rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Concluding remarks**

This paper investigates the impact of Internet job search on employment in Egypt, a country with a large population, high unemployment rates, and fast-growing Internet adoption rates. Given that in Egypt and more generally in the MENA region poor labor market prospects for the youth have contributed to large-scale protest movements and in some cases political instability, it is especially important to understand whether Internet job search could help reduce unemployment rates. The question is all the more relevant given that unemployment is particularly pronounced among the youth and that the younger cohorts are also the most likely to use the Internet.

Relying on panel data from the 2012 and 2018 rounds of the ELMPS, this paper thus examined the impact of Internet job search on the employment prospects of unemployed indivduals. Focusing on unemployed individuals in 2012, the results suggest that men who used the Internet for job search had a higher likelihood of finding a job within a five year timeframe compared to those who did not use the Internet or to those who used the Internet for purposes other than job search. While Internet job search seems to be an effective search method for men, as it increases men’s employment, we find that Internet job search is associated with an increase in women’s labor force participation but does not have an effect on women’s employment. Accounting for unobserved heterogeneity through first-difference and fixed effects models yields consistent results. Meanwhile, placebo regressions confirm that the results are not driven by pre-existing trends in labor market outcomes between individuals who use the Internet for job search and those who do not or between neighborhoods with high versus low Internet adoption rates.

Finally, this paper also investigates whether Internet job search among employed job seekers is associated with wage growth and tackles the question of digitalization at the workplace and its effect on earnings growth. The findings of this paper suggest that Internet job search does not have any significant effect on wage growth for employed job seekers. Even though digitalization and Internet penetration have been associated in other
) for details.

Finally, the third condition (perceived availability of alternative sources of drinking water) was also likely satisfied given that well-sharing was already practiced in the area (see Section 3). To some extent households were therefore already aware of the possibility of using neighbours’ well water for drinking. Data from neighboring Araihazar show that the BAMWSP testing campaign conducted about 10 years earlier led to a substantial degree of well sharing (Balasubramanya et al. 2014). On the one hand, the figures in Table 1 show that about three quarter of wells had been found to be unsafe by BAMWSP in the study area, and this may have reduced the perceived chance to have safe options nearby in case of bad news. On the other hand, the average household had more than 10 other wells within a short 50 m radius and about 30 within 100 m, and such dense network of wells likely increased the perceived likelihood of having safe wells close by.

Overall, differences in switching rates between any two experimental arms could have emerged either from different selection into purchase or from the way information was provided (in which case even identical buyers may have reacted differently). The decision to change source was still likely to depend primarily on any new information made available by the test results. Hence, regardless of the

> 14In principle, such value needs not be positive, for instance if knowledge of a high-arsenic well is perceived as lowering land value, or signaling poor health among household members, or is more generally stigmatized. In Bihar, India, Barnwal et al. (2017) find that placards indicating unsafe arsenic levels were more likely to be removed by households than those indicating low levels of arsenic two years after installation, although such behavior may have also been justified by the desire not to be reminded constantly about the health risks. However, earlier research has shown that households very rarely refuse testing when this is offered for free, even when the results are posted on the wells (see for instance Madajewicz et
The new parameters express the differences between the earnings equations of workers with Di = 1 and Di = 0. Parameter δt denotes the difference in the public premium, and γ denotes the difference in the relative returns to the workers' comparative advantage. The estimated equation is a simple extension of equation (2), with the interaction terms between the employment history dummies and the dummy Di.

# **RESULTS**

The literature on earnings inequality between two sectors (public and private; formal and informal) is extremely rich and active, with some widely used techniques, such as quantile regressions with a sector dummy or wage equations with different coefficients for each sector. In this section, before estimating our model, we present and discuss the results that can be obtained with these models. These results will help us to identify some robust stylized facts and to make the case for using a different approach.

# _Sample and descriptive statistics_

We restrict ourselves to the workers who worked for wages for two consecutive surveys: 1993 and 1998 for the first panel and 2002 and 2004 or 2004 and 2006 for the second panel. As discussed earlier, the VLSS and VHLSS samples are representative of the Vietnamese population but contain few wage earners. Hence, our final sample is relatively small, with 875 individuals in 1993–1998 and 3893 individuals after pooling the 2002–2004 and the 2004–2006 surveys. As we saw, our model's identification further relies upon the workers who switched to the public or to the private sector between the 16
**Figure 39: U.S. Household Disposable Income and Net Worth**

<!-- Start of picture text --> 70 700%<br>60 600%<br>50 500%<br>40 400%<br>30 300%<br>20 200%<br>10 100%<br>0 0%<br>Disposable personal income<br>Net Worth of Households and Non-profit Organizations<br>Household net worth as percentage of disposable personal income (right axis)<br>US$, thousands<br><!-- End of picture text --> Source: Federal Reserve, Flow of Funds Account.

