rafmacalaba/datause-encoder-data huggingface dataset:
Table 1A: Harnessing Entrepreneurship and Job Creation in Cities-Formal Manufacturing

||Base<br>estimation|District<br>traits|Full<br>estimation|Adding<br>consumption|Using log<br> entry count|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||_DV i_|_s log entry e_|_mployment b_|_y district-ind_|_ustry_|
|Log of incumbent employment in district-industry|0.229+++<br>(0.043)|0.186+++<br>(0.040)|-0.028<br>(0.048)|-0.030<br>(0.047)|0.032+<br>(0.018)|
|Log of district population|0.531+++<br>(0.179)|0.483+++<br>(0.155)|0.475+++<br>(0.156)|0.482+++<br>(0.161)|0.216+++<br>(0.056)|
|District Traits:||||||
|Log of district population density||-0.569+++<br>(0.088)|-0.563+++<br>(0.080)|-0.562+++<br>(0.079)|-0.197+++<br>(0.029)|
|Share of population with graduate education||0.211+<br>(0.110)|0.235++<br>(0.107)|0.230++<br>(0.111)|0.078+<br>(0.042)|
|Demographic dividend for district (age profiles)||0.605<br>(0.458)|0.567<br>(0.446)|0.535<br>(0.468)|0.271<br>(0.177)|
|Index of infrastructure quality for district||0.018<br>(0.100)|0.096<br>(0.094)|0.086<br>(0.097)|0.015<br>(0.038)|
|Strength of household banking environment||0.143<br>(0.104)|0.095<br>(0.100)|0.085<br>(0.106)|0.027<br>(0.036)|
|Stringency of labor laws in district's state||-0.210+++<br>(0.070)|-0.161++<br>(0.064)|-0.157++<br>(0.065)|-0.095+++<br>(0.023)|
|Log travel time to closest large city||-0.275+++<br>(0.090)|-0.241+++<br>(0.083)|-0.237+++<br>(0.083)|-0.091+++<br>(0.031)|
|Log per capita consumption||||0.152<br>(0.505)||
|Local Industrial Conditions by Incumbent Firms:||||||
|Labor market strength for district-industry|||0.161<br>(0.102)|0.164<br>(0.102)|0.026<br>(0.041)|
|Inputs / supplier strength for district-industry|||0.485+++<br>(0.098)|0.485+++<br>(0.098)|0.154+++<br>(0.043)|
|Outputs / customer strength for district-industry|||0.388+++<br>(0.140)|0.387+++<br>(0.140)|0.167+++<br>(0.057)|
|Chinitz small suppliers metric for district-industry|||0.279<br>(0.213)|0.279<br>(0.212)|0.337+++<br>(0.129)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|4843|4843|4843|4843|4843|
|Adjusted R-squared|0.128|0.166|0.218|0.218|0.279|

Notes: Estimations quantify the relationship between district-industry employment in new establishments and local district conditions. District-level traits are taken from the 2001 Census. Industrial conditions are calculated from 2005-06 using incumbent establishments in the district-industry. Labor regulations are a composite of adjustment and disputes laws. Estimations weight observations by an interaction of district size and industry size, include industry fixed effects, and cluster standard errors by district. Non-logarithm variables are transformed to have unit standard deviation for interpretation. Source: Ghani, Kerr, O'Conell, 2011b 14
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE standards). But the latter would also guarantee road access to less than one-third of the land with suitability for agricultural production.

**Table 19. Indicative infrastructure spending needs in South Sudan for 2006 to 2015**

|$ million per year||||GDP shares|
|---|---|---|---|---|
|Sector|Capital expenditure|Operations and<br>maintenance|Total needs|Total needs|
|ICT|78|21|99|1.64|
|Irrigation|—|—|—|—|
|Power (nontrade)|179|29|209|3.46|
|Transport|524|187|711|11.80|
|Water supply and sanitation|303|45|348|5.78|
|Total|1,084|282|1,367|22.7|

_Source_ : Mayer and others 2009 **;** Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others, AICD 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ ICT = information and communication technology. — = Not available.

The water and sanitation sector is also in disarray, with the second-highest spending needs. About $350 million per year—or 6 percent of South Sudan’s GDP—is needed to meet the MDGs in water and sanitation. The water supply and sanitation estimates consider improving the technology mix used to provide improved services. This means that population would get improved services not only through cheaper and simpler technologies (protected boreholes or latrines) but the share of the population getting high-end services (piped water or sewerage) would increase, albeit modestly, over time relative to the other means. The power infrastructure requirements for South Sudan represent another big item, at an estimated $209 million per year. Yet, this only assumes meeting domestic demand.

Needs estimates for ICT are the lowest only because of the recent developments of the ICT sector, which has been financed by, and is expected to receive more, private sector financing. The estimates are modest as they do not include the cost of linking any of the regional gateways to submarine cables.

