rafmacalaba/datause-encoder-data huggingface dataset:
14 distribution, there is no apparent relationship between both legal entitlement to and actual severance pay received.

If a job separation is due to an economic cause beyond the control of the employee, the compensation package (both severance pay and long-service leave) is significantly larger and the cost to the firm is greater. Hence, it is important to identify those employees who were fired for an economic cause. In the IFLS (2007) survey 16.3 percent of wage and salary employees with a job separation were fired in the past five years because the firm merged or restructured. While the proportion of these job separators who were legally eligible to receive severance pay on termination was 0.8807, only 0.4041 actually received a payment.<sup>11</sup> The average severance entitlement amounted to 11,827,577 IDRs and the average amount of severance paid was 4,864,027 IDRs; about 40 percent of the amount to which an employee dismissed for an economic reason was entitled. This proportion of severance pay actually received (0.4112) was similar to the overall average of 0.3853 for all employees legally entitled to severance payments. Given that a job separation due to an economic cause beyond the control of the employee would automatically confer eligibility, the similarity in both proportions suggests a high degree of non-compliance.

In the Sakernas (2008) survey, 16.0 percent of employees were separated from their job in the last two years because they were laid-off by their firm; approximately one-third were laid-off because the company failed.<sup>12</sup> This proportion represents 4.2 percent of all job separations in the previous two years. While the proportion of these job separators who were legally eligible to receive severance pay on termination was 0.7859, only 0.1607 actually received it. The average severance entitlement amounted to 45,256,847 IDRs and the average amount of severance paid was 15,646,787 IDRs; about 34.6 percent of the amount to which an employee dismissed for an economic reason was entitled. This proportion of severance pay actually received (0.3457) was similar to the 11 Employees are legally eligible if (1) they have a work contract or letter of appointment which is permanent/open-ended; (2) dismissed for reasons stipulated by legislation.

> 12 Question R18 of the Sakernas (2008) survey asks wage and salary employees who were laidoff to identify the main reason for the lay-off.  The proportion who responded that their job separation was due to the rationalization of the firm is 35.8 percent; followed by 26.0 percent for company bankruptcy/loss/disaster.
|VARIABLES|Growth|of real outputp|er capita|
|---|---|---|---|
||[1]|[2]|[3]|
|log ODA inflows (residual)|0.022***|0.025**||
||[0.007]|[0.010]||
|log FDI inflows (residual)|0.003**|0.002*||
||[0.001]|[0.001]||
|log public debt inflows (residual)|-0.003|-0.005*||
||[0.002]|[0.003]||
|log ODA inflows (residual) (t-1)||-0.010<br>[0.009]||
|log FDI inflows (residual) (t-1)||-0.001<br>[0.002]||
|log public debt inflows (residual) (t-1)||-0.004<br>[0.005]||
|log ODA inflows (residual) (t-2)||-0.008<br>[0.009]||
|log FDI inflows (residual) (t-2)||-0.000<br>[0.001]||
|log public debt inflows (residual) (t-2)||0.005<br>[0.003]||
|Growth of Trade partners|0.905|-0.482|1.452**|
||[1.161]|[0.919]|[0.627]|
|Commodity Index|0.111|-0.041|-0.015|
||[0.072]|[0.096]|[0.103]|
|Constant|-10.954*|-139.496***|-14.233**|
||[5.890]|[7.435]|[6.970]|
|Observations|770|547|1,080|
|R-squared|0.288|0.363|0.229|

Clustered-Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Table 4: Impacts of capital inflows on output growth

# **VI. Conclusion**

This paper investigates the causal impacts of economic growth on capital flows and of capital flows on growth in a set of 38 Sub-Saharan African countries. Using a two-step approach to address reverse causality and omitted variable issues, we find two sets of results. First, contrary to popular belief, economic growth in Sub-Saharan Africa has not managed to attract capital inflows. Second, we find that “exogenous” capital flows have positive impacts on economic growth. Among the three measures of 23
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Policy Research Working Paper 10526

