rafmacalaba/datause-encoder-data huggingface dataset:
substitutes, but there is no distinction between foreign countries. That is, firms demand foreign workers without regard to their country of origin (Aguiar, 2009).

In addition, this version of the model also includes unemployment of endowments (capital, skilled and unskilled domestic and foreign labor) through the inclusion of an elastic segment in the previously inelastic labor supply curve.<sup>8</sup> This is achieved through a complementarity which sets employment equal to the natural rate of employment, unless a fall in demand is sufficient to drive the expected real wages down by more than a threshold rate of change. In the next period, the employment rate will attempt to move back to the natural rate, but this will only be achieved if demand is sufficient to return the economy to the natural rate without further lowering wages more than the threshold rate of change. Provided the economy does not continue to be hit by negative shocks, employment is expected to gradually move back to the natural rate of employment. This allows us to capture unemployment resulting from the global financial crisis.<sup>9</sup> In Equation (1) the real wage is adjusted to take account of the probability of employment once the migrant arrives in the host region. Real wages ( _RWi,c,r_ ) are calculated as the average expected real wage of the entire labor force, regardless of employment status; hence the expected real wage that a migrant faces once the migrant arrives in the host region includes unemployment. If unemployment in the host economy rises then the expected real wage falls, due to a higher probability of unemployment, and hence migration falls. This reflects a main assumption of the Harris-Todaro model that the migration decision between rural and urban areas is based on expected income differentials, rather than just wage differentials.

Underlying the _GMig2Dyn_ model is a database that captures both bilateral labor (GMig2 Database) and foreign ownership of capital (GDyn Database); as well as the core GTAP 7 Data Base (Narayanan and Walmsley, 2008). For this study, we have updated the GMig2 database to

> 8 For simplicity of the diagrams, we assume that labour supply is fixed.  In the model there will be some interaction between total labour supply in an economy and the demand and supply of migrants, which responds endogenously to real wage changes and unemployment, hence the total labour supply curve is slightly upwardslopping.

> 9 See Strutt and Walmsley (2010) for more details on how unemployment was incorporated.

13
expenditure on different types of innovation. It also provides information on technology use, new technology incorporated during the period of analysis, and relations between technology adoption and the labor force. We match the data from ENDEI with information on informality in employment at the district level, in order to assess whether the relation between ICT and labor demand depends on local labor market institutions.

The sampling frame consisted of 18,900 private manufacturing firms registered in the social security administration. Once the sector and firm size strata were defined, 3,995 firms were selected using a systematic algorithm with equal probability in all strata. The survey is representative at the 2‐digit industry level and by firm‐size.

Table 1 provides basic descriptive statistics for the year 2012. Firms hire on average 49.5 workers, the average annual wage is 17,065 dollars, and average annual sales are 7,164,000 dollars per year. Unskilled workers account for 87 percent of total employment, skilled workers account for 7 percent, and managers account for 6 percent. These groups earn an average annual wage of 13,984, 22,860, and 37,194 dollars, respectively. The wage gap between skilled and unskilled workers is 40 percent.

In columns (2) to (4) of the same table we show the 5<sup>th</sup> , 50<sup>th</sup> and 95<sup>th</sup> percentile of each variable. Firms are highly heterogeneous, with differences in size of 5,750 and 1,100 percent, measured in sales and employment, between the 95<sup>th</sup> and 5<sup>th</sup> percentile. The dispersion of the average wage is comparatively much lower, with a gap of 180 percent. Wage differences between the 95<sup>th</sup> and 5<sup>th</sup> percentile are higher for managers, then skilled workers, and finally unskilled workers, at 477, 242 and 183 percent. The decreasing dispersion in wages across skill categories is compatible with two non‐mutually exclusive explanations. The first explanation is that within‐category variance in skills decreases in the skill category. The second explanation is that skilled workers and especially managers have more negotiating power and their salaries have more correlation with firm performance.

The bottom panel of Table 1 reports descriptive statistics on ICT. During the period 2010‐2012, 32 percent of firms report having invested in ICT. The average share of ICT investment in sales is 0.25 percent. Firms report an average of 6.2 workers per computer, and 9
**<u>Table 2: Effects of Parent Insurance on Utilization by Child Eligibility</u>**

|Panel A: Ever Visit|Any<br>Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public<br>Facilities<br>(4)|Private<br>Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|0.007|0.013|0.226***|0.059|-0.006|
||(0.066)|(0.080)|(0.046)<br>-|(0.078)|(0.074)|
|Parent Enrolled*Ineligible|-0.209|-0.158|0.204***|-0.142|-0.296**|
||(0.145)|(0.149)|(0.053)|(0.137)|(0.117)|
|Ineligible|0.073|0.009|0.019|0.104**|0.050|
||(0.048)|(0.049)|(0.017)|(0.044)|(0.039)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.088|0.066|0.15|0.037|0.076|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.145|0.301|0.483|0.516|0.003|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|1.260**|0.336|0.564***|0.210|0.110|
||(0.622)|(0.292)|(0.150)|(0.283)|(0.263)|
||||-||-|
|Parent Enrolled*Ineligible|-3.003***|-0.801|0.519***|-0.355|1.092***|
||(1.028)|(0.519)|(0.148)|(0.446)|(0.385)|
|Ineligible|0.690**|0.134|0.037|0.333**|0.192|
||(0.342)|(0.170)|(0.050)|(0.134)|(0.134)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.092|0.061|0.108|0.031|0.042|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.048|0.314|0.409|0.714|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