South Sudan’s infrastructure spending needs, at about 23 percent of GDP, are twice as high as other resource-rich economies in Africa and broadly comparable to those of LICs such as Kenya, Senegal, and Madagascar (figure 22). Investment needs are about 18 percent of GDP, accounting for 75–80 percent of total needs.

In absolute terms, South Sudan already spends over $450 billion per year to meet its infrastructure needs. This is equivalent to around 7.5 percent in terms of the size of the economy (table 20). These numbers position both South Sudan among average spenders in Africa, comparable to what MICs spend and perhaps slighter higher than other resource-rich countries (figure 23). Most of the traceable spending goes to capital investment, and maintenance flows seem to be almost nonexistent. Most of the spending of the south comes from the public sector and has garnered minimal interest from non–Organization for Economic Co-operation and Development (non-OECD) investors and the private sector, until recently.

40
Finally, a limitation of this study is that, since the VMS 2004 conducted interviews only in eleven provinces, the data should not be viewed as representing the migration patterns of Vietnam as a whole. In particular, whereas this research emphasizes differential migration patterns depending on job outcomes, it is not within the scope of this paper to analyze the relative importance of each type of migration. This paper also suggests that the impact of foreign job opportunities on internal migration must be understood in the context of the external environment. Specifically, the year 2004 and the preceding years covered by the VMS were a time when the labor demand of foreign firms expanded substantially, mainly stimulated by the U.S.-Vietnam BTA. The expansion of export-oriented and female-intensive manufacturing production in foreign firms is likely to have accelerated a migration flow of women towards the areas where foreign firms are concentrated. How negative external shocks would affect labor demand, urban unemployment, and migration patterns in a developing country is an important issue and a subject for future research.

# **References**

Asian Development Bank (ADB), 2006, _Vietnam: Poverty Assessment_ .

Athukorala, P. and T. Q. Tien, 2012, Foreign Direct Investment in Industrial Transition: the Experience of Vietnam. _Journal of the Asia Pacific Economy,_ **17** (3), pp. 446-463.

Dang, N. A., S. Goldstein and J. McNally, 1997, Internal Migration and Development in Vietnam. _International Migration Review_ , **31** (2), pp. 312-337.

Dang, N. A., C. Tacoli and X. T. Hoang, 2003, Migration in Vietnam: a Review of Information on Current Trends and Patterns, and their Policy Implications. Paper presented at the Regional Conference on Migration, Development and Pro-Poor Policy Choices in Asia, on June 22-24, 2003 in Dhaka, Bangladesh.

Davies, P. S., M. J. Greenwood and H. Li, 2001, A Conditional Logit Approach to U.S. State-toState Migration. _Journal of Regional Science_ , **41** (2), pp. 337-360.
of evidence showing that the dramatic influence of CCT programs on attendance is not accompanied by similarly large improvements in _learning._<sup>_17_</sup> Having established the treatment effects for the average individual, we want to understand how treatment effects differ according to the highest grade completed at baseline. We may expect strongly differential effects depending on whether the individual was within two years of a ‘transition’ year (i.e. a grade at the end of which a diploma is received) because the marginal value of additional schooling without an additional diploma may be significantly lower. Schultz (2004) finds enrollment impacts of _Progresa_ to be strongest in the highest year of primary school, and the Cambodian program studied by Filmer and Schady (2009) offers treatment _only_ to those in the transition year from primary to secondary school. Therefore, the evidence in the existing literature that CCTs can improve enrollment in nontransition years is scant at best.

In Figures 3a and 3b we plot follow-up schooling attendance by highest grade attended at baseline for dropouts and schoolgirls, respectively. While it is true that the effects are large and relatively constant for those whose highest grade attended at baseline was between Standard 8 and Form 3 for both groups, we also see large enrollment impacts for baseline schoolgirls throughout the distribution of grades. On the other hand, while the treatment effects are very large for baseline schoolgirls between Standard 8 and Form 2, but muted otherwise. These impacts suggest that CCTs can generate impacts across a much broader range of baseline schooling status when individuals who had already dropped out as of baseline are included and examined.

Figures 4a and 4b repeat the above exercise, but use reported changes in English literacy rather than attendance as the outcome. Baseline dropouts re-enroll in school in grades at which literacy is low and improving quickly. A separate analysis of the changes among dropouts (not shown here) indicates

> 17 World Bank (2009) finds that CCTs led to large increases in school enrolment, particularly among those with low enrolment rates to begin with. However, evidence on the impact of educational transfer programs (in kind or cash) on ‘final outcomes’ such as test scores, is not as encouraging – see, e.g., Miguel and Kremer (2004) or Glewwe, Kremer, and Moulin (2008). Filmer and Schady (2009) argue that the lack of any discernible effect of such programs on learning (despite large impacts on school enrolment) may be due to the fact that they draw lower ability students back to school.

