rafmacalaba/data-use-mentions-extended huggingface dataset:
be higher or lower depends partly on the expected relative returns to education in the children’s generation, i.e., whether _R_<sup>_cn_</sup> _> R_<sup>_cf_</sup> or _R_<sup>_cn_</sup> _< R_<sup>_cf_</sup> . It is, however, much more difficult to estimate _expected returns_ to education for children. One can argue that parent’s expectation would depend on their information set, a salient element in which is his own returns to education. In other words, the evidence of higher returns to education in nonfarm in the parental generation suggests that the parents would expect similarly higher returns for children in nonfarm occupations. Note that even when _R_<sup>_cn_</sup> _> R_<sup>_cf_</sup> , the intercept for the nonfarm households can be smaller as we find in the empirical analysis above (Table 2), if the households with zero (or very low) parental schooling have sufficiently lower income in nonfarm occupations, i.e., _Y_ 0<sup>_pn_</sup> _< Y_ 0<sup>_pf_.</sup> The estimates of _Y_ 0<sup>_pn_</sup> and _Y_ 0<sup>_pf_,i.e.,theinterceptsoftheincomeequationforparents,</sup> using data from NSS 1993 round (employment and unemployment round), are reported 27
# **1. Introduction**

Good jobs are associated with greater productivity and better living standards and are critical to achieving sustainable growth and poverty reduction (World Bank 2012). However, most working poor in developing countries do not have access to good jobs with enough earnings and benefits to allow them to meet their basic needs or with adequate working conditions to improve their well-being (Fields 2012). Global studies measuring job quality across countries have typically relied on a single indicator, such as formality or jobs. Likewise, only a few multicountry studies have examined job characteristics beyond a single indicator. They have also usually focused on single regions, such as Latin America, Europe, and Africa, particularly because of the difficulty in harmonizing microdata across countries (Bocquier, Nordman, and Vescovo 2010; Brummund, Mann, and Rodríguez-Castelán 2018; Leschke and Watt 2014; Sehnbruch et al. 2020).

To close this knowledge gap and to support global efforts to measure and monitor trends in job quality across developing countries, particularly in light of Sustainable Development Goal 8 (SDG 8) on “fostering sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all,” this paper proposes a multidimensional measure of job quality that can track and benchmark the quality of employment across developing countries. In the presence of the increased availability and improved quality of the labor force and household surveys in many developing countries (Lewandowski, Góra, and Lis 2017), this paper also contributes to a growing body of literature on the quality of jobs by producing all indicators from microdata, which also allows meaningful disaggregation across sociodemographic groups, industries, and locations in multiple countries. This flexibility is important in uncovering the characteristics of employment that may be correlated with poverty, especially among disadvantaged groups, such as women and low-skill workers.

The analysis follows well-established international conventions in the selection of job quality characteristics. Indicators to assess decent work for all under SDG 8 include disaggregated measures (including by sex, age, occupation
frequency phone surveys to collect data on input and even output variables, though the data quality implications of such a survey design ought to be rigorously tested before mainstreaming.

In sum, there is a need for survey practitioners and agencies charged with collecting agricultural sector data to carefully consider the data quality implications of survey design choices that implicitly affect the length of the recall period for the respondent, and to develop and promote the adoption of survey designs that can ensure data quality without excessive implications for the survey budgets and logistics.

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in their firms. A beneficiary of a matching grant said: “There is no need of hiring more workers now, I prefer to treat better my current workers.”

# **7. Conclusion**

We analyzed the short-term impacts of two innovative interventions to enhance private sector development and to create jobs in a resource-poor and rather rural setting. The interventions relied on a rigorous targeting based on a business plan competition and offered sizeable grants and complementary training in business practices as well as a careful monitoring. These are all features that many previous interventions that have been subject to rigorous evaluations were missing and, at least partly, may explain that they generated only moderate or no effects.

Yet, the interventions of this study had to deal with many difficulties, including institutional frictions, an increasing countrywide political and social instability, and the COVID-19 pandemic. Together, these seriously affected the project implementation, especially by delaying the transfers to the beneficiaries and by dampening demand, making it difficult to draw benefits from new investments. Nevertheless, the results based on the first follow-up survey can provide many interesting insights and are rather promising from the perspective of the implementers, at least with respect to the beneficiaries of cash grants, who were served on average a bit earlier than recipients of matching grants.

