rafmacalaba/datause-encoder-data huggingface dataset:
force participation or unemployment, because the pandemic might have affected the validity of the traditional definitions of such measures, thus rendering the pre-pandemic and ongoing-COVID-19 comparison results somewhat unclear (Lemieux et al., 2020). We disaggregate our analysis by sex, age group, highest education level, sector, and region (more on this in Section 5.3).

**Table 2. Placebo cutoff impacts on employment**

||||E<br>|mploymen<br>|t<br>|||
|---|---|---|---|---|---|---|---|
||February<br>2017|August<br>2017|February<br>2018|August<br>2018|February<br>2019|August<br>2019|February<br>2020|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A:**||||||||
|**Female**||||||||
|COVID|-0.003|-0.007|-0.003|0.002|-0.003|-0.002|-0.003|
||(0.007)|(0.007)|(0.007)|(0.006)|(0.007)|(0.004)|(0.007)|
||[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|
|**Panel B:**||||||||
|**Male**||||||||
|COVID|0.004|0.004|0.004|-0.004|0.004|0.004*|0.004|
||(0.004)|(0.004)|(0.004)|(0.003)|(0.004)|(0.003)|(0.004)|
||[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|[0.983]|
|Test:βm=|0.055|0.099|0.055|0.415|0.055|0.059|0.55|
|βf (p-value)||||||||

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Parentheses indicate standard errors of coefficient estimates, while square brackets indicate the False Discovery Rates (FDR). The dependent variable in the regression is the unconditional dummy variable of employment status (i.e., no missing values over the population). The main variable of interest in the above results is the COVID dummy, which takes on the value of 1 if the time period is August 2020. Different placebo treatment cutoffs are used across the table columns. Control variables in the regressions include age, squared age term, marital status, household sizes, education level dummies, training experience dummy, and urban/rural dummy. All specifications include year, month and province fixed effects.

# **_5.2 Transition in and out of Employment_**

The Sakernas data is pooled cross-sections that does not allow us to look at transitions in and out of employment. To understand the movement of individuals in and out of the labor force, we define mutually exclusive categories that sum to 100 percent of the working age population: (i) 24
# **Table of Contents**

|_Motivation and main findings ............................................................................................... - 1 -_|
|---|
|_What determines transport costs in Malawi? ....................................................................... - 4 -_|
|_Economic implications of infrastructure shortfalls and high transport costs .................... - 11 -_|
|_Geographically prioritizing infrastructure investments ..................................................... - 18 -_|
|_References .......................................................................................................................... - 22 -_|

# **List of Tables**

|Table 1: Physical road conditions, GIS+HDM4, by road class ............................................. - 6 -|
|---|
|Table 2: The determinants of tobacco transport costs to markets: all tobacco farmers ......... - 7 -|
|Table 3: The determinants of tobacco transport costs to markets: tobacco farmers using lorry|
|transportation ......................................................................................................................... - 8 -|
|Table 4: Trucking Survey - Summary of main findings ........................................................ - 9 -|
|Table 5: Determinants of trucking prices ............................................................................ - 11 -|
|Table 6: Crop productivity, by agronomic potential ........................................................... - 13 -|
|Table 7: Cash crop production, by agronomic potential and travel time to urban centers .. - 13 -|
|Table 8: Tobacco production, by agronomic potential and travel time to urban centers .... - 13 -|
|Table 9: Factors influencing decisions to produce cash crops ............................................ - 15 -|
|Table 10: Varying industrial specialization and bottlenecks in Blantyre and Lilongwe ..... - 17 -|
|Table 11: Location characteristics and the probability of living in poverty ........................ - 20 -|

# **List of Figures**

|Figure 1: Malawi's road network ........................................................................................... - 4 -|
|---|
|Figure 2: Road quality and traffic volumes ........................................................................... - 5 -|
|Figure 3: Identifying drivers of transport prices .................................................................... - 9 -|
|Figure 4: Tobacco Production Intensity and Agro climatic Conditions .............................. - 12 -|

# **List of Boxes**

|Box 1: Linking spatial data on road networks with engineering estimates of quality ........... - 6 -|
|---|
|Box 2: Route-specific trucking survey in Malawi ............................................................... - 10 -|

ii
suffer trauma which, in turn, affects schooling and health outcomes. Children who lose a parent due to AIDS specifically may be additionally stigmatized relative to other causes of death.

