rafmacalaba/datause-encoder-data huggingface dataset:
The available time series between 1961 and 2010 is relatively short in terms of number of observations ( _N_ = 50). A cross country analysis with a particular focus on low income countries is handicapped by data limitations. However, in the case of developed countries, longer time series with higher resolution, e.g. quarterly, or monthly, are often available and should be preferred for the gains in statistical significance.

First, we present the results of the wavelet analysis of the actual Output, Consumption, and Investment levels. Then, the same analysis is repeated for the growth rates of the latter components. In both cases a MODWT described in Section A.1 using the Haar filter is performed. Each level captured the changes in, for example Output, and Output growth at dyadic scale.

# **4.1 Growth Analysis**

This section introduces a time and frequency decomposition of trending data by dyadic levels. We perform the wavelet analysis of the Output levels for a set of five countries. Figure 6 shows the MODWT based scalogram of the GDP per capita levels, which decomposes the changes in GDP per capita levels by time and frequency dimensions. The horizontal axis shows the time dimension, whereas the vertical axis denote the wavelet scales that identify the frequency categories: The first scale (1) shows the frequency intervals between 2 and 4 years,whereas the second scale (2) shows 4-8 years, the third scale (3) shows 8-16 years, and the fourth scale (4) shows 16-32 years. The shades of colors, on the other hand, correspond to changes in the GDP per capita at different frequencies: green shows an increase in GDP per capita, and red shows a decrease. The legends on the right hand sides of the figures map the different shades of green, yellow, and red with the corresponding magnitudes of change in each case. e.g. the greener the color is, the more positive is the change. The white areas around the edges are the omitted values that were affected by the wavelet circularity assumption (see Section A.1).

The figure for Brazil exhibits two economic downturns in the time frame of this analysis. A reduction in GDP per capita in the first half of the 1980s was followed by a short recovery, and then by a second decline in late 1980s and early 1990s. Our decomposition shows that the second downturn had stronger long-term impacts than the first one as shown in the decline of Output on the fourth scale (approximately 30 13
**Table 2: Patent Output by Country**

|**Rank in**<br>**2003**|**Number of**<br>**Patents in**<br>**2003**|**Share of**<br>**All Patents**<br>**in 2003**|**Country***|**(Rank**<br>**in**<br>**2002)**|**(Number**<br>**of patents**<br>**in 2002)**|**(2002 to 2003)**<br>**(Change in**<br>**Number of**<br>**patents)**|
|---|---|---|---|---|---|---|
|1|37,250|19.9%|Japan|(1)|(36,340)|(2.5%)|
|2|12,140|6.5%|Germany|(2)|(11,957)|(1.5%)|
|3|6,676|3.6%|Taiwan|(3)|(6,730)|(-0.8%)|
|4|4,132|2.2%|South Korea|(6)|(4,009)|(3.1%)|
|5|4,127|2.2%|France|(4)|(4,421)|(-6.7%)|
|6|4,031|2.2%|United<br>Kingdom|(5)|(4,196)|(-3.9%)|

Source: U.S. Patent and Trademark Office 45
ambitious round to date of the ICP and entailed a substantial improvement in data quality for estimating PPP’s. For the purpose of measuring global poverty, an important feature of the 2005 ICP is that it did a much better job of collecting the prices needed to measure living costs. Reliable price surveys are quite difficult to do, particularly in poor countries where non-traded goods are a large share of spending. The new surveys done for the 2005 ICP used far more elaborate product descriptions to help identify comparable goods, so that we do not make the mistake of judging people to be better off because they consume lower quality (and hence cheaper) goods. However, there are also a number of concerns about the 2005 ICP round in this context.<sup>25</sup> These include a likely “urban bias” in the price surveys for some countries and the fact that the ICP is designed for comparing national accounts aggregates rather than poverty measurement.<sup>26</sup> Third, we use our own compilation of 675 household surveys for 115 countries; the latest survey rounds cover a total of 1.23 million randomly sampled households. Chen and Ravallion (2008a) provide a listing of the countries and years; further details can be found in the _<u>PovcalNet</u>_ site. The surveys were mostly done by governmental statistics offices. We have estimated all poverty measures from the primary (unit record or specially commissioned tabulations) survey data. Households are ranked by either consumption or income per person; we have preferred consumption, which is available for about 60% of the surveys. The distributions are weighted by household size and sample expansion factors. Thus our poverty counts give the number of people living in households with per capita consumption or income below the poverty line. Interpolation methods (described in Chen and Ravallion, 2004, 2008) are used to line up the survey-based estimates with the reference years at three-yearly intervals over 1981-2005.

