rafmacalaba/datause-encoder-data huggingface dataset:
flexibility of firing.<sup>1</sup> The natural logarithm of the index of labor regulations ranges in value from 3.66 to 4.09 in the sample of 108 countries that we use. Next we develop an index of entry regulations, using data on the number of procedures and the time it takes to start a business in each country. We take the logarithm of an equally-weighted average of the data from both sets of entry regulations (number of procedures and days) after rescaling each dataset series. The final range of values for the natural log of the constructed entry regulation index is 3.71 to 4.12. We then average the index of labor regulations and the index of entry regulations, with final values of the regulation index ranging from 3.71 to 4.09. The index of regulations is constructed such that higher values reflect a greater degree of regulation both in the labor market and the business sector.

Appendix Table A4 shows the mean of the regulation index for various income groups and regions. While high income countries do tend to have less regulation, the index is not monotonically increasing in income. Low- and middle-income countries tend to have roughly the same level of regulation on average. Regional differences are more acute. Latin America and the Caribbean is the most regulated region, North America is the least regulated region. United States, Denmark, New Zealand, Canada, and Hong Kong are the five least regulated countries. Brazil, Belarus, Mozmbique, Venezuela, and Paraguay are the five most regulated.

We use two measures of trade openness. The first is a measure of current total trade relative to current GDP in local currency. The second is a PPP-adjusted measure that uses 2000 trade data (exports and imports at current dollars) from the WDI and is then deflated to be

> 1 The raw data we have from the Doing Business database for the labor and entry components are rescaled such that their mean values are 50 and standard deviation 5. The final series range in value from 39 to 60 for the employment laws  index, from 38.36 to 62.34 for the number of entry procedures and 43.26 to 69.20 for the number of days it takes to start a new business over the sample of 108 countries used in our regression analysis.

8
While the elasticities are statistically significant in both cases, they are appreciably lower for the upper-bound measures, which are also less responsive (in terms of elasticities) to inequality.

For the upper bound there is also a significant interaction effect between the growth rate in the mean and the (log) initial value of the mean, implying a lower growth elasticity at higher means; the elasticity reaches zero at a point close to the highest mean. However, no such interaction effect was in evidence for the lower bound.<sup>53</sup>

# **7. Conclusions**

A strong case can be made that welfare-relevant concerns about relative incomes need to be built into prevailing assessments of global poverty and how it is changing. National poverty lines have provided the main data for setting global poverty measures. However, there is a poorlyrecognized but deep identification problem in using data on national lines to infer welfareconsistent international lines. The World Bank’s longstanding international line has been anchored to the national lines (at purchasing power parity) found in the poorest countries. The line is then held fixed across all countries. This approach assumes that relative income does not matter to welfare. A large body of research since the World Bank’s approach was introduced in 1990 suggests this is an overly strong assumption, and that a fixed real line will not be welfare-consistent in any reasonable sense. People at the same level of welfare are not being treated equally.

One might argue instead for using national lines for setting an international schedule of relative lines, and a strand of the literature has followed this approach. However, this makes an equally strong assumption, namely that one can statistically ignore the latent heterogeneity across countries in the “social norms” that define the critical welfare level to not be considered poor. We do not know whether higher lines in richer countries reflect higher expenditures (in real terms) needed to attain the same level of welfare globally or higher reference levels of welfare.

Recognizing the uncertainty on this point leads us to propose lower and upper bounds to welfare-consistent global poverty measures. The natural choice for the lower bound is the World Bank’s $1.90 a day international line, at 2011 purchasing power parity (corresponding to the $1.25 a day line at 2005 PPP). In setting the upper bound we have proposed a new approach to measuring relative poverty. We follow other recent work in proposing that the relative line should only be “weakly relative,” meaning that its elasticity with respect to the mean should be less than unity.

> 53 The t-ratio on the interaction effect for the lower bound is -1.24. The regression is not reported for this case.

31
domestic goods declined as predicted. Interestingly, the predicted and observed declines in consumption are extremely close (1.4 and 1.3 percent, respectively). Yet, the actual consumption per capita merely changed, hence pointing to the accelerated outmigration and to the reversal in refugees’ trends (Figure 4).