16
tiny at 0.006. When we try to explain city effects for walking trip using the same approach as in table 10, the only robust correlate of our walking mobility index is a measure of average slope in the city. As Google Maps’ algorithm reflects, steeper slopes slow down walking.

As described in Appendix A, we also collected transit data. These data have two important limitations. Google Maps only appears to return transit information for formal transit, and it bases its information on official timetables. This ignores informal transit and delays or missed services in formal transit. With these caveats in mind, we first note that only about 20% of our trip instances have a transit alternative that we define as ‘viable’: it requires less than an hour wait, and is strictly faster than walking. Despite this selection, viable transit trips take on average 2.3 times as long as trips with private vehicles. In regressions not reported here, we additionally find that unsurprisingly, the transit time penalty is higher for shorter trips, trips further from the center, and nighttime trips.

Next, for 141 cities we can estimate an index analogous to our baseline mobility index for transit. Unlike with walking, there is a lot of cross-city variation for transit. The standard deviation for our transit mobility index is about twice that of our baseline mobility index for private vehicles. This variation does not seem to be due to sampling problems as these indices are precisely estimated and alternative transit indices are all highly correlated.

The correlation between our mobility index for transit and our baseline mobility index (for private vehicles) is extremely low at 0.02. This correlation even becomes negative when we focus on the largest cities. However, when we re-estimate our mobility index for private vehicles on a sample limited to trip instances for which a viable transit alternative is possible, this correlation increases from 0.02 to 0.17. This difference suggests a fair amount of selection regarding which trip instances have a viable transit alternative known to Google. To confirm the low correlation between transit and vehicle travel times we regress log transit travel time on log private vehicle travel time and log walking time. In this regression, the coefficient on log vehicle travel time is only 0.19 while the coefficient on log walking time, which is essentially a measure of trip length, is 0.52.

Finally, we also replicated the regressions of table 10 for our transit mobility index. We did not find any robust correlates of transit mobility at the city level. Given the sizable variation across cities in transit mobility, this may seem surprising. Nonetheless, this result is consistent with the weak correlation between (private vehicle) mobility and transit mobility. Although we must remain cautious given the caveats that apply to our transit data, taken together these results suggest to us to that transit mobility depends much more on the coverage and 35
<!-- Start of picture text --> Ply 9aning, and Reo arch |<br>World Development Roprt I<br><!-- End of picture text -->

Marx, Shumpeter, Kuznets, and others have written about how technical innovations affect economic development, but little attention has been paid to how financial and fiscal innovations affect development.

Financial innovations tend to reduce transaction costs and risks - both subjective and - objective and thus to expand and integrate capital markets. Financial development acce.erates the pace of economic development by encouraging savings and investment and increasing output.

- Public or govenmient intervention through a govenmment's central bank - is essential to a sound credit system.

A central bank in a developing country must have a development orientation. The single most important criterion of central bank effectiveness is the extent to which the bank's policies reduc,. overall costs and risk on credit transactions.

To perform its regulatory function effectively and to promote a sound, healthy financial or credit system, a central bank must have a degree of autonomy. Since 1965 this autonomy has been eroded in both the developed and the developing countries because of the impact of fiscal policy.

The result has been unstable credit and the intemational debt crisis, which already affect the development process and the functioning of intemational credit markets.

This is a background paper for the 1989 World Development Report. Copies area available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room S13-060, extension 31393 (32 pages with tables).

The PPR Working Paper Series dissemninates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
Kyyrä, T., Pirttilä, J., & Ravaska, T., (2021). The corona crisis and household income: The case of a generous welfare state. VATT Institute for Economic Research.

- Lastunen, J., Rattenhuber, P., Adu-Ababio, K., Gasior, K., Jara, H. X., Jouste, M., McLennan, D., Nichelatti, E., Oliveira, R. C., Pirttilä, J., Richiardi, M., & Wright, G. (2021). The mitigating role of tax and benefit rescue packages for poverty and inequality in Africa amid the COVID-19 pandemic. WIDER Working Paper, 2021/148. Helsinki: UNU-WIDER.

- Moreira, A., & Hick, R. (2021). COVID-19, the Great Recession and social policy: Is this time different? Social Policy & Administration 55(2): 261–279

- Müller T and Schulten T (2020). Ensuring Fair Short-Time Work – a European Overview. ETUI Policy Brief. European Economic, Employment and Social Policy. Covid-19 impact series No. 7. Brussels: ETUI.

- Paulus, A., & Tasseva, I. V. (2020). Europe Through the Crisis: Discretionary Policy Changes and Automatic Stabilisers. Oxford Bulletin of Economics and Statistics, 82(4), 864–888.