26
**_Energy demand models for policy_** **_<u>formulation</u>_**

resulting from uncertainty; in contrast, forecasts aim to identify the most likely pathway and estimate uncertainties” (Ghanadan and Koomey, 2005).

The strength of the scenario approach is its ability to capture structural changes explicitly by considering sudden or abrupt changes in the development paths. The actual level of disaggregation and inclusion of traditional energies and informal sector activities depend on model implementation. Theoretically it is possible to include these aspects but how much is actually done in reality cannot be generalized. Moreover, the development of plausible scenarios that could capture structural changes, emergence of new economic activities or disappearance of activities is not an easy task.

# **3.2.2.5 Hybrid approaches**

This, as the term indicates, approach relies on a combination of two or more methods discussed above with the objective of exploring the future in a better way. The hybrid methods have emerged to overcome the specific limitations of individual approaches. These models have become very widespread now and it is really difficult to classify any particular model into a specific category. For example, econometric models now adopt disaggregated representation of the economy and have internalized the idea of detailed representation of the energy-economy activities. Similarly, engineering-economy models use econometric relationships at the disaggregated levels thereby taking advantages of the econometric estimation method. The end-use approach heavily relies on the scenario building approach to enrich itself.

There is a growing interest in the hybrid energy models in recent times with the objective of reconciling the differences between the top-down and bottom-up approaches. This is evident from a set of recent studies:<sup>17</sup>

- To reconcile the “efficiency gap”, models with top-down structure are using bottom-up information to estimate parameters. See for example Koopmans and te Velde (2001) for such an exercise.

> 17 See for example Special Issue of Energy Journal (November 2006) on this theme.

40
# **Abstract**

This paper reviews the process of job creation and destruction across a sample of 16 industrial and emerging economies over the past decade. It exploits a harmonized firm-level data-set drawn from business registers and enterprise census data. The paper assesses the importance of technological factors that characterize different industries in explaining cross-country differences in job flows. It shows that industry effects play an important role in shaping job flows at the aggregate level. Even more importantly, differences in the size composition of firms - within each industry - explain a large fraction of the overall variability in job creation and destruction. However, even after controlling for industry/technology and size factors there remain significant differences in job flows across countries that could reflect differences in business environment conditions. In this paper, we look at one factor shaping the business environment, namely, regulations on hiring and firing of workers. To minimize possible endogeneity and omitted variable problems associated with crosscountry regressions, we use a difference-in-difference approach. The empirical results suggest that stringent hiring and firing costs reduce job turnover, especially in those industries that require more frequent labor adjustment. Regulations also distort the patterns of industry/size flows. Within each industry, medium and large firms are more severely affected by stringent labor regulations, while small firms are less affected, probably because they are partially exempted from such regulations or can more easily circumvent them.

# **JEL Classification** : J23, J53, K31.

**Keywords** : gross job flows, firm dynamics, firm size, product and labor market regulations, firm-level data.

2
Policy Research Working Paper 8980

# **Abstract**

This paper investigates the sources of growth in manufacturing productivity in Cote D’Ivoire, Ethiopia and Tanzania in comparison with the case of Bangladesh. Based on the analysis of establishment census data since the mid-1990s, it finds that reallocation of market share between firms contributed substantially to productivity growth in each of the four countries, although to a varying extent. In Ethiopia, the impact of market share reallocations among survivors tended to be larger than those associated with increases in within-plant productivity. In addition, plant closure (or exit) boosted productivity more than new plant openings (or entry) did in the sense that the relative productivity of survivors (or continuing plants) was higher relative to that of closing plants (or exit cases) than it was relative to the productivity of newly opening plants (or new entrants).

Reallocation of market share plays an important role in raising aggregate productivity in Côte d’Ivoire as well. But the pattern here is opposite to that in Ethiopia in that in Côte d’Ivoire entering (or newly opening) plants have larger impact on aggregate productivity growth than closing (or exiting) plants. Unlike the case with Cote D’Ivoire and of Ethiopia, the reallocation of market share among surviving plants is a smaller source of manufacturing productivity growth in Tanzania than the new plant openings and plant closure. The data suggest that the reallocation of market share among surviving plants and exiting plants has larger impact on productivity growth in Bangladesh than the productivity gap between new plants and survivors, as in the case of Ethiopia.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. 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 jonesp@newpaltz.edu, elartey@worldbank.org, tmengistae@worldbank.org, and azeufack@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
423-429.

