rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Parametric Regressions_

Our main results are shown in tables 2a and 2b. We pooled the surveys of 1999 and 2001 and estimated the regressions with a six-month bandwidth.<sup>24</sup> Tables A.1 and A.2 in the appendix show only the results with the PNAD of 1999. The results are very similar, though less precise.

Tables 2a and 2b show the results with three different specifications of the smooth function, linear and quadratic polynomials, and piecewise linear. We add mothers’ years of schooling as controls. In addition, the tables present the mean values of the outcomes of the comparison group.

Table 2a shows a decrease in paid-work incidence of 4.1 percentage points (p.p.) for all males or 27 percent. The result is very stable across the three specifications and is statistically significant at a 5-percent level. The same results are obtained when only using the 1999 PNAD and are shown in table A.1 in the appendix. For 1999 only, the child-labor decline is mostly driven by the decrease in paid formal work.

To shed more light on the time allocation in work and schooling activities, we look at the proportions only working, working and studying, and neither working and studying. We decompose working into working in paid and unpaid activities respectively. The results are shown in table 2.b.

There is fall of 3.4 p.p. (about 28 percent) in the proportion of boys doing paid work and studying and an increase of 5.6 p.p. (about 8 percent) in the proportion of boys only studying. Both these results are statistically significant, different from 0 at 5 percent. It seems that some affected boys returned to school after the ban. Although not shown in the table, we estimate that the affected cohort is 2 p.p. more likely to be delayed in school just after the ban. Given that 45 percent of 14-year-old boys were delayed in school in 1999,
. This is especially so given the ban on pregnant girls being able to re-enrol post-crisis.

# **4 Data, Implementation and Research Design**

## **4.1 Data Sources**

Our data was collected before, during and after the Ebola outbreak. Figure 2 shows the timeline of data collection activities, and how these relate to the timing of the crisis, and ELA club openings/activities. Our evaluation takes place in four districts: Port Loko, Kambia, Moyamba and Pujehun, where 20% of the population resided pre-crisis. In preparation for our evaluation of ELA, in October 2013 we conducted a census in the 200 sample villages in these districts, covering 94 _,_ 338 individuals in 17 _,_ 233 households. This recorded information on demographics, education, occupation and dwelling and allows us to construct a pre-crisis poverty score for each household. This census was used to draw a sample of women aged 12 to 25 and thus eligible for ELA.

12
services trade facilitation in Mode 4 and what does not (see Table 1 for examples). The distinction in this context is between liberalizing regulation that may be unnecessarily restrictive or burdensome and which can be streamlined, if not eliminated in its entirety, and between facilitating complying with regulation that may be necessary. Illustratively, suggestions for eliminating or easing regulations (e.g. the requirement for professionals to retrain in-country and get local qualifications) are more likely classified as services trade liberalization issues rather than trade facilitation. In contrast, suggestions for reducing visa fees; reducing the number of documents needed to get a visa; streamlining processing times; allowing for multiple entry visas etc. fall under the rubric of trade facilitation. While the focus of this paper is on trade facilitation (see Section 4 for details), it also provides an analysis of services trade barriers vis-à-vis Mode 4 as contained in the OECD STRI database (see Section 3 for details) for comparison. The analysis of services trade barriers outlined in Section 3 is also warranted by the fact that the distinction between trade cost and trade facilitation measures is not always watertight. For instance, while reducing heterogeneity in regulation clearly forms a part of a possible TFiS agenda, regulatory heterogeneity also imposes significant trade costs on suppliers, accounting for 21 percent of total trade costs in services along with trade policy barriers (WTO, 2019). We thus provide an analysis of predominantly trade cost measures from the OECD STRI database in Section 3 followed by an analysis of largely trade facilitation measures using the same data source in Section 4, while acknowledging links between the two.

3
. and Koch, S. (2018).’Exploring child poverty and inequality in postapartheid South Africa: A multidimensional perspective’, _Journal of Poverty and Social Justice_ , 26(3), pp.417–437.

- Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). _Informing Durable Solutions by Microdata : A Skills Survey for Refugees in Ethiopia_ (English). Washington, D.C.: World Bank Group. -

- <u>http://documents.worldbank.org/curated/en/996221531249711200/Informing durable-solutions-by-micro-data-a-skills-survey-for-refugees-in-Ethiopia.</u>

32
Figure 37. Adjusted net savings share in GNI and transparency score in Sub-Saharan African countries, 2004-2014 average.