Popova, D., & Navicke, J. (2019). The probability of poverty for mothers after childbirth and divorce in Europe: The role of social stratification and tax-benefit policies. Social Science Research, 78, 57–70.

21
Policy Research Working Paper 9765

# **Abstract**

A better understanding of how individual wealth and time use are linked—across paid, unpaid, and leisure activities —is important for targeting widespread gender inequalities in time allocation, as well as in accessing economic opportunities. The lack of reliable, individual-level data on asset ownership across different subpopulations, however, has limited discussions of these issues in the literature. Using a unique nationally representative survey from Cambodia, this paper shows that individual wealth, as measured through self-reported ownership of physical and financial assets, is significantly associated with time allocation to different activities. The role of asset ownership in time use is also stronger, particularly among women, vis-à-vis the competing proxies for socioeconomic status. Ownership of financial accounts, motorized vehicles, and mobile phones —all of which can improve access to networks, markets, and services—is associated with less time in unpaid work, and in some cases greater time in paid work, specifically among women in off-farm jobs. There are also distinct gender differences in how men and women shift their time away from leisure and childcare, highlighting the importance of social norms in choices over time use. The analysis highlights the utility of integrated, intra-household, individual-disaggregated data collection on asset ownership, time use, and employment in lower-income contexts.

This paper is a product of the Development Data Group, Development Economics. 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 tkilic@worldbank.org and gkoolwal@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
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_31_ _Ways Out of Poverty - Diffusing Best Practices and Creating Capabilities_ societies. Some basic measures to establish such societies are known, for example respect for property rights and contracts as well as freedom for firms of all types to enter markets as well as freedom to fail. Just as we cannot predict exactly which type of firm will succeed so we cannot predict which type of government policy and institutional mix will be most successful. Competition among jurisdictions is as important to institutional development as that among firms. We have leamed some basics on how to facilitate development, but we do not know how to engineer it and have to remain open to experiments. Hopefully we can improve the chances of success of unavoidable experiments by studying in more detail the way markets, firms and governments interact.<sup>68</sup>

**68** World Bank, 2001b.
|Strong x 2021|0.138|-0.096|0.083|-0.015|-0.037|
|---|---|---|---|---|---|
||(0.091)|(0.061)|(0.114)|(0.101)|(0.117)|
|PSU dummies|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|Yes|
|Partner controls|Yes|Yes|Yes|Yes|Yes|
|Observations|2,024|1,990|1,233|1,583|1,894|
|R-squared|0.221|0.149|0.283|0.196|0.136|

The dependent variables are the sub-indices of the female decision-making index (FDI). Standard errors clustered at the district level.

# b. Sensitivity checks

Our baseline results have established that the female decision-making index has significantly improved in medium conflict districts relative to low conflict districts. To check the sensitivity of the results, we proceed with a series of robustness checks based on column 2 (i.e., full sample) of the main results in Table 1.<sup>7</sup> We repeat the analysis reported in the main results while controlling for the intensity of conflict, controlling for children living in the household, excluding polygamous households, excluding women that were not privately interviewed, considering alternate measures of conflict intensity, excluding districts that saw extreme levels of conflict intensity, and excluding a district from the sample one-by-one. These checks, summarized in Table 4, prove that the results reported in this paper are satisfactorily robust in the face of alternate specifications and conditions.

_Table 4: Sensitivity checks_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||FDI|FDI|FDI|FDI|FDI|FDI|FDI<br>|FDI<br>|FDI<br>|
||Baseline|Weigh by<br>number<br>of<br>fatalities|Control<br>for<br>children<br>in HH|Exclude<br>polygamous<br>HH|Exclude<br>women not<br>privately<br>interviewed|Sum of<br>events<br>(2017-<br>2019)|Average<br>number of<br>events<br>(2015-<br>2021)|Drop<br>extreme<br>conflict<br>districts<br>(1%)|Drop<br>extreme<br>conflict<br>districts<br>(5%)|
|Medium x 2021|0.173**|0.233**|0.179**|0.186**|0.325|0.161*|0.173**|0.173**|0.160*|
||(0.082)|(0.093)|(0.079)|(0.085)|(0.303)|(0.084)|(0.082)|(0.085)|(0.087)|
|Strong x 2021|0.055|0.067|0.074|0.065|-0.002|0.060|0.055|0.060|0.046|
||(0.062)|(0.086)|(0.065)|(0.064)|(0.096)|(0.064)|(0.062)|(0.062)|(0.063)|
|Medium x 2021 x number of<br>fatalities||0.0001||||||||
|||(0.002)||||||||
|Strong x 2021 x number of<br>fatalities||0.001||||||||
|||(0.002)||||||||

7 We find a similar level of robustness when considering the couples sample (i.e., in comparison to column 3 of Table 1), but these are not reported for the sake of brevity and are available upon request from the authors.

16