rafmacalaba/data-use-mentions-extended huggingface dataset:
. When we are drawing individual countries randomly from a large population, the use of a random effects specification is appropriate. To strengthen our choice we should perform a specification test (Hausman’s test).

Finally, further empirical research should concentrate efforts in targeting the problem of endogeneity. This has been attempted for the telecommunications sector (Gutiérrez (2003)), but not so much for the electricity supply industry (with the exception of Zelner and Henisz (2000) of the papers reviewed in this Subsection). As suggested by our conceptual reform model outlined in Section 3, performance is not only influenced by reforms but also itself influences reform decisions. Gutiérrez (2003) proposes a dynamic model that uses lagged variables as instruments and show that neglecting the effect of lagged dependent variables may distort or overestimate the effects of all variables, including reform variables. Dynamic panel data modelling and estimation is one way to tackle the problem; however the estimation itself is not so straightforward when the time span is limited (as is usually the case for policy analysis). However, this problem becomes more tractable over time as datasets become larger.

> 4 The proposed regulatory index is actually the result of the interaction of three dummy variables. In addition, there is a dummy for vertical integration.

20
interesting to note that most of the effects we observed in Ghana are also present in Uganda. There are, however, interesting differences.

The main difference from Ghana is that women were more likely than men to be involved in agriculture and this effect was the same for both survey years. However, female household heads were distinguished from women as a group in that they were found to participate significantly less in agriculture in both years. This supports our earlier findings that female heads were moving into non-farm much more than other female household members. For non-farm activities, women were less likely than men to participate but again this effect is mitigated by the opposite, positive and significant effect of being a female head of household on non-farm participation. The results from Model 3 show that like female heads, members of female headed households were significantly less likely to participate in agriculture and significantly more likely to participate in nonfarm activities. This stronger attachment of female heads and their family members to non-farm as opposed to agriculture is only positive to the extent that earnings are higher in non-farm activities. It is possible that female headed households have fewer options and are forced to enter non-farm activities that are less profitable. Since we do not have reliable individual income and hours data for Uganda we cannot fully resolve this question as we have been able to do for Ghana. At least, the poverty analysis suggests that non-farm activities are helping female-headed families out of poverty marginally more so than those in agriculture.

As for Ghana, higher education is an important determinant of participation in nonfarm activities in Uganda. In both years, individuals who pursued primary education and higher education have a higher likelihood of participating in non-farm activities than 28
contribute to looser global financial conditions to stimulate the economy. They may also lead to “search for yield” effects which tend to drive up asset prices and may induce more risk taking. If crypto-assets are perceived as risk assets, crypto volumes would tend to rise when real yields are low. It is important to keep in mind that asset purchases by the Federal Reserve, including Treasury securities, took place during the sample period (e.g., to combat the impact of the pandemic) which has influenced price formation and the signals that can be extracted from it. C is a dummy which assumes a value of one after March 2020 and zero otherwise. This variable captures the broad and global impact of the pandemic on crypto-assets jj PPCCLLPPC PPCCPPC including public health, behavioral, and economic impacts as well as the unprecedented monetary, fiscal, and financial sector policy response. Finally, represents country-fixed effects to account for time-invariant country characteristics (e.g., level of economic development, institutional framework) that may drive differences of crypto jj γγ volume across countries. is the error term. j Second, we run cross-sectional OLS regressions on 2020 data to explore the role of financial εε development, remittances, economic freedoms, and ICT factors to explain variation of crypto volume across countries. Our main specification is: C <u>C C</u> L ( ) 0 1 C C C 2 C C + G jjC EEjjC CCjj 3 3 C 4C C C , (2) jj jj jj CC = ββ + ββ LLCCaaGGP PPC + ββ CCPPLLCCLLCCPPC PPCCaaC LLCC ββ RRC PPC CCLLC ttjj + ββ EEC LLC PPC C <u>CCCPPC</u> ttjj + ββC IIPPCCaaC LLCCjj + εεjj Where the dependent variable Ln ( ~~)~~ represents the log of the yearly value of cryptoG jjC EEjjC CCjj assets transactions as a fraction of a country’s nominal annual GDP for country CC jj _j_ in 2020, C C C captures the general level of economic development, C
The message from Figure 17 is quite clear. The end of civil war is associated with large positive changes in foreign investment inflows - regardless which dataset we use. Estimates vary somewhat but lie around 0.5 which implies that investment inflows increase by 50 percent on average. The second observation is that at a cut-off between 0.005 and 0.02 battle-related deaths per 1000 population all point estimates move up and very close together. In other words, across a large variation of investment measures results are fairly similar.<sup>43</sup> This means that at these cutoffs we have divided countries with similar experiences above and below the threshold. In what follows we focus on the threshold of 0.008 battle related deaths per 1000 population. Observations below this threshold are, for example, from China, the United Kingdom, the Russian Federation, India and Bangladesh. In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i.e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources.