_Notes: Adjusted net savings as % of GNI vs Country Policy and Institutional Assessment (CPIA) transparency, accountability, and corruption in the public sector rating 2004-2014 average. The CPIA rating captures the level of performance of a country in a given year rated on a scale of 1 (low) to 6 (high). Oil and gas rich countries (in blue) are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries as defined by Lundgren, Thomas, and York (2013) are identified with color red. It excludes Burundi and Liberia which have ANS of -32 % and -61 % of GNI, respectively. Source: authors’ calculations using World Bank data and the CPIA database._ 47
<!-- Start of picture text --> Migrants can Move Freely Migrants as Neighbors Born Non−Ugandan Able to Naturalize<br>0.3<br>0.2 0.041 0.092<br>0.071 0.032<br>−0.006<br>0.1<br>−0.019<br>0.0 −0.098<br>−0.1<br>−0.2<br>Baseline Pres x Pres x Baseline Pres x Baseline Baseline<br>Presence 2016 2020 Presence 2020 Presence Presence<br>Felt Unsafe in Community Feared Crime Any Violent Event (ACLED)<br>0.3 0.175 0.01<br>0.02 0.013 0.011<br>0.2<br>0.062<br>0.045 0.01<br>0.1<br>−0.016 −0.005<br>−0.059<br>0.0 −0.087 −0.109 −0.126 0.00<br>−0.016<br>−0.1 −0.01<br>−0.2 −0.02<br>Baseline Pres x Pres x Baseline Pres x Pres x Pres x Pres x Baseline Pres x Pres x Pres x Pres x<br>Presence 2016 2020 Presence 2006 2011 2016 2020 Presence 2006 2011 2016 2020<br>Estimate<br><!-- End of picture text --> Figure 6: Effect of refugee presence on public opinion and insecurity. The figure shows that a greater refugee presence does not lead to backlash against migrants, but does increase fears of crime (Afrobarometer), and slightly more experience with violence (ACLED). OLS models for Afrobarometer respondents within 150km with Nearest + 20km exposure measure interacting demographic controls with year, and including year
6 26th level corresponding to a female life expectancy of 82.5 was added. The appropriate level for each time period was chosen given life expectancy, with interpolation if necessary between adjacent levels. Choice among life table families was based on region and mortality level, as follows: Region and mortality level Life table family Sub-Saharan Africa Below 12 South 12-17 North 18 and above West North Africa, the Middle East, Latin America, and the Caribbean Below 18 South 18 and above West Northern America, Europe, Asia, and Oceania All levels West Over time, countries in particular regions may therefore switch from one life table family to another. To avoid discontinuities, the switch is made gradually, using life tables linearly interpolated between the two families involved for five intervening periods.

# Fertility level and trend

Base-period total fertility rates (TFRs) are derived from the same mix of sources as mortality rates, including the U.N. 1988 revision; official sources, as cited in Population and Vital Statistics Reports or in government publications; the U.N. 1984 assessment; the U.S. Bureau of .he Census (1985); Eurostat (1987);-and Celade (1987). We relied on two sources more frequently for information on fertility than mortality: various surveys, especially Demographic and Health Surveys and World Fertility Surveys; and the Institut National d'ttudes Demographiques (Monnier 1987). Often, all these sources give fertility for a different period or in terms of the crude birth rate, and corresponding TFRs had to be estimated using available data on age-sex composition and age-specific fertility, or obtained from a projection using an estimated TFR for an earlier period. Where no reliable source was available, an unattributed Bank estimate may be indicated in the Appendix.

Future fertility trends are specified by selecting a year when the net reproduction rate (NRR) reaches unity, i.e., when the average woman's fertility results in exactly replacing herself. (This is referred to here as the
probability of being rejected. A high risk index re‡ects a high sensitivity to food safety regulations. Since, rejection occurs when a product does not comply with food safety requirements as set in the regulation, the index can be interpreted as the gap between standard and actual product quality. "Risky" products are products far away from the standard. The gap deepens if the regulation is changing and/or if current production technologies do not allow to reach adequate quality. As a consequence, laying far away from the standard, leaves ...rms with two options, either conform or drop the market. As shown in section 2.2, compliance is a lengthy and costly process. Thus, for complying ...rms, the risk index captures the need for capital to conform with EU markets food safety requirements, and acts as a proxy for product ...nancing needs. In the remainder of the paper we refer to our measure of ...nancing needs as the risk index. Here, risk should be understood as the risk of su¤ering alerts.

We now brie‡y discuss some important features of the index. First, to our knowledge, there is no available measure of ...nancing needs at the product level<sup>7</sup> . As explained in

> 4We take tari¤s data for the year 2005.

> 5Even though we control for a potential protectionist agenda of the destination markets and for the exporting country time invariant characteristics, our index may still su¤er from omitted variable bias. The limited time span of our data on alerts does not allow us to estimate the risk index controlling for time invariant sectorial characteristics speci...c to the exporting country.

> 6A Poisson would give similar results, as the consistency of second-stage estimates does not depend on the correct speci...cation of the ...rst-stage equation. In addition, we have over dispersion and little excess of zeros in the sample
## **Figure 27. Who is shortlisted?**

_Source_ : Authors.

_Note_ : Shown are regression results that examine how much characteristics of applicants/applications affect the probability of being shortlisted or interviewed. The regression controls for job postings fixed effects. The dependent variable is a dummy indicating that an application is shortlisted or interviewed. The other controls are marital status dummies and job postings fixed effects. The mean probability of being shortlisted is 9.5%. The standard errors are clustered at the job posting level.

# 5. Conclusions

This paper provides unique, granular pictures of labor markets by using novel online job data. The data of Rozee.pk enable us to analyze labor markets in real time on both employer and worker sides and understand skill demands and supply based on rich text information.

The online job portal examined in this paper represents a high-skill segment of the labor market in Pakistan. Job postings in the job portal offer higher salaries than the national average, and jobseekers are younger and better educated than the average labor force in the country. This focus on a high-skills segment is relevant to tackle the ongoing labor market issues of Pakistan such as the youth bulge and the higher unemployment rate of better educated people.

32
Introduction How did the economic impact of the crisis caused by Covid-19 impacted poor households in Sub-Saharan Africa? While statistics on infections and deaths suggest that Sub-Saharan Africa (SSA) may have escaped the worst of Covid-19, the economic cost was major. Poverty in the region was projected to increase by 2 percentage points or more in 2020 (Montes et al. 2020 and Lakner et al. 2021). While the full extent of the economic damage is still unknown, data from phone surveys collected since the pandemic provide an indication of the adverse impact on livelihoods in the region.<sup>1</sup> However, little is known about how impacts differed for poor and wealthy households within countries. This note aims to help fill this gap in order to better understand the distributional impacts of the crisis and inform recovery efforts.

We combine data collected through 73 phone surveys in SSA since the early stages of the global pandemic with richer multi-topic household surveys collected prior to the pandemic to examine how the impact of Covid-19 differed for poorer and wealthier households, as measured by predicted household per capita consumption. There are six main findings:

1. The initial impact of Covid-19 on the prevalence of income declines and the ability to work as usual was widespread throughout the predicted consumption distribution.

2. During the summer of 2020, there were large (20 pp) increases in the share of households in the bottom 40 percent that engaged in farming activities, and a more gradual increase 10 pp increase in the share of these households that owned or partially owned a non-farm enterprise.

3. During the latter part of 2020 and early 2021, food insecurity and the incidence of distressed asset sales declined faster for the bottom 40 than the top 60 percent. Conversely, the prevalence of income declines and the inability to work as usual declined faster for the top 60 percent, who were better able to cope with the medium-term impacts of the crisis.

4. Children in the bottom 40 percent were less likely to engage in educational activities in
br>|0.09<br>|-0.00<br>|
||(0.01)|(0.02)|(0.02)|(0.01)|(0.02)|
||0.07|0.07|0.00|0.06|-0.01|
|**Household owns an air c/fans**|(0.01)|(0.01)|(0.02)|(0.01)|(0.02)|
|**Household owns a radio**|0.44<br>(0.01)|0.44<br>(0.03)|0.00<br>(0.03)|0.45<br>(0.03)|0.01<br>(0.03)|
||085|086|001|086|001|
|**Household owns a mosquito net**|.<br>(0.01)|.<br>(0.02)|.<br>(0.02)|.<br>(0.02)|.<br>(0.02)|
||045|046|001|046|001|
|**Log of perca residential area**|.<br>(0.01)|.<br>(0.01)|.<br>(0.01)|.<br>(0.01)|.<br>(0.01)|
|**Roof is made of concrete/metal**<br>**sheets/tiles**|0.84<br>(0.01)|0.84<br>(0.03)|-0.00<
Policy Research Working Paper 10859

