rafmacalaba/datause-encoder-data huggingface dataset:
S interpolate --, within this trend,<sup>for the missing years. When the beginning</sup> or the end of a time series is<sup>missing, asjessing a trend is&more delicate</sup> procedure and experts have to resort<sup>to their knowledge of the country's</sup> conditions to supplement their projections.<sup>Such extrapolations are</sup> definitely less reliable than actual<sup>data and interpolations.</sup> Because of different projection<sup>or estimation methods, data compiled by</sup> different sources can show substantial<sup>discrepancies. Such discrepancies may</sup> not always be easy to understand or<sup>justify. For instance, consider the case</sup> of data on infant mortality rates.<sup>2</sup> Table 1 shows differing infant mortality data for a few selected countries from<sup>three different sources: the World</sup> Bank, the United Nations, and Westinghouse''s Demographic and Health Surveys (DHS).3 2For a good discussion of the data problem relative to mortality, see Christopher Murray, "A Critical<sup>Review of International Mortality Data," Soc.</sup> Sci. Ned., vol. 25, no.7 (1987),<sup>pp. 773-781.</sup> 3The World Bank, SociaL Indicators of Devplmuent 1988 (Washington, D.C.: The World Bank, 1988); United Nations,<sup>Mortalitv of Children under age 5! World</sup> Estimates and ProJections. 1950-2025<sup>(New York: The United Nations, 1988)</sup> Population Studies, No 105.
Policy Research Working Paper 10757

# Spillovers in ICT Adoption from Formal to Informal Firms

## Evidence from Zambia

_Filip Jolevski Gaurav Nayyar Regina Pleninger Shu Yu_ Equitable Growth, Finance and Institutions Practice Group & Global Indicators Group April 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
# Policy Bias Againgt Agriculture Comparison with Othcr Cotntrics

Given the similarity between agricultural policies in Pakistan and India, some useful insights can be gained fi-om looking at the Indian incent-vc structurc. According to Gulati and Pursell (1993), at official exchange rates, lndian agriculturc had an efrcctive protcction coefiTcient of 0.86 betwecn 1980-81 and 1987-87, while in 1986-87, manufacturing's ceTcctivc protection cocfficicnt was 1.34. Taking account of nontraded inputs reduced disprotcction of agriculturc close to zero, but the protection of manufacturing relative to agriculture is indicative of substantial anti-agricultural bias within the traded goods sector, with consequent misallocation of resources.

According to a recent World Bank report, Pakistan also has protected industry relative to agriculture (Table 5), at least as revealcd by import tax rates. Import tax rates arc far lower on agricultural imports than other kinds of imports.

Table 5: Tradc-Wcightcd Mcan All-lnclusivc Import Tax Rates, 1989-90

|Wholc Economy|70.1|
|---|---|
|Agriculturc|46.7|
|Manufacturing|73.8|
|Consumer Goods|92.4|
|Intemiediate Goods|71.8|
|Capital Goods|65.5|

Source: World Bank (1992).

Of course, import tax rates are imposed rates and actual collection rates may be less; in addition the protection may be latent, since some goods may still be produced cheaper domestically than at the world price. However, other indicators also point to substantial protection for industry. The three main industrial sectors are chemicals, engineering, and textiles, which receive average effective protection of 24 percent. In addition, the dispersion in rates of protection is huge. Within the three industrial subsectors, 70 percent of domestic resources are employed in inefficient and highly protected industries. This diversion of resources to inefficient sectors (and to rent-seeking) is at the expense of unprotected sectors, including agriculture.

Historically, over a broader range of countries, the effect of protection for other sectors (tradable and non tradable) on agriculture has been relatively high in Pakistan. The effect of trade policy can be quantified by its impact on nominal rates of protection for agriculture (Table 6). The total effect can be disaggregated .ito direct and indirect efforts. Direct effects measure the percent by which producer prices diverged from prices at free trade (given the actual exchange rate and degree of industrial protection). Indirect effects take account of the impact of trade and macroeconomic policies on the real exchange rate, and the extent of protection afforded to nonag,icultural tradable cornmodities.

In the early 1980s, direct effects were having a small effect on agricultural incentives in both Pakistan and Chile. However, indirect policy had the effect of reducing the price received by Pakistani cotton farmers by 35 percent relative to the world price, while producers of Chile's primary export crop (grapes) were only losing 7 percent of the world price for the same reason.

8
in the previous year "refund" was greater than total levy paid. Finally, the ratio of total profit to total employment is on average negative indicating that most fmns incur losses. Note however that this average is most likely affected by outliers (minimum value is - 218 and maximum value stands at 41.67). The median value, which is a better measure of central tendency, has a value of 0.0146. Finally, note that the coefficient of variation varies a great deal. It goes from 29.80% _(EL)_ to 6,069.44% _(Profemp)._ The typical variation is between 100% and 1000%. There seems to be a great difference in variations between discharges using total suspended solids (3,840.85%) and those for chemical oxygen demand (306.01%). Care should thus be exercised when interpreting results using variables with greater variability since the variables might contains some outliers.

