rafmacalaba/data-use-mentions-extended huggingface dataset:
this point in mind as a caveat.<sup>5</sup>

# **3 Estimation**

We now turn to an attempt at estimating _c_ following the logic of expression (7). There are two difficulties. First, as the model’s logic rests on a comparison of financial returns, we must find a way of valuing the return to subsistence activity. We do so in the simplest possible way, by using market prices to value output. However we do not have farm accounting data to assess profits as precisely as if we used corporate balance sheets. In principle, one would want to measure profits as the difference between output value (actual sales plus self-consumed output valued at producer prices) and intermediate consumption. However farmers typically report their income with considerable error, and cost data is non-existent. We go round these difficulties by taking consumption expenditures (reported much more accurately than income in household surveys) net of other (non-agricultural) revenue.<sup>6</sup> Second, farms must be classified as “subsistence” or “market” ones on the basis of a reasonable criterion. The most natural one is the proportion of farm output that is sold vs. self-consumed. However this settles only half the issue, because the cutoff must then be set somewhere.<sup>7</sup> We choose, as explained in section 3.2 below, to let the data generate the cutoff value endogenously.

## **3.1 Data**

The data is from Madagascar’s four household surveys (1993, 1997, 1999 and 2001), called in French “ _Enquˆete Permanente des M ́enages_ ” (EPM). Each is made of a household survey per se covering about 5,200 households on average and a “community questionnaire” collecting

> 5 We are grateful to an anonymous referee of the World Bank’s Policy Research Working Papers for attracting our attention to this issue.

> 6We actually also estimated our
Firstly, we use the _<u>estimation of shadow production as percentage share on official GDP</u>_ of countries. The source of the data is research of Schneider et al. (2010), who provide a unique database of the size and trends in the shadow economies of 162 countries between 1999 and 2006/2007. The estimations are based on a Multiple Indicators Multiple Causes (MIMIC) model approach.<sup>9</sup> The clear advantage of this dataset is the unified methodology and a broad sample. Many other studies also use this data (see e.g. Loayza et al., 2005, Perry et al., 2007). Besides stating the share of shadow economy output on overall official output of the economy, this indicator estimates the share of employment in the informal sector as well under the assumption that the trends in productivity of informal labor track the similar development as the productivity of formal labor force. Still, this model approach also has considerable shortcomings and the data should be considered with caution. The main concern about this approach is the theoretical background of relation between the shadow economy and its indicators and question of causality that might be subject to discussion. Nevertheless, although some other approaches may give a different picture about the situation in shadow economy, we believe that the unified methodology offers an opportunity to consistently study the differences among countries and development in time. For comparison of estimation by different methods see Schneider and Enste (2000).

As the second set of indicators we utilize _<u>shadow employment as the share of labor force in</u> - -_ _<u>unregulated self and wage employment</u>_ . To estimate this variable we have four proxies with different data sources coming from the Eurostat. Firstly, we use one indicator from the household survey European Union-Statistics on Income and Living Conditions (EU-SILC), stating the share of labor force not contributing to pension system (both private and
# Figure 5. EAP: Drivers of potential output growth

# A. Potential GDP growth

<!-- Start of picture text --> Percent<br>10 EAP EAP excl. China Median<br>8<br>6<br>4<br>2<br>0<br>2000-21 2000-10 2011-21<br><!-- End of picture text --> B. Contributions to potential GDP growth <!-- Start of picture text --> Percent<br>TFP Capital Labor Potential growth<br>10<br>8<br>6<br>4<br>2<br>0<br>EAP EAP excl. China<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> <!-- Start of picture text --> C. Investment growth D. Potential TFP growth<br>Percent EAP EAP excl. China Median Percent EAP EAP excl. China Median<br>14 4<br>12<br>10 3<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-2021 2000-2010 2011-2021<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent EAP EAP Excl. China Median<br>100 EAP EAP ex. China Median 6<br>5<br>80<br>4<br>60 3<br>2<br>40<br>1<br>20<br>0<br>0 -1<br>2000-20 2000-10 2011-20 2000-2021 2000-2010 2011-2021<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Educational, Scientific, and Cultural Organization (UNESCO) Institute of Statistics; UN Population Prospects; World Bank;
# **IV. Empirical strategy**

