rafmacalaba/datause-encoder-data huggingface dataset:
_15_ exceeded (Figs. 10-14) suggest that both profitability and the relaxation of credit and interest rate policy in the second half of 1990 played a role.

<!-- Start of picture text --> Wage and Wage Norm profitability and the relaxation of credit and<br>Metallurgy interest rate policy in the second half of 1990<br>played a role.<br>SI bn<br>1200<br>/ooo - The role of profits and credits is<br>1000 . / / . captured by the specification: wages =<br>f(cumulative wage norm, gross value added,<br>aoo O increase in credits), where gross value added<br>is approximated by pre-tax profit (wynik<br>ew0 - finansowy brutto) plus labor costs plus<br>depreciation. This specification fits in well<br>400 with the changed milieu facing the worker<br>controlled firms, namely, cost plus pricing no<br>200 - longer feasible, even for so-called<br>monopolies, and the growing importance of<br>al Jani n Fsb WIer Apr Vqf Jul Jul Aug Ilop Oci Nw D P.6 Mur Apr May Jun. Jul Aug Gap Oct Now Doe marketing and profits with the elimination of<br>l mo l subsidies and the gradual hardening of the<br>budget constraint, captured by the inclusion of<br>-w. Wage norm value added. Firms could be regarded as<br>facing a two-step maximization problem: in<br>the first step, value added is maximized; in<br>the second, its distribution among wages,<br><!-- End of picture text --> Figure 10 taxes (income, dividends, PPWW), investments, retained earnings and bonuses is made.

Regressions based on the above specification using a sample of 65 firms yielded the following conclusions on wage evolution (details in Annex IV): (i) The importance of the wage norm in wage determination steadily diminished through 1990, while that of value added ("disposable surplus") steadily grew. This fits the story that firms were initially uncertain about what the ETP would bring, and were therefore guided by the norm; but by the end of the fourth quarter of 1990, were anxious to consume the surplus available, as indicated by a rise in the wage bill value added elasticity from 0.19 in the first quarter to 0.58 for the year as a whole.

(ii) There was a definite and statistically significant credit-wage link in the second half of 1990, albeit weak, the elasticity for the year as a whole being only 0.07. Thus, the relaxation of credit and lowering of interest rates may have contributed to the consumption of the unutilized norm during the second half of 1990; but given the small size of the elasticity, this would have probably taken place anyway.

(iii) First quarter 1991 displays a similar pattern to that in 1990, with starting monthly wages having reached unsustainable levels owing to the consumption of the unused norm in the second half of 1990.
**_<u>Table 7</u>_** _– Marginal effects after Probit - Loans_