60. Greenspan and Kennedy (2008) show that the increase in housing prices contributed substantially to the consumption boom, primarily via home equity loans and mortgage refinancing cash-outs. Empirical evidence for the link between housing wealth and consumption is found by Bostic et al. (2006), which uses micro-data to estimate the elasticity of consumption to both housing and financial wealth in the United States over the period 1989-2001. They find a substantially larger marginal propensity to consume from housing wealth than from financial wealth. In addition, Case et al. (2005), using panel data for 14 countries, report: ―The importance of housing market wealth and financial wealth in affecting consumption is an empirical matter....[] We find at best weak evidence of a stock market wealth effect. However, we do find strong evidence that variations in housing market wealth have important effects upon consumption. [The results...] support the conclusion that changes in housing prices should be considered to have a larger and more important impact than changes in stock market prices in influencing household consumption in the U.S. and in other developed countries.‖ 61. Equity can be extracted from houses through three channels: (i) free cash available to home sellers, which is equal to the value of existing
the polls – and, conversely, these objective benchmarks may force governments to make structural reforms if there is popular demand for improving the investment climate. The DB ranking can thus serve both as a carrot and stick for driving the regulatory reform process.

The DBs rankings are well publicized and are of interest to international investors, donors, and voters alike. They signal a government’s resolve to improve opportunities for business at home. If visibility is the goal, governments should focus on reforms that will give them the largest gains on their overall ranking.<sup>4</sup>

**Impact** , on the other hand, refers to the effect of DB-related reform on economic performance. This can be linked to visibility to the extent that visible improvements in the business environment can lead to a larger volume of FDI inflows which can eventually stimulate the economy. But more broadly, impact refers to a direct link between the DBIs and economic growth. The difference between maximizing visibility and maximizing impact is that the goal for the former is the highest possible absolute improvement on the DB ranking; for the latter it is to improve indicators irrespective of the absolute improvement but with the highest potential for enhancing economic activity.

There are two ways of identifying reforms that maximize impact. A more indirect way is to survey perspectives of the business community on what they identify as key constraints to doing business. The DBIs are ‘neutral’ in that they aim to objectively compare the quality of regulatory systems across countries. Accordingly, they do not weight particular areas of the business environment according to priorities. Indeed, such priorities are bound to vary across countries. For example, a country that does

> 3 Claiming credit for improvements in overall rankings can be more controversial as such rankings are relative and thus strongly influenced by reforms in other countries.

> 4 The DB website offers a _DBI Reform Simulator_ which can be used to calculate the effect of reforms in particular areas on a country’s DBI ranking: http:/
# **4 Empirical application to Malawi**

## **4.1 Model selection and estimation of model parameters**

### **4.1.1 ELL approach: Household level data with household expenditure as dependent variable**

We rely on variables that are available in both the survey and the census. The first step of our model selection procedure groups candidate variables by type: (i) demographics, (ii) education, (iii) employment, (iv) dwelling characteristics, (v) asset ownership, and (vi) village level variables. This latter group includes variables that are created by computing mean values of selected variables from the population census at the enumeration-area (EA) levels (think of local employment rates, share of the population with given levels of education and the penetration of phone use) and variables derived from the publicly available remote data (think of night-time-lights, distance to nearest city and river, local climate, and agricultural land quality).

Next, we regress the log of per capita expenditure (combined with region and urban dummies) on each group separately using Ordinary Least Squares (OLS). Independent variables that are not significant at the 5% level are sequentially dropped from the regressions, starting with those that are least significant. Once all groups have been considered we select the group that provides the best fit, combine this with the group that provides the second-best fit, and continue to trim the variables that cease to be significant. This process is continued until every single group has been given a chance to contribute one or more variables to the model. We allow for interactions with the urban-rural dummy to capture selected area heterogeneities (see Tarozzi and Deaton, 2009; Tarozzi, 2011). Finally, LASSO model selection is implemented to verify that all key variables are accounted for. The same procedure is applied to obtain the heteroskedasticity model describing the variance of the errors. The resulting regression models are included in Appendix 3.

Among the different
using the demeaned data. Since there is more than one treated unit, based on Abadie et al. (2010), I aggregate the treated units into a single unit (pooled SCM). Pooled SCM can yield poor unit-specific fits. Thus, I check the fit for each treated unit and find a good fit mostly because the characteristics of households in affected units are similar (Figure S3). Moreover, following Ben-Michael et al. (2021), I use the partially pooled SCM as a robustness check and find similar results.