We estimate the effect of robotics on export quality using a difference-in-difference estimation, complemented with additional fixed effects, in the next section we discuss our instrumental variable: Where ln quality p p refers to the log quality for a product , from an exporter , in a sector , at time (defined by equation 3 in the previous section). r p represents is the log robot stock pp qq ss in exporter , for sector at time t. Note we add one to the robot stock before taking logs, to avoid pp qq qqll r qq dropping some zero values. p represents a broad range of fixed effects and control variables. qq ss We include exporter*HS 10-digit product and exporter*year fixed effects, in order to control for pp χχ potential trends across countries overtime, as well as potential time-varying multilateral resistance terms highlighted by the trade literature (Anderson and van Wincoop, 2003).<sup>17</sup> Note the seminal paper in the literature, Graetz and Michaels (2018) estimate a similar specification for European country-industries, also allowing for country-trends, but measuring long-differences in productivity rather than differences-in-differences as we do here. We also add control variables for (log) industry employment, which is similar to estimating log robots per worker, but allows for differential coefficients on log robots and log employment. We include real value added per worker to control for potential scale effects and the effects of differential productivity growth across sectors. p represents the error term. All standard errors are clustered at exporter-industry level. pp εε First, we examine heterogeneous effects by exporter country income – developed vs developing economies. Exporter income status is defined in 2000, the first year of our data, using the World Bank classification. We interact our main robot measure with a developed and developing country dummy, in order to report the estimated effect for each country group against zero. We do this in preference to sample splitting in order to maximize the power in our
# **Appendix A.  The Monte Carlo Experiments**

The Monte Carlo experiments use 10,000 replications of the model: _w f_ = α + β ln ( _x n_ ) +γ ln _n_ + _u_ , where α = 1.4, β = -0.06, γ = -0.06 and each series is 1000 observations. Parameter values match the results using data from the benchmark diary, in column (8) of Table 3. To implement the experiments, total consumption, _x_ was partitioned into food consumption, _xF_ = _x_ ⋅ _wF_ and nonfood consumption, _xNF_ = _x_ − _xF_ and three different types of errors were added to food ( _vF_ ) and non-food ( _vNF_ ) and the error-ridden total expenditure and food share variables were ~ ~ ~ ~ ~ reconstructed as _x_ = _xF_ + _xNF_ and _w_ = _xF_<sup>~</sup> _x_ , before the food Engel curve regressions were reestimated.

In case (1) the measurement errors were independent of any of the variables in the model and of each other, with _vF_ ~ _N_ (,0 σ _v_ 2 _F_ ), and _vNF_ ~ _N_ (,0 σ _v_ 2 _NF_ ). The errors, σ _v F_ and σ _v NF_ took each of nine values, ranging from 0, 0.05, 0.1, 0.15, ..., 0.35, 0.4.

> In case (2) the errors were correlated with true values, _vF_ =φ ln _xF_ + ε and _vNF_ =ψ ln _xNF_ + ε where ε ~ _N_ ()1.0,0 and the values used for φ and _ψ_ were 0, -0.05, -0.1, -0.15, ..., -0.35, -0.4.

In case (3) errors were correlated with household size, _n_ : _vF_ = λ ln _n_ + ε and _vNF_ =η ln _n_ +
Table 2 and Table 4 (evaluated at the median). The marginal e¤ects con...rm the conclusion that the IV estimates of the e¤ects of the land restrictions on wages are substantially larger compared to the OLS estimates; the marginal e¤ects are -0.46 (OLS, column (5) Table 2) and -1.41 (column (1) in Table 4). This substantial increase in the estimated e¤ect of land restrictions on wages after instrumentation is indicative of the importance of unobserved positive productivity as the main source of omitted variables bias as discussed in details earlier.

