rafmacalaba/datause-encoder-data huggingface dataset:
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Kotlikoff, L., 2000. The A-K OLG Model: Its Past, Present and Future, in: Harisson, G. W. et al (Eds.), Using Dynamic General Equilibrium Models for Policy Analysis. Elsevier, Amsterdam.

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39
**<u>Table 9:  Robustness: Excluding Tigray</u>**

|||**Excludin**<br>|**g Tigray**<br>||**Urb**<br>|**an areas &**<br>|**excluding T**<br>|**igray**<br>|
|---|---|---|---|---|---|---|---|---|
||**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|Reform* Non-<br>homework|0.277**<br>(2.14)||||0.397**<br>(2.81)||||
|Reform* Paid-<br>work||0.326**<br>(2.18)||||-0.046<br>(-0.38)|||
|Reform* All-year-<br>work|||0.403**<br>(2.02)||||0.328*<br>(1.77)||
|Reform*Education||||0.015**<br>(2.10)||||0.011*<br>(1.75)|
|Constant|0.678***<br>(14.13)|0.684***<br>(14.71)|0.672***<br>(14.8)|0.676***<br>(16.29)|0.407<br>(8.05)|0.426<br>(7.82)|0.410<br>(8.28)|0.119<br>(0.87)|
|N|156|156|156|156|156|156|156|156|
|R sq.|0.72|0.72|0.72|0.72|0.71|0.69|0.7|0.7|

The result from estimating equation 1 for all occupations and all geographic areas but Tigray. The dependent variable is women share in occupation and t-stats are in parentheses. Occupation share in the geographic area, the geographic area-time and occupation dummies were controlled for but omitted from table. Standard errors are clustered at the level of the geographic areas. ***significant at 1%, **significant at 5%, *significant at 10%.

# **VI. Conclusion**

Household heads cannot be regarded as “benevolent dictators” where they always internalize the preferences of other members. Given the difference in preferences, the welfare of all individual members is unlikely to be accorded equal considerations given power asymmetries. While bargaining positions have traditionally been viewed from who controls the most income flowing into the household, the institutional environment can also play a significant role in shaping power and consequently the bargaining position of individuals within households.

In this paper, we analyze the effects of changes in family law on women’s labor market outcomes in Ethiopia. The Revised Family Code 2000 came into effect across some Ethiopian regions and chartered cities shortly after 2000. Using difference-in-difference, we exploit the variation in the timing of the implementation of this new family code by using DHS data sets from 2000 (before implementation) and 2005 (after implementation in five geographic areas). Our results show that the implementation of the new Family Code is associated with a significant 33
followed by emergency expenses (11 percent). The popularity of borrowing for emergencies indicates the common tendency of individuals to use credit to manage financial risks, corresponding to the low penetration of formal insurance in the country (1.26 percent of GDP in 2018).<sup>19</sup> Insurance could have been a more appropriate product for such instances, as it also offers additional benefits (Karlan and Morduch, 2010).

Across the world, common motives cited by consumers for saving range from the need to meet future expenses and unexpected emergencies to investment purposes. The same was found in Sri Lanka, where being prepared for emergency expenses was the main motivation for saving (56.9 percent). Women seemed more risk averse and inclined to save, with 59 percent saving for a rainy day (compared to 54 percent of men).

Globally, the main reason cited by individuals for not having an account is lack of money to use an account (Demirgüç-Kunt et al., 2017). This same rationale may be extended to Sri Lanka. While account penetration is high in the country, 17 percent of respondents mentioned not having made any deposit or transfer to their account in the past year. In terms of savings, the main reason cited by respondents for not saving was that they had no excess cash to save (56 percent). The results were similar for both men and women. This indicates that accounts are being opened by individuals without the intent to make ongoing transactions. Reiterating our earlier discussion, despite the country recently graduating to upper middle-income status, the stark reality is that there is a large proportion of the population who live just beyond the poverty line and hence believe that they do not have sufficient funds to regularly utilize a transaction account.

Usage of digital methods to conduct financial transactions was also found to be low. For instance, only 15 percent of respondents used debit cards for payments at retail stores and less than 10 percent used credit cards for the same purpose. A preference for cash use (42 percent) followed by not having a card (35 percent) were cited as the main reasons for low usage of digital channels. The strong influence of traditional banking practices was evident from the fact that a majority of respondents (85 percent) preferred going to a bank cashier to conduct financial transactions, which nearly half of respondents stated they could access in less than 15 minutes. The enduring popularity

> 19 There are 26 insurance companies in operation (CBSL Annual Report, 2018).

12
the strong selection inherent in world migration patterns. Across all regional groups, a far higher proportion of both college educated and women emigrate to OECD destinations. This selection on skills is most pronounced in the cases of low income and least developed countries from which only 4.1% and 3.5% of emigrants to non-OECD nations have college education as opposed to 38.0% and 34.6% in OECD nations respectively. These patterns are also reflected strongly in the data for 1990.