**Table 2 Means and coefficients of variation for variables of interest in 1997**

|**VariableNames**|**Definition**|**Mean**|**CVin**%|
|---|---|---|---|
|EL|Waterlevypaid/waterlevycharged|0.9566|29.80|
|State|**1=a**plantisastate-ownedenterprise;<br>0=otherwise|0.3228|144.83|
|Coll|l=aplantisacollectively-owned<br>enterprise;0=otherwise|0.5964|82.26|
|Fjv|1=aplantisajointventure;<br>0=otherwise|0.0706|362.75|
|Textile|1-textileindustry; 0=otherwise|0.0189|721.16|
|Chemical|I=chemicalindustry; 0=otherwise|0.0224|660.42|
|Profemp<br>(profitability)|Totalprofit/totalemployment|-0.1056|6,069.44*|
|Lref<br>(refund)|Total refundfromthelevyfunddivided<br>by totallevypaidin1996|2.2256|337.60|
|Ratwtop<br>(pollutioncontrol<br>effort)|Totaloperationcostofwaterpollution<br>abatementdividedby total production<br>operationcost|0.0016|418.75|
|Cmpaccon<br>(socialimpact)|I-=havingenvironmentalaccidents,<br>environmental conflictsorcitizen<br>complaints onitspollution;0=otherwise|0.1848|210.06|
|Bentss<br>(TSSviolation)|Levelofdischargesoftotalsuspended<br>solidswithrespecttothe standard|0.0674|3,849.85|
|Bencod<br>(CODviolation)|Levelofdischargesofchemicaloxygen<br>demand withrespecttothestandard|1.5923|306.01|
|Insp(numberof<br>inspections)|Totalnumberofinspectionstoaplant|7.7604|127.17|

'CV stands for coefficient of variation. It is calculated as the ratio of the standard deviation to the mean. "*" indicates the absolute value.

15
Other important socioeconomic determinants of MNCH outcomes and services are education and place of residence. We examined differences in health inequalities by these two outcomes (Table 3b). An important observation in Table 3b is that of the improvement in the rural-urban gap. In all the indicators, the rural–urban difference in health inequality has either improved (narrowing inequalities) or shown no significant indication of worsening or widening inequalities. Although it is not as strong as the spatial dimension, there is also a similar profile by education-based health inequalities. The results for most of the indicators show that education-related absolute inequalities in health have been narrowing. The exceptions are undernutrition indicators, where mixed results were found.

**Table 3b. Trends in absolute gaps MNCH inequalities by education and place of residence, 2000-201** Diff: Primary & below-

||Diff: Pri<br>Seconda<br>|mary & be<br>ry+<br>|low-|Diff: Ru<br>|ral-Urban<br>||
|---|---|---|---|---|---|---|
||Earlier<br>Survey|Latest<br>Survey|P-value|Earlier<br>Survey|Latest<br>Survey|P-value|
|Stunting|22|23|0.649|12|16|0.147|
|Wasting|6|2|0.439|5|0|0.347|
|Underweight|18|22|**0.022**|15|9|**0.012**|
|NMR|21.4|17.6|NA|4.3|-11.6|NA|
|IMR|31|27|NA|10|-12|NA|
|U5MR|75|55|NA|38|5|NA|
|Measles Vaccination|-37|-35|0.112|-40|-27|**0.105**|
|Full Immunization|-34|-34|0.506|-32|-28|0.588|
|Contraceptive|-32|-23|0.362|-29|-20|0.110|
|ANC4+|-42|-49|0.411|-38|-47|0.545|
|SBA|-41|-64|0.131|-32|-49|0.376|

Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data.

Note: The p-values are for the F-test of the difference-in-differences (Diff-in-Diff). Tests for education and rural-urban difference in differences are: _Is [(less educated (primary and none)in the Latest Survey)- educated (secondary+) in the Latest Survey)] equal to [((less educated (primary and none) in the Earlier Survey)-( educated (secondary+) in the Earlier Survey)]?_ And, in the second panel of Table 4: _Is [(rural in the Latest Survey) - (urban in the Latest Survey)] equal to [(rural in the Earlier Survey) - (urban in the Earlier Survey)]?_ Test is not applicable for NMR, IMR and U5MR.