# **Abstract**

This paper presents global evidence on the impact of expansion in mobile telephony and broadband Internet services on economic development at the subnational level. Leveraging two decades of satellite data on nightlights and the global expansion of 2G, 3G, and 4G mobile networks in over 34,000 subnational districts in 120 countries, it documents three main findings on the effects of mobile phones on local economic development (proxied by nightlights): I. The expansion of mobile coverage has a positive effect on economic activity. Using the GDP—nightlights elasticity from Henderson et al. (2012), the estimates suggest a GDP growth—mobile phone penetration elasticity of 0.018–0.023; II. While mobile broadband (3G & 4G) Internet connectivity is associated with economic development across all countries, 2G connectivity boosts local economic growth mainly in developing countries; III. The economic effects of expansion in mobile network connectivity are more pronounced in countries that hitherto had limited access to fixed-line telephone infrastructure, thus highlighting the importance of mobile phones to developing countries in leapfrogging the technology ladder.

This paper is a product of the Office of the Chief Economist, Africa 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 author may be contacted at jmensah2@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
delaying entry, it is likely that some fraction of would-be entrants are now tied to other responsibilities and constrained from entering at later ages. This constraint is represented by the fraction _e_<sup>_a_</sup> <u>1</u><sup>_−_7,where</sup><sup>_a_isequal</sup> to the age of entry being considered, in this case 9. As the post-reform age gets closer to 7, the legal age of entry, this constraint approaches 1 and binds fewer students from delayed entry.

The full starting-age-adjusted intensity of the FPE reform for the 1985 cohort is then Equation (6) is a combination of (4d) for pre-FPE starting ages and (5) for post-FPE starting ages. Iterating equation (6) forward three years, when even six-year-old entrants are post-reform, demonstrates that the 1988 cohort is the first fully post-reform cohort: Every cohort born in 1988 or later is affected by the maximum potential magnitude of the reform, _Mz_ .<sup>4</sup> The average of the FPE intensity measure _Izy_<sup>FPE</sup> in regions without any MTI introduction is shown as the solid black line in fig. 2, for each cohort. For comparison and to demonstrate the type of variation in the intensity measure, the FPE measure is also shown for Addis Ababa, the most educated region in the country. The height of each line can be considered the number of additional free years generated by the FPE reform. Prior to the reform, there were more students in higher grades in Addis Ababa, leading to a greater effect size in earlier cohorts; but due to the higher level of initial schooling, the maximum magnitude of the effect in Addis Ababa is much smaller. Not only does the intensity measure predict larger increases in areas with lower levels of initial schooling, but it also generates significant variation in the path of the predicted effect across cohorts prior to the post-reform period.

# **4 Data**

## **4.1 Data Sources**

Individual-level outcome data for Ethiopian women are from the 2005, 2011, and 2016 rounds of the Ethiopian Demographic and Health Survey (DHS) (Central Statistical Agency of Ethiopia 2005; 2011; 2016). The DHS data used in this article are from the merged individual women and birth history datasets, and include data

> 4The explicit set of equations used for all cohort-specific intensity calculations can be found in Appendix Section A.

7
- 41 -

# TABLE 6 SUMMARY FEATURES OF 1962 AND 1982 MICROCONSISTENT DATA SETS USED TO EVALUATE INPUTS OF TAX **POUCIES**<sup>IN KOREA'S</sup> OUTWARD ORIENTED GROWTM STRATEGY

||1962 Microconsistent<br>Data Set|1982 Microconsistent<br>Data Set|
|---|---|---|
|Value of GDP<br>(millions U.S. dollars)|1935.59|92587.56|
|Ratio of employment|||
|<br>in manufacturing|||
|<br>to agriculture|1:15|1:2|
|% of GDP in|||
|importsa|16.0|43.9|
|exportsa|6.0|36.9|
|Manufactured exports|||
|<br>as % total exportsb|27.0|93.7|
|Average tariff rate<br>on imports (%)|13.4|7.09|
|Average export subsidy<br>rate (%)|16.6|21.3|