Figure 1 plots the national poverty lines for developing countries against private consumption per capita from the National Accounts, both converted to international $’s using the 2005 household consumption PPP from the ICP. We see that the national poverty line tends to rise with mean consumption, which we call the “economic gradient.” The least squares estimate of the elasticity of _Z_ to _M_ is 0.655 (with a t-ratio of 13.68, based on a robust standard error).<sup>27</sup> This is significantly less than unity (t=7.21). So these data are not consistent with strongly

> 25 For an overview of the issues in constructing PPPs see Deaton and Heston (2008). On the impacts of some of the methodological choices on global poverty measures see Ackland et al. (2008).

> 26 China is an important example of this urban bias; for further discussion and a description of how we have attempted to correct for this bias see Chen and Ravallion (2008b).

> 27 The estimate is also robust to outliers; a median quantile regression gave 0.647 (t=9.57).

12
but also an area where infrastructure (especially roads) has improved in recent times (after the genocide in Rwanda). This has opened up the Kagera area, including the introduction of information about living standards elsewhere.

While the Tajikistan and Guatemala surveys were entirely verbal, in the case of Tanzania, the survey teamed used a paper diagram of the “ladder of life” onto which respondents placed blocks for each of the household vignettes and their own household. This was developed after piloting showed that verbal-only implementation did not work well. Half-steps were also allowed (scoring at, for example, 2.5).

In Table 1 we compare the pre- and post-vignette responses of the household’s subjective welfare. While there are some off-diagonal elements, the correspondence is quite strong. Most respondents place themselves in the same position in the pre- and post-vignette. (The footnote to each panel of the table gives summary statistics on the strong correlation in the cross-tab.) In all three cases, the modal responses are around the second or third rung of the ladder. Notice that very few respondents put themselves on the top rung of the ladder; pre-vignette the proportion is only 0.2%, 0.1% and 0.4% for Tajikistan, Guatemala and Tanzania respectively. This could well reflect an unwillingness of the rich to reveal their true economic position.

# **3. Subjective poverty measures**

The vignettes allow us to make explicit at least a sub-set of the characteristics that define “poverty.” This is in contrast to the standard approach to measuring subjective poverty in which no characteristics are explicit.

# _3.1 Theoretical representation_

Let _SWi_ denote the subjective welfare of respondent _i_ in a sample of _N_ respondents. This is a continuous but latent variable. The survey provides an ordinal response<sup>_R_</sup> _i_<sup>on subjective</sup> welfare, which is assumed to be increasing in _SWi_ as follows: 8
**Figure 8: Gender gaps in property ownership are not closely related to GDP per capita**

<!-- Start of picture text --> Property<br>5 6 7 8 9<br>log GDP per capita<br> sole ownership sole and joint ownership<br>60<br>40<br>20<br>0<br>-20<br>Male ownership (%) - female ownership (%) -40<br><!-- End of picture text --> _Note:_ Data for 28 countries from 2010-16. Population aged 20-49 years. GDP per capita in constant 2010 US$. _Source:_ DHS and WDI.

# **3.4 Patterns within countries**

As discussed in section 2.1, property acquisition is often related to life events, especially inheritance and marriage. Figure 9 shows, for the 16 most populous countries, gender gaps in property ownership by marital status.<sup>22</sup> In all countries, gender inequality in sole property ownership is largest for the married population. These gaps among married men and women tend to decline if joint ownership is included, but remain above 40 percentage points in the West African countries Niger, Nigeria and Burkina Faso. There are no distinct pan-African patterns for other demographic groups. In Ghana, Mali, Mozambique and Zambia, gender gaps for the never-married population are small, whereas in Burkina Faso, Kenya and Uganda they amount to 25 percentage points or even higher. The latter group of countries (and Niger) also have large gender gaps for the population currently widowed/separated.