Ivanic, M. and W. Martin, and H. Zaman, 2012. Estimating the Short-Run Poverty Impacts of the 2010 ̈ı¿ � 11 Surge in Food Prices, World Development, Elsevier, vol. 40(11), pages 2302-2317. Kojima, Y, J. Parcell, J. Cain (2014), A Demand Model of the Wholesale Vegetable Oils Market in the U.S.A, Working paper.

Lenin, V.I., 1964. Complete Collected Works, vol. 35. Moscow.

Minten, Bart & Kyle, Steven, 1999. The effect of distance and road quality on food collection, marketing margins, and traders’ wages: evidence from the former Zaire, Journal of Development Economics, Elsevier, vol. 60(2), pages 467-495, December.

Nakamura, E., and D. Zerom (2010): Accounting for Incomplete Pass-Through, Review of Economic Studies, Vol. 77, No. 3, pp. 1192-1230.

Smith, J K (1987), Trade Credit and Informational Asymmetry. Journal of Finance, September, 1987, 42(4), pp. 86369.

Srinivasan, P V (2005), Impact of Trade Liberalization on Indias Oilseed and Edible oils sector, IGIDR, Working Paper.

Uddin, H and M. Taslim (2010), An Assessment of Competition in the Edible Oil Market of Bangladesh, Bureau of Economic Research, University of Dhaka, Bangladesh.

Weyl, E. G., and M. Fabinger (2013): Pass-Through as an Economic Tool: Principles of Incidence under Imperfect Competition, Journal of Political Economy, Vol. 121, No. 3, pp. 528- 583.

37
Table A.6: Effects on household per capita saving in financial institutions: Gradually adding fixed effects

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|_Firstborn girl_ * _Expected dowry_|-18.499|4.888|31.328|58.159|50.885|61.393|350.138*|419.244**|556.246*|
||(114.494)|(99.852)|(111.277)|(126.133)|(121.251)|(162.986)|(190.602)|(190.016)|(271.315)|
|_Expected dowry_|132.781|-61.710|65.782|95.694|235.628|221.049|78.864|60.857||
||(107.702)|(255.374)|(160.598)|(341.418)|(314.434)|(321.656)|(319.867)|(306.283)||
|_Firstborn girl_|-309.800|-169.534|-202.498|-269.8|-292.774|-597.086|-1,605.135|-406.126|-639.849|
||(296.990)|(184.202)|(208.785)|(228.960)|(239.105)|(430.449)|(1,025.921)|(1,728.706)|(3,342.154)|
|N|2,840|2,840|2,840|2,840|2,840|2,840|2,840|2,840|2,840|
|_Xi_|x|x|x|x|x|x|x|x|x|
|YOB FE||x|x|x|x|x|x|x|x|
|Caste FE||x|x|x|x|x|x|x|x|
|State FE||x|x|x|x|x|x|x|x|
|Caste FE*State FE|||x|x|x|x|x|x|x|
|State FE*YOB FE||||x|x|x|x|x|x|
|Caste FE*YOB FE|||||x|x|x|x|x|
|Firstgirl*Caste FE||||||x|x|x|x|
|Firstgirl*State FE|||||||x|x|x|
|Firstgirl*YOB FE||||||||x|x|
|Caste*State*YOB FE|||||||||x|

NOTES: This table reports the coefficients corresponding to specifications (1) and (2) estimated for households where all children have the same mother. Each column is a separate regression. The dependent variable is total household saving in financial institutions. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Caste refers to indicators for SC, ST, OBC, and upper castes. YOB refers to the year of birth of the firstborn child. State refers to the state of residence at the time of survey. **X** _i_ controls for parents’ age, schooling, religion, and month of survey. Standard errors in parentheses are clustered by state. *** 1%, ** 5%, * 10%.
||(-1.83)|(-1.22)|(-1.57)|
|---|---|---|---|
|_ρHealth x Misperception_|0.631***<br>(2.68)|||
|_ρHealth x Overuse_|-0.062<br>(-1.01)|||
|_ρMisperception x Overuse_|0.087<br>(0.48)|||
|Observations|820|||
|Log likelihood|-1398.210|||

z-scores in parenthesis; * - significant at the 10% level; ** - significant at the 5% level; *** - significant at the 1% level

> The first column of Table 2 presents the determinants of the _health effects_ equation. Variables found to be significantly associated with the probability of a farmers’ health impairment from pesticides are: _the amount of pesticides applied_ and _income_ .  As expected, the probability of sickness increases significantly with greater use of pesticides. After controlling for the amount of pesticides, however, we did not find any statistical significance for the toxicity of pesticides. Income, although significant, has a perverse sign which we could not explain.<sup>10</sup> Neither the age nor the nutritional status of the exposed had any significant effect on health.  The number of protections taken by the farmer is also insignificant, however, low variation in the sample may be a possible explanation.  District controls reveal that health impairments from pesticides are more likely in the _Bogra_ district, and less likely in the district of _Munshiganj_ .