The second section of the top and bottom panels of Table 3, again reveal strong patterns of selection. The proportions of both the high-skilled and women emigrants are far larger in OECD destinations when compared to non-OECD destinations in 2000; with the exception of women from Latin America and the Caribbean who have a greater tendency to emigrate to non-OECD destinations. This almost certainly reflects intra-regional migration in that part of the world. Examining how this selection between OECD and non-OECD destinations has changed over time - in other words the difference of the differences - also yields interesting results. The selection of emigrants from all regions to OECD nations, in terms of high-skill composition increased between 1990 and 2000, with the exception of those from the GCC and the Commonwealth of Independent States, which over time both sent more highly skilled migrants to other non-OECD destinations. Similarly, although many regions send larger numbers of female migrants abroad in both 1990 and 2000 e.g. the GCC, Latin America and the Caribbean, Sub-Saharan Africa, the Commonwealth of Independent States, India and China, the selection on females increasingly favoured the OECD from all these regions with the exception of the GCC and the Commonwealth of Independent States.

32
- 3 -

In section 2, we present a two-goods model of optimal growth along the lines of the technology argument by dividing capital accumulation in a typical developing country into two parts: the accumulation of traditionally, home-produced capital and the accumulation of imported foreign technology. Revenues from exports are used to purchase foreign consumer goods and foreig technology imports. We formally show that a developing country's economic growth rate increases as foreign technology imports increase. In section 3, we conduct empirical tests of the hypotheses generated by the model, using panel data from developiL, countries. Section 4 concludes.

# 2. AN ENDOGENDUS GROWT0H MOE0M **FOREIGN TECHIOLOGY IMPORTS**

The model developed in this section has its orgins m the neoclassical growth model. The standard version of the neoclassical growthi model developed first by Solow (1956) has the property that the only potential sources of growth are susained exogenous increases in factor supplies (e.g., population growth) and exogenously given technological change (see, for example, Jones & Mamuelli 1989). Thus, except for the possibility of exogenous tecbnical change, ihese models of growth lead to the starting conclusion that there is no per capita growth in the long rn. Rather, depending on inital conditions, there is growth until the capital stock reaches a steady state where things settle down permanenty. The fundamental problem with he neoclassical growth model, as Solow (1970) acknowledged, is that it is not able to explain the wide differences in rates of productivity growth across countries. Faced with the phenomenal sustined growth in per capita output that many developing couries have experienced, the only explanation the model has to offer is exogenous technological change, which sheds no new light on cross-country differences.
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Table A3: Determinants of attendance (self-reported take-up)

||(1)<br>Attended at least|
|---|---|
||one safe space session|
|T1 (safe spaces)|0.456***<br>(0.065)|
|T1 + Leaders|0.466***<br>|
||(0.062)|
|T2 (safe spaces + IGA)|0.615***<br>|
||(0.069)|
|T2 + Leaders|0.479***<br>|
||(0.054)|
|T3 (safe spaces + husbands’ clubs)|0.449***|
||(0.074)|
|T3 + Leaders|0.560***|
||(0.058)|
|Rural|0.086**|
||(0.039)|
|Household size|0.002|
||(0.002)|
|Housedhold head is female|-0.029|
||(0.025)|
|Household head is polygamous|-0.033**|
||(0.015)|
|Household head had no formal education|-0.005|
||(0.017)|
|Household head is Muslim|-0.014<br>|
||(0.024)|
|Adolescent age|0.003<br>|
||(0.003)|
|Adolescent has been married/lived with a husband/partner|-0.008<br>|
||(0.021)|
|Adolescent has gone to school|0.014|
||(0.019)|
|Index decision-making: individual issues (categories 1–6)|-0.000<br>(0.002)|
|Ado has been engaged in any IGA (last 30 days)|0.003|
||(0.019)|
|T1 = T1 + L|.913|
|T2 = T2 + L|.12|
|T3 = T3 + L|.216|
|T1 = T2|.088|
|T2 = T3|.0944|
|T1 = T3|.938|
|T1 + L = T2 + L|.867|
|T2 + L = T3 + L|.307|
|T1 + L = T3 + L|.256|
|N|2,999|
|Control mean|.018|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, OLS regression with standard errors in parentheses clustered at the locality level. IGA = income-generating activity; OLS = ordinary least squares. The dependent variable for column (1) is a dummy for attending at least one safe space session. When the baseline value is missing, it is imputed and a dummy indicating whether the variable is imputed or noted is added as control. This regression controls stratification fixed effects.

36
(4) ττ ττ ττ ττ Under our second assumption, the first term is positive, so the second term must be negative, pp. EE[uu (ττ(1),1) −uu (ττ(0),1)] + (1 −pp). [uu (ττ(1),0) −uu (ττ(0),0)] = 0 , implying that given that welfare in the unshocked state is concave in ττ . ττ uu (ττ(1),0) < uu (ττ(0),0) ττ(1) > ττ(0) Thus, we predict that countries that experience a big shock will allocate more resources ττ Thus, we predict that countries that experience a big shock will allocate more resources to adaptation and protection from future big shocks. This is the hypothesis we aim to test, drawing on both the literature and new evidence across countries. A further implication of our assumptions is that among countries that experience the shock in the second period, those that were shocked in the first period will be better off _ex post_ , in the sense that . (This follows from the fact that is increasing in .) uu(ττ(1),1) > As noted, the investment in adaptation and protection will also depend on the probability uu(ττ(0),1) uu(ττ, 1) ττ The more likely the future shock, the more the country invests in protection from that shock.

