educational success. In our case, educational success is measured by the stock of cognitive skills (Hanushek 1979).

Some of the more recent economic literature makes an attempt to examine the effect of these input factors on educational outcomes. While results from these studies vary, systemic effects seem to matter hugely: Several studies, mostly using data from PISA and TIMSS, reveal large 2"
two assumptions a market for a single good is independent from the rest of the economy. That is to say, changes in the dynamics of the specific market can be assumed to have no or limited effects on the rest of the economy.

In partial equilibrium models, supply and demand of the commodity under analysis is modeled in detail. Demand is generally found by maximizing some kind of objective function under pertinent constraints. For water markets, farmers are consumers while water authorities are suppliers. Thus the demand is be found by maximizing either the profits or surplus of producers. An alternative and frequently applied method is using a biophysical farm model to determine crop and time specific water demand. The second method requires more detailed data about yields, water requirements, irrigation areas and crops produced. Under the assumption of a functional relationship between inputs and output, the first method is rather simple and water demand can be obtained from the first order conditions of the optimization problem.

The supply side, on the other hand, is more difficult to model due to the uncertainty. Water supply is largely dependant on unpredictable weather conditions. One way to overcome this difficulty is to use outputs from hydrological models or historical data to determine the level of future availability. In this way water availability is made exogenous to the model variables and thus supply side shocks are given exogenously to the model. If the water availability can be assumed to be non-binding then uncertainty problem can be ignored. In this case, supply side can be determined in the same way as the other economic goods, i.e. by maximizing the profits of suppliers under an appropriate structural assumption (monopolistic, oligopolistic or competitive) about the water market. However, in most cases such an assumption is unrealistic and generally water supply functions are calibrated to historical data and effects of changes in water supply are analyzed exogenously.

# **3.1.2 Recent Work**

Johansson _et. al_ ., (2002) and Johansson (2005) give an extended survey of partial equilibrium models. The former covers the models until 1998 while the latter extends up to 2004. Jonhsson _et. al._ (2002) describes the general framework of partial equilibrium models and surveys the deviations of the models from the standard theoretical framework. Johnsson (2005), on the other hand, focus is on the taxonomy of the models according to their mathematical approaches and surveys the contribution of different models to different modeling methods. In this section we will give a survey of recent work aiming to model the water markets by using partial equilibrium analysis. We will focus on the contributions of 26
Policy Research Working Paper 9265

# **Abstract**

Are changes in services markets provoking reform, restrictions, or inertia? To address this question, this paper draws on a new World Bank-World Trade Organization Services Trade Policy Database. The paper analyzes the services trade policies of 68 economies in 23 subsectors across five broad areas—financial services, telecommunications, distribution, transportation, and professional services. Policy measures are quantified into a Services Trade Restrictions Index (STRI) following a novel, consistent and transparent framework. The paper identifies patterns of services trade policies across sectors and economies, and secular trends over the past decade. Higher income economies are still more open on average than developing economies, but the chronology of reform differs markedly across sectors. In telecommunications and finance, there is convergence toward greater openness driven by liberalization in the previously more restrictive developing economies. In the hitherto universally protected transport and professional services, there is policy divergence, as some higher income economies pioneer reform. But while explicit restrictions are being lowered in most services sectors—in contrast to recent developments in goods trade policy—there is greater recourse to regulatory scrutiny, especially in higher income economies. These measures could reflect legitimate prudential or security concerns, but they could also reflect recourse to less transparent forms of protection.

This paper is a product of the Development Research Group, Development Economics and the Office of the Chief Economist, East Asia and Pacific Region. 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://www.worldbank.org/prwp. The authors may be contacted at i.borchert@sussex.ac.uk, Joscelyn.Magdeleine@wto.org, juan.marchetti@wto.org, and amattoo@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
Figure A13: Specification Charts for Median Daily Temperature <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2.1 .1<br>0 0<br>-.1 -.1<br>* Weather Product * -.2-.3 * Weather Product * -.2<br>MERRA-2 MERRA-2 -.3<br>ERA5 ERA5<br>CPC CPC<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 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>1 .6<br>.5 .4<br>.2<br>0 0<br>* Weather Product * -.5 * Weather Product * -.2<br>MERRA-2 MERRA-2<br>ERA5 -1 ERA5<br>CPC CPC<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 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .2<br>.1<br>0<br>0<br>* Weather Product * -.1-.2 * Weather Product * -.2<br>MERRA-2 MERRA-2 -.4<br>ERA5 ERA5<br>CPC CPC<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 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<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 18 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.