Notes: a) The numbers used in the model are smaller due to netting<sup>out of two-way trade.</sup> b) These figures are based on actual data. In the model Korea<sup>only exports one</sup> manufactured good on a net basis.
# **Appendix 2. Robustness Check**

## **Table A.1. E-commerce development and total household expenditure per capita using alternative measures**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West|
|---|---|---|---|---|---|---|
|Z-score of purchase/census pop in|0.303***|0.247***|0.718***|0.267***|0.313**|0.538***|
|2013|(0.0671)|(0.061)|(0.098)|(0.083)|(0.119)|(0.157)|
|Z-score of buyers/census|0.189***|0.150***|0.455***|0.127**|0.344***|0.422***|
|population in 2013|(0.064)|(0.057)|(0.145)|(0.057)|(0.104)|(0.117)|
|Z-score of buyers share in the|0.208***|0.175***|0.734***|0.179***|0.660***|0.664***|
|national market in 2013|(0.064)|(0.051)|(0.143)|(0.040)|(0.206)|(0.194)|
|Z-score of purchase share in|0.136***|0.114***|0.545***|0.118***|0.485**|0.649***|
|national market in 2013|(0.042)|(0.033)|(0.117)|(0.026)|(0.185)|(0.199)|
|Z-score of purchase over GDP in|0.275***|0.269***|0.266***|0.431***|0.171**|0.252**|
|2013|(0.067)|(0.079)|(0.088)|(0.097)|(0.084)|(0.105)|
|Z-score of per consumer purchase|0.126***|0.149***|0.093***|0.148***|0.112***|0.022|
|in 2013|(0.021)|(0.026)|(0.029)|(0.028)|(0.036)|(0.049)|
|Observations|11,107|5,183|5,945|4,458|3,486|3,163|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, regional dummies, and year fixed effect except for gift and house repair and upgrading. For readability, we present only the results of interest - the coefficients of the z-score each measure in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 30
<!-- Start of picture text --> Sons' Primary Completion Estimates by Birth Year<br><!-- End of picture text --> <!-- Start of picture text --> Intercept Linear Coefficient<br>4 .04<br>.02<br>2 $ it<br>> bf +<br>as ieeela iain need tind Rn Seed ie De De -_——-<br> ¢ > 4%<br>° L}/_ | @io¢ jo) fo><br>-.02 7<br>><br>><br>-.04<br>-.2<br>59 60 61 62 63 64 65 66 67 68 69 70 71 72 59 60 61 62 63 64 65 66 67 68 69 70 71 72<br><!-- End of picture text -->

Birth Years Omitted Birth Year 1957-58 © Comparison Cohort © Partially Exposed Cohort @ Fullly Exposed Cohort
where a firm hired for this purpose digitized and scored them. Specifically, the responses were aggregated into a composite “dropout-risk score”, with the weights given by the extent to which individual characteristics predicted the likelihood that a child would fail to enroll in 7<sup>th</sup> grade after completing 6<sup>th</sup> grade, as estimated from a recent nationwide household survey, the Cambodia 2003/04 Socioeconomic Survey. The formula used, and the weights, were the same for all CSP schools. Finally, the firm produced a list of the dropout-risk score for each applicant, and ranked applicants within each CSP school by this score. In “large” CSP schools, with total enrollment above 200, 50 students with the lowest value of the score were then offered a scholarship for 7<sup>th</sup> , 8<sup>th</sup> , and 9<sup>th</sup> grade; in “small” CSP schools, with total enrollment below 200 students, 30 students with the lowest value of the score were offered the scholarship.<sup>3</sup> In total, just over 3800 scholarships were offered.<sup>4</sup> Two-thirds of the scholarships were given to girls; this is because girls are more likely than boys to drop out of school in Cambodia, so they were given a lower dropout score by the formula.

The list of students offered scholarships was posted in each CSP school, as well as in the corresponding feeder schools. The families of children who had been selected to receive scholarships received the cash award three times a year, conditional on school enrollment, attendance, and satisfactory grade progress. Payments were made at open school ceremonies, with the school principal publicly handing over the cash to parents.