Table 7. Comparing simulations and observations for the period 2005-2020 (change, in percent)

|Change in|2004|2005-2010|2011-2016|2004|2017-2019|1912|2020|
|---|---|---|---|---|---|---|---|
|remittances &|Simulated|Observed|Observed|Simulated|Observed|Simulated|Observed|
|foreign savings|10.0|14.1|7.0|-10.0|-10.8|-90.0|-50.3|
|Total Income|4.9|6.8|2.1|-4.8|-2.8|-39.7|-16.5|
|Private Consumption|1.4|6.3|2.7|-1.4|-1.3|-33.7|-27.4|
|Private Consumption<br>per capita|0.1|5.2|-1.8|-2.7|-0.1|-34.7|-24.4|
|Investment|4.5|10.8|3.4|-4.8|-16.9|-31.3|-65.9|
|Exports|-2.6|6.7|-1.5|2.7|-1.0|7.9|-34.2|
|Imports|4.1|6.9|3.6|-4.0|-2.2|-37.0|-33.4|
|Demand of domestic<br>goods|0.7|6.9|3.4|-0.7|-4.2|-17.6|-23.0|
|Price of domestic<br>good|5.7|3.6|4.7|-5.5|6.2|-36.1|40.5|
|Real exchange rate|5.7|0.1|8.1|-5.5|5.5|-36.1|-43.7|
|Real GDP at market<br>price|0.1|6.7|2.0|-0.2|-2.7|-2.6|-25.9|

Source: author simulations and observations from Lebanon National Accounts 2004-2020 (CAS, 2022) and WDI, 2023.

Figure 4. Population in Lebanon 2004-2022 (thousands, right hand axis) and share of ages 15-64 (percent, left hand axis) <!-- Start of picture text --> 70% 6,500<br>69% 6,250<br>68% 6,000<br>67% 5,750<br>66% 5,500<br>65% 5,250<br>64% 5,000<br>63% 4,750<br>62% 4,500<br>Total Population Share Ages 15-64<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> Source: World Bank, WDI 2023.

The most striking outcome is the similarity between the observed changes in 2020, the year of fullblown crisis, and the simulation with the structure of the economy in 1912. Here, with the exceptions of the decline in growth that substantially overshoots predictions, and exports and prices of domestic goods that present reversed signs,<sup>3</sup> all other changes are within the range of the 1912 simulation. Indeed, the simulated 90 percent collapse in foreign inflows for the economy of 1912 depresses income by 39.7 percent. The actual decline in

> 3 The simulation using the structure of 1912 predicts a decline in domestic prices and an increase in exports which attenuates GDP decline. Indeed, in 1912, exports represented over 60 percent of GDP (see Tables 2 and 3). In 2019, exports were only 21 percent of GDP, domestic prices increased because of massive increase in money supply, and, more importantly, the massive disruptions in service provisions – including energy shortages – prevented the economy from turning the decline in real exchange rate into an opportunity for a better competitive position.

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and was statistically significant (t=2.85). However, when we added district effects it became negative (-0.360), but not significantly different from zero (t=-1.64).

Thus the two sources of bias appear to work in opposite directions. First, the excluded village externality leads to an over-estimation of the welfare impact of acquiring electricity; this stems from the fact that the externality is positive. Second, the omission of geographic effects leads to under-estimation. This suggests that districts with lower consumption levels were more likely to acquire electricity over the period. That is consistent with a model in which the early benefits of electrification were captured by better off areas.

Notice also that the upward bias from using OLS is small compared to the bias from using an IV estimator for which the exclusion restriction is not valid. The Khandker et al. impact estimate without district effects is over four times our preferred IV estimate, while the OLS estimate is only about one third higher. With district effects, the direction of bias switches sign, though the estimated impact is still larger than with OLS.

**_Labor supply and wage rates:_** Turning to labor supply, a more complex picture emerges, involving the substitution of some activities for others. Table 4 gives the results for the various categories of labor supply that can be identified in the REDS survey data and compared over time. (Again the homogeneity restriction performed well and so was imposed.)