> 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discussion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure.

> 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows.

> 42We also distinguish two different ways of calculating the cut-off of intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off.

> 43Each coefficient is also estimated quite precisely at this cut-off.

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provided in Jensen and Tarr (2010, Appendix E). Given the importance of foreign direct investment in services in our model, we mention here that Arnold, Mattoo and Javorcik (2007) show that in the Czech Republic, services sector liberalization led to increased productivity of downstream industries, and the key channel through which reform led to increased productivity was allowing foreign entry. Fernandes and Paunov (2008) found a positive and significant effect of foreign direct investment in services on productivity growth in Chile. Fernandes (2007) finds a positive and significant effect of services liberalization in both finance and infrastructure on the productivity of downstream manufacturing in the fifteen Eastern European countries.

In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of new technologies is the elasticity of varieties with respect to the price. Based on the literature summary in Jensen and Tarr (2010, Appendix E), we assign central values to this elasticity based on the region and the research and development intensity of the sector. The assigned central values for these parameters by sector and region are in table 6B. We conduct extensive sensitivity analysis on this parameter, both piecemeal and systematic.

# **V.  Data of the Model**

# **Social Accounting Matrix**

The key data source for our study is the social accounting matrix taken from Kiringai. Thurlow and Wanjala (2006). This is a social accounting matrix (SAM) for the year 2003. The table is very rich in agricultural detail, with 20 agricultural sectors. Given our focus on services, we found it necessary to disaggregate the single transportation sector into five sectors (based on value of output data of the various transportation sectors published in the _Economic Survey, 2006_ for Kenya by the Central Bureau of Statistics (2007a, p. 198)) and the single financial services sector into insurance, and banking and other financial services, from data in Central Bureau of Statistics (2007a, pp. 95-98. We assumed
for the household level model decline only modestly in Tanzania and increase significantly in Sri Lanka in comparison with the direct estimates. In Sri Lanka, the coverage rate reported in Table 15 for the noisy welfare measure is remarkably similar to the one for non-monetary poverty reported in Table 8 (84.9% vs. 83.7%). In Tanzania, the coverage rate is higher for the noisy welfare measure (78.0% vs. 73.6%). Accuracy and coverage are particularly important measures to consider because, unlike estimated precision, they are only observed when a “true” census population is available. Overall, the results presented in Table 15 indicate that the accuracy and coverage of the household level model remain high, in comparison with both the Fay-Herriot and direct estimates, when additional noise is introduced into the welfare measure.

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0296|0.0344|
||[0.00226]***|[0.00225]***|[0.00292]***|[0.00420]***|[0.00258]***|
|Household head|0.103|0.102|0.0978|0.104|0.119|
||[0.00386]***|[0.00386]***|[0.00525]***|[0.00672]***|[0.00333]***|
|Spouse works|-0.00507|-0.00496|-0.00671|-0.00746|-0.0216|
||[0.00343]|[0.00342]|[0.00462]|[0.00969]|[0.00466]***|
|Income stability|0.15|0.15|0.128|0.156|0.184|
||[0.00432]***|[0.00432]***|[0.00469]***|[0.0110]***|[0.00327]***|
|At least one person receives remittances regularly|-0.061|-0.0605|-0.0562|-0.0674|-0.0731|
||[0.00248]***|[0.00250]***|[0.00308]***|[0.00370]***|[0.00381]***|
|Household Home & WASH index|0.00841|0.00861|0.00722|0.00747|0.0161|
||[0.00163]***|[0.00163]*
41 in 1981 to 0.35 in 2010.

The decline in the incidence of total poverty was not sufficient to assure falling numbers of poor over the period as a whole. The number of people who are “purely relatively poor” (poor by this new measure but not absolutely poor) has been rising, as can be seen from Figure 13. Roughly two-thirds of the increase in the number of people who whose poverty is purely relative is due to the fall in the number of absolutely poor (Chen and Ravallion, 2012).