20
_- 18 -_ <!-- Start of picture text --> y<br>M = md (O,r,p,Zm)<br>y<br>Ms Md<br>y y<br><!-- End of picture text --> and it is represented in Figure 12 where demand intersects supply. Along the demand curve, wealth-holders demand higher real rate of returns of money 0 (or lower inflation rates) in order to hold increasing stocks of money relative to GDP. If the real rate of return on money 0 were higher than the rate at which asset holders are willing to hold the existing amount of money relative to GDP, then equilibrium would be restored through an increase in the real rate of returns on financial assets, r and p, thus prompting asset holders to hold the same amount of money relative to GDP at a higher real rate of return on money 0.

**Figure 12: Money market equilibrium**

<!-- Start of picture text --> 6<br>rni<br>p m<br><!-- End of picture text -->

# **_The quasi-money market equilibrium_**

In the quasi-money market, the quasi-money supply as a ratio to GDP

- _(QM =_ qm') is exogenous, whilst the quasi-money demand as a share to GDP **_y_**

- _(QM_ = **_=d M)_** is a positive function of the real rate of return on quasi-money (r), a _y_

negative function of the real rate of return on stocks (p), a negative function of the real rate of return on money (0), and a function of a vector (Zqm) of exogenous variables that
for new keynesian models. _NBER Working Paper_ , (w27486).

- Auclert, A., Rognlie, M., & Straub, L. (2018). The intertemporal keynesian cross. (25020).

- Baird, S., Ferreira, F. H., Özler, B., & Woolcock, M. (2014). Conditional, unconditional and everything in between: a systematic review of the effects of cash transfer programmes on schooling outcomes. _Journal of Development Effectiveness_ , 6(1), 1–43.

- Balboni, C., Bandiera, O., Burgess, R., Ghatak, M., & Heil, A. (2020). Why do people stay poor?

- Bandiera, O., Burgess, R., Das, N., Gulesci, S., Rasul, I., & Sulaiman, M. (2017). Labor markets and poverty in village economies. _The Quarterly Journal of Economics_ , 132(2), 811–870.

- Banerjee, A., Breza, E., Duflo, E., & Kinnan, C. (2019). Can microfinance unlock a poverty trap for some entrepreneurs? (26346).

- Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., Shapiro, J., Thuysbaert, B., & Udry, C. (2015). A multifaceted program causes lasting progress for the very poor: Evidence from six countries. _Science_ , 348(6236).

- Banerjee, A., Duflo, E., & Sharma, G. (2020). Long-term effects of the targeting the ultra poor program. (28074).

- Banerjee, A., Karlan, D., Osei, R. D., Trachtman, H., & Udry, C. (2018). Unpacking a multi-faceted program to build sustainable income for the very poor.

- Bedoya, G., Coville, A., Haushofer, J., Isaqzadeh, M. R., & Shapiro, J. (2019). No household left behind: Afghanistan targeting the ultra poor impact evaluation.

- Blattman, C., Fiala, N., & Martinez, S. (2020). The long-term impacts of grants on poverty: Nine-year evidence from uganda’s youth opportunities program. _American Economic Review: Insights_ , 2(3), 287–304.

47
Policy Research Working Paper 5851

# **Abstract**

In this paper, a unique post-earthquake survey designed to provide a rapid assessment of food insecurity in Haiti is used in order to see how adequately emergency assistance programs have been allocated. When modelling the impact of various covariates upon assistance allocation, the location of households emerges as the main criterion. This helps to explain why, five months after the quake, government and agencies still seemed unable to provide an efficient allocation of emergency assistance. What is more, those who benefited less from assistance appeared to be on the one hand families headed by women and on the other hand households with disabled members: this obviously runs counter to an “optimal” targeting that would make the most vulnerable ones eligible for assistance in priority. Furthermore, the fact that associations may favour assistance allocation is an interesting result that should be considered further. It is also found that asset losses had no significant impact on the food consumption score, whereas household preearthquake wealth did. This result demonstrates that the impact of the shock has been buffered when households had previously enforced coping strategies, regardless of the effects of emergency assistance programs.

This paper is a product of the Social Protection Sector, Latin America and the Caribbean 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://econ.worldbank.org. The author may be contacted at damien.echevin@usherbrooke.ca.

_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
appears to be an important determinant of post-earthquake participation. What is more, cash-forwork is very rarely declared as the main source of household income.

We can therefore consider that asset-poverty trends as observed during the five months following the earthquake, has little to do with the allocation of assistance programs. It seems that those households who have lost the most succeeded in recovering more rapidly from the shock, regardless of the effects of assistance, and probably more in line with coping strategies that are specific to households. This might be an expression of the remarkable capacity of resilience of the population in Haiti.<sup>17</sup> Yet, it is not possible to confirm the existence of poverty traps with available data.

> 17 _Cf._ World Bank (2007).