The largest increase in days of labor supplied attributed to household electrification is for the regular wage work of men, which rises by 16.6 days with electrification, representing 33 percent of the mean days of regular work in 1999. (There is also an increase in men’s non-farm self-employment, though it is not statistically significant.) This extra work came mainly from reduced casual wage work (10.4 days). Thus our results indicate a significant substitution in male labor supply from casual to regular work attributed to electrification. This is consistent with the argument made in section 2, whereby electricity allows the male to switch leisure time from daylight hours to night time, allowing a more regular supply of labor, as required by regular salaried work.

For women the main effect was to increase their casual wage work, amounting to about 6 days per year. It is plausible that this involved the women taking up the casual work that was displaced by men. There are signs that this came in part from reduced days of regular wage work and of non-farm self-employment, although these displacement effects are not statistically 27
# EFFECTS OF FOUR PERCENT INCREASE IN THE U.S. MONEY SUPPLY IN MULTIMOD, TAYLOR AND LIVERPOOL MODELS _ON_ THE JAPANESE ECONOMY

JAPAN <!-- Start of picture text --> 02- I<br>0 1<br>0~~~~~~~~~~~~~~~~~~~~~~~0<br>-01 I<br>-0?<br>-03~~~~~~~~~~~~~~~~~~~~~~~~~~~~-<br>-05~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>-06~~~~~~~~~~~~~~~~~~~~~0<br>-05a<br>-01<br>1985 186 1987I 1969 it"<br>CUIP<br><!-- End of picture text --> JAPAN <!-- Start of picture text --> o, Wo/ t<br>LT 70<br>-02<br>-03 .<br>-05<br>~~~~~~~~~~~~~~~~~~~~-07<br>1965 198I 1987 t 3909 1990<br>rPG<br><!-- End of picture text --> <!-- Start of picture text --> JAPAN<br>52 24<br>'2 *t<br>3 4R<br>296 90 19815 90t8<br>28<br>1985 3987 '988 1968 1989 3990<br><!-- End of picture text -->

**JAPAN**

<!-- Start of picture text --> __ _ _ _ _ __ _ _ _ _ _ __ _ _ _ _ __ _ _ _ _ _ __ _ _ _ _ _<br>-0.01I<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-002<br>-008<br>-00 7<br>-0 1<br>-007 -0.1I 8 9tZ41e 9<br>429 3<br>15 1985 1986 1987 twa 1989 3990 .P0<br>-0 16 -~~~~-1a~RS AT<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 5: Illustrative Data, Period 2 Figure 6: Illustrative Data, Period 2<br>6 11<br>6 6 11 11<br>6 6 11 11 11 19<br>6 7 11 31 11 12 19 54<br>6 6 11 31 51 11 11 19 54 89<br><!-- End of picture text -->

**<u>Table 3:  Illustrative Data, Period 2</u>**

**<u>Table 4:  Illustrative Data, Period 2</u>**

||Period 1|Period 2|Growth(%)||Period 1|Period 2|Growth(%)|
|---|---|---|---|---|---|---|---|
|_AoL_(Pixels)|6.9|15.0|150|_AoL_(Pixels)|6.0|15.0|150|
|_R_(Digital Number)|23.3|13.4|-43|_R_(Digital Number)|23.3|23.6|1|
|_SoL_ (Digital Number)|140.0|201.0|44|_SoL_(Digital Number)|140.0|354.0|153|

When the stable lights data set is examined, most countries display positive growth in _AoL_ and negative growth in _R_ . There is a weak negative relationship in levels (Figure 7) and a somewhat stronger negative relationship in growth rates (Figure 8). This outcome is not surprising. For all but a handful of rapidly growing countries, the periphery of an expanding _AoL_ is not as bright as the center. Thus, _R_ must fall because _R_ = _SoL_ ÷ _AoL_ .

<!-- Start of picture text --> Figure 7: AoL and R in Logged Levels Figure 8: AoL and R in Log-Differences<br>16 200<br>14 150<br>12 100<br>10 50<br>8 0<br>6 -50<br>4 -100<br>1.6 2.0 2.4 2.8 3.2 3.6 -80 -60 -40 -20 0 20 40 60<br>ln(R) Dln(R)<br>ln(AoL) Dln(AoL)<br><!-- End of picture text --> Several characteristics of the night-time lights products have the potential to confound accurate measurements in levels and in growth rates. These include omitted observations, a lack of sensor calibration, blooming, sensor saturation, and thresholding (censoring) of low radiance pixels. These are reviewed below.