The optimal weights are positive for three industries information, education, and health with values 0.148, 0.169, and 0.683, respectively and take value zero for the other potential controls in the donor pool. Two features of these industries protect them from the sanctions. First, these industries are heavily regulated by the government. Therefore, their wages and employment size are little responsive to the market conditions.<sup>37</sup> Second, these industries are not dependent on trade, thus making them unaffected by the changes in the exchange rate due to trade

> 35Since the synthetic control is supposed to reproduce the outcome variables for treated industries in the absence of the sanctions, I discard financial, real estate, and administrative and support service industries from the donor pool. As explained above, some firms in the financial sector that owned or control by either IRGC or Setad are targeted by the sanctions, but in HIES only the job sector is observable not the firm. Thus, I exclude the financial industry. I also exclude households in real estate and administrative and support service industries from the donor pool because, as explained above, these households have a large difference in their characteristics relative to treated households which makes them unsuitable controls. Based on Abadie (2021), while the SCM puts small weights for dissimilar units, it is still important to limit the donor pool to units with similar characteristics to the affected unit to avoid interpolation biases.
general<sup>this methodology may</sup> cause aggregation biases; it may: (1) overestimate the group effect,<sup>that is, the firm effect,</sup> namely productivity; and (2) underestimate the individual effect<sup>on wages and</sup> productivity. Multilevel estimation takes aggregation biases iinto account.<sup>The firm level</sup> effect impacts average production but also the individual slope of,<sup>for example, gender.</sup>

> 5Groshen (199la) mentions that education, age, occupation, ethnicity, gender and union variables<sup>only</sup> account for 51 percent of the variation in the log of wages, analyzing (US)<sup>data from the Current</sup> Population Survey One Quarter Earnings Sample, 1986. Introducing demand<sup>related factors is likely</sup> to explain parts of the 49 percent of the variation in wages that workers'<sup>characteristics cannot</sup> account for.

6
experienced certain behaviors from their husband/partner including pushing, slapping, arm twisting, punching, kicking, choking, attacking with weapon, forceful sexual intercourse, forceful performance of other sexual acts. When an ever-partnered woman reports experiencing physical or sexual IPV, they are asked when the first episode occurred in years after marriage or having a partner. They are also asked if there were any episodes in the 12 months preceding the survey.<sup>19</sup> Because we exploit the timing and location of IPV in the BH affected area, our analysis focuses on women’s experiences of physical or sexual IPV in the year preceding the survey – commonly taken to mean that women are currently experiencing violence (Abramsky et al. 2011). Using this information, we create a dichotomous variable measuring past year physical or sexual IPV, which equals 1 if a woman answers yes to experiencing any behaviors about physical or sexual IPV from her husband/partner in the past 12 months and 0 otherwise.

We use information on men’s controlling behavior and women’s autonomy in decision-making to examine key drivers of IPV. Both the 2008 and 2013 NDHS contain some information on women’s autonomy in household decision-making and individual behaviors that are related to gender norms that can be used to examine BH’s impact on these behaviors. We explore whether women’s experiences of controlling behavior from her husband/partner, women’s household decisionmaking autonomy and women’s decision-making autonomy over her own earnings are affected by the BH insurgency. To determine if a woman experiences controlling behavior from her husband/partner, the NDHS asks if a husband/partner gets jealous when she talks to other men, accuses her of unfaithfulness, does not permit her to meet female friends, limits her contact with family or insists on knowing where she is at all times. Using this information, we create the variable for controlling behavior, which equals 1 if a woman answers yes to any of these questions and 0 otherwise.Women
percent or more of financial wealth in developing countries is held offshore, free of taxes. This wealth is presumed to be held almost entirely by the very wealthy, and thus presents a major loss of revenue and of progressivity for developing country tax systems.<sup>119</sup> In some cases, inequities may arise from national policy choices. At the national level, tax incentives and exemptions can generate substantial inequities across different businesses – with, in some cases, businesses in the same sector paying significantly different tax rates. This is a particular risk where government officials enjoy significant discretion in granting exemptions.<sup>120</sup> Special tax regimes granted to some extractive companies are a particularly prominent example.<sup>121</sup> However, such inequity is also possible where exemption regimes appear relatively transparent and rule-bound. Such regimes might, for example, create benefits for foreign companies over local companies. Alternatively, where incentives are time-bound, some firms may manage to extend

> 115 Bird, R., & Zolt, E. (2005). Redistribution via taxation: The limited role of the personal income tax in developing countries. _International Tax Program Paper 0508_ . Toronto: Joseph L. Rotman School of Management.