**Figure 9: Gender gaps in property ownership are linked to marital status**

<!-- Start of picture text --> Sole ownership of property Sole and joint ownership of property<br>NGA ETH COD TZA NGA ETH COD TZA<br>755025 13 53 27 17 30 2 11 33 18 12 52 6 755025 18 47 33 21 3 6 12 14 18 14 15 5<br>0 0<br>KEN UGA GHA MOZ KEN UGA GHA MOZ<br>755025 25 48 23 31 53 27 5 39 22 0 41 755025 29 14 24 33 22 26 8 25 29 4<br>0 0<br>-0 -2 -4<br>CIV BFA ZWE MLI CIV BFA ZWE MLI<br>755025 9 42 24 35 60 35 11 22 16 41 11 755025 15 20 27 39 42 30 13 10 16 6 22 17<br>0 0<br>-1<br>MWI NER ZMB SEN MWI NER ZMB SEN<br>755025 11 23 15 55 52 3 32 1 31 24 755025 14 3 19 41 39 3 6 19 9<br>0 0<br>-8 -6 -3 -5 -14<br>never married married never married married<br>widowed/separated widowed/separated<br>Countries sorted by population size. Countries sorted by population size.<br><!-- End of picture text --> _Note:_ Data for 16 most populous countries from 2010-16. Population aged 20-49 years. _Source:_ DHS.

> 22 Co-habiting is combined with married. Divorced is combined with widowed/separated.

17
those found with labor market surveys, we confirm both that they are very high for poorer countries and that they decrease sharply with development, consistent with the literature. However, we also find that across a wide range of measures and functional forms, physical productivity elasticities are very low or negative in developing countries. These findings are consistent because while agglomeration appears to put downward pressure on firm mark-ups, marginal costs rise steeply with density. Hence, the higher wage and TFPR elasticities are largely driven by higher urban costs which, once adjusted for, leave no gains in efficiency of physical productivity with density. The adverse combination of very low efficiency and high marginal cost elasticities appears to diminish on both fronts with development - Chile, the richest country in our sample, shows both positive efficiency and negative cost elasticities. The finding of limited or negative physical productivity growth with urbanization, which we term “sterile agglomeration,” is likely due to the fact that developing world cities are often not being driven by structural transformation and positive agglomeration effects, but more likely by natural resource and aid rents, in-migration driven by a variety of push factors (e.g. poor basic services in rural areas), access to political power, and location near markets given poor transport infrastructure.

# **References**

Ackerberg, Daniel A, Kevin Caves, and Garth Frazer. 2015. “Identification Properties of Recent Production Function Estimators.” Econometrica 83 (6):2411–2451.

Ades, Alberto F.and Glaeser, Edward L. 1995. Trade and Circuses: Explaining Urban Giants. The Quarterly Journal of Economics, 110(1): 195-227 Amiti, M., O. Itskhoki, and J. Konings 2019. International shocks, variable markups, and domestic prices. The Review of Economic Studies.

Atkin, D., B. Faber, and M. Gonzalez-Navarro. 2018. “Retail Globalization and Household Welfare: Evidence from Mexico.” Journal of Political Economy 126 (1): 1–73.

Bairoch. P (1988). Cities and economic development: from the dawn of history to the present. University of Chicago Press Beaudry, Paul & Green, David A. & Sand, Benjamin M., 2014. "Spatial equilibrium with unemployment and wage bargaining: Theory and estimation," Journal of Urban Economics, Elsevier, vol. 79(C): 2-19. Blum, Bernardo S, Sebastian Claro, Ignatius Horstmann, and David A Rivers. 2018. “The ABCs of Firm Heterogeneity: The Effects of Demand and Cost Differences on Exporting.” Unpublished mimeo Bond, Stephen and Måns Söderbom. 2005. “Adjustment Costs and the Identification of Cobb Douglas Production Functions.” Unpublished Manuscript, The Institute for Fiscal Studies, Working Paper Series No. 05/4.

13
receive text-message reminders about completing their job search goals before the end of the week. This _Reminder_ sub-treatment differed for each group. The Workshop group received a general reminder to use the lessons learned in the workshop to identify job opportunities and apply for jobs. The Workshop Plus group received a specific reminder about the job search and application goals, as specified in the job seeker’s plan (for the Peer sub-group, the peer also received a reminder).<sup>8</sup> Participants in the control group did not receive a reminder. The study design is shown in Figure A1 in the Appendix.

# **2.3 Data**

We collected data on study participants through in-person and phone interviews. Once participants arrived to the Labour Centres, surveyors first registered them and confirmed that their ID was among those scheduled for that day. Next, surveyors administered a baseline survey through an in-person interview. The survey takes on average 20 minutes and includes modules on demographic information, work history and current search activities. Baseline data were collected between September and December 2015.

Two rounds of follow-up data were collected, via phone, from all participants five and twelve weeks after the intervention. The attrition rates in the first and second follow-up round was 5% and 15%, respectively, and did not differ by treatment group (Appendix Table A2).