The available evidence of the impact of orphanhood, some of which is discussed in the next section, typically relates to the impact of orphanhood from HIV/AIDS and other causes in the short-run, often by examining a sample of school-age children in cross-sectional survey data. A small number of studies use longitudinal data over short-run (perhaps 1-2 years) periods in which household coping strategies may successfully mitigate the impact. Studies of long-run impacts and outcomes are rare. Understanding short-run outcomes is important, but short-run effects may not ultimately translate into worse welfare outcomes in the long-run (that is, in adulthood). For instance, the socio-economic consequences of a parent’s death may affect outcomes around the time of illness or during a period of funeral/mourning. However, as extended families foster in orphans, these outcomes may recover over time.<sup>1</sup> The next section reviews some of the existing empirical analysis. Section 3 discusses the data used in this study. Sections 4 and 5 present the empirical specifications and results. The final section concludes.

# **2. Background**

The existing literature on the consequences of orphanhood tends to focus on education outcomes and use cross-sectional data, which may limit the robustness of the outcomes. The findings of these studies are mixed<sup>2</sup> as are the findings of the few studies looking at other outcomes such as health. Few studies have also used panel data, which allows for extensive control of initial conditions.

Among the studies using large cross-sectional household survey data, several control for concurrent household characteristics to identify the impact of the loss of a parent on schooling. To the extent that orphans are found in relatively better-off households that also have higher demand for schooling, simple cross-sectional comparisons of enrollment rates between orphans and non-orphans may underestimate the true impact (for example, see Hargreaves and Glynn, 2002, and Ksoll, 2007). On the other hand, depending on mortality patterns, in some settings, an orphan’s household may be poorer prior to death, thus over-estimating the impact of orphanhood.

Ainsworth and Filmer (2006) find considerable diversity in the orphan/non-orphan differential across countries and conclude that it is difficult to draw generalizations about the extent to which orphans

> 1 See the studies cited in Golden (1994) regarding catch-up growth among stunted malnourished children.

> 2 Even estimates of the number of orphans is debated, with some research showing that the commonly-cited statistics from the UN may be seriously over-estimating the number of orphans (Bennell, 2005).

2
# Contents

|I|. Introduction .......................................|.<br>I|
|---|---|---|
|II.|Ghana Registered Midwives Association Family Planning Program ....<br>Background ............................................<br>|.....<br>4<br>4|
||The GRMA Program .<br>.....................................|5|
||Factors Contributing to GRMA Program Success ....................|7|
|Im.|The Rwanda Community Project ...............................<br>|8|
||Background ..............|**8**,.............,|
||The Ruhengeri Project..................................|...<br>9|
||Factors Contributing to Success in Ruhengeri .......................|12|
|IV.|The Sudan Community-Based Family Health Project ..................<br>|13|
||Background .................<br>,,,,,,,..............|13|
||The Original Project .................<br>,.<br>.............<br>|14|
||The Extension Project .................<br>|15|
||Revitalization in the Original Area ..............................|16|
||Factors Contributing to Family Health Project Success .................|17|
|V.|Discussion...........|18|
|Refer|ences ......................|20|
approved by the ERC and are for implementation during the subsequent regulatory period. Generation projects are subject to the Certificate of Compliance by the ERC and endorsements by the Securities and Exchange Commission and the DoE.

# 3.2 Access and affordability

## 3.2.1 Performance

_Access_ to grid electricity is a problem in many developing countries, but here the Philippines has had moderate success. From 1990, when the overall rate of access was 62 percent, the level has increased steadily, to 74 percent in 2000, 84 percent in 2010 and 91 percent in 2016. **Figure** **_8_** shows that the rate of urban electrification was high throughout the period, and that rural electrification made very substantial progress, increasing from 40 percent in 1990 to 86 percent in 2016. Nevertheless, there are large regional differences in access—Bangsamoro (a Muslim area in Mindonao) has a rate below 40 percent.

**Figure 7: Rates of access in urban and rural areas**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Urban Rural<br><!-- End of picture text --> Source: Tracking SDG 7, 2018 The _affordability_ of electricity to most consumers is still determined by tariffs set by the government, because the RCOA still applies only to 1 MW and above customers. The tariff structure uses an increasing block approach. The first block is a “social” block of 20 kWh with zero price, although there is a monthly fixed charge of US$ 0.49 for all users irrespective of the amount used. The price of the highest block is US$ 0.21, and the average residential tariff in 2017 was US$ 0.17. The average household consumption was 98 kWh/month, equivalent to an annual expenditure on electricity of $205. Households in the bottom 40 percent of Gross National Income had an income of $2795, so that to purchase the national average consumption of electricity these households would have had to spend 7.4 percent of income (figure 9). While this 30
# Moving to Adaptation?

Understanding the Migratory Response to Hurricanes in the United States A. Patrick Behrer,<sup>1</sup><sup>_∗_</sup> Valentin Bolotnyy<sup>2</sup>

> 1The World Bank Washington, D.C., USA

> 2The Hoover Institution Stanford University, 434 Galvez Mall, Stanford, CA 94305, USA

> _∗_ To whom correspondence should be addressed; e-mail: abehrer@worldbank.org.

