rafmacalaba/datause-encoder-data huggingface dataset:
between respondents’ ability to accurately recall their time use and enumerators’ abilities, and speaks to the importance of proper enumerator training.

# **5. QUALITY OF TIME**

Traditionally, time use research has focused on the _quantity_ of time. Research on _quality_ of time research moves beyond this, and attempts to understand not only how much time people devote to different activities but also how they experience time. For instance, how do people’s emotional states change as they move from one task to another over the course of a day? Some tasks may bring pleasure or happiness, whereas other tasks may be more unpleasant or physically taxing, due to higher energy expenditure or some element of danger or risk. Information about quality of time might also shed light on how labor burdens are shared within households, by making comparisons between individuals within the same household more meaningful. Indeed, women often perform multiple activities at once (e.g., livelihood activities and childcare), which can be more stressful or demanding than engaging in a single activity.

Accurately measuring the quality of a person’s time is, however, a difficult proposition. One approach is to draw inferences about a person’s physical or emotional state based on the activities he or she engages in, though this requires strong assumptions on the part of researchers. For example, Floro (1995) and Floro and Pichetpongsa (2010) draw conclusions about the deleterious effects of overlapping work activities on women’s well-being, based on assumptions about the physical and emotional demands of combining work activities. Similar assumptions are implicit in the workload indicator of the WEAI, which is based on the total amount of time a person spends working: time spent on secondary work activities is given half the weight of primary work activities, regardless of the type or sequence of activities being combined.

# **5.1. EXPERIENCE SAMPLING**

19
firms in African manufacturing sectors are integrating in global supply chains. Unfortunately, as was the case for the second measure, the necessary question to identify suppliers was not included in the surveys for most firms. For comparability, we show the results for all domestic, privately-owned firms, combining both producers and suppliers.

The results in columns (5) and (6) of Table 2 show the average for each country and the change over time. The average fraction of output that is exported is naturally low, averaging only 6% across all countries. Over time, it went down slightly, again confirming that the globalization process that transformed the economies of many other countries has not been a very strong force in sub-Saharan Africa.

Naturally, the distribution of firm-level exports is dominated by firms that do not export at all, which is clearly visible in the top graph of Figure 4. Among domestic firms, only 15% export anything. Among exporters, shown in the dashed line, the fraction of output exported declines gradually, but there is again a mass point at one: 12% of firms that export a positive amount, report to be exporting their entire production. The median value for exporters is 30% and the average is 42%. It is clearly a bimodal distribution with equally important mass points at 20% and 100%.

Averaged over all firms in a country, in the middle graph, the fraction exported ranges from 1% to 20%. The distribution is concentrated among the lowest values. Only in Mauritius does the average reach 20% and for only five countries is the average between 10% and 15.2%.<sup>8</sup> The country-average limited to exporters smooths over the bimodal distribution at the firm level. The average is almost normally distributed with a very high (unweighted) mean around 45%. When firms export, they export a lot. We expect exporters to perform a lot better on most performance dimensions, for example achieve higher productivity (Van Biesebroeck, 2005).

Naturally the average fraction of output exported is larger if we limit the sample to manufacturing firms, but the difference is not huge. Slightly more than half of the domestic firms in the sample are in the manufacturing sector. On this sample, the average export intensity is 8.6%, while it is 4.2% for other firms. Among manufacturing firms, only 22% export, while exporters represent fewer than 10% of non-manufacturers.

The results for the changes in the average export intensity indicate that there twice as many countries where exporting increased as decreased. The average change, across the 33 countries for which we can calculate it, is 3.1 percentage points. The median change is only half as large. The standard deviation is rather large (at 7.9), but this is driven by a single negative outlier. Only 7 of the 33 countries show a change in export intensity of more than 5% either positive or negative.

In sum, we can conclude that only few firms export, firms that do export a lot, but the fraction of exporters is barely growing over time.

> 8 The question on exported output has missing observations for all firms in the surveys of Liberia and Sierra Leone in 2009.