|**Dependent variable = 1 if the househol**|**d received a lo**<br>[1]|**an in the last**<br>[2]|**12 months**<br>[3]|[4]|[5]|[6]|
|---|---|---|---|---|---|---|
|**treated**|**0.0086**|**0.004**|**0.002**|**0.002**|**0.007**|**0.007**|
||[0.024]|[0.025]|[0.024]|[0.024]|[0.028]|[0.028]|
|**Village level shocks**|||||||
|Lower yields due to drought or floods||**0.022**|**0.011**|**0.011**|**0.011**|**0.004**|
|||[0.025]|[0.026]|[0.026]|[0.025]|[0.025]|
|Rise in food prices||**0.035**|**0.038**|**0.038**|**0.028**|**0.023**|
|||[0.025]|[0.025]|[0.025]|[0.025]|[0.025]|
|**HH level shocks**|||||||
|Crop disease|||**0.061 ****|**0.061 ****|**0.059 ****|**0.064 ****|
||||[0.030]|[0.030]|[0.030]|[0.030]|
|Livestock died or stolen|||**-0.028**|**-0.025**|**-0.025**|**-0.026**|
||||[0.029]|[0.030]|[0.028]|[0.028]|
|Theft|||**0.082 ***|**0.081 ***|**0.062**|**0.068**|
||||[0.052]|[0.052]|[0.049]|[0.049]|
|House damage|||**0.000**|**0.001**|**0.000**|**0.002**|
||||[0.025]|[0.025]|[0.024]|[0.024]|
|Safety net (food)|||**-0.029**|**-0.029**|**-0.026**|**-0.028**|
||||[0.025]|[0.025]|[0.025]|[0.025]|
|Safety net (agr. inputs)|||**-0.004**|**-0.002**|**-0.020**|**-0.025**|
||||[0.025]|[0.025]|[0.025]|[0.025]|
|**HH assets**|||||||
|Furnitures (mattress, table, chairs)||||**-0.002**|**-0.002**|**-0.009**|
|||||[0.043]|[0.043]|[0.042]|
|Animal (chickens, pigs, etc)||||**-0.026**|**-0.027**|**-0.031**|
|||||[0.034]|[0.033]|[0.033]|
|Agr tools (axe, sickle, hoe)||||**0.012**|**0.004**|**0.008**|
|||||[0.038]|[0.039]|[0.037]|
|**HH characteristics**|||||||
|HH size|||||**0.005**|**0.005**|
||||||[0.006]|[0.007]|
|Chronically ill adults|||||**0.020**|**0.004**|
||||||[0.026]|[0.026]|
|Presence of orphans|||||**0.019**|**0.010**|
||||||[0.027]|[0.027]|
|Deaths in the past 5 years|||||**0.000**|**0.000**|
||||||[0.026]|[0.026]|
|Disable persons|||||**0.003**|**0.007**|
||||||[0.030]|[0.030]|
|Credit in past year|||||**0.210 *****|**0.206 *****|
||||||[0.063]|[0.063]|
|Credit*treated|||||**-0.055**|**-0.057**|
||||||[0.038]|[0.036]|
|**Head of HH's charact.**|||||||
|Female headed||||||**0.018**|
|||||||[0.027]|
|Over 65 yrs old||||||**-0.042**<br>[0028]|
|No education||||||.<br>**-0.012**|
|||||||[0.024]|
|P hlh i h||||||**0062****|
|oor eat status n te past year||||||**. **|
|||||||[0.024]|
|Employment in past year||||||**0.009**<br>[0032]|
|**Nr. of obs.**|749|749|749|749|748|.<br>744|
|**R-sq**|0.01|0.01|0.02|0.02|0.06|0.08|

**Notes:** Table reports estimated marginal effects after probit. Significant at the *** 1% level, ** 5% level, * 10% level. Robust standard errors in squared brackets.

26
To estimate equation (5), the share equation, we divided the ASI data into two subperiods, from 1984 through 1989 and from 1990 through 1998. (We exclude the 1989-90 transition because of inconsistencies in the redefinition of industries, as noted above).

**Table 5: Descriptive Statistics - Manufacturing Industries 1984-98**

||**Annualized C**<br>**Mean**|**hange 1984-89**<br>**Std. Dev**|**Annualized C**<br>**Mean**|**hange 1990-98**<br>**Std. Dev.**|
|---|---|---|---|---|
|Non-manual share<br>in wage-bill|-0.0041|0.0062|0.0031|0.0036|
|Log capital|0.059|0.084|0.131|0.052|
|Log value added|0.043|0.074|0.104|0.045|
|Log imports|0.112|0.146|0.126|0.113|
|Log exports|0.139|0.144|0.115|0.092|

**Note:** Annualized changes for 76 four-digit (ISIC) manufacturing industries for all of India in each period.

Table 5 reports summary statistics. Recall that the 1984-89 period largely preceded economic reforms, and was characterized by a decreased proportion of non-manual (skilled) workers by 0.41 percentage points per year. In contrast, in 1990-98 the proportion of non-manual workers increased by 0.31 percentage points per year.<sup>20</sup> Note that imports and exports also grew rapidly in both periods, but the 1990s stand out in the rapid growth of investment and value added. For instance, capital grew by over 13.1 percent per year and value added by 10.4 percent so that the capital/value added ratio increased by 2.7 percent annually.