27
Policy Research Working Paper 8510

# **Abstract**

Violent conflict and instability affect men and women in heterogeneous ways, including differentiated impacts on economic, social, physical, and mental well-being. This study assesses the impact of the post-2013 conflict in South Sudan on adolescent girls and young women. The analysis uses data from the Adolescent Girls Initiative endline survey and the Armed Conflict Location & Event Data to measure conflict exposure using constructed cluster-level, self-reported, and external conflict exposure variables. The impact of conflict exposure is then estimated on a set of socioeconomic outcomes of adolescent girls by comparing exposed and non-exposed clusters before and after the conflict. The results suggest that girls from clusters more affected by the conflict had statistically different outcomes compared with girls from less affected clusters. Specifically, there is strong evidence that the conflict negatively affected outcomes related to income opportunities, aspirations, marriage, and household characteristics, but increased self-reported empowerment and entrepreneurial potential scores. The results indicate that impacts on labor supply, personal motivation, household conditions, and other forms of victimization are important channels through which conflict negatively impacts adolescent girls.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at upape@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
supplement these data with 12 additional public opinion surveys. The second dataset is on refugee populations at the sub-national level. We obtain preliminary estimates from the United Nations High Commissioner for Refugees (UNHCR), develop a methodology to impute missing totals, and aggregate at the level of sub-national regions as reported in GWP. The third dataset covers policies on camps, which we also obtain from UNHCR, and on _de jure_ access to the labor market, which we obtain from the authors of the Developing World Refugee and Asylum-seeker Policy dataset (Blair et al. 2021a) and supplement by coding the additional LMICs in our sample.

In order to causally identify the effect of refugees’ presence on attitudes, we use large, sudden arrivals of refugees that create clear before and after time windows at the sub-national level. We examine how attitudes changed within affected regions in our first set of specifications using event study designs. We extend the framework to include regions within the same countries that did not experience a similarly large increase for a difference-in-differences design.

Our preliminary findings suggest that the large arrivals of refugees do not have a negative effect on attitudes in the period we study (up to four years after arrival). The coefficients are positive, small, and statistically insignificant but, importantly, can rule out meaningful negative effects. We then find little evidence that the pattern differs across camp and non-camp settings or across liberal and restrictive right-to-work environments, although the analysis is still at a preliminary stage.

Our strategy allows us to estimate the average effect across contexts, which is arguably the parameter of interest to both policymakers and academics.<sup>4</sup> Studies within a single context have important advantages, but the external validity is always a concern. Meta-analyses may also not produce the true average effect if the set of contexts is systematically biased, perhaps due to data availability or potential publication bias. However, Pottie-Sherman and Wilkes (2017) conduct a metaanalysis of immigration attitudes and group size across 55 studies. Among 487

> 4Our strategy identifies the effect of large waves only. The effects of smaller populations of refugees could evolve differently.

2
and infrastructure made investments in infrastructure a priority again for the donor community. This is resulting in renewed flows of official development assistance for infrastructure projects in sub-Saharan Africa to try to bring the continent at par with other regions of the world.

The rejuvenated emphasis on infrastructure in Africa is welcome, as today Africa is lagging behind in the coverage of network infrastructure services compared to all the other regions in the world. Most of the countries in Eastern Europe and Central Asia have almost universal access or coverage (we will use both terms without differentiating them in this paper<sup>2</sup> ) so that the infrastructure policy debate revolves there around improving service quality. Latin America and the Middle East and North Africa are following closely behind. With respect to gross national income (GNI), Africa comes closest to South Asia (SA) among the regions of the world. Yet population growth and urbanization trends are highest in Africa, which results in faster rising infrastructure needs at the household level than in South Asia. Overall, Africa lags behind other regions in the provision of improved water and sanitation services. In case of fixed and mobile phone coverage, Africa is ahead of South Asia, a testimony to the explosion of cell phone networks, services and subscribers (Table 1).

While there is a renewed consensus towards providing more support for improving the coverage of modern infrastructure services in Africa, the capacity to monitor and evaluate the progress achieved or to be achieved remains limited. Our objective in this paper is to provide detailed evidence on the trends in household access to modern infrastructure services in subSaharan Africa. To this end, we rely on a large number of Demographic and Health Surveys because these surveys have very wide coverage (they are fielded in most countries at regular intervals of time) and they are also relatively comparable between countries and over time. In a few countries where there is no DHS or there is only one survey in the past 15 years, Multiindicator cluster surveys (MICS) have been sparingly employed. MICS are the closest to DHS with respect to sample and questionnaire design.

How does our work compare with previous work? In general, water supply and sanitation (WSS) is the only sector whose evolution is directly monitored because of the target set forward in the MDGs to “ _halve the number of people without access to safe drinking water_

> 2 In other work, some of the authors of this paper have differentiated between access, to be understood as a household living in an area where access to a service is potentially available, and coverage, which represents the actual use by the household of the service (coverage thus represents the product of the access rate and the take-up rate for the service where access is available).  In this paper, this distinction is not essential, hence we will use both terms of access and coverage interchangeably.

3