The monetary value of the CSP scholarship is small—it is approximately equivalent to the direct cost of lower secondary school in Cambodia, without taking account of the opportunity cost of attending school. On average, it represents only 2 to 3 percent of the consumption of the median recipient

> 3 In practice, within every large school, the 25 students with the lowest dropout-risk score were offered a scholarship of $60, and the 25 students with the next lowest scores were offered a scholarship of $45; in small schools, the comparable numbers were 15 students with scholarships of $60, and 15 with scholarships of $45. We do not focus on this distinction in this paper. Rather, we compare applicants who were offered a scholarship, regardless of the amount, with others that were not. Because the identification strategy is regression-discontinuity, we are implicitly comparing applicants who were offered a $45 scholarship, with those who were offered no scholarship at all. Students who were offered a $60 scholarship help estimate the control function that relates enrollment to the dropout-risk score. We have elsewhere shown that there is no evidence that the effect of the CSP on enrollment is larger among students who were offered the $60 scholarship rather than the $45 scholarship (Filmer and Schady 2009b).

4 Occasionally, there were tied scores at the cut-off. In these cases, all applicants with the tied score at the cut-off were offered the scholarships.

7
- 15 -

Appendix (2) derives the following investment equation in terms of the stock of physical capital K; and the rate of depreciation ap for purposes of

# the CIT:

- _-_

- (5) _(Ppe) (Iot) (atpt) (P'pt) (K;c) + (Potl (K-c.1) (P t) (Kt)_

The following investment constraint for physical capital is obtained by substituting the sum of replacement investment and net investment into the lefthand side of (5):

- _(6) 5p(Ppe) (Kpt,) + (Pp,.,) (Kp,e.i) - (Ppe) (Kpc) = (cpc) (Ppt) (Kpt)_ +

An analogous investment constraint for knowledge capital is given by:

- (7) **_jk(Pjtc)_** _(J4t) + (Pk,.,) (Kk,.,) - (Pst) (Kkt) = (kt) (Pk,) ('kZ,)_ + _(PktV 1) (4t+I)_ **_-_** _(Pkt) (4Kkt)_

Substituting for gross investment in physical and knowledge capital in objective function (1), augmenting it by production function (2), borrowing constra.'nt (4) and investment constraints (6) and (7), one obtains the following Lagrangean:

- 2

- _(8) L_ e0 **_E_** _D,,t{ (Pyt) (Yt) - E (Wj,)_ **_(vj,) -_** _(it,..a) (At)_ **_-_** _(uWp) (p*t)_ t=g j.1

- **-** _,p(P,p) (Kpt)_ - (Ppt.2) _(Kxpt.)_ + _(Ppt)_ (Kpt) **-** **_8 k(Pkt)_** _(K_ **_-_** _(Pkt-d (Kkt.l) + (Pkt) (Kt) + DAt_ - _CITPt - k3 f Ye - Y. (Vj,, Kpo, Kktk_ **_XpC gk,t_** _t)]_ **-** _k 2 [DAc,l_ - _(Ppt+.)_ (K,pc.) _+ (Ppt) (Rlo)_ - **_(Jkcv1)_** (Rktv,) +(!kc) _(Kkc)] - k1 [8.(P'p) (Kot) + (Ppt. 1 )_ **_(Kpe,)_** _(Ppt) (K,pt)_ - _(apt) (P (K) -(Pptl) (Kpft.1) + (Ppt)(Kpt)] k4_ **_Iak(PkC)_** _(Kkt) + (Pkrd (4kt-d) (P,) (Kkt) (akt) (PkF) (Rkt) k(P,te)_ (4K.t) + _(Pkt) (Rkt)_ ]}

where kl to k4 are Lagrangean multipliers.
Table 2: Marginal effects - probability of positive outcome.