Investment significantly increased for the beneficiaries of cash grants. More firms in the group of cash beneficiaries invested, and they invested on average much higher amounts. Yet, the period was too short to have triggered any additional investment beyond the grants handed out. There is also no effect on investment for recipients of matching grants; yet, matching grants were earmarked for BDSs only, so any investment effect would be a second-order effect. The impact on profits for beneficiaries is slightly negative. Although, we cannot pinpoint the exact mechanism behind this, a plausible explanation could be that the investments caused additional costs and that more time is needed to increase production and sales especially in the context of several overlapping crises. There is also not yet an
<!-- Start of picture text --> Global growth incidence curves<br>(Percentage change in welfare between years)<br>$1.90 $3.20 $5.50 $10 $20 $50<br>6 fb -6<br>0 20 40 60 80 100<br>Percentile<br>— 2019-2020 —— 2020-2021 —— 2019-2021<br><!-- End of picture text --> <!-- Start of picture text --> on<br>—_—_ —__—<br><!-- End of picture text -->
skilled (Lazear, 2000) or, more pessimistically, the highly intrinsically motivated. Second, given the longer time frame, teacher characteristics could have changed. Experienced P4P may have eroded a given teacher’s intrinsic motivation (as hypothesized in the largely theoretical literature on motivational crowding out) or, more optimistically, encouraged a given teacher to improve her classroom skills. In this section, we conduct an exploratory analysis of these dynamic effects.<sup>35</sup>

**Retention effects** We begin by exploring whether experienced P4P affects retention rates among recruits. Specifically, we look for an impact on the likelihood that a recruit is still employed at midline in February 2017 at the start of the Year 2; i.e. after experiencing pay-for-performance in Year 1, although before the performance awards were announced. To do so, we use a linear probability model of the form

where _employediqd_ 2 is an indicator for whether teacher _i_ with subject-family qualification _q_ in district _d_ is still employed by the school at the start of Year 2, and _γq_ and _δd_ are the usual subject-family qualification and district indicators.

As the first column of Table 5 reports, our estimate of _τE_ is zero with a randomization inference _p_ -value of 0.96. There is no statistically significant impact of experienced P4P on retention of recruits; the retention rate is practically identical—at around 80 percent—among recruits experiencing P4P and those experiencing FW.

It is worth noting that there is also no impact of experienced P4P on _intentions_ to leave in Year 3. In the endline survey in November 2017, we asked teachers the question:“How likely is it that you will leave your job at this school over the coming year?”. Answers were given on a 5-point scale. For analytical purposes we collapse these answers into a binary indicator coded to 1 for ‘very likely’ or ‘likely’ and 0 otherwise, and estimate specifications analogous to equations (5) and (6). As the
OAMDI, 2016. Labor Market Panel Surveys (LMPS), http://erf.org.eg/data-portal/. Version 2.2 of Licensed Data Files; ELMPS 2012. Egypt: Economic Research Forum (ERF).

Stanton, C.T. and Thomas, C., 2015. Landing the first job: The value of intermediaries in online hiring. _The Review of Economic Studies_ , _83_ (2), pp.810-854.

United Nations, Department of Economic and Social Affairs, Population Division, 2019. _World Population Prospects 2019_ . Online edition. Rev. 1.

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

the go-to for data storage when COVID-19 impeded the acquisition of additional server capacity. In terms of analytics, although there was a tool for producing standardized mobility statistics available on the GitHub repository, we chose to write our own script. This was to accommodate the system parameters on PURA’s premises. Since much of the analytical work was done through remote access, it was restricted by network capacity and could be interrupted by electrical outages. This required breaking computationally intensive tasks into multiple steps with intermediate results, so that any interruption would only disrupt the current computation and not the whole script.

# **7. Conclusion**

In this paper, we demonstrate the uses of four indicators for examining how interventions and events under COVID-19 are reflected in the patterns of phone usage, population distributions, levels of mobility, and population flows. Results show that CDR aggregates are relevant for capturing changes in these indicators, which continued to vary over a few months. It indicates that CDR data provide timely and granular population statistics that can complement conventional statistics.

The use of CDR data in the context of COVID-19 in The Gambia demonstrates the hidden potential of big data, including CDR data, to inform decision making. Due to lack of investments in statistical systems, severe data deprivations are likely to remain a challenge for governments, the private sector, and civil society, and this approach offers an opportunity to leapfrog, and exploit data which is available in real-time, highly localized, and at relatively low cost. However, the use of CDR data will require future investments into the institutional and organizational framework of national statistical systems, including improvements of IT infrastructure and technical and statistical capacity. The analysis also demonstrates that CDR data are unlikely to substitute traditional data, such as administrative and survey data, as linkages between telecommunication data and individual level and household characteristics need to satisfy strict technical and ethical requirements.

This application focuses on a well-defined
accounting, auditing and disclosure standards.

# **VI. References**

**Arrau P. and K. Schmidt-Hebbel** (1993) Macroeconomic and Intergenerational Welfare Effects of a Transition from Pay-As-You-Go to Fully Funded Pensions. Background Paper for the World Bank Study on Old-Age Security, Washington, D.C. (mimeo).