Keywords: cyclones, climate change, adaptation, migration JEL codes: Q54, R23, R30
- John Van Reenen and C Patterson. The rise of star firms has been better for investors than for employees. _Harvard Business Review_ , 2017.

- Mahmut Yasar, Rafal Raciborski, and Brian Poi. Production function estimation in stata using the olley and pakes method. _Stata Journal_ , 8(2):221–231, 2008. URL `https:// EconPapers` _._ `repec` _._ `org/RePEc:tsj:stataj:v:8:y:2008:i:2:p:221-231` .

36
# IV.2 Survey of Industry Sectors

# A. Hypotheses and an Aggregate Pattern

We began our survey of SME marketing support with certain hypotheses about the behavior of SMEs.

e Firms in sectors that have a long history of exports, and where private exporting arrangements (such as subcontracting and local trader networks) are well developed, are likely to have proportionately less need for collective marketing services. This implies that firms in the woven textiles sector, with its relatively long exporting history, would need public marketing support less than firms making electronic parts, for instance, who have a shorter export history and less well-developed networks to turn to. In a similar vein, we expected that firms in more mature exporting indust **i** es like woven textiles would be less likely to change their choice of exporting chanmels than those in less experienced exporting sectors, who are on a steeper learning curve.

* Within this framework, we expected to find an industry cycle at work. In other words, firms that begin exporting early, relative to other firms in the same sector, would rely relatively more on collective support at the outset of their export activity. As firms in the sector gain experience, the reputation of their products in overseas markets would grow and export markets in the sector would thicken. Thereafter, firms that follow them into the market would tend to have less need of collective supports. Instead, they would be likely to increasingly rely on direct interactions with agents and foreign buyers:

* We also expected to find a firm cycle at work. Within a given sector that already has export experience, fims that are relatively new to exporting would logically tend to use collective marketing services more heavily than others. Similarly, if a sector's product mix (and presumably buyers) changes frequently, as is the case in factory automation, firms in the sector would be likely to rely on collective support services more heavily.

* Finally, we looked at firm size. Even though the need of small fihms might be greater, we expected that large firms would tend to use public marketing support more than smaller firms, simply because they find it relatively cost-effective on a per-unit basis. Small finms seemed likely to find subcontracting arrangements a simpler and more cost-effective export channel.

Our survey data indicates an interesting time lag pattern between the founding of the firm 7i.e. start-up) and the firm's first export. For those sample SMEs that have ever exported, Table _IV.1_ indicates that the average number of years between start-up and first export is 6.3 years across all sectors. Across all sectors, the lag from start-up to first export accrued from 9.9 years for the most mature firms, to 1.7 years for the more recent firms. Looldng just at factory 24
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<!-- Start of picture text --> bg<br>Jobs<br>. 1255 - 1904<br>. | © 1904-2467<br>© 2467- 3275<br>© 3275 - 7076<br>5 © 7076 - 37525<br>. * 2 r Impact on income net of<br>#- ° oHiRiemS _ | transportationMi -11.58--8 costs (%)<br>* =oe985%8%ope MmMm -s--4-4--3<br>. wethee - @goLe€° eeteeet e MM -3--1.5a<br>a eof ee MM -1.5--05<br>Scene, © , phlie MM -0.5--0.3<br>Pacehie > : é © 7 0,33 ---0.2-0.<br>we° ° .“ oseeoE. a £ eo ' 0.2--0.10.1-0.05- -0.05- 0<br>S<br>. oo?<br>é = e<br>é<br><!-- End of picture text -->
# **<mark>Table</mark>** **<u><mark>7: Changes in Firm-Level Employment, 2003–2013</mark></u>**

# **<mark>a. Light manufacturing</mark>**

|||l|,<br>og<br>_i t_<br>_ManEmp_|||
|---|---|---|---|---|---|
||Light<br>(1)|Food<br>(2)|Clothing<br>(3)|Paper<br>(4)|Other<br>(5)|
|×<br>×<br>MDist<br>Year<br>South|0.089|0.229**|0.062|–0.224|0.159*|
||(0.080)|(0.116)|(0.105)|(0.183)|(0.082)|
|MDist|0.076|–0.054|0.085|0.254*|0.070|
||(0.057)|(0.079)|(0.068)|(0.132)|(0.044)|
|Year|–0.644***|–0.494***|–0.712***|–0.421***|–0.756***|
||(0.043)|(0.080)|(0.060)|(0.154)|(0.051)|
|South|–0.035|0.181**|–0.141**|0.056|–0.125***|
||(0.039)|(0.085)|(0.056)|(0.123)|(0.042)|
|Constant|4.695***|4.915***|4.293***|4.427***|3.497***|
||(0.176)|(0.311)|(0.228)|(0.342)|(0.280)|
|_N_|5,895|1,398|2,224|874|1,399|
|_R_<sup>_2_</sup>-statistic|0.358|0.457|0.250|0.392|0.318|