# 6.3. Additional Robustness Checks: Can Long Run Adverse E¤ects of Early-Age Malaria Exposure Drive the Results?

Although we control for education, age, and current malaria infection rates in the IV regressions, one might still worry that they might not be adequate controls for potential long term e¤ects of early-age malaria on health and education. For example, if a signi...cant proportion of the individuals in our sample belong to age cohorts that were a¤ected by malaria prevalence during 1937-1941, then they are likely to have lower educational attainment and adverse health status. We have reasonably good controls for education, as both individual and DSD level indicators of education are used in the IV regressions. However, the Sri Lanka HIES 2002 lacks good measures of health of the individuals. To check if our results are driven by long term negative e¤ects of early-age malaria, we perform robustness checks by excluding early age malaria cohorts from the sample, those born before 1947, 1950, and 1960 respectively. The oldest person in our sample is born in 1937 and the youngest in 1981. The results from estimating the IV regressions using the three subsamples are reported in Table 5. It is reassuring that the estimates are consistent with the results reported earlier in Table 4 using the full sample. Both the direct e¤ect of land restrictions and the
variation in unemployment rates, and also leads the small area estimation procedure to underestimate the variance of the municipal random effect, which in turn gives these inaccurate predictions more weight relative to the survey data in generating the estimates. Therefore, the small area estimates for unemployment suffer from high levels of relative bias and are more weakly correlated with the census values than the direct estimates.

While a non-linear estimation model might improve these estimates, a natural question is whether larger samples might improve the small area estimates for unemployment. This ties into a larger question about the potential benefits of expanding the second stage of samples when combining survey data with alternative data sources. To shed some light on this question, we repeat the small area estimation exercise after simulating a sample in which every household in selected AGEBs is included in the sample. This will reduce measurement error in the dependent variable, which substantially improves the quality of the predictions (Lobell et al., 2021, Engstrom et al., 2021). The exercise in intended to provide an upper bound estimate of the impact of expanding the second stage of the sample. But since listing exercises are common parts of household surveys in many developing countries, it may be possible to include selected questions as part of the listing exercise. The full enumeration results also shed light on the potential benefits of expanding the second stage of samples when combining survey data with alternative data sources.

<u>Table 7 - Sample of sampled AGEBs vs. full enumeration of sampled AGEBs</u>

||(1)<br>Male LFP|(2)<br>Female LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
||**Panel A: All m**|**unicipalities**|||
|**Average estimated mean**<br
could cost this generation of students trillions of dollars (Psacharopoulos et al. 2021).

Also, global studies – that is, studies using data from international assessments where students in many countries take the same tests and under the same conditions, thus, making them comparable across country and time – document significant learning losses (Jakubowski et al 2023; Kennedy and Strietholt 2023). International reading scores declined an average of 2
_Services PTAs_ The variables coded for these services PTAs, as well how the WB-DTA relates to, and improves upon, earlier attempts to quantify preferential services trade agreements are described by Gootiiz, Jonetzko, Magdeleine, Marchetti and Mattoo in Chapter 4 of the Handbook by Mattoo, Rocha and Ruta (2020). Of the 143 services PTAs in this dataset, 47 are plurilateral agreements (such as the East African Community) and 96 are bilateral agreements. Bilateral and pluriateral services PTAs exhibit systematic differences in terms of scope and depth. We document these differences below and in the estimation results. Bilateral agreements also contain on average a higher number of services provisions compared to other types of services PTAs (see Appendix Figure 12).

In the empirical analysis, we restrict our attention to the PTAs in force since 2000 in order to be able to match the WB-DTA 2.0 data to bilateral services trade and valueadded data, respectively, which are described below. This is inconsequential for the analysis as most agreements prior to 2000 would arguably contain shallow services provisions or would be superseded. Moreover, the majority of observations that we lose from this constraint is accounted for by the European Economic Area (EEA) agreement, and all estimations include an EU dummy variable anyway (see equation 4.1). The services chapter of the WB-DTA database covers various aspects of services PTAs, from the general architecture of the agreements, to the specific commitments and reservations on liberalization negotiated by the parties. This information is coded in 64 variables, which span eight policy areas: agreement structure (coverage of modes, presence of separate chapters and annexes), scope and coverage (liberalization approach - positive list, negative list, other type; sectoral and policy inclusions or exclusions), substantive disciplines (definition of market access, non-discrimination, national treatment, local presence requirements, performance requirements, monopolies, domestic regulation, transparency), 16
<!-- Start of picture text -->
-25 -20 -15 -10 -5 0 -25 -20 -15 -10 -5 0<br>Change in GDP growth Change in GDP growth (pct points)<br>Earnings growth change Employment growth change Unemployment Participation<br>Fitted earnings change Fitted employment change Fitted unemployment Fitted participation<br>10<br>0<br>-10<br>-20<br>8<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text -->