In order to address concerns about mis-reports on the outcomes, we supplement the survey data with an observed measure of job search. This may be particularly relevant in the context of an intervention designed to assist job seekers. Specifically, we sent participants a text message (from a number they could not associate with the research study) in which we

> 8The content of the text-message reminder for each group was the following. Workshop group: “ _Dear XX. Labour Centre Reminder: Apply the steps you learned in the job search workshop about finding job opportunities and applying for jobs_ .” Workshop Plus group: “ _Dear XX (Dear YY). Labour Centre Reminder: Your (Your friend XX’s) plan-making is to search for X hours, find X job opportunities and apply for X jobs by Sunday_ .”

8
one year after the intervention – and an endline survey – approximately two and a half years after the intervention. Figure 1 shows the study timeline. All surveys were administered face to face except for the midline survey, which had to be conducted over the phone due to COVID-19 restrictions. For this reason, data on a small number of the pre-specified outcomes could not be collected at midline (details in Section 2.3.2 below).

# **2.3 Empirical strategy**

## **2.3.1 Treatment assignment**

After baseline data collection, block randomization was used to assign each ELA club (cluster) to one of three study arms. The randomization was stratified by (a) BRAC branch office and (b) community-level depression, categorized as high or low based on whether the PHQ-8 scores were above or below the median severity among screened-in adolescents. This resulted in a total of 16 (8x2) randomization blocks. The three study arms are:

1. **Control group (36 clubs):** ELA clubs to operate as usual, with no other intervention introduced until the completion of the study.

2. **IPT-G (35 clubs):** ELA clubs to operate as usual, with a 14-week IPT-G intervention delivered by ELA club mentors trained and supported by SMU staff.

3. **IPT-G+ (35 clubs):** Same as the IPT-G intervention arm, followed by a one-time, lump-sum, unconditional cash transfer, announced and delivered after the end of therapy.<sup>14</sup>

## **2.3.2 Outcomes**

The outcomes of interest are divided into two main categories: those relating to mental health and those relating to human capital accumulation. We registered a set of primary and secondary outcomes within each of these families of outcomes (Tables 1 and 2, respectively).<sup>15</sup> Table 1 shows the mental health outcomes. The primary mental health outcomes consist of two binary indicators: (i) having a Patient Health Questionnaire 8 (PHQ-8) score _≤_ 4, which is indicative of showing no or minimal depression (Kroenke et al., 2009); and (ii) having a General Health Questionnaire 12 (GHQ-12) score _<_ 3, which indicates one is not

> 14The study design deviates from a classical 2x2 factorial design as there is no ‘cash only’ treatment arm. While a 2x2 factorial design would have allowed estimating the marginal impact of therapy over cash alone, a cash-only arm was not included – given that cash transfers were not something BRAC Uganda was considering to add to their adolescent programming.

> 15The JDE Registered Report can be accessed at `https://afosterri.org/jdepreresults/ sample-page/` .

10
# **Table A13: Simulated effect of temperature on poverty and inequality**

**_Panel A: Poverty_**

|||SSP1-1.9|SSP1-2.6|SSP2-4.5|SSP3-7.0|SSP5-8.5|
|---|---|---|---|---|---|---|
|Temperature|Mean|1.400|2.000|3.000|4.100|5.000|
||Upper|2.200|2.900|4.300|6.200|7.400|
||Lower|0.900|1.300|2.100|3.000|3.800|
|Increase in poverty rate $1.90|Mean|0.749|1.070|1.605|2.194|2.675|
||Upper|1.177|1.552|2.301|3.317|3.959|
||Lower|0.482|0.696|1.124|1.605|2.033|
|Increase in poverty rate $3.20|Mean|1.736|2.480|3.720|5.084|6.200|
||Upper|2.728|3.596|5.332|7.688|9.176|
||Lower|1.116|1.612|2.604|3.720|4.712|
|Increase in poverty rate $5.50|Mean|2.811|4.016|6.024|8.233|10.040|
||Upper|4.418|5.823|8.634|12.450|14.859|
||Lower|1.807|2.610|4.217|6.024|7.630|

_Notes:_ Data on simulated temperature are from the Coupled Model Intercomparison Project (CMIP6) climate projections. The projection is estimated using the coefficient on the effects of temperature on inequality reported in Columns (2), (4), and (6) (Panel B) of Table 1.