The share equations derived demonstrate the relative importance of output growth, capital, imports and exports in demand for skills. Table 6 reports the result of estimating equation (5) using the national data for 76 “4 digit” (ISIC)<sup>21</sup> industries. The left four columns of results report separate estimates for each period, revealing the contrast between periods. In 1984-89 growth in 20
Overall wage work is negatively associated with both enrollment and attendance disproportionately more than household chores, regardless of gender or work intensity. Intensity seems to play a smaller role, but still matters for girls participating in household chores. Moreover, it is important to remember that even though working longer hours might not directly be correlated with enrollment and attendance, it could affect learning outcomes as it reduces the available time to study and do homework as shown in other studies (Emerson, Ponczek and Souza 2014;, Heady 2003;, Patrinos and Psacharopoulous 1997). The analysis highlighted some other important factors that contribute to the probability of being enrolled in school. The education level of the household and who holds the highest level of education, the level of household welfare, income and whether they receive remittances, and having an alive parent positively affect schooling, while the child’s age, the number of siblings, disabilities and having a female head of household negatively affect our outcomes of interest. However, as Myanmar quickly developed after 2015 before being hit by multiple crises in the early 2020s, the findings might not reflect this changing socioeconomic environment and the most recent challenges faced by Myanmar.

# Bibliography

Akabayashi, Hideo, and George Psacharopoulos. 1999. "The trade‐off between child labour and human capital formation: A Tanzanian case study." _Journal of Development Studies_ 35: 120-140.

- Allais, Federico Blanco. 2009. _Assessing the gender gap: Evidence from SIMPOC surveys._ International Labour Organization. International Programme on the Elimination of Child Labour (IPEC), Statistical Information and Monitoring Programme on Child Labour (SIMPOC). Geneva, Switzerland: ILO.

- Basu, Kaushik, and Pham Hoang Van. 1998. "The economics of child labor." _American Economic Review_ 88 (3): 412-427.

Beegle, Kathleen, Rajeev Dehejia, and Roberto Gatti. 2009. "Why should we care about child labor? The education, labor market, and health consequences of child labor." _Journal of Human Resources_ 44 (4): 871-899. https://www.jstor.org/stable/20648923.

- Bezerra, Márcio Eduardo G., Ana Lúcia Kassouf, and Mary Arends-Kuenning. 2009. _The impact of child labor and school quality on academic achievement in Brazil._ Discussion Paper No. 4062. Bonn, Germany: IZA. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1369808.

23
usable and accessible sample frame based on enumeration areas demands a workable and up-to-date national sampling frame in the country. Thus, given the unsuitability of the existing sample frames, this paper develops a national sampling frame based on pre-defined enumeration areas (pre-EAs) in Armenia that conceptually prevails over the existing sampling frames evaluated in this section.

**Table 3: Sampling frames of major surveys in Armenia**

|Surveys|PSUs in the sampling frames|
|---|---|
|Demographic and Health Survey (DHS)|Enumeration areas in the Armenia Population and Housing Census|
|Integrated Living Conditions Survey (ILCS)|Population census enumeration areas|
|UNICEF Multiple Indicator Cluster Survey<br>(MICS,first ever in Armenia)|Currently in search of a suitable sampling frame|

_Notes_ : The PSUs stand for primary sampling units.

# **3 Data and Methods**

## **3.1 Input Datasets**

This section describes the datasets obtained from different sources to establish the national sampling frame and support collecting field data in Armenia.

## **3.1.1 Gridded Population**

The gridded population for Armenia is obtained from WorldPop (Bondarenko et al., 2020) and is based on the 2020 population census or projection-based estimates for 2020. The population data contain an estimated total number of people per grid cell (panel (a) **Figure 2** ). The data at a resolution of 3 arcs (about 100 meters at the equator) can be downloaded in Geotiff format with the projection of Geographic Coordinate System, WGS84. The estimated number of people is in units of one pixel. The values marked with “NoData” denote regions mapped as unpopulated according to the results of the Built-Settlement Growth Model (BSGM) developed by Nieves et al. (2020). The WorldPop gridded population dataset was produced by disaggregating the projected subnational population totals into grid cells using machine learning techniques and a variety of geospatial layers derived from satellite imagery (Stevens et al., 2015).

## **3.1.2 Digitized Features Visible from the Ground**

The outline of enumeration areas (EAs) should be positioned in line with prominent visible ground features to facilitate ground-based data collection. Extensive digitized visible ground features are needed to ensure that the outline of the pre-EA meets the criteria during the automatic creation of the national sample frame in Armenia. Thus, extensive digital features, both natural and man-created, mostly from OpenStreetMap (OSM) are compiled. Panel (b) of **Figure 2** illustrates information obtained from the OSM data, including road networks, waterways, and railways. This data provides modifiable and updatable inputs to enable multiple iterations of EA generation. The figure depicts the input datasets where the two datasets have not been combined yet. The subsequent figures show how the input datasets have been used to divide the country with their estimated total population.