**Bailliu J. and H. Reisen** (1997) Do Funded Pensions Contribute to Higher Aggregate Savings? A Cross-Country Analysis., OECD Development Centre Technical Papers Nol130.

**Baltagi B. H.** (1995) Econometric Analysis of Panel Data., John Wiley & Sons, Chicester.

**Bodie Z.** (1990) Pensions as Retirement Insurance., Journal of Economic Literature, 28(1): 28-49.
Lanjouw (2003). A recent review article considers the issue of transformations for small area estimation in detail (Tzavidis et al, 2018). It reports design-based simulation results using Mexican data that considered alternative transformations, including log-shift and Box-Cox transformations (Box and Cox, 1964). A log-shift transformation involves adding a constant to welfare prior to taking the log, while the Box-cox transformation can be written as: <u>y</u> −1 , and log y for λ=0 . Tzavidis et al (2018)<sup>λλ</sup> conclude on the basis of the simulation results that log-shift transformations tend to perform well. Stata λλ for λλ≠0 has implemented two commands that can quickly estimate the parameters for the log shift and Box-Cox transformations that minimize the skewness of the welfare distribution. These commands, however, only go part of the way towards normalization, because they cannot guarantee that the resulting distribution will have a kurtosis of three, which is characteristic of a normal distribution. Furthermore, the log shift transformations is not monotonic when the underlying distribution is left-skewed.<sup>18</sup> Finding an appropriate monotonic transformation for the welfare variable is important, as failure to satisfy the normality assumption can lead to systematic bias in EBP models. While benchmarking can be used to at least partially correct this bias, relying on benchmarking becomes more difficult to justify as the extent of the bias increases.

For these reasons, our preferred estimates utilize a monotonic transformation called the “ordered quantile normalization” to transform welfare. This method transforms the scaled rank of the welfare variable to conform with a normal distribution. Variants of this method date back more than 70 years (Bartlett, 1947, Van der Waerden, 1952 ) . Among several methods proposed in the literature, the ordered quantile normalization most consistently transforms an underlying variable to follow a normal distribution (Peterson and Cavanough, 2019). We utilize the Ordernorm function in the Bestnormalize R package to implement this transformation.
foreign inputs. Thus, the cost structure of multinationals differs from national only service providers. Multinationals incur costs related to both imported primary inputs and Armenian primary factors, in addition to intermediate factor inputs. Foreign provision of services differs from foreign provision of goods, since the service providers use Armenian primary inputs. Domestic service providers do not import the specialized primary factors available to the multinationals. Hence, domestic service firms incur primary factor costs related to Armenian labor and capital only. These services are characterized by firm-level product differentiation. For multinational firms, the barriers to foreign direct investment affect their profitability and entry. Reduction in the constraints on foreign direct investment will induce foreign entry that will typically lead to productivity gains because when more varieties of service providers are available, buyers can obtain varieties that more closely fit their demands and needs (the DixitStiglitz variety effect).

# **Trade facilitation and border costs**

According to the World Bank Logistics Performance Index of 2010, Armenia ranks 111<sup>th</sup> in the world out of 155 countries.<sup>9</sup> This is an improvement from 131<sup>st</sup> in the world in 2007,<sup>10</sup> but still leaves considerable room for improvement. Given the focus of the EU on institutional development for trade facilitation, a deep and comprehensive free trade agreement with the EU is likely to reduce these costs for exports to the EU. We therefore assume that the costs of exporting to the EU from Armenia will fall and the costs of importing into Armenia from the EU will also fall. Moreover, improved institutional development for trade facilitation is likely to reduce trade facilitation costs for imports from and exports to all regions. If customs is more efficient in processing imports from the EU, these procedures will generally facilitate trade with all regions. For example, if trucks with imports from the EU can pass through Armenian borders more quickly, trucks with imports from other countries are also likely
al., 2021). For the analysis, we regroup the 16 land cover classes based on the Annual University of Maryland (UMD) Classification into 8 categories. These categories include: water bodies, forests, shrub and grasslands, wetlands, croplands, cropland/natural vegetation mosaics, urban, and non-vegetated/barren. This gives an annual breakdown of land cover by pixels of approximately 500 meters by 500 meters for the whole world. In order to smooth away biases from potential misclassification, we allocated each pixel to the predominant class over these 3 consecutive years. For the analysis, we use a restricted sample that covers the years 2003-2018. Our main focus is on “cropland” defined as pixels that have at least 60 percent of their area cultivated. The category of “cropland mosaic” (pixels that have between 40 and 60 percent of cropland with natural tree, shrub, or herbaceous vegetation) is a hybrid category that can capture intermediate steps towards cultivation or degradation or mixed uses such as agricultural and residential at the periphery of cities. Figure A1.1 in the Appendix shows the 2018 global land cover by land cover category.