> 116 These patterns are more likely to vary by country. Mascagni and Mengistu (2016) offer an example of an effort to estimate this variation in Ethiopia. Mascagni, G., & Mengistu, A. (2016). The corporate tax burden in Ethiopia: Evidence from anonymised tax returns. _ICTD Working Paper 48_ . Brighton: International Centre for Tax and Development.

> 117 Piracha, M. & Moore, M. (2015). Understanding low-level state capacity: Property tax collection in Pakistan. _ICTD Working Paper 33_ . Brighton: International Centre for Tax and Development.; Khan, A., Khwaja, A., & Olken, B. (2016). Tax farming redux: Experimental evidence on performance pay for tax collectors
in North-East Nigeria.</mark> _<mark>Journal of Development Economics 141,</mark>_ <mark>102249</mark> <mark>Bill and Melinda Gates Foundation (n.d.). Fundamental Enablers: Education. Retrieved 27</mark><sup>th</sup> <mark>April 2020:</mark> <u>https://www.gatesfoundation.org/equal-is-greater/element/education/</u> <mark>Government of Nigeria (2003). Child’s Right Act. Retrieved 21 February 2021: https://lawsofnigeria.placng.org/laws/C50.pdf</mark> <mark>Dang, H. A. H. and Dabalen, A. L. (2019). Is Poverty in Africa Mostly Chronic or Transient? Evidence from Synthetic Panel Data.</mark> _<mark>The Journal of Development Studies 55</mark>_ <mark>(7), p</mark> .1527-1547 <mark>DHS (n.d.). Guide to DHS Statistics DHS-7. Nutritional Status. Retrieved 26th March 2019:</mark> <u><mark>https://dhsprogram.com/data/Guide-to-DHS-Statistics/Nutritional_Status.htm</mark></u> <mark>Gordon, D., Nandy, S., Pantazis, C., Townsend, P. and Pemberton, S. (2003a).</mark> _<mark>Child poverty in the developing world</mark>_ <mark>. Policy Press.</mark> <mark>Gordon, D., Nandy, S., Pantazis, C., Pemberton, S. and Townsend, P. (2003b). The Distribution of Child Poverty in the Developing World. Report to UNICEF.</mark> _<mark>Bristol: Centre for
paper is organized as follows. The next section develops hypotheses about how the crisis affected different firms and how foreign bank participation affected the depth of the crisis in different countries in the region. The third section discusses the two sources of data used in the paper: the Financial Crisis Survey (FCS), which was conducted in six

> 5 When firm managers were interviewed, most said that the biggest problem that their firm faced during the crisis was a drop in demand—between 70 and 80 percent of the surveyed firms in each country identified this as the most serious problem.  By comparison, less than 10 percent said reduced access to credit was the most serious problem (Ramalho et al., 2009).

> 6 See Morgan et al. (2004).

5
# Poverty lines

How to establish a relevant poverty line for FDPs is another important question. The choice is between international poverty lines such as 1.90 USD/day at PPP value (Ravallion, Chen and Sangraula, 2009), national poverty lines of host countries or contextual poverty lines specific to FDPs. International and national poverty lines can be considered after the use of appropriate deflators. These choices can be controversial for regular populations and carry additional complexities when FDPs are considered.

One question is how to adapt poverty lines for FDPs living inside and outside camps. FDPs in camps often receive shelter, education and health assistance that is not – or not in the same way – provided to the population residing outside camps. This can create a paradox that refugees that pay rents in urban areas would appear much richer than refugees living in camps as their shelter is free of charge and not necessarily accounted for in the consumption aggregate. In traditional poverty estimations, a similar challenge occurs when one considers poverty lines for urban and rural populations since the rental market is often absent in rural areas making it impossible to estimate or impute rent. To allow for the inclusion of rent for urban populations, separate urban and rural poverty lines are estimated, called contextual or regional poverty lines. Only the urban poverty line and its corresponding consumption aggregate would include rent.

Similarly, other services like education and health are extremely hard to value given that cost data for camps’ services are incomplete and grossly available only in aggregate form. Hence, they would also need to be excluded in the contextual poverty line for camps. Given the difficulty to estimate contextual poverty lines, however, this approach is usually not feasible in FDP contexts, while it also adversely affects the comparability of estimated poverty rates. Furthermore, a comparison between camp and non-camp population would ignore differences in housing, health and education between these populations, substantially limiting the value of a comparison. These questions are still largely unresolved and the search for poverty lines adapted to FDP contexts is still