17
Table 6: LPM estimates for regressions as correlations for Vietnam

||**(1)**<br>**R&D**|**(2)**<br>**R&D new**|**(3)**<br>**Patent Nat.**|**(4)**<br>**Patent Int.**|**(5)**<br>**Collab.**|
|---|---|---|---|---|---|
|Index * (D/T)|0.305|‐3.490**|0.006|0.003|0.072|
||(0.148)|(0.043)|(0.914)|(0.882)|(0.149)|
|Constant|0.064***|0.763***|0.008***|0.003**|0.006**|
||(0.000)|(0.000)|(0.006)|(0.024)|(0.034)|
|Observations|20462|1123|20473|20473|20392|
|R2A|0.283|0.485|0.251|0.067|0.172|
|R2W|0.000|0.007|0.000|0.000|0.000|
|RMSE|0.230|0.355|0.079|0.053|0.088|

Note: * p<0.10; ** p<0.05; *** p<0.01, representing p‐values not standard errors. The term (D/T) is computed as the proportion of ICT‐services as input in total input expenditure for each sector. R&D measures whether the firms undertakes any R&D activities Yes = 1 | No = 0; R&D new measures whether the firms has R&D activities that target at an innovation that is new to the market or world Yes = 1 | No = 0; Patent Nat. measures whether the firm has national patents Yes = 1 | No = 0; Patent Int. measures whether the firm has international patents Yes = 1 | No = 0; Collab measures whether the firm is involved in any research collaborations Yes = 1 | No = 0. Robust standard errors clustered by sector. Fixed effects by firm, sector and year are applied. A lag of 2 year is also applied.

28
# Figure 11: LAC: Correlates of investment growth

# A. Investment growth and commodity price movements

<!-- Start of picture text --> Investment growth (percent)<br>15 2001-2011<br>2012-2022<br>5<br>-5<br>-15<br>-30 -15 0 15 30 45<br>Commodity price growth<br><!-- End of picture text --> <!-- Start of picture text --> C. GDP growth<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>2000-21 average<br>8<br>4<br>0<br>-4<br>-8<br>LAC EMDEs<br>2010 2012 2014 2016 2018 2020 2022 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text -->

# B. Growth of investment and credit

<!-- Start of picture text --> Percent Percent<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 Investment growth -10<br>Real credit growth (RHS)<br>-15 -15<br>D. Corporate tax and investment growth<br>in LAC<br>Percent Percent<br> EMDEs corporation<br>30 6<br>tax rate<br>25 EMDEs investment<br>growth (RHS)<br>20 4<br>15<br>10 2<br>5<br>0 0<br>Corporate tax rate Investment growth<br>(RHS)<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Organisation for Economic Cooperation and Development; World Bank.

Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. A. Annual commodity price growth is a simple average of annual changes in the prices, in US dollars, of energy, metals (excluding precious metals), and agricultural commodities.

B. Last observation is 2020. Investment-weighted average growth rates.

C. GDP-weighted average growth rates.

D. Corporation tax is average effective corporation tax from 2017-19. Investment growth is average annual investment growth from 2017-21. Sample contains 27 EMDEs (horizontal lines), with 7 countries from LAC (vertical bars).
for groundwater extraction. Compared with the baseline, agricultural growth declines by 4.1 percentage points in 2011 in the accelerated scenario and slows between 1.1 and 1.4 percent annually during the gradual reform scenario. However, in both cases, agricultural growth resumes due to substitution and adjustment effects. The withdrawal of petroleum subsidies also affects the industrial sector relative to the baseline during the early years of reform. But as in the case of agriculture, growth is estimated to quickly recover and accelerate, initially because of the adjustment process and subsequently because of higher productivity and related new investment opportunities arising after reform, which improve competitiveness. Mainly due to the low substitutability and the domestic orientation of most services, the service sector is the least affected sector and continues to grow modestly during reform.<sup>14</sup> Table 7. Economic Growth and Poverty Impact of the Subsidy Reform