**Figure 7 Figure 8**

To assess the role of these other factors, this paper classifies countries along seven dimensions. The first two are region and level of per capita GDP, which are intended to capture structural differences in the economy and in labor market institutions. Countries are also classified according to the relative size of the manufacturing and export sector, two areas of the economy where crisis impacts were most severe. Exports were also affected by countries’ exchange rate regimes, as those with currencies pegged to the dollar suffering larger falls in exports, since the dollar appreciated roughly 15 percent against the Euro in the latter half of 2008.<sup>19</sup> Finally, countries are grouped according to their level of labor regulation (high/low) to investigate the expectation that stringent labor market regulations, if enforced, discouraged employers from shedding workers during a downturn. Two measures of firing costs from the 2009 Doing Business Indicators are used for this purpose: the difficulty of firing index and the firing cost.<sup>20</sup> Only a few of these characteristics are closely correlated with each other.<sup>21</sup>

> 19 We use the IMF’s _de facto_ exchange rate-regime to classify countries by their currency regime. http://www.imf.org
Average living standards in the Philippines have improved markedly since 2000. GDP per person (adjusted for inflation) rose at greater than 4 percent per year in nine of the 18 years between 2000 and 2017, contributing to an overall increase in real income per capita of close to USD 1,300 over this period. Overall, the distribution of income growth since 2006 has been inclusive and pro-poor, but especially so between 2012 and 2015 when income per capita among the poorest 20% of households increased by 16 percent.<sup>35</sup> As a result, between 2006 and 2015, poverty rates estimated on the basis of the national poverty line fell from 26.6 to 21.6 percent (and from 14.5 to 6.6 using the USD 1.90 international or “extreme” poverty line).

Other risk factors or interventions closely associated with stunting that clearly improved between 2000 and 2017 and that now affect less than 25% (or cover at least 75%) of the population, include anemia prevalence among women (pregnant or lactating), anemia prevalence among children between the ages of 1 and 5 years, diarrhea prevalence among children under the age of 3, breastfeeding prevalence, the share of births assisted by a skilled health practitioner and the share of the population with access to an improved, non-shared sanitation facility.

Risk factors or interventions that improved but still affect more than 25% (or cover less than 75%) of the population include anemia prevalence in children between 6 months and 1 year of age, exclusive breastfeeding prevalence among infants between 0 and 5 months of age, and children between 6 and 23 months of age receiving a diverse or acceptable diet.<sup>36</sup> Amid these positive trends are a few key areas of concern, some of which were pointed out previously. Adolescent fertility and childbearing prevalence are on the rise. Preterm births are estimated to have more than doubled since 2000<sup>37</sup>
such as Fintech companies and cellular providers, do not consult nor report to credit reporting bureaus fully or regularly. In large part, this is because credit reporting bureaus were set up for a pre-digital era. They are not inclusive of all types of lenders and face challenges in the frequency of uploading data (daily uploads are necessary for these short-term loans) and the ease of connection with other players in the credit reporting ecosystem. As a result, many lenders do not ensure that credit bureau information is updated and correct, even when they do consult it prior to lending. Because account activity can be monitored, credit bureau information is not required for nano-

> 5 https://baobabgroup.com/en/announcement/meeting-a-diversity-of-client-needs-nano-loans-with-potential-forgiga-impact/

> 6 Buri et al. (2019).