Extrapolating the rate of progress forward under the optimistic scenario, the combined poverty rate is 45% in 2012, and will be 43% in 2017 and 40% in 2022. The number of poor

> 32 López-Calva and Ortiz-Juarez (2011) offer a way of setting a dividing line between vulnerability and “economic security” using panel data to quantify the probability of falling into poverty at various income levels. Also see the discussion of this approach and alternatives in Ferreira et al. (2012). The issue remains open as to what probability is deemed acceptable and for what poverty line.

> 33 Note that these average lines do not play any role in the calculations of the poverty measures, which are built up from the country-level estimates. The averages given in Table 3 are for purely expository purposes. Note also that the poverty rate in this table can be below 50% even though the mean poverty line is above the median; see, for example, the total for 2008. This stems from the nonlinearities and heterogeneity in the underlying cumulative distribution functions.  The implicit global relative line (that yields the global poverty rate) is $2.18, closer to the median.

> 34 For the remainder of the paper, the discussion of “relative poverty” refers to the weakly relative measures. 35 Note, however, that some of the homeless in rich countries may well be this poor, but that they are underrepresented in standard household surveys.

16
**_Figure 3_** _: Comparison of Proxy LPMT and True LPMT for treated households_

_Source_ : Author’s calculations from NaCSA administrative Data and CIMS (2020) The comparison of true LPMT scores and the proxy LPMT for treated households provides a further justification of the relevance of the chosen threshold of 4. In Figure 3, we match administrative data from NaCSA to the CIMS (2020) data to compare the distribution of the true LPMT and proxy LPMT for treated households. The figure shows that for households’ whose true LPMT are between 7 and 10, we can see that a large majority have a proxy LPMT equal to 4 or more. We argue that this threshold allows us to strike the balance of minimizing exclusion and inclusion errors.

# _4.2. Identification Method_

Since we do not have experimental data and although we implement a cautious identification of the control group, there remain significant differences between the treatment and control groups (see Appendices 3 and 4). For instance, 60.7 percent of ECT-R1 beneficiaries were working the week prior to the Round 1 of survey in comparison to only 50.6 percent of the control group. For this reason, we use an Inverse Probability Weighting method to rebalance treated and control observations along a set of 15
# **Figure 2: Home-based work and face-to-face interactions do not always go hand-in-hand**

<!-- Start of picture text --> 0.90<br>Management of Professional, Scientific,<br>0.80 Education Services Companies and Technical Services<br>0.70 Finance and Insurance<br>Information<br>0.60<br>Wholesale Trade<br>0.50 Real Estate and Rental<br>and Leasing<br>0.40<br>Public Administration<br>Utilities<br>Arts, Entertainment,<br>0.30 and Recreation Other Services Administrative and Mining and Extraction<br>Support Services<br>of Natural Resources<br>Health Care and Social<br>0.20 Assistance Transportation and Manufacturing<br>Warehousing Construction<br>Retail Trade<br>0.10<br>Agriculture<br>Accommodation and<br>Food Services<br>0.00<br>0.00 0.20 0.40 0.60 0.80 1.00 1.20<br>More to less face-to-face<br>Less to moe home-based work<br><!-- End of picture text --> Source: Authors’ calculations based on Dingel and Neiman (2020), the O*NET database and the US. Bureau of Labor Statistics.

Among industries that are amenable to home-based work, the supply of those least intensive in faceto-face interactions, such as professional, scientific and technical services, might be least affected.<sup>6</sup> However, those intensive in face-to-face interactions are likely to experience negative productivity shocks during lockdown even if some tasks can be delivered digitally. For example, high school teachers can provide lectures through web-based applications, but the teaching quality might be lower compared to interactive sessions in the classroom. Similarly, branch managers and investment advisors in financial services can liaise with clients through online
diet quality and inadequate nutrient intake.<sup>47</sup> And micronutrient deficiencies have been linked to multiple negative outcomes for children, as well as adults; these include impaired cognitive development, physical and mental disabilities, child and maternal deaths, and lower productivity (UNICEF, 2004). Additionally, young children, lactating and pregnant women and the chronically ill are particularly susceptible to decreases in nutrient intakes due to their high nutrient requirements.<sup>48</sup> The quality-quantity trade-off observed at the national level is also observed at the sub-national level, when we look at differences across rural and urban areas. Rural households experience small declines in calorie intake, whereas urban households are able to buffer the shock to calories by reducing the diversity of their diets. We also explore the possibility that access to agricultural land and producing wheat (since net sellers of wheat can benefit from higher prices) can help to buffer the shock of higher prices. We find that these two groups of households are in fact better able to buffer the shock of increasing food prices with respect to their food consumption and dietary diversity; though, with respect to calorie intake, they are unable to buffer as well as their counterparts.