||**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|
|---|---|---|---|---|---|---|---|
|**Baseline**||_Annual ch_|_ange_|||||
|GDP (billion YER)|5.090|4.52|4.30|4.27|4.24|4.20|4.18|
|Poverty|42.8|3.07|3.26|3.22|3.39|3.31|3.22|
|**Reform 1(accelerated)**||_Annual ch_|_ange from_|_base_||||
|**1A**||||||||
|GDP (share)|100|0.00|–0.13|0.13|0.27|0.23|0.22|
|Agriculture|8.3|0.00|–4.05|0.21|0.19|0.14|0.09|
|Industry|38.3|0.00|–1.15|–0.22|0.21|0.17|0.17|
|Services|53.4|0.00|0.79|0.29|0.40|0.35|0.33|
|Poverty|42.8|0.00|3.00|2.91|2.71|2.70|2.21|
|Rural|47.6|0.00|2.91|2.75|2.53|2.61|1.99|
|Nonfarm|50.4|0.00|2.08|2.32|2.06|2.28|1.41|
|Farm|42.0|0.00|4.58|3.62|3.48|3.29|3.18|
|Urban|29.9|0.00|3.23|3.34|3.19|2.94|2.79|
|Compensation required for poor|est one-third of p|opulation u|nder 1A|||||
|Per household (YER)|0|0|18,997|20,405|20,200|19,764|18,886|
|Total(billion YER)|0|1|17|18|18|18|17|
|**1B**||_Annual ch_|_ange from_|_base_||||
|GDP (share)|100.0|0.00|–0.12|0.11|0.24|0.21|0.20|
|Agriculture|8.3|0.00|–3.56|0.18|0.17|0.11|0.07|
|Industry|38.3|0.00|–1.20|–0.26|0.17|0.15|0.14|
|Services|53.4|0.00|0.75|0.26|0.37|0.32|0.31|
|Poverty|42.8|0.00|1.82|1.66|1.72|1.25|1.15|
|Rural|47.6|0.00|1.29|0.98|1.17|0.60|0.54|
|Nonfarm|50.4|0.00|0.34|0.43|0.68|0.15|0.01|
|Farm|42.0|0.00|3.23|2.10|2.15|1.51|1.62|
|Urban|29.9|0.00|3.22|3.44|3.18|2.96|2.75|
|**1C**||_Annual ch_|_ange from_|_base_||||
|GDP (share)|100.0|0.00|–0.04|1.15|0.89|0.91|0.88|
|Agriculture|8.3|0.00|0.26|0.15|–0.16|–0.17|–0.19|
|Industry|38.3|0.00|2.18|1.88|1.44|1.46|1.39|

regions, often to irrigate qat plantations. Water transported by truck is used on 2.3 percent of the total irrigated land, and this share has most likely increased since the time of the census from which these data stem. 14 Table A.5 in the Appendix gives an overview of the effects of both reform options on the main macroeconomic accounts under each investment option.

14
# **REFERENCES**

- <mark>Aaronson, Daniel, Rajeev Dehejia, Andrew Jordan, Cristian Pop-Eleches, Cyrus Samii, and Karl Schulze.</mark> _<mark>The effect of fertility on mothers’ labor supply over the last two centuries</mark>_ <mark>. No. w23717. National Bureau of Economic Research, 2017.</mark>

- <mark>Attanasio, Orazio, and Marcos Vera-Hernandez. "Medium-and long run effects of nutrition and child care: evaluation of a community nursery programme in rural Colombia."</mark> _<mark>The Institute for Fiscal Studies</mark>_ <mark>. (2004): 1-97.</mark>

- <mark>Baker, Michael, Jonathan Gruber, and Kevin Milligan. "Universal child care, maternal labor supply, and family well-being."</mark> _<mark>Journal of Political Economy</mark>_ <mark>116, no. 4 (2008): 709-745.</mark>

- <mark>Barua, Rashmi. "Intertemporal substitution in maternal labor supply: Evidence using state school entrance age laws."</mark> _<mark>Labour Economics</mark>_ <mark>31 (2014): 129-140.</mark>

- <mark>Bauernschuster, Stefan, and Martin Schlotter. "Public child care and mothers' labor supply— Evidence from two quasi-experiments."</mark> _<mark>Journal of Public Economics</mark>_ <mark>123 (2015): 1-16.</mark>

- <mark>Benjamini, Yoav, and Yosef Hochberg. "Controlling the false discovery rate: a practical and powerful approach to multiple testing."</mark> _<mark>Journal of the Royal Statistical Society. Series B (Methodological)</mark>_ <mark>(1995): 289-300.</mark>

- <mark>Berlinski, Samuel, and Sebastian Galiani. "The effect of a large expansion of pre-primary school facilities on preschool attendance and maternal employment."</mark> _<mark>Labour Economics</mark>_ <mark>14, no. 3 (2007): 665-680.</mark>