> 7 Center for Financial Inclusion/Accion. “Making Digital Credit Truly Responsible.” September, 2019. Washington, D.C. https://www.centerforfinancialinclusion.org/making-digital-credit-truly-responsible. - See also Beth Rhyne, “Red Flags in Kenya’s Nano-Credit Market.” https://www.centerforfinancialinclusion.org/red <u>flags-in-kenyas-nano-credit-market</u>

8
24.23]||
|Reduced Coping Strategies Index|13.03|12.85|12.16|12.85|13.51|13.39|13.09|0.54|
||[19.19]|[20.91]|[14.07]|[18.62]|[16.96]|[17.36]|[17.47]||
|Livelihoods Coping Strategy Index|4.71|4.65|4.34|5.59|4.46|4.66|5.24|-0.93***|
||[5.50]|[6.00]|[4.68]|[5.79]|[4.68]|[3.74]|[4.41]||
|Household has some amount of debt|0.78<br>|0.77<br>|0.78<br>|0.81<br>|0.77<br>|0.77<br>|0.81<br>|-0.04|
||[0.71]|[0.71]|[0.63]|[0.59]|[0.77]|[0.66]|[0.63]||
|Total cumulative stock of debt|<br>1235.69|<br>1245.75|<br>1122.20|<br>1313.21|<br>1172.42|<br>1295.10|<br>1336.34|-18.11|
||[2897.40]|[3029.59]|[2547.79]|[2361.03]|[2813.99]|[2840.49]|[2932
seekers, and led many European governments to require Gambians to have a visa before traveling. Faced with more restrictive immigration policies, many Gambians wishing to migrate increasingly turned to irregular migration.

This irregular migration typically takes place through what is known as the “backway”, which involves travel overland through the Sahara Desert, through Morocco or Libya, and then across the Mediterranean Sea to Spain or Italy. Each stage of this journey involves many dangers. Travel through the Sahara Desert typically takes place in crowded open trucks (from which migrants often fall and are left behind to die) to get to Libya, where they face a variety of serious challenges, including abductions for ransom, slavery, torture and other ill-treatments.<sup>8</sup> Considering the additional risks of drowning in the Mediterranean, having the smuggler boat caught and being brought back to Libya,<sup>9</sup> and being deported upon arrival in Europe,<sup>10</sup> results in an estimated overall 4% success rate of attempted backway migration journeys.<sup>11</sup> Despite these risks, the irregular emigration rate from The Gambia to Europe grew substantially in the first half of the 2010s. These trends can be seen in data from the European border agency, Frontex, shown in Figure 1.<sup>12</sup> Recorded irregular migration flows from The Gambia grew from 8 Numbers on the death rates of irregular migration from West Arica to Italy through the Central Mediterranean route are very scarce and hence have limited reliability. According to our calculations combining the evidence collected by the North Africa Mixed Migration Hub (MHub) with the existing numbers on death rates in the Mediterranean, 22% of those attempting to cross the Saharan desert and other transit countries to reach Libya died in the process. Of those who reached Libya successfully, another 23% died mostly due to physical violence. <u>http://www.mixedmigrationhub.org/wp-content/
_CHAPTER 1. INTRODUCTION_ 4 reviews the existing SAE procedures, starting with the very simple direct estimates obtained from area-specific survey data, progressing through the simple past indirect methods, and concluding with the more sophisticated and recent model-based procedures, some of which can incorporate various, possibly heterogeneous, data sources.

The review is based on several documents, including other recent review papers. Specifically, it heavily relies on Molina (2019), Corral Rodas, Molina and Nguyen (2021), Molina, Corral and Nguyen (2022), Corral et al. (2022) and Molina and Rao (2023). Drawing upon this literature review, this paper then identifies challenges or gaps in the current SAE methodology that are relevant for practitioners and could potentially be addressed through research in the next 3-5 years.

Chapters 2 to 6 review a significant portion of the existing SAE literature. Chapter 2 describes direct estimators based on area-specific survey data and related methods. Chapter 3 goes over early indirect estimators, including the first synthetic estimators as well as composite estimators. Then, Chapters 4–6 provide an overview of modern model-based SAE procedures, describing many recent extensions of these models and methods. We emphasize the main ideas behind each method, focusing on model types and on the estimation methods based on them. The description includes the pros and cons of each procedure from a practical standpoint, while details such as fitting procedures and other technical issues are omitted due to space restrictions. Finally, Chapter 8 enumerates open problems or topics deserving further research, once again considering the perspective of practitioners. It suggests potential topics of interest that could be addressed through research in the next 3-5 years.