# **3. Insights from the literature**

Here we review evidence from two different literatures that can be interpreted as offering partial support for the hypothesis formalized above, though they are not conclusive. The first is literature indicating that residents of a place exposed to war violence tend to be more cooperative and altruistic after the war. The effect also persists over time, regardless of whether the war was experienced by a person herself or by family members or friends; the impact of exposure seems even to increase over time. Societies exposed to wars often were able to return to pre-war levels of institutional qualities and to a high level of trust in a relatively short time. Grosjean (2014) uses survey data from 35 countries in Europe and Central Asia to investigate the effects of exposure to WW2 and more recent conflicts among respondents’ parents and grandparents. Her results show a positive correlation between past war experiences and contemporary participation in collective actions and community groups. At the same time, war seems to diminish trust in politics. A study based on a representative sample of adults from 14 European countries demonstrates that early-life exposure to WW2 increases individual resilience and optimism about 7
neighborhoods. In neighborhoods of higher socioeconomic status, restaurant clients also tend to be locals who can afford the upper-tier eating possibilities or visitors who are from equally wealthy neighborhoods. Commuters from poorer areas tend to buy not from restaurants, but rather from markets or street vendors that resemble the food options available in their socioeconomic group.

# **5 Results**

## **5.1 Descriptive results**

Figure 1 shows the geographical distribution of the four socioeconomic groups, the share of women who consume FAFH on a daily basis and the PDRNI of restricted nutrients in 40 neighborhoods of the Lima Metropolitan Area. The first map shows that neighborhoods with a lower socioeconomic background are predominately situated in the north and south, whereas high socioeconomic status neighborhoods are located in the center. A similar distribution can be found for the share of women consuming FAFH on a daily basis.<sup>14</sup> While, on average, 49 percent of all women ages 15–49 consume FAFH on a daily basis (such as in restaurants; see the appendix, table A1), neighborhoods with the highest shares are mainly located in the north and south of the Lima Metropolitan Area. Furthermore, it becomes apparent that most of these neighborhoods show the highest PDRNI of restricted nutrients, while neighborhoods of higher socioeconomic status in the center of Lima exhibit lower PDRNI values. From a simple visual observation, it appears that especially women in neighborhoods of low socioeconomic status consume FAFH and also face a lower nutritional quality of menus in these food environments.

> 14 The average household expenditures per year for the four socioeconomic groups are based on calculations using data of the ENAHO survey. Neighborhoods of low, mid-low, mid- and high socioeconomic status exhibit average per capita household expenditures of S/. 7,500, S/. 9,000, S/. 11,500 and S/. 20,500 (see the appendix, figure A1). Since the socioeconomic strata was based on quintiles of the household expenditure distribution, we estimated first the five socioeconomic status groups and then collapsed into the four that are used in this study.

10
# Log scale

Source of death data: 2 Official© COVID-19~ deaths Vital statistics (Exp(Expected all cause mortalityy rate rate) © Vital statistics (Excess deaths) <!-- Start of picture text --> Brazil Chile<br>6,400 Cumulative deaths in 2020: Cumulative deaths in 2020:<br>3,200) Official: 201,651 Official: 16,987<br>1,600 Net excess deaths: 167,439 Net excess deaths: 16,567<br>800<br>400<br>200<br>100<br>50<br>20<br>10<br>5<br>Colombia Mexico<br>6,400 Cumulative deaths in 2020: Cumulative deaths in 2020:<br>3,200) official: 50,752 Official: 151,540<br>1,600 Net excess deaths: 50,695 Net excess deaths: 290,909<br>800<br>400<br>200<br>100<br>50<br>20<br>10<br>5<br>Peru USA<br>6,400 Cumulative deaths in 2020: Cumulative deaths in 2020:<br>3,200) Official: 94,064 Official: 392,351<br>1,600 Net excess deaths: 87,370 Net excess deaths: 533,558<br>800<br>400<br>200<br>100<br>50<br>20<br>10<br>5<br>————s>ssss<br>15 45 55 65 75 85 15 45 55 65 75 85<br><!-- End of picture text -->
aggregate level as compared with spatial variation in maximum and minimum temperature across different climate zones. This is as expected since the climate zones are partly based on long-term temperature ranges. We are not sure of the reason for the dip in minimum temperature in the early 1980s, but one possible reason may be a lesser number of meteorological observation stations in the early years. However, this does not affect our analysis of the health outcome variables in the 1990s.

Casual observation of these figures indicates that at an aggregate level of the region, twenty years of precipitation and temperature data do not show any signs of climate change, but the time period is too short to drawn any conclusions.

# **6. Results and interpretