rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 3: Net Capital Gains<br>19<br>14<br>0. 9<br>2<br>S -1 L=-_ -d -i'. - -. , ;<br>-11<br>1951 1958 1965 1972 1979 1986 1993<br>Private Sector ------- Public Sector - External Sector<br>Source:Authors Calculations<br>Figure 4: Net Capital Gains of the Private Sector<br>4<br>z -1<br>0<br>-6--<br>1951 1958 1965 1972 1979 1986 1993<br>On Public Internal Debt ------- On Private External Debt<br>- - - - On Base Money Holings Total Net Capftal Gains<br>Source: Authors Calculations<br><!-- End of picture text --> caused by the real exchange rate appreciation plus those due to domestic debt holdings are higher than the inflation tax it pays on money holdings (Figure 4).

(iii) According to the National Accounts, in 1992 the private saving rate attained its lowest level since 1950 when it reached 9.3% of GNP, and was 3.5% of GNP below the average private saving rate of the period 1951-1993. Although this rate recovered in 1993, its level was still 2.5% of GNP below the average of the last 23 years. In contrast, the decline of the adjusted measure of private savings continued in 1993 but its level was 0.5% of GNP below the average of the period 1951-1993. Therefore, the low level attained by private savings in the 1990s is more dramatic in the National Accounts than in the adjusted measure. However, the decline in the adjusted measure is steeper. In fact, between 1988 and 1993 the private saving rate fell 5.4% of GNP and 4.6% of GNP according to the adjusted and traditional measures, respectively. The purpose of the next 8
analysis was shortened by one year. Fifth, whereas the CTNS data set only covers protests and strikes, MEC also includes all non-institutional acts of civil disobedience such as sit-ins and nonviolent occupations, as long as they include at least 1,000 participants and aim to replace the government. In 79.45% of the cases where MEC identifies an ongoing nonviolent uprising, the CNTS data set also records at least one demonstration or strike.

_Table 5 Alternative dependent variables_

||(1)<br>Ongoing<br>uprisings<br>(MEC)|(2)<br>Riots<br>(CNTS)|(3)<br>Violent<br>conflict<br>(UCDP/PRIO)|(4)<br>Battle-related<br>deaths<br>(UCDP/PRIO)|
|---|---|---|---|---|
|Suffering (%)|0.125**<br>(0.064)|0.013ꝉ<br>(0.007)|0.025<br>(0.104)|-0.012<br>(0.011)|
|Struggling<br>(%)|0.113*<br>(0.065)|0.008<br>(0.006)|0.114<br>(0.12)|0.008<br>(0.008)|
|Constant|-122.665***<br>(41.705)|-0.686<br>(4.749)|30.467<br>(30.469)|9.964<br>(8.605)|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|
|R-squared|0.402|0.307|0.380|0.063|
|Observations|152|862|178|882|
|Countries|23|118|22|121|

Note: R-squared refers to pseudo R-squared for the logit models in Column 1 and Column 3. Estimates are obtained with an OLS model. Cluster robust standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05, ꝉ p<0.10 We estimate a logistic regression with clustered robust standard errors because nonviolent uprisings are measured as a binary variable in the MEC data set. In this model only those countries for which there is variation in the ongoing uprising variable, capturing either the onset or offset of an uprising, can be included, which limits the set of countries included in the logit model to only 23. In the regression of subjective well-being on uprising, as recorded in the MEC data set (Table 5, Column 1), the effect of _suffering_ remains positive and statistically significant. On average, a one-percentage point increase in the _suffering_ rate increases the probability of a large-scale nonviolent uprising with 1.3 percentage points. Also the coefficient on the _struggling_ variable remains positive and significant with an average 29
<!-- Start of picture text --> Need-based grants Merit-based grants<br>10 studies 6 studies<br>=F 7 om<br>a] = =:a =<br>re r<br>= =<br>= Maw. CANS 000 (erly com =<br>a a0 a<br>rae rat|<br>— AS54000 —<br>a a<br>i] aMax 8,00 fecrly commitment) a<br>a a S105 17 14 1 200-64 00<br>E sa yA AS 3 SS10/517 14 ae. Mar. 99038<br>Man. BR000 aa? a<br>S130<br>a a<br>6-3-1173Reported effect5 fF 9111315171921 in percentage points 29 6-3-1913Reported effect5 Fin9111315177percentage points1921 23<br><!-- End of picture text -->

Filed symbols indicate statistically significant estimates. Labels indicate the annual amount of the grant evaluated Source: Tables 6.C2 and 6.C3 in annex
The coefficient of GDPpc should have a positive impact, but selectivity effects may trigger a different outcome. OPEN is the ratio of exports plus imports to GDP, and aims at capturing the degree of openness of the economy in the origin country. This variable is also available in the Penn World Tables. The coefficient of OPEN is expected to be positive provided that a high degree of an economy’s openness implies more information is available to prospective migrants. The English dummy in the model is based on CIA – The World Factbook (2002) and takes value 1 if English is the main spoken language in a country. This variable should have a qualitative effect on performance; therefore the sign of the coefficient is expected to be positive. DIST is a selectivity indicator measured as the log of distance in miles to the US<sup>32</sup> . It affects migrants’ ability distribution by its influence on migration costs. A country in the proximity of the US has a higher propensity to send unskilled migrants compared to one that is further away. Therefore distance should have a positive impact on the entry level of occupational placement. The presence of military CONFLICT has a negative selection effect. In estimations for the 1975 arrival, military conflict<sup>33</sup> is evaluated for the 1970s decades, whereas estimations for the 1985 arrival use an index based on data for the 1980s. Variable COM<sup>34</sup> indicates whether a country has a communist regime in 1970, and respectively 1985.

The next step is to determine whether country attributes impact the change in occupational placement over a decade. The model in the equation is estimated six times for the 1975 arrival, and three times for the 1985 arrival. Thus, for each pair of Census years t=1980, 1990, 2000:  _predicted_  5'' * _CONFLICT_ ln _OPc c_  '' 6  '' *  _COMc_ * _predicted_   _c_ '', _c_ ln  _OPcountryinitial_ , _c_   2'' * _OPENc_   4'' * _DISTc_ (4) GDPpc and English are not included in specification (4) because they are correlated to a large degree with the initial predicted occupational placement.

> 32 From Andrew Rose’s datasets available at http://faculty.haas.berkeley.edu/arose

> 33 Constructed using  www. prio.no, “Armed Conflict Version 2.1” by Gledisch, Wallensteen, Eriksson, Sollenberg and Strand (2004)

> 34 Robert Barro’s  “Religion Adherence Data”

> http://www.economics.harvard.edu/faculty/barro/data_sets_barro

18
the two columns (due to missing observations) and partly due to the control for age of the store. Our main result of a positive and significant effect of labor regulation on computer usage holds for the _Law Book_ index.<sup>10</sup>

# **5. Conclusion**

The paper shows that rising computer usage in India’s retail sector is in part driven by burdensome labor regulations. The findings suggest a labor saving motive which we confirm in a companion paper. We have also shown that existing work on the labor regulation-technology-employment/wages nexus may suffer from specification problems in that it treats the choice of technology by firms as exogenous to labor regulations. Our results contradict the exogeneity assumption.

# **References**

Alesina, A. and J. Zeira (2006), “Technology and Labor Regulations,” NBER, WP 12581.

Banerjee, A and L. Iyer (2005), “History, Institutions and Economic Performance: The Legacy of Colonial Land Tenure Systems in India,” _American Economic Review_ , 95(4), 1190-1213.

Besley, Timothy and Robin Buregess (2004), “Can Labor Regulation Hinder Economic Performance? Evidence from India,” _Quarterly Journal of Economics_ , February 2004, 91-134.

Blanchard, Olivier and T. Philippon (2006), “The Quality of Labor Relations and Unemployment,” mimeograph.

Botero, Juan, Simeon Djankov, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2004), “The Regulation of Labor,” _Quarterly Journal of Economics_ , November, 118: 1339-82.

Card, David, Francis Kramarz and Thomas Lemieux (1999), “Changes in Relative Structure of Wages and Employment: A Comparison of the United States, Canada and France,” _Canadian Journal of Economics_ , 32(4), 843-877.

Krueger, A.B and J. Angrist (2001), “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments? _Journal of Economic Perspectives_ , 15, pp. 69-85.

> 10 For the specification in column 5 of Table 1, the coefficient of the _Law Book_ index equals .020 which is significant at less than 5% level. The coefficient remains significant at less than 5% level with just a few basic controls such as _Sex ratio_ and _Regulation_ , irrespective of the remaining controls in column 5.

6
= ( _−νγ_ ) exp( _−z_ ) + _ν_ exp( _−z_ ) � _x_ exp( _−x_ + _z −_ exp( _−_ ( _x − z_ ))) _dx_ We know that exp( _−z_ ) = _m_<sup>_ij_</sup> from McFadden (1973). Substituting this in: Then we set _y_ = _x − z_ , thus 24
The large potential gains from deeper integration highlight a deficiency of the focus on the second-best problems of regional trade integration in the literature. Exaggerated concerns over trade discrimination could lead to the unwarranted conclusion that big countries would be better off as a number of small countries. For example, some trade is certainly diverted away from foreign countries to US states, yet disintegration of the United States is unthinkable, precisely because of the deep integration. A shift of focus toward the gains from deeper integration and to how RTAs may help to facilitate it is therefore desirable.

# **6. Conclusion**

In the last fifteen years regionalism has become, by far, the most popular form of reciprocal trade liberalization. This trend has been met with scepticism by trade economists, many of whom are concerned with the distortions from the discriminatory policies inherent to these arrangements. While legitimate, these concerns seem excessive. Empirical analyses indicate that trade creation, not trade diversion, is the norm. Evidence suggests that this is both because governments “choose well” when forming RTAs and because they adjust other trade policies to moderate the distortions from discrimination. The other main concern is that regionalism could endanger multilateralism. Maybe it does, maybe it does not; at the moment we just do not know. Settling this issue theoretically has proven very difficult. Empirical analyses, which rely on data from a handful of trade rounds, will take time. Perhaps trade economists may do better by looking for ways to better integrate regionalism with multilateralism, recognizing that regionalism is and will probably remain the preferred form of reciprocal liberalization for 41
# **_A.2. School Enrollment-Income elasticities_**

We estimate the income elasticity to schooling using data from 130 household surveys, using the latest available Global Monitoring Database (GMD) for all available countries. We estimate this relationship by welfare quintile, which has the advantage of allowing for non-linearities. We estimate non-parametrically the following relationship, O ... qq=1,aa,cc qq=1,cc �O OO × WW where, qq=5,aa,cc qq=5,cc OO × WW O , is the share of out-of-school by welfare quintile _q_ , for age group **_a_** , in country **_c_** (see figure 3 for the out of school variation across welfare quintile, per country income group) OO W, is the share of children in welfare quintile **_q_** , in country **_c_** We apply the income shock to all children by multiplying the per capita welfare of all children by the available macro projections of contraction in 2020. In our reported estimates, we use the latest published Macro Poverty Outlook (MPO) projections for GDP per capita growth, with the regional average imputed if no country value was available. Preserving the baseline cutoff values for each welfare quintile, we observe how this shock changes the distribution of children across the original quintiles. The total out of school children is obtained by reweighting the number of children on each welfare quintile, and assigning them the observed shared of out of school children ( _OoSq,ac_ ). O ... qq=1,aa,cc qq=1,cc O OO × WW′ � where, qq=5,aa,cc qq=5,cc OO × WW′ where, O , is the share of out-of-school by welfare quintile _q_ , for age group **_a_** , in country **_c_** (see figure 3 for the out of school variation across welfare quintile, per country income group) OO W’, is the share of children in welfare quintile **_q_** , in country **_c,_** after the income shock is applied, but considering the same cut-offs of each quintile as in the vector _W_ (table A.2.1 shows the transition probabilities per quintile from W to W’) If a country does not have a household survey available, we imputed the overall change in out-of -school rates of their income group.

At baseline, the within- and between-countries inequalities of access to school for the 4 to 11 age group are extremely high. While in high-income countries the range of out-of-school children from the poorest to the richest is close to 0.2pp, in low-income countries this range remains close to 15pp. However, this inequality rapidly falls to 12pp and 3pp, as we move to lower-middle-income and upper-middle-income countries, respectively.

For the 12 to 17 age group, the within country inequality by income group is almost the same (15pp) across all country groups. However, gender differences persist, with girls being less likely than boys to attend schools in low-income countries, and the reverse in high-income countries. Moreover, important between countries inequalities are evident. The poorest households in highincome countries have on average, a lower share of out-of-school female children (Q5=12%), than 42
Financial Regulation and Development Table 3: Profits, Branches and Regulation: Annual Reports

|Variables|(1)<br>Real Profts<br>(million birr)|(2)<br>Number of<br>Employees|
|---|---|---|
|Policy _×_ Big Bank|24.47*<br>(14.86)|393.3***<br>(114.6)|
|Bank FE|Yes|Yes|
|Year FE|Yes|Yes|
|Observations|52|52|
|Adjusted _R_<sup>2</sup>|0.938|0.971|
|Mean Dep. Var.|126.2|1,461|
|SD Dep. Var.|96.42|976.7|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank and month fixed effects are included. Davidson and McKinnon (1993) robust standard errors are reported in parentheses, which for this case are more conservative than bootstrapped bank-level clustered. Real Profits is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in million birr. The number of branches denotes the total number of branches as shown in banks’ annual reports. The variables are made inter-temporally comparable using the core inflation figures from National Bank of Ethiopia (2012, 2013). Their means and standard deviations are reported in the final two rows. These variables are regressed over a Policy dummy, which takes unit value after the introduction of the regulation policy and a dummy for Big Banks, taking unit value for the six banks classified as large in Figure 4. Bank and Year fixed effects are included. The adjusted _R_<sup>2</sup> of these regressions is reported in the row “Adjusted _R_<sup>2</sup> ”. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

_cy_ their differential trend. As highlighted in Figure 8, there seems to occur a discontinuity at the introduction of the policy, both in real profits (upper panel) and employee numbers, supporting the previously reported hypothesis. In the next section, we clarify how these are in line with the evolution of branch installation.

# **2.3 Robustness Checks**

In this section, we explore possible alternative explanations, which might be related to the policy change and invalidate our inference. The most important feature, which has not been previously addressed, is the destination of the funds collected by the NBE, through this new bill. One powerful argument regarding the effects observed in Figure 6 on deposits and loans could be the following. This regulation drained substantial resources from the banking system and placed them in long-term investment in some geographical areas to which big banks had a comparative advantage in access. In a sense, leaving aside the safe asset increase verified in Figure 5, this hypothesis would identify the regulation policy as an indirect transfer of resources from small to big banks. We believe this is implausible for two reasons. First, the Ethiopian government heavily relies on its state-owned – bank, CBE, which is the largest in the country, not affected by the policy and massively profitable in 2011/12, it amassed eight billion birr of profits, corresponding to roughly 400 million US dollars (USD). If there had to be a redistribution of resources, then the two state-owned banks (CBE 28
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**Figure 3: Economically active and inactive population by sex (% of population 15-64 years old)**

<!-- Start of picture text --> 100.0<br>80.0 31.3 20.9 40.7 26.4 23.9 28.7 24.0 18.5 29.1 21.5 18.3 24.4<br>60.0<br>40.0 58.3 71.8 68.5 71.6 65.6 61.3 67.9 55.0 70.5 74.0 67.3<br>20.0 46.1<br>0.0<br>All Male Female All Male Female All Male Female All Male Female<br>Ethiopia (ESS 2018/19) Malawi (IHPS 2019) Nigeria (GHS 2018/19) Uganda (UNPS 2019)<br> Currently working Temporarily absent Unemployed Economically inactive<br><!-- End of picture text --> _Source_ : Pre-COVID-19 face-to-face LSMS-ISA surveys.

**Figure 4: Working respondents by economic sector (% of population 15-64 years old currently working)**

<!-- Start of picture text --> 100.0 5.49.0 11.7 14.9 14.6<br>80.0 15.6 8.2<br>23.8<br>60.0<br>40.0 78.0<br>63.8 57.9<br>20.0 40.7<br>0.0<br>Ethiopia (ESS 2018/19) Malawi (IHPS 2019) Nigeria (GHS 2018/19) Uganda (UNPS 2019)<br>Agriculture Manufacturing & Utilities<br>Construction, Transport & Professional Act. Commerce<br>Public Administration Services<br><!-- End of picture text --> _Source_ : pre-COVID-19 face-to-face LSMS-ISA surveys.

# **High-Frequency Phone Surveys**

We use data from HFPS in Ethiopia, Malawi, Nigeria, and Uganda. The sampling frame for these phone surveys was the most recent nationally representative LSMS-ISA-supported national longitudinal household survey. This enables a methodologically sound comparison with the pre-COVID-19 survey data. We chose these four countries because the latest face-to-face survey was conducted in 2019.

These surveys have been designed to track the social and economic implications, as well as household behavioral responses to the pandemic, with sections on Knowledge, Behavior, Access to Services, Employment, Income Loss, Food Security, etc. The LSMS-ISA COVID-19 HFPS were implemented in Ethiopia, Malawi, Nigeria, and Uganda. In each country, the aim has been to conduct monthly phone interviews over a period of 12 months.

Phone surveys have the advantage of creating opportunities and learning experiences for the direct and timely analysis of developments in dynamic labor markets; however, there are several drawbacks as well. These include (i) selection bias since households that do not own a phone are automatically excluded; (ii) non-response bias that may arise particularly in longitudinal studies; (iii) heterogeneity in phone coverage across population groups and regions; and (iv) a limited scope for verifying the accuracy of answers. Phone surveys face restrictions with respect to the questionnaire design; since interview times are limited, phone surveys must therefore rely on short, simple questions with similarly short response options. In addition, interviews have to rely on data provided by one representative household member, usually the “household head”, because it will be cumbersome to try to interview all household members. These drawbacks need to be actively considered when implementing the surveys and analyzing the data collected. However, these 6
23 which is solved numerically for<sup>Po (using Newton's method). The poverty</sup> gap measure P1 can be written<sup>as</sup> where B(k,m,n) = _f_ pm- (l-p) dp. The FGT measure for a = 2 is 0 evaluated as follows. From<sup>the definition of P</sup> 2<sup>we know that</sup> Thus, given (p, a, 7, _6),_ the FGT poverty measures<sup>for any poverty line are</sup> calculated from (AB), (A9)<sup>and (AIO). The probability densities at the</sup> poverty line (as required<sup>for calculating %) are readily estimated using</sup> the fact that f(z) = l/(#LC(PO)).<sup>All of the above calculations can be</sup> performed using SAS.