# **Abstract**

This paper estimates the profitability of inorganic fertilizer use in maize production in Malawi. It employs a two-wave, nationally representative panel of data on smallholder households and plots to estimate household fixed effects, plot fixed effects, and multilevel regressions. The results suggest that inorganic fertilizer use is generally unprofitable at prevailing market prices when, assuming that farmers incur positive transaction costs in the use of fertilizer. The low fertilizer profitability is driven by low nitrogen use efficiency, the kilograms of maize produced per kilogram of nitrogen, which is estimated to range from 9.2 to 12.1. For fertilizer use to be profitable, the nitrogen use efficiency would have to increase by at least 137 percent (from 11.89) if maize output is valued at the farmgate price and by 50 percent (from 11.89) if maize is valued at the lean season market price. For farmers who receive the fertilizer subsidy, it improves the profitability of fertilizer use by increasing the maize-nitrogen price ratio at all rates of subsidy (0 to 100 percent). However, unless farmers can store their produce and sell during the lean season when the output price is relatively higher, they would be better off by at least MKW 66.16 (US$0.18) per kilogram of subsidized nitrogen with the cash equivalent of the subsidy than with subsidized fertilizer. The analysis also finds that, compared to the current rate of nitrogen application, the government recommended rate of application is between 116 and 119 percent more profitable on smallholder fields.

This paper is a product of the Agriculture and Food 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/prwp. The authors may be contacted at fdarko@worldbank.org.

_The Policy Research Working Paper Series
Washington, DC: World Bank.

Krugman, P. 1979. “A Model of Balance-of-Payments Crises.” Journal of Money, Credit, and Banking 11 (3): 311-325.

Krugman, P. 1999. “Balance Sheets, the Transfer Problem, and Financial Crises.” International Tax and Public Finance 6 (4): 459-472.

Krugman, P. 2014. “Currency Regimes, Capital Flows, and Crises.” IMF Economic Review 62 (4): 470-493.

Krugman, P. 2019. “Perspectives on Debt and Deficits.” Business Economics 54 (3): 157-159. Laeven, L., and F. Valencia. 2020. “Systemic Banking Crises Database II.” IMF Economic Review 68 (2): 307-361.

Ley, E. 2009. “Fiscal Policy for Growth.” PREM Note 131, World Bank, Washington, DC.

Lian, W., A. F. Presbitero, and U. Wiriadinata. 2020. “Public Debt and r - g at Risk.” IMF Working Paper 20/137, International Monetary Fund, Washington, DC.

Lo, S., and K. Rogoff. 2015. “Secular Stagnation, Debt Overhang and Other Rationales for Sluggish Growth, Six Years On.” BIS Working Paper 482, Bank for International Settlements, Basel.

Mauro, P., and J. Zhou. 2021. “r – g < 0: Can We Sleep More Soundly?” IMF Economic Review 69 (1): 197-229.

Mendoza, E. G., and M. E. Terrones. 2012. “An Anatomy of Credit Booms and Their Demise.” NBER Working Paper 18379, National Bureau of Economic Research, Cambridge.

20
**In this scenario, the increase in the minimum wage has a more substantial and positive impact on income inequality compared to Scenario 1.** This is evident in the reductions of the Gini index and the increases in the bottom 50% income share, as detailed in Table 8. When all dismissed workers move to unemployment or inactivity, the Gini index is expected to decrease substantially, from 0.356 to 0.344, and the bottom 50% income share could increase by 0.7 percentage points. As in Scenario 1, the lower bound of inequality suggests that reduction in inequality can be muted if dismissed workers end up not leaving the labor force but finding new employment opportunities with negligible wages.  In this case, the increase in compensation from rising wages among one group is almost fully offset by the decrease in income for those employees who lose their jobs.