**_Panel B: Inequality_**

|||SSP1-1.9|SSP1-2.6|SSP2-4.5|SSP3-7.0|SSP5-8.5|
|---|---|---|---|---|---|---|
|Temperature|Mean|1.400|2.000|3.000|4.100|5.000|
||Upper|2.200|2.900|4.300|6.200|7.400|
||Lower|0.900|1.300|2.100|3.000|3.800|
|Gini index|Mean|0.489|0.698|1.047|1.431|1.745|
||Upper|0.768|1.012|1.501|2.164|2.583|
||Lower|0.314|0.454|0.733|1.047|1.326|
|Theil index|Mean|0.8288|1.184|1.776|2.4272|2.96|
||Upper|1.3024|1.7168|2.5456|3.6704|4.3808|
||Lower|0.5328|0.7696|1.2432|1.776|2.2496|

_Notes:_ Data on simulated temperature are from the Coupled Model Intercomparison Project (CMIP6) climate projections. The projection is estimated using the coefficient on the effects of temperature on inequality reported in Columns (2), and (4) (Panel B) of Table 2.

61
Our estimates of the event-time coefficients _βk_ represent the causal effect of being in a target firm under standard difference-in-differences assumptions. In particular, we require that conditional on covariates, the outcomes of the matched comparison group of firms (and their workers for worker-level outcomes) represent a valid counterfactual for the target firms.

To verify the plausibility of our difference-in-differences assumptions, we look at how similar firms and their workers involved in takeovers are to the matched firms and their workers. Examining the cross-sectional characteristics of the firms and workers (Tables 2 and 3, respectively), we find that four years prior to the takeover, the target firms and their matched control firms and workers appear quite similar. Such cross-sectional comparisons, however, do not guarantee that the outcomes of workers at the target and its matched control firm trend similarly. One particular concern is that even if Table 3 suggests that workers do not differentially select into target firms four years prior to the takeover, they may still be differentially retained or compensated in the interim. We validate the comparability of incumbent workers at target and matched control firms by comparing pre-trends in outcome variables in Section 3.

A final potential concern, given the general equilibrium findings of Arnold (2021), is that there could be spillovers from firms involved in takeovers onto control firms in which case the Stable Unit Treatment Value Assumption would be violated. However, as discussed in Section 2.3, this is unlikely to be a large concern in this context since very few of our events generate a meaningfully large change in labor market concentration. Similarly, we will show that our results are not driven by takeovers that are likely to lead to large changes in product market power, suggesting that our results are not affected by spillovers in product market concentration or price-cost markups (see Section 4.1).

# **2.5 Estimation of Firm Wage Premia**

We are interested in the effect of takeovers on the quality of firms where workers find employment. We estimate a standard Abowd, Kramarz and Margolis (1999) (AKM) model using the universe of Dutch firms for the time period 2006 to 2019. We proxy firm quality by the estimated firm fixed settings with staggered timing of treatment and heterogeneity in treatment effects over time. Further, as demonstrated in Sun and Abraham (2021), these same conditions are present not only when estimating a static parameter but can also affect TWFE estimates of coefficients on lead and lag indicators, which can be contaminated with information from other leads and lags. The authors propose an alternative approach to TWFE which weights cohort-specific treatment effects using predictions of treatment timing to produce an overall average treatment effect.

11
# Table 11: Caregiver handwashing practices and cleanliness

||HWWS after|fecal contact|HWWS before|handlingfood|Anyexposure eve|nt is accompanied by|
|---|---|---|---|---|---|---|
|VARIABLES|Observed|Reported|Observed|Reported|water only|soap and water|
||(1)|(2)|(3)|(4)|(5)|(6)|
|HWWS (β1)|-0.028|0.042|0.016*|0.077***|0.011|0.006|
||(0.030)|(0.033)|(0.010)|(0.020)|(0.018)|(0.009)|
|TSSM ( β2)|-0.056*|0.033|0.009|-0.023|-0.005|-0.010|
||(0.031)|(0.029)|(0.008)|(0.017)|(0.020)|(0.008)|
|HWWS + TSSM (β3)|0.003|0.025|0.016*|0.022|0.017|0.011|
||(0.029)|(0.033)|(0.008)|(0.019)|(0.020)|(0.008)|
|p-values β1= β2|0.297|0.787|0.490|0.000|0.369|0.062|
|for F-test β1= β3|0.224|0.640|0.976|0.010|0.741|0.614|
|β2=β3|0.031|0.813|0.456|0.014|0.275|0.014|
|_Observations_|961|3,307|2,238|3,307|4,126|4,126|
|_R-squared_|0.074|0.106|0.034|0.119|0.035|0.027|
|_Control Mean_|0.127|0.474|0.013|0.150|0.273|0.038|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

47