- <mark>Berlinski, Samuel, Sebastian Galiani, and Patrick J. Mc Ewan. "Preschool and maternal labor market outcomes: Evidence from a regression discontinuity design."</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>59, no. 2 (2011): 313-344.</mark>

- <mark>Berthelon, Matias, Diana Kruger, and Melanie Oyarzún.</mark> _<mark>The effects of longer school days on mothers' labor force participation</mark>_ <mark>. No. 9212. Institute for the Study of Labor (IZA), 2015.</mark>

- <mark>Bettendorf, Leon JH, Egbert LW Jongen, and Paul Muller. "Childcare subsidies and labour supply—Evidence from a large Dutch reform."</mark> _<mark>Labour Economics</mark>_ <mark>36 (2015): 112-123.</mark>

- <mark>Blau, David, and Janet Currie. "Pre-school, day care, and after-school care: who's minding the kids?."</mark> _<mark>Handbook of the Economics of Education</mark>_ <mark>2 (2006): 1163-1278.</mark>

- <mark>Brinkman, Sally Anne, Amer Hasan, Haeil Jung, Angela Kinnell, Nozomi Nakajima, and Menno Pradhan. "The role of preschool quality in promoting child development: evidence from rural Indonesia."</mark> _<mark>European Early Childhood Education Research Journal</mark>_ <mark>25, no. 4 (2017): 483-505.</mark>

30
|Pollutant=<br>Emiss|ions|
|---|---|
|<br>CO2 in Mt|1441|
|CO2from coal (Mt)|315|
|CO2from gas (Mt)|761|
|CO2from oil (Mt)|364|
|PM in kt|2973|
|SO2 in kt|5254|
|CO in kt|5148|
|NOx in kt (only stationary sources)|1679|
|CnHm in kt|2724|
|VOCinkt|1131|

<!-- Start of picture text --> PollutantEmissions=<br>CO2 in Mt 1441<br>CO2 from coal (Mt) 315<br>CO2 from gas (Mt) 761<br>CO2 from oil (Mt) 364<br>PM in kt 2973<br>SO2 in kt 5254<br>CO in kt 5148<br>NOx in kt (only stationary sources) 1679<br>CnHm in kt 2724<br>VOC in kt 1131<br><!-- End of picture text -->
_-7_ the cash flow from the representative new project a2(s) must exceed the sum of the cost of Investment, the expected capital loss on the existing investment, and the deadweight cost of taxation: If the first three terms in equation (9) are positive, the new project will be welfare improving. and a govenment with access to a lump-sum tax to finance the transfer to the nonentrepTreneur should undertake the economic policy reform. Equation (9) indicates, however, that under the benclhmark analysis of free entry of new firms the government will be forced to forego -some potentially welfare-improving policy reforms due to the deadweight costs of tax collection.

# 3. Debt Financing of the New Project by the Existing Firm

As an alternative to the.benchmark scenario, assume that the government constrains the representative entrepreneur to finance the. new project from her existing bank holding company. The entrereneur will then be forced to issue new debt that compensates the representative nonentrepreneur for the risk associated with the old debt of the firm: _5min{r, A,*[r(s) +-a2(s)]}f(s)ds_ = _rf._ (10) where _r8_ is the contracted rate on new debt and _A1_ = is the share of the firm's cash flows that _rf_ (l-w,) _+r._ go to pay off the nonentreprenfeus new debt claim in the event of the firn's default.