Omitted observations occur because of weather related constraints. In theory, the number of observations per country (or region) per year could be as high as 365 a year and 366 in a leap year. In practice, as noted above, the number of observations per country per year varies between 20 and 100. Fewer observations will reduce the precision of the annual averages. The northern latitudes and tropical areas close to the equator tend to have more cloudy days.

Sensor inaccuracies occur because the DMSP-OLS sensors are not calibrated over time. The US Airforce satellite operators frequently adjusted the sensitivity (gain) of the sensors in order to best detect moon-light
# **Box 1:   License Renewal Regime and Process:  Significant Determinants of the Operator’s Attractiveness and Value for Current or Potential Investors.**

In a note for potential investors in Sri Lanka Telecom, investors are advised to consider the risks relating to the company, and specifically identify as a significant risk, the lack of clear license renewal guarantees: "The Company’s telecommunications business is subject to governmental regulation regarding licensing, competition, and costs and arrangements pertaining to interconnection and tariffs. Changes in laws, regulation or governmental policy affecting the business activities of the Company could adversely affect its financial condition and company’s operational results [...]. The initial term of the license expires on 8 August 2001. Neither the Telecommunications Act nor the company’s license provide for the renewal of the license. Accordingly, the company may be required to comply with the new license issuance requirements provided by the Telecommunications Act at the expiration of the Company’s license. If the company fails to renew its license on its existing terms or obtain a new license on terms favorable to the company, this would have material adverse consequences for the company’s business, financial condition, results of operation and prospects." (Source: http://www.slt.lk/section_two.pdf) Where the regulators enjoy a discretionary power in areas of license modification and revocation, similar discretion is usually extended to the case of license renewals. In Kenya CCK, the regulator, is empowered to attach to the license any condition it considers appropriate, and to modify the conditions attached even after the license has been granted and the licensee has started its activities.<sup>14</sup> This is a cause for concern, since it reduces the possibility to predict the future returns on private investments. In order to renew their licenses, both telecommunications and radio-communications licensees must follow the same procedure<sup>15</sup> (and pay an application fee) as those

> 14  See http://mkaccdb.eu.int/study/studies/22.doc

> 15  This implies paying an application fee, and submitting a request, plus any information CCK finds appropriate, and releasing a public notice asking for objections regarding the proposed licensing.

- 5 -
Table 3 shows the descriptive statistics of the rescaled variables in which we see that the variability of the data is somewhat "normalized." Table A.2 in the appendix gives the correlation coefficients among these rescaled variables for the MENA sample.<sup>29</sup>

**<u>Table 3.</u>** <u>Descriptive statistics of the rescaled data for the MENA and OECD samples</u>

|SC dimension|Variable<br>||MENA<br>|<br>||OECD<br>|<br>|
|---|---|---|---|---|---|---|---|
|||Obs.|Mean|Sd.|Obs.|Mean|Sd.|
||rf_pp<br><br>|266|0.49|0.22|532|0.77|0.14|
|Protection (P1)|rf_rp<br><br>|266|0.46|0.16|532|0.79|0.10|
||rf_rp<br><br>|266|0.47|0.16|532|0.81|0.14|
||rf_ff<br><br>|263|0.10|0.06|532|0.27|0.11|
|Provision (P2)|rf_mf<br><br>|265|0.49|0.21|532|0.55|0.09|
||rf_ff<br><br>|249|0.11|0.10|531|0.50|0.17|
||rf_ifpi<br><br>|244|0.01|0.01|438|0.02|0.10|
||rf_pvp<br><br>|266|0.30|0.11|532|0.83|0.11|
|Participation (P3)|rf_mp<br><br>|266|0.35|0.17|532|0.70|0.19|
||rf_di<br><br>|247|0.28|0.13|494|0.80|0.11|
||rf_rp<br><br>|266|0.28|0.19|532|0.25|0.12|
|Controls|rf_iu<br>|266|0.28|0.19|532|0.20|0.11|
|<br>|rf_hdi<br> <br>|209<br> <br>|0.66<br> <br>|0.16<br> <br>|418<br> <br>|0.89<br> <br>|0.08<br>|

29 We note that these correlation coefficients are generally high, which suggests that the three dimensions used to represent an SC may be relevant, an aspect of the data that we further explore in the next section. 30Note, as pointed out by Cámara and Tuesta (2018), that PCA is robust to information redundancy.