10
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Table B2: FDI concentration dynamics on Mergers and Acquisitions

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>CR5|
|---|---|---|---|---|---|
|Trend _×_ Developing|0.007***|0.119**|0.168|0.190|0.266**|
||(0.002)|(0.054)|(0.104)|(0.122)|(0.119)|
|Observations|1,542|1,542|1,542|1,542|1,311|
|R-squared|0.148|0.772|0.786|0.800|0.810|
|FEs|No|Country|Country, Year|Country, Region-Year|Country, Region-Year|
|Period|||2003-2022||2003-2019|

_Note_ : Each observation corresponds to a destination-year, represented as _it_ . “CR5” refers to the concentration ratio of the top 5 investing firms. The indicator variable, _Developing_ , is assigned the value 1 for countries classified as Low, Lower-Middle, or Upper-Middle income, as per the 2003 World Bank classification. Standard errors are clustered at the destination country level. Statistical significance denoted by; *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

Table B3: FDI concentration dynamics controlling for Share of services in GDP

|VARIABLES|(1)<br>CR5|(2)<br>HHI|(3)<br>CR5|(4)<br>HHI|(5)<br>CR5|(6)<br>HHI|
|---|---|---|---|---|---|---|
|Trend _×_ Developing|0.410**|0.019**|0.375**|0.019**|0.382**|0.017**|
||(0.162)|(0.008)|(0.158)|(0.007)|(0.179)|(0.008)|
|Share of services in GDP (standardized)|||0.830|0.089|1.630|0.162**|
||||(1.074)|(0.058)|(1.589)|(0.080)|
|Observations|2,283|2,283|2,612|2,612|1,981|1,981|
|R-squared|0.835|0.755|0.859|0.795|0.802|0.724|
|Minimum # of|||||||
|Foreign Investors||5|1||1|0|

_Note_ : Each observation corresponds to a destination-year, represented as _it_ . “CR5” refers to the concentration ratio of the top 5 investing firms. Analysis in Columns 1 and 2 excludes destination countries averaging less than 5 foreign investors; Columns 3 and 4 include all destinations without restrictions on the number of foreign investors; Columns 5 and 6 exclude destinations averaging less than 10 foreign investors. “HHI” denotes the HerfindahlHirschman concentration index, computed using the corresponding sample for “CR5”. For ease of interpretation, HHI values are expressed in natural logarithm form. The indicator variable, _Developing_ , is assigned the value 1 for countries classified as Low, Lower-Middle, or Upper-Middle income, as per the 2003 World Bank classification. The analysis includes country fixed effects and (world) region by year fixed effects. Standard errors are clustered at the destination country level. Statistical significance denoted by; *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