||Non-agricu<br>employmen|ltural<br>t|Seasonal e|mployment|Job outside village|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Poor reg. road quality|-0.054***|-0.035***|-0.054***|-0.052***|-0.028*|
||(0.006)|(0.008)|(0.007)|(0.009)|(0.015)|
|Average reg. road quality|-0.019***|-0.014**|-0.015***|-0.015**|-0.033***|
||(0.005)|(0.006)|(0.006)|(0.007)|(0.011)|
|Log distance to market||-0.013***||-0.023***|-0.015***|
|||(0.003)||(0.003)|(0.005)|
|Log distance to kindergarten||-0.008***||-0.009***|-0.007**|
|||(0.002)||(0.002)|(0.003)|
|Log distance health center||-0.006**||0.012***|-0.006|
|||(0.003)||(0.003)|(0.005)|
|Individual controls|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|
|Region-year FE|Yes|Yes|Yes|Yes|Yes|
|Survey month FE|Yes|Yes|Yes|Yes|Yes|
|Observations|49,443|37,245|49,442|37,244|7,228|

Individual controls include: _gender, age, age squared, household head, marital status, education categories_ . Household controls include: _family size, number of children, family member abroad as an immigrant._ Robust standard errors in parentheses clustered on household level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 20
cities. Nevertheless, in both cities the observed patterns generally follow the predictions of the monocentric city model.

This consistency with the model disappears in the next two graphs: In both Moscow and Johannesburg the negative gradient disappears. The Moscow graph is from Bertaud and Renaud (1997), which examines the spatial structure of Moscow after 70 years of non-market planning. They show that a tightly controlled government investment and rationing system created rusting factories in prime locations, and high-density residential areas in distant suburbs. Such a spatial outcome, they argue, reduces economic and social efficiency, and worsens the city’s environment.

The Russian work was followed by work in South Africa, where the Apartheid system also had similarly rigorous laws controlling land use, in this case by race. The Group Areas Act required black workers to live in dormitory towns separated from the white core cities by buffer zones. This law resulted in extremely long commutes and economic hardship for black South Africans. Brueckner (1996) models the welfare gains that might result from the repeal of the Group Areas Act in 1991. He shows that allowing all South Africans the freedom to locate where they could maximize their utility would increase aggregate welfare. Thus, like Bertaud and Renaud’s (1997) conclusions about Moscow, he also implies that the Apartheid policies, apart from being racist, were also highly inefficient.

From the perspective of the effects of land market restrictions, the fundamental points of the graphs in figure 1 are two: first, land use restrictions can and have imposed heavy costs on urban dwellers, decreasing both efficiency and equity; and second, these constraints are undoubtedly highly variable across cities and history. To get a sense of just how variable such costs might be, it is useful to contrast Bertaud and Brueckner (2004) who examine the welfare impact of what is known as Floor Area Ratio (FAR) restrictions in the Indian city of Bangalore with those of Bertaud, Buckley and Owens (2003) who analyze the same sorts of 18
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-5 -

# Ambient **Air** Quality Standards **for** Annual Averages (micrograms/M3)

|Pollutant|Proposed<br>Indonesian|EPA|WHO|California|
|---|---|---|---|---|
|Total Suspended|||||
|<br>Particles (TSP)|90|75*|60-90|55#|
|Lead|0.5|N/A|0.5-1.0|N/A|
|Nitrogen Dioxide|100|100|N/A|N/A|
|Sulfur Dioxide|60|80|50|N/A|
|Ozone|200|240|150|180|

*In 1986, the EPA standard was replaced by a PM1O standard. The California standard has been converted from the current PMl0 standard to a TS? equivalent for the purpose of this analysis. N/A signifies standards with averaging time other than annual average.

# With this infonnation, the estimated health impact can be represented as follows:

dHi = b * POP; * dA where: dH; = change in population risk of health effect i bi = slope from dose-response curve

# POP; = population at risk of health effect i

dA = change in air pollution under consideration To complete benefit estimation for health effects, one would calculate the economic valuaLion of this effect (V.), as well. The valuation could be developed from estimates of the willingness to pay (WTP) for reducing risk, in order to atach values to the expected changes in prmature mortality, or a modified cost of illness (CO1). approach, to value changes in morbidity. Thus, the change in total social