The government's-guarantee associated with the policy is te following: _V1(s) = r1(1-w)-min{r 1_<sup>_(1-wj)(l-A1)[a(s)+a_</sup> _2_<sup>_(s)]}_</sup> _=_ max{O. rf(l-wi)-(1-A)[4(s)+a21S)I}
obtain our measure of variable costs, _OPEX*_ : The standard definitions of markups rely on the cost shares framework by Foster et al. [2008] or the production framework by De Loecker and Warzynski [2012] and De Loecker and Eeckhout [2017]. To estimate markups, we primarily rely on the cost shares approach where markups can be computed directly from the data (Markups is simply Sales/Variable Cost, that is _Sales/OPEX*_ ), as discussed in Foster et al. [2008]. While requiring constant returns to scale, the derivation is transparent and is not subject to econometric and optimization challenges faced by alternative methods that rely on explicit estimates of productivity using the control function approach Rovigatti and Mollisi [2018]. Furthermore, this is close to the Lerner Index (measured by the difference between the output price of a firm and the marginal cost divided by the output price) that is widely used in the literature as a measure of market power (see e.g. Grullon et al. [2017], Guti ́errez and Philippon [2017]). However, for consistency with the preceding literature we also detail our estimation of markups using the production function approach in the Internet Appendix. The latter is based on the cost minimization of a variable input of production, without additional assumptions on firm demand or competition. Heuristically, this measure takes the firm’s capital stock as given, and estimates the markup that the firm can charge customers over its variable costs.

Table A1 of the Appendix presents summary statistics of the main variables in our analysis. We drop top and bottom 1% outliers in constructing all our firm-level variables. In addition to the variables discussed above we also use a proxy for firm age which is defined as the number of years since the firm first appears in Compustat following Giroud and Mueller [2010].

The mean _ROIC_ in our sample once we adjust for intangible capital is 13%. By definition, 10% of our sample is classified as star firms. Once we take into account intangible capital, the average markup is 1.31 using the cost shares approach ( _Markups_ ) and 1.221 using the production function approach ( _Markups_ _~~p~~ rodfn_ ). The latter has fewer observations because they are first estimated within each industry necessitating a minimum number of firms in that industry. The average Herfindahl industry concentration is 0.09 and the average firm market share is 0.015.

15
name or enhance their claim to their husband’s land by bearing an heir. This is the mechanism that seems best supported by our data. In what follows, we explore the evidence for this hypothesis and other potential, alternative mechanisms.

# **5.1 Increase in Income**

## **5.1.1 Sons as a pension asset**

As explained above, age gaps between spouses are both common and large in Sub-Saharan Africa, with widowhood being a near-certain event in the later part of women’s lives. Moreover, in patrilineal contexts (such as the ones from which almost all of our data derives) prevailing norms exclude women from property ownership and inheritance. Having a child—particularly a male child—can thus be a way for a woman to reduce her risk of dispossession in the event of her husband’s death. Furthermore, in contexts where formal pensions are non-existent or inaccessible for much of the population, and where the practice is for daughters but not sons to move away upon marriage, women are more likely to rely on their son in old age. Having a son may thus be women’s best strategy to secure long-term economic security.

In line with this hypothesis, Table 6 shows that women without a son increase their fertility, while women with sons do not. Controlling for baseline number of children, female beneficiaries randomly assigned to receive DOT without a son at baseline have 25% and 21% more children in period 1 and 2 respectively, in comparison to the control group. Looking at the magnitude of the coefficient, the entirety of the treatment effect on fertility shown in Table 4 was driven by women without a son at baseline. For Togo PI, this effect is even larger, with treated women having one more child under five on average compared to control women. The test of equality between the effects in each group shows that we can reject the hypothesis of equal impact.

Table 6: Heterogeneity by Son

||||Num|ber of children|||
|---|---|---|---|---|---|---|
||_Eth_|_iopia DOT_||_Togo PI_|_Eth_|_iopia WALN_|
||No son<br>(1)|At least one son<br>(2)|No son<br>(3)|At least one son<br>(4)|No son<br>(5)|At least one son<br>(6)|
|Treatment follow-up 1|0.229**|0.025|1.070*|0.313|0.242|0.282|
||(0.088)|(0.071)|(0.499)|(0.198)|(0.613)|(0.263)|
|Treatment follow-up 2|0.193**|0.050|||0.561|0.055|
||(0.078)|(0.073)|||(0.655)|(0.278)|
|Observations|489|891|130|261|37|141|
|Control group mean|0.917|2.924|0.349|0.489|3.889|3.472|
|p-val—follow-up 1||0.108||0.054||0.898|
|p-val—follow-up 2||0.137||||0.164|

Notes: This table displays the results of an OLS regression of the number of children at each follow-up round on the treatment status in the subgroup of women who had a son at baseline and on the subgroup who had not. Controls include: number of children at baseline, marital status, highest level of school completed, age at baseline, age square, a dummy equal to one if women is household head and household size. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

20