28
||Table 8: Aid &|Bilateral TRA|DE – 1<sup>st</sup>stage|||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_Z_<br>_aid_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|0.135***|0.128***|0.128***<br>|0.128***|0.136***|
||(0.008)|(0.008)|(0.008)<br>|(0.008)|(0.008)|
|_Ln(Affected)_|0.062***|0.055***|0.055***<br>|0.060***|0.057***|
||(0.003)|(0.004)|(0.004)<br>|(0.004)|(0.004)|
|_Ln(GDP p.c.)_|-0.197***|-0.205***|-0.205***|-0.149***|-0.161***|
||(0.017)|(0.021)|(0.021)<br>|(0.022)|(0.022)|
|_Ln(Population)_|-0.399***|-0.372***|-0.372***|-0.374***|-0.384***|
||(0.015)|(0.016)|(0.016)<br>|(0.016)|(0.016)|
|_Trade (% of GDP)_|-0.005***|-0.004***|-0.004***|-0.004***|-0.004***|
||(0.001)|(0.001)|(0.001)<br>|(0.001)|(0.001)|
|_Distance_|-0.050***|-0.053***|-0.052***|-0.052***|-0.053***|
||(0.004)|(0.004)|(0.004)<br>|(0.004)|(0.004)|
|_Affinity index_|-0.661***|-0.647***|-0.649***|-0.645***|-0.698***|
||(0.092)|(0.101)|(0.101)<br>|(0.101)|(0.102)|
|_Common religion_|0.130***|0.100***|0.100***<br>|0.103***|0.112***|
||(0.034)|(0.038)|(0.039)<br>|(0.039)|(0.039)|
|_Common language_|0.216***|0.212***|0.211***<br>|0.216***|0.190***|
||(0.048)|(0.057)|(0.057)<br>|(0.056)|(0.057)|
|_Former colony_|-0.227***|-0.226***|-0.225***|-0.226***|-0.208***|
||(0.051)|(0.054)|(0.054)<br>|(0.054)|(0.054)|
|_Ln(GDP p.c.) donor_|0.514***|0.516***|0.516***<br>|0.516***|0.515***|
||(0.017)|(0.018)|(0.018)<br>|(0.018)|(0.018)|
|_Pop. (in mio.) donor_|0.001***|0.001***|0.001***<br>|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)<br>|(0.000)|(0.000)|
|_Trade with donor_|0.136***|0.126***|0.127***<br>|0.127***|0.176***|
||(0.008)|(0.009)|(0.010)<br>|(0.009)|(0.011)|
|_Regulatory quality_||0.018|0.001|||
|||(0.035)|(0.049)|||
|_Trade with donor_×|||0.005|||
|_Regulatory quality_|||(0.010)|||
|_Corruption control_||||-0.191***|-0.515***|
|||||(0.036)|(0.052)|
|_Trade with donor_×|||||0.085***|
|_Corruption control_|||||(0.010)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|Constant|1.209***|0.937**|0.935**<br>|0.397|0.445|
||(0.376)|(0.422)|(0.422)<br>|(0.418)|(0.421)|
|N|26585|22259|22259<br>|22259|22259|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is aid, a dummy that switches to 1 if the donor has made a contribution. ***, **, * indicate significance at the 1, 5 and 10%level, respectively.

22
<!-- Start of picture text --> oe ° ° & &<br>; eerk y é 8<br>® ° ®, ¥<br>8S °<br>° @, e<br>°& o® -<br>bs “S<br>Optical fibre nodes (2009 and 2010) Optical fibre nodes (2009 and 2015)<br>6 3 oJ<br>oe °° 2<br>S2aaah;doraSteals¢¥) °<br>Bim, Meat<br>° Bf a “ °<br>° fear 8a<br>at é<br>Optical fibre nodes (2009 and 2019) © Existing nodes<br>@ New nodes<br><!-- End of picture text -->
- Hasan, R., and K.R.L. Jandoc. (2008), “The Quality of Jobs in the Philippines: Comparing SelfEmployment with Wage Employment”, UP School of Economics, Discussion Paper No.0811.

- Hungerford, T. and G. Solon. (1987). “Sheepskin Effects in the Returns to Education”, Review of Economics and Statistics, 69:175-177.

Lächler, U. (1998). “Education and Earnings: Inequality in Mexico”, Policy Research Working Paper, No. 1949 , World Bank, Washington D.C.

Mincer, J. (1974). Schooling, Experience and Earnings. Columbia University Press, New York.

- Psacharopoulos, G. and H.A. Patrinos. (2004). “Returns to Investment in Education: A Further Update”, Education Economics, Vol. 12, No. 2, August 2004 .

- Sakellariou, C. (2006). “Education Policy Reform, Local Average Treatment Effect and Returns to Schooling from Instrumental Variables in the Philippines”, Applied Economics, 38, 47381.

- Schady, N.R. (2000). “What Education Pay? Non-Linear Returns to Schooling Among Filipino Men”, Mimeo.

Schady, N.R. (2003). “Complexity and Sheepskin Effects in Human Capital Earnings Function: Recent Evidence for Filipino Men”, Oxford Bulletin of Economics and Statistics, 65(2), 171-196.