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100 grams per child on average, and hemoglobin reductions of 1.6 g/L. However, a diarrhea reduction of 2.1 percentage points, when taken at face value, can be considered biologically important, although this measure is only marginally significant (statistically) and does come with reasonable concerns linked to selfreporting, making it less reliable than the biometric markers.

The implications for policy and program implementation require some extrapolation from the data and should therefore be considered only as suggestive. If the primary objective of the policymaker is to reduce child morbidity and mortality, then intervention intensity needs to be considered carefully since there is a tradeoff between going to scale and delivering interventions effective enough to generate health impacts through TSSM and HWWS behavior change campaigns. Exactly how to increase intensity is beyond the scope of this study. Broadly this could focus on doing more of the same interventions more intensively (e.g. multiple follow ups within the same villages) or including a broader range of tools (e.g. sanitation subsidies, animal fecal containment practices).

If, on the other hand, the primary objective for policy makers is increased coverage of improved practices and materials in their own right (reducing open defecation or increasing handwashing with soap), then TSSM seems to be an effective tool to deliver on these objectives, whereas changing behavior through HWWS remains a challenge. In summary, the evidence would suggest that, whichever objective the policymaker seeks to achieve (increased coverage or health improvements), HWWS should be a complementary rather than stand-alone activity.

# **10. Conclusion**

Three systematic reviews over the past decade have shown that handwashing with soap consistently reduces diarrhea by between 39% and 47% (Fewtrell _et al_ , 2003; Curtis & Cairncross _,_ 2005; Ejimot-Nwadiaro _et al_ , 2008). This evidence, however, comes mostly from small-scale efficacy trials exploring proof of concept, or matched studies with high levels of handwashing compliance. While useful, it is not clear how scalable these interventions are, and thus how much national policy can and should draw from this evidence when designing at-scale programs where handwashing compliance is more limited. One outlying piece of recent evidence using emotional drivers in India has found sustained changes in handwashing practices at scale but does not measure resulting health impacts (Biran _et al._ , 2014). Evidence on rural sanitation is less comprehensive, and a series of ongoing evaluations will provide evidence on sanitation efficacy in the coming years. Moving beyond efficacy trials, this study explores, for the first time, what can be achieved _at scale_ through independent and combined sanitation and handwashing campaigns and builds on evidence from the global WSP program looking at the impacts of government-led, at-scale interventions in Vietnam and Peru (HWWS), and India and Indonesia (TSSM). Consistent with the evidence from these evaluations, we find significant improvements in intermediate outcomes resulting from the TSSM intervention (latrine 30
in Alesina et al (2003). Neither of these two measures significantly predict Property Rights protection. These results confirm that its ethnicity of the countries, where ethnicity is based on a combination of racial and linguistic differences, rather than purely linguistic or religious differences that explain Property Rights protection.

In Panel D, we explore an alternative measure of Property Rights protection used in the literature, Risk of Expropriation. The Risk of Expropriation index is the Political Risk Services’ assessment of the protection against government expropriation in the country and is scaled 0-10, where higher scores mean less risk of expropriation of private foreign investment by the government. Using these variables instead of the Property Rights variable does not alter our main results. Ethnic Fractionalization still has a direct effect on the dependent variable except when we drop African countries when Latitude has a direct effect on Property Rights protection.

Acemoglu and Johnson (2006) distinguish between determinants of property rights institutions and contracting institutions, and suggest that while the endowment view determines property rights, legal origin determines contract enforcement. In Panel E, we use the three contract enforcement variables used in their study to see if we also observe these differences - Legal formalism from Djankov et al. (2003) measures the number of formal legal procedures necessary to resolve the simple disputes of collecting on an unpaid check or evicting a non-paying tenant; Number of Procedures is the number of formal procedures involved in registering a new business; and Procedural complexity is an index varying between 0 and 100 where higher values indicate more complexity in contract enforcement procedures. Number of Procedures and Procedural Complexity are from the World Bank’s Doing Business database.

25