The MODIS dataset provides an estimate of global cropland of 1.2 billion hectares in 2018, which is very close to that of Potapov et al. (2021), who combined MODIS and Landsat to enhance the accuracy of cropland extent measurement. If we include cropland mosaics, we obtain a total mass of cultivated land of slightly more than 1.3 billion hectares. This is smaller than FAO’s estimate of global “cropland” of 1.6 billion hectares based on a broader definition, which includes temporary meadows and land temporary fallow. This gives us confidence in focusing on the raw MODIS product and its cropland category for our analysis. Cropland mosaic is a hybrid category that can be considered a transitional category in the dynamic processes whereby land is put into or out of cultivation and thus needs to be treated separately for the purpose of studying cropland dynamics.

3
the likelihood of protection being successfully implemented is low (McMichael et al., 2020). We did not include sea level rise in our review because much analysis on this topic makes use of _modeled_ projections of exposure rather than retrospective empirical analyses using historical data (Neumann et al., 2015b; Davis et al., 2018).<sup>6</sup> Fourth, future research should look at alternative outcomes, such as survival or risk management migration, voluntary vs involuntary migration, jointly rather than separately, to improve our understanding of the response heterogeneity with respect to wealth and income. More generally, gaining a more systematic understanding of the irreducible heterogeneity of the climate-migration nexus should be considered as a primary task for future research (Hoffmann et al., 2020). Fifth, more research is needed on the role played by institutions and policies in ‘interfering’ with the decisions to migrate in response to climatic stress. Development policies can affect migration outcomes in a way which is difficult to know _a priori_ , as they could either facilitate or inhibit migration depending on the type of intervention and the related welfare outcome. For instance, while local investments in climate-resilient infrastructures or in the development of early-warning systems may reduce the need to migrate and improve _in-situ_ adaptation, social protection interventions or emergency responses alleviating weatherinduced liquidity constraints may make voluntary migration possible. This is especially important in order to provide evidence-based recommendations on national and international climate migration policies.

# _2.3. A closer look at Sub-Saharan Africa_

Finally, bearing in mind the major insights from the main review, we take a closer look at recent studies with an exclusive focus on Sub-Saharan Africa (SSA), one of the parts of the world indisputably more vulnerable and exposed to climate change (IPCC, 2014) and the area where LSMS-ISA surveys (which we use as benchmark in the assessment of data gaps in household survey data to understand the climatemigration nexus) are currently implemented.

A work by Lilleør and Van den Broeck (2011)
built-up data are at 300 meters by 300 meters grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the total built-up area at the district level.

# (4) Empirical Strategy and Estimating Equations

Our empirical strategy follows closely the approach due to Duo (2001, 2004) that exploits both cross sectional variation across districts and over time variation across birth cohorts. The cross sectional variation comes from the dierences in the intensity of exposure depending on

> 27The district that received the highest number of schools received 8.6 schools per 1000 children. In contrast, the district that received the lowest number of schools received only 0.59 schools per 1000 children. The mean is 1.86 schools per 1000 children. After normalization, the school construction intensity value ranges from 0.0678 to 1 with a mean of 0.215.

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identifying assumption is that the effect of reforms in specific services sectors should be more pronounced in manufacturing sectors relying more heavily on those services inputs. The specification also controls for the level of import tariffs on output and inputs as well as for firm and year fixed effects.

The analysis is based on firm-level data from the Capitaline database, a commercially available database including balance sheets, profit and loss statements, and ownership information on large private and public firms operating in India. Firms included in the database account for 62 percent of India‟s total manufacturing output during the period covered by the analysis. Our data set forms an unbalanced panel covering 3,771 firms or 22,558 firm-year observations during the 1993-2005 period.

Our results suggest that policy reforms in services sectors had a significant impact on firms in the manufacturing sector. The aggregate effect of services liberalization was an increase in productivity of 11.7 percent for domestic firms and 13.2 percent for foreign firms for a one-standard-deviation increase in the liberalization index. When the individual services sectors are examined in the same specification, a one-standard-deviation change in the banking sector index corresponds to a 6.5 percent change in productivity for both domestic and foreign firms. A one-standard-deviation change in the telecommunications liberalization index corresponds to a 7.2 percent increase in productivity for domestic firms and a 9.8 percent increase in productivity for foreign firms. A similar change in the transport index leads to a 19 percent improvement in productivity of all firms. Only foreign firms appear to benefit from the insurance reform enjoying a productivity boost of 3.3 percent.

Our results are confirmed by an instrumental variables approach in which we instrument for reform in India using measures of services reform in two countries that can be viewed as India‟s competitors, namely China and Indonesia. The results are also robust to focusing on structural breaks in services reform instead of using the reform index. Finally, the results remain unchanged if we control for delicensing and lifting of restrictions on FDI inflows in a given manufacturing