15
where the shares are sc � P<sup>C</sup> CN(1++�P<sup>CI</sup> <u>)IP+</u><sup>C</sup> PCN<sup>G</sup> CGN<sup>; si�</sup> P<sup>C</sup> CN(1++P�<sup>II</sup> I)+PP<sup>I</sup> I<sup>G</sup> CGN<sup>and, as explained earlier, the</sup> value of s<sup>�If the latter is cho-</sup> CG<sup>depends on the assumptions made about government consumption.</sup> sen so as to optimize the representative household’s welfare, then s<sup>�</sup> CG<sup>= sc</sup> G<sup>�</sup> P<sup>C</sup> CN +PP<sup>GI</sup> <u>CIG+NP</u><sup>G</sup> CGN<sup>.</sup> Alternatively, if government consumption is pure waste, s<sup>�</sup> CG<sup>=0.RecallthatCtandCG;tdenote</sup> private and public consumption per capita, while N<sup><u>It</u></sup> t<sup>denotes investment per capita.Equation (57),</sup> based on domestic absorption, is appropriate also in the open economy case, and allows us to consider a variety of cases with respect to taxation and government spending: 1) wasteful government spending with lump sum taxes (in which case distortionary taxes are set to zero in the productivity equation); 2) optimal government spending with lump sum taxes; 3) wasteful government spending with distortionary taxes; 4) optimal government spending with distortionary taxes.

For the cross country welfare comparisons, our empirical calculations are based on the following equation, which generalizes (26): where the productivity of the reference country (US in our benchmark case) is: while the productivity for country i is calculated as: ~~i~~ Shares and tax rates used in the calculation of log PRt<sup>andlog PR</sup> t<sup>us</sup> +s<sup>arenowthoseofthe</sup> reference country (the US in our basic set of results) and shares are de...ned as in the within case.

# 5.1 Data and Measurement

In order to discuss how our index of welfare changes over time within a country and how it compares across countries, we use yearly data on consumption, investment, capital and labor services for the years 1985-2005 and for seven countries: the US, the UK, Japan, Canada, France, Italy and Spain. We are unable to include Germany in the sample, since data for uni...ed Germany are available only since 1995 in EU-KLEMS. We use two di¤erent data sets to compare welfare within a country and across countries.

To analyze welfare changes over time within a country, we combine data coming from the OECD 24
Table A3: Alternative transformations of the dependent variable

|Dependent variable:|N|umber of|Daesh recrui|ts|
|---|---|---|---|---|
||ihs|linear|log (1+...)|dummy|
||(1)|(2)|(3)|(4)|
|_Panel A: OLS with delegation _|_FE_||||
|Internet access (rate)|0.072<br>(0.028)|0.121<br>(0.047)|0.056<br>(0.022)|0.048<br>(0.021)|
|N|7878|7878|7878|7878|
|R2|0.228|0.207|0.228|0.216|
|_Panel B: 2SLS second stage_|||||
|Internet access (rate)|0.318<br>(0.159)|0.555<br>(0.264)|0.247<br>(0.123)|0.217<br>(0.123)|
|N<br>Kleibergen-Paap test|7878<br>66.183|7878<br>66.183|7878<br>66.183|7878<br>66.183|
|_Notes:_<br>Standard errors are reported <br>following Conley (1999) using a 50 km<br>(log), unemployment (rate), married <br>1995-2010 rainfall (log). Panel A prese<br>Panel B presents 2SLS estimates that <br>access (rate). Column 1 replicates the <br>transformation of the number of Daes<br>the number of Daesh recruits as depen<br>number of Daesh recruits as dependent<br>variable that assumes value 1 if the nu<br>Appendix Table A1 provides complet<br>statistics.|in parenthes<br> distance cut<br>(rate), area <br>nts OLS esti<br> use lightnin<br> baseline resu<br>h recruits as<br>dent variabl<br> variable. Co<br>mber of Daes<br>e defnitions|es and are co<br>of. All specif<br>(log), 1995-20<br>mates, which a<br>g strikes (log) <br>lt in Table 2 w<br> dependent va<br>e. Column 3 t<br>lumn 4 uses a<br>h recruits is str<br> of variables a|rrected for spa<br>cations control <br>10 nighttime lig<br>lso account for <br> as an instrume<br>ith the inverse <br>riable. Column<br>akes the logarit<br>s dependent var<br>ictly positive, a<br>nd Table 1 disp|tial correlation<br> for population<br>hts (log), and<br> delegation FE.<br>nt for internet<br>hyperbolic sine<br> 2 simply uses<br>hm of 1 + the<br>iable a dummy<br>nd 0 otherwise.<br>lays summary|

28