**Table 8. Distributional Effect of Minimum Wage Increase on Labor Market Outcomes in Scenario 2: Convergence to Living Wage Estimate**

(16.41% of Full-Time Employees Affected by the Raise)

|**Indicator**|**Baseline**<br>Before MW increase|**Simulation**<br>After the MW increase,<br>sample of wage earners<br>andjob losers*|**Simulation**<br>After the MW increase,<br>sample of wage earners<br>only**|
|---|---|---|---|
|Gini coefficient|0.3560|0.3554|0.3444|
|Theil Index|0.2504|0.2499|0.2391|
|Mean logdeviation|0.2037|0.2030|0.1904|
|Bottom 50% income<br>share|26.43%|26.46%|27.16%|
|Middle<br>40%<br>income<br>
# VI. CONCLUSIONS

When international food prices first rose sharply in 2007 and 2008 there was understandable concern that higher prices constituted a grave risk for the world’s poor. While there was much debate about the true extent of these risks, extensive evidence on the relationship between food prices and poverty is only now emerging (Ivanic and Martin 2014; Jacoby 2016). Consistent with recent simulation exercises for subsets of developing countries, this paper finds that increases in international food prices typically predict reductions in national poverty rates in a large sample of developing countries over periods of one to five years.

This core result is subject to two important caveats. First, the magnitudes of these associations are much larger in IV-GMM regressions than in OLS and dynamic panel GMM regressions, but the IV-GMM regressions potentially suffer from a weak instruments problem and certainly suffer from imprecision. Second, in section III we noted that the POVCAL data utilize CPIs to deflate their income and expenditure series, and it is quite possible that these CPIs series understate true increases in the cost of living for the poor during food price increases. While the World Bank has taken some steps to address this problem, these potential biases warrant further investigation.

Bearing these caveats in mind, a negative relationship between changes in food prices and changes in poverty could stem from three factors. First, the most recent estimates suggest that around three-quarters of the world’s poor are still rural (Ravallion et al. 2007) and are therefore either directly dependent on farm income or on non-farm incomes that are likely to be strongly influenced by agricultural production decisions, particularly the demand for labor. Second, we find that higher food prices appear to have produced a positive, large, and relatively quick agricultural supply response in developing countries. Third, there are some indications from Bangladeshi data that wage increases provide compensation for rural wage earners relatively quickly, though there is no evidence that urban workers benefit from any wage response except perhaps in the long run.

These findings potentially point to some important implications
- 9 -

Since there is no quarterly data for GDP at market prices, quarterly series were formed from annual series through interpolation. The results of the regression are presented in Table 2a.

As expected, the foreign interest rates do have a high correlation with the domestic rates with a t-statistic of 4.0. The coefficient of

[GDP) representing the degree of tightness of the monetary policy, has a very low number (.003), with a t-statistic of 0.3. and is insignificant. The inflation rate (Xt) is also insignificant. When M2 is replaced by the monetary base, the results are almost identical, and therefore omitted from the table.

Table 2a: Determinants of the Interest Rate for Thailand

|Variable<br>Parameter Estimate<br>T-Statistic|
|---|
|1974-87) Quarterly Using Money Supply (M2)<br>ariable-<br>rTHA<br>CONST<br>0.009<br>0.8|
|rSGP<br>0.484<br>4.0|
|GDP/M2<br>0.003<br>0.3|
|iX<br>-0.079<br>0.6|
|rFTHA_1<br>0.627<br>7.0|

I. Thailand (1974-87) Quarterly Using Money Supply (M2) DeR Variable - rTHA R-Square - 0.838 Durbin-Watson D - 1.81 Source: IFS.
p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 12c: Impacts of PROSPERA on Type of High School: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008

||Neares<br>|t-Neighbor Matching <br>|Models<br>|<sup>h</sup>|
|---|---|---|---|---|
|Variables|12th grade<br>General|12th grade<br>Technical<br>T|12th grade<br>echnicalprofessional|12<sup>t</sup>grade<br>Private|
||Propensity score mat|ching with exact matc|hing on age and state||
|Girls|0.046***|-0.046***|0.004|-0.0037|
||(0.017)|(0.018)|(0.004)|(0.0037)|
|Observations|4,931|4,931|4,931|4,931|
|Boys|0.074***|-0.066***|- 0.0062|-0.0015|
||(0.017)|(0.017)|(0.004)|(0.0037)|
|Observations|4,751|4,751|4,751|4,751|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0