61
Policy Research Working Paper 5162

# **Abstract**

The global financial crisis has already led to sharp downturns in the developing world. In the past, international aid has been able to offset partially the effects of crises that began in the developing world, but because this crisis began in the wealthy countries, donors may be less willing or able to increase aid in this crisis. Not only have donor-country incomes fallen, but the cause of the drop—the banking and financial-sector crisis—may exacerbate the effect on aid flows because of its heavy fiscal costs. This paper estimates how donorcountry banking crises have affected aid flows in the past, using panel data from 24 donor countries between 1977 and 2007. The analysis finds that banking crises in donor countries are associated with a substantial additional fall in aid flows, beyond any income-related effects, perhaps because of the high fiscal costs of crisis and the debt hangover in the post-crisis periods. In most specifications, aid flows from crisis-affected countries fall by an average of 20 to 25 percent (relative to the counterfactual) and bottom out only about a decade after the banking crisis hits. In addition, the results confirm that donor-country incomes are robustly related to per-capita aid flows, with an elasticity of about 3. Because all donor countries are being hit hard by the current global recession, and several have also suffered banking-sector crises, there are reasons to expect that aid could fall by a significant amount (again, relative to the counterfactual) in the coming years—just when aid may be most clearly justified to help smooth exogenous shocks to developing countries.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to determine how aid can more effectively promote development.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at hrogers@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
<!-- Start of picture text --> 2 ;*<br>rao //i \ \ \ N _----~<br>o --------"— x ---<br>os i ~ as<br>5 / ~s LL<br>g<br>ir<br>ne<br>by<br>se]<br>c<br>2<br>Do<br>5x<br>£° aX<br>E i N<br>a} / N ---- -<br>=? i t N7 _u---77 “s ~ “or<br>e/—“<br>i~ae<br>é<br>oO<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br><!-- End of picture text --> <!-- Start of picture text --> ire) ee<br>“ a<br>ne ~ -_—_—Oo _ ~~ a<br>oOo Seo saa T--~ aia<br>ad<br>2<br>iL<br>rey<br>G<br>Ee<br>2<br>2<br>io<br>i<br>°o<br>Eo<br>oT<br>= one<br>oO ” “ey<br>aPa x ~ _wee ee - e e -——_<br>Neo ~~<br>wo<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br><!-- End of picture text -->
**Figure 13b Criteria used by banks to make small business loans, by type of country**

<!-- Start of picture text --> 100%<br>Developed Developing<br>80%<br>60%<br>50% 49%<br>(5) (38)<br>40%<br>20%<br>16% (2)<br>20% 10% (12) 10% 9% 10%<br>(1) 8%(6) (1) 4% (7) (1)<br>(3)<br>0%<br>Collateral Financial Firm's credit Firm's credit Firm's owner Size of the loan Purpose for the<br>assessment of the history with your history from a characteristics loan<br>business bank credit registry (age, sex, etc.)<br>Percentage of banks<br><!-- End of picture text -->

**Figure 13c Criteria used by banks to make small business loans, by type of bank**

<!-- Start of picture text --> 100%<br>Government Private (Domestic) Foreign<br>80%<br>58%<br>60% 53% (23)<br>(9)<br>37%<br>40% (11)<br>20% 20%<br>(6) (6)<br>20% 13% 13% 12%<br>7% (5) 6% (5) 7% 5% 6% 6% (2) 7% 5%<br>(2) (1) (2) (2) (1) (1) 3% (2) (2)<br>(1)<br>0%<br>Collateral Financial Firm's credit Firm's credit Firm's owner Size of the loan Purpose of the<br>assessment of history with the history from a characteristics loan<br>the business bank credit registry (age, sex, etc.)<br>Percentage of banks<br><!-- End of picture text --> 37
Table 2: Change in monthly sales relative to 2019 and recovery-Heterogeneity

|||Chan|ge in sales|||R|ecovery||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Size|(2)<br>Age|(3)<br>Female-led|(4)<br>Subsector|(5)<br>Size|(6)<br>Age|(7)<br>Female-led|(8)<br>Subsector|
|Informality|-18.20**|-11.70**|-8.740**|-13.49**|-9.919**|-9.339**|-4.412*|-8.810**|
||(2.637)|(2.145)|(1.760)|(2.316)|(3.263)|(2.873)|(2.452)|(3.083)|
|Micro (less than 9 employees)_×_Informality|11.65**<br>(2.832)||||6.759*<br>(3.479)||||
|Young_×_Informality||8.130**||||7.172|||
|||(3.917)||||(5.274)|||
|Established_×_Informality||0.239||||6.652**|||
|||(2.345)||||(3.090)|||
|Female-led_×_Informality|||-7.718**||||-5.479||
||||(2.917)||||(4.074)||
|Const and utilities_×_Informality||||12.36**||||2.728|
|||||(6.116)||||(8.221)|
|Retail and wholesale_×_Informality||||14.87**||||12.49**|
|||||(3.075)||||(3.931)|
|Transp and storage_×_Informality||||0.457||||-0.923|
|||||(6.366)||||(9.204)|
|Accomm_×_Informality||||-7.319||||-6.129|
|||||(6.396)||||(8.865)|
|Food prep serv_×_Informality||||5.216||||6.367|
|||||(4.751)||||(5.479)|
|ICT_×_Informality||||12.02||||3.710|
|||||(8.789)||||(12.69)|
|Financial serv_×_Informality||||1.373||||14.24|
|||||(12.11)||||(28.55)|
|Education_×_Informality||||-12.45||||-19.75|
|||||(12.56)||||(23.37)|
|Health_×_Informality||||-16.60*||||-21.10|
|||||(9.896)||||(14.19)|
|Other serv_×_Informality||||1.567<br>(3.242)||||5.053<br>(4.438)|
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|No|No|No|No|No|No|No|
|Lag change in sales|No|No|No|No|Yes|Yes|Yes|Yes|
|<br>Observations|19569|19569|19569|19569|10203|10203|10203|10203|

**N** otes: Each regression also includes a control for the variable interacted with the informality measure. All continuous variables are winsorized at the 1% and 99%. Recovery is calculated as the difference in the changes in sales across rounds of data collection. Robust standard errors in parentheses * p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
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