Finally, we find evidence that among certain subpopulations, the demand for wheat products is upward sloping (i.e., Giffen good); specifically, among urban households, large decreases in purchasing power brought about by the spike in staple food prices caused per capita consumption of wheat products to increase as the income effect of the price change dominated the substitution effect.

These new survey data provide a unique opportunity to calculate indicators of socioeconomic development and to analyze changes in these outcomes over time.<sup>49</sup> Such analysis is particularly crucial in conflict areas, which may be most susceptible to shocks, but which usually have little quantitative data. Our findings inform current policy discussions within Afghanistan and, more generally, within the international development community. They

> 47 See
mechanisms that traditionally underpinned trust in and accountability by such leaders (</mark> Acemoglu et al. 2014).<sup>3</sup> Chiefs whose downward accountability has been compromised may ‘privatize’ land for personal benefit even if this reduces social welfare (Leeson & Harris 2018), widens inequality (Obeng-Odoom 2014), or undermines sustainability (Mihaylova 2023). They may act as land brokers for outsiders (Lentz 2010); behaving like owners rather than custodians of land (Lanz et al. 2018) or let themselves be co-opted by politicians (Chimhowu 2019) to offer political support for personal benefit rather than that of the group they represent (de Kadt & Larreguy 2018).

# **2.2 Land registries and the benefits of documented land rights**

Land registries comprise a spatial and a textual component. The former, normally referred to as cadaster, describes boundaries of the objects (land parcels) over which rights are defined, reducing the incidence of land disputes over long time horizons (Libecap & Lueck 2011) and making land use change (Dippel _et al._ 2020) or assembly (Leonard & Parker 2021) easier. The textual part, referred to as the register, provides

> 2 A documented exception is Siam's traditional usufruct land rights system in the 19th century where women’s land rights were very secure, protected in court against challenges by investors or local elites, and supported high living standards (Chankrajang & Vechbanyongratana 2021).

> 3 <mark>Systematic differences in the use of indirect rule between British and French colonial powers are still visible in higher levels of chiefs’ perceived corruption and lower s</mark> tate capacity in former British vs. French territories (Ali _et al._ 2020c). This also holds for historical tribal homelands that were split between British and French colonies, and for counties colonized by either of the two powers within Cameroon (Ali _et al._ 2019c).

8
however, have made additional access commitments on the temporary entry of business persons, including on length of stay and types of occupations. For example, Australia provides categories defining "business visitors" and spells out the conditions and limitations for each category such as "service sellers" who are permitted an initial stay of 6 months up to a maximum of 12 months.

# **III. SERVICES TRADE POLICY AND COMMITMENTS: DATA AND MEASUREMENT**

Our goal is to compare the market openness agreed in the TPP with three other benchmarks: Doha offers, bilateral and plurilateral agreements, and applied MFN policy. The approach taken is similar to that in Borchert, Gootiiz, and Mattoo (2011) which compared Doha offers with applied policy. We define the scope of the comparison and the approach to quantification on the basis of the World Bank Services Trade Restrictions Database, the most comprehensive source of information on applied policies in services. The updated Database covers key services sectors of 12 TPP countries. We use the same methodology to measure the commitments in each negotiating fora.<sup>9</sup> The applied policy information is from a survey conducted in 2008 and updated for some of

> 8 Some of the previous PTAs examined, namely the NAFTA, the US- Singapore, the US- Chile, Canada-Peru FTAs contain a chapter on temporary entry of business persons, while other FTAs such as the US-Australia, US-Peru FTAs do not have such chapters.

> 9 A detailed description of the World Bank Services Trade Restrictions Database—including details on the data collection process, the policy measures covered and the questionnaire used in the data collection—is provided at http://iresearch.worldbank.org/servicetrade.  The global policy patterns of services trade policy emerging from the Database are presented in Borchert, Gootiiz and Mattoo (2014).  We updated the applied policy information for

6
controls|Yes|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|
|Observations|3,687|7,768|3,687|3,687|3,687|
|Number of individuals|1,609|2,182|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38. Permanent job is defined as working fulltime/parttime with permanent contract (the reference group is fixed-term contract and casual employment). Skilled occupation is defined as jobs with occupational scales above medium.

34