The second column of Table 2 gives the determinants of the farmers’ _misperception of pesticide risk_ . The negative sign and significance (at the 1% level) of _health impairment_ clearly shows that the probability of misperception is lower with increases in health impairments. The positive and significant sign of two _pesticide toxicity_ variables in the misperception equation reveal the incidence of misperception is higher for farmers with higher proportional use of WHO Ia & Ib (extremely and highly hazardous) and WHO II (moderately hazardous) pesticides. This finding is analogous to that found by Warburton, Palis and Pingali (1995), where farmers tended to group WHO I and II pesticides as equally hazardous. It is worth noting that neither formal education nor training in use/ safe-handling had any effect on misperception. Once again, the very low (4%) number of farmers actually trained in the sample may be the cause of its

> 10 A t-test among income groups confirms the statistical difference in pesticide use.  Note however that the marginal impact of changes in income is relatively small.

13
<!-- Start of picture text --> 80 15 80 15<br>80 15<br>High Income Countries East Asia and Pacific Europe & Central Asia<br>60 60 60<br>10 10 10<br>40 40 40<br>Life Exp 5 Life Exp 5 5<br>20 School Year 20 School Year 20 Life Exp<br>School Year<br>0 0 0 0 0 0<br>1911 1919 1927 1935 1943 1951 1959 1967 1975 1983 1931 1937 1943 1949 1955 1961 1967 1973 1979 1985 1935 1942 1949 1956 1963 1970 1977 1984<br>80 15 80 15 80 15<br>Latin America and the Caribbean Middle East & North Africa South Asia<br>60 60 60<br>10 10 10<br>40 40 40<br>5 5 5<br>20 Life Exp 20 Life Exp 20 Life Exp<br>School Year School Year<br>0 0 0 0<br>0 0<br>1922 1929 1936 1943 1950 1957 1964 1971 1978 1985 1924 1932 1940 1948 1956 1964 1972 1980<br>1942 1947 1952 1957 1962 1967 1972 1977 1982<br>80 15<br>Sub Saharan Africa<br>60<br>10<br>40<br>5<br>20 Life Exp<br>School Year<br>0 0<br>1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985<br>Year of Birth<br>152<br>Years in School<br>Life Expectancy at Birth<br><!-- End of picture text -->

**Figure 2** : Life Expectancy and Schooling by Birth Cohort across Regions
for dialogue: they are expected to inform policy decisions and programs aimed at creating more and better-quality jobs.

The paper is organized as follows. After a brief description of the analytical framing and methodology in Section 2, Section 3 presents the _history and demographics_ of the labor market in Sudan, focusing on indicators by gender and age across the three main sectors of employment: services, agriculture, and industry. Section 4 examines _formal institutions_ : the institutional setting, service delivery, and laws and regulations as they relate to economic opportunities. Section 5 examines _informal institutions_ , where the social norms and networks can be a barrier to women’s and youth’s full economic participation. Section 6 analyzes how the _market_ is supporting or constraining economic activity, which includes a closer look at the labor market itself and access to assets. Section 7 discusses how all of these aspects are considered when it comes to the _household- and individual-level_ decision-making that directly affects women’s and youth’s accumulation of human capital, overall agency, and—ultimately—their economic opportunities. Section 8 concludes with considerations for _policy and action_ .

# **2. ANALYTICAL FRAMING AND METHODOLOGY**

To identify and examine the constraints that women and youth face in accessing employment opportunities, including employment, the study applies a conceptual framework derived from the World Development Report 2012: Gender Equality and Development analytical framework. While the framework was developed for gender equality, it is also applicable to, and highly relevant for, better understanding the societal conditions and individual-level outcomes of youth. The focus is on the economic opportunities dimension, particularly on employment, but relevant variables related to the interconnected dimensions of endowments and agency are also considered (Figure 1). The study examines the role of _informal institutions_ (norms, stereotypes, trust, sense of security/safety, and so on); _formal institutions_ (legal restrictions, employer policies, and so on); and _markets_ (access to finance, transport, day care, and so on) as they affect employment-related outcomes for women and youth in Sudan. The study analyzes how these aspects influence intra-household decision-making processes, especially as they relate to the participation of women and youth in the economic sphere. These decisions directly affect individual-level endowments and agency as well as access to economic opportunities.

3