# **<mark>b. Heavy manufacturing</mark>**

|||<br>l|<br>,<br>og<br>_i t_<br>_ManEmp_|||
|---|---|---|---|---|---|
||Heavy|Chemical|Nonmetallic<br>mineral|Metal|Machinery|
|×<br>×<br>MDist<br>Year<br>South|0.267***|0.482*|0.257|0.209*|0.247|
||(0.076)|(0.270)|(0.172)|(0.116)|(0.156)|
|MDist|0.047|–0.425***|–0.001|0.185***|0.114|
||(0.053)|(0.146)|(0.109)|(0.064)|(0.110)|
|Year|–0.564***|–0.530**|–0.388***|–0.686***|–0.465***|
||(0.055)|(0.212)|(0.109)|(0.093)|(0.106)|
|South|–0.078|–0.041|–0.023|–0.124|–0.145|
||(0.048)|(0.205)|(0.113)|(0.082)|(0.101)|
|Constant|4.258***|5.236***|4.474***|4.199***|3.385***|
||(0.283)|(0.446)|(0.452)|(0.373)|(0.258)|
|_N_|2,206|378|400|1,050|378|
|_R_<sup>_2_</sup>-statistic|0.454|0.546|0.301|0.419|0.433|

_Source:_ Authors’ estimates based on data from the 2003 National Industrial Census and the 2014 Integrated Business Establishment Survey.

_Note:_ This table reports estimates for equation 4. Dependent variable = log employment in firm _i_ at time _t_ . All regressions control for the informality status of a firm, the type of ownership, the type of legal organization, and region. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

28
A declining share to capital spending may have implications for present or future enrollment rates. However, the effects depend on the original spending levels: capital spending may decline because construction tapers off once universal enrollment is attained, or because donors and local communities increasingly bear capital costs. A growing share of the education budget allocated to personnel costs could signal a deteriorating quality of education as materials become scarce while teaching j( is or wages are protected. But it could also indicate that textbook provision is increasingly financed by external sources.

2.15 Real expenditures per student are a function of changes in both the relative or absolute sector resource allocation and enrollments. Per student expenditures could decline although real public expenditures increase, if the number of students increases even more. Changes in real per capita education expenditures are not a reliable indicator of the<sup>impact</sup> of adjustment. If school enrollments grow faster than the total population, the increases in per capita education expenditures observed in several adjusting countries do not preclude declines in per student expenditures.

2.16 The effects of changes in financing on coverage, quality, and equity are not obvious. According to the World Bank (1990d), the effects of a decline in sector spending depend on: 1) the adequacy of the original level of sector spending, 2) the absolute, rather than relative, decline in spending, 3) the ability of the sector to recover expenditure losses in years of increasing government spending, and 4) the relationship of spending to the number of beneficiaries in the system (supply versus demand). Although reductions in public allocations to education may lead to school closure or quality deterioration, Berstecher and Carr-Hill (1990) point out that some governments only reduced available spaces (and expenditures) in response to a decline in the number of students registering. Furthermore, a decrease in real education spending does not necessarily induce sector decline, if wastage declines while equity and efficiency improve.

2.17 Enrollment rates trace changes in sector coverage, but do not indicate whether supply or demand has changed. In Ghana, for example, real education spending increased, but the increase in enrollments did not keep up with population growth, and enrollment rates declined. Finally, changes in compliance with or enforcement of official age limits may change the gross enrollment rate independent of any change in supply<sup>or demand.</sup> While repetition and dropout rates may change in response to changes in quality, these rates are also affected by other factors. For example, in a number of countries repetition rates are largely a function of national regulations, and dropout rates are significantly affected by perceptions of the opportunity costs and benefits of education.

19
# **Job creation through infrastructure investment in the Middle East and North Africa**

Elena Ianchovichina, Antonio Estache, Renaud Foucart, Grégoire Garsous and Tito Yepes<sup>1</sup>

# _JEL:_ H54, J21

_Key words:_ infrastructure, investment, employment, job creation Sector Board: EPOL

> 1 Elena Ianchovichina is Lead Economist at the Chief Economist Office of the World Bank’s Middle East and North Africa region. Estache, Foucart and Garsous are with the Université Libre de Bruxelles and the European Center for Advanced Research in Economics and Statistics (ECARES), Brussels, Belgium. Yepes is with Fedesarollo, Bogota, Colombia. The authors are grateful to Caroline Freund, Jonathan Walters, Ilhem Salamon, Robert Bacon, and many other colleagues at the World Bank. Any mistake is ours and should not be blamed on any of the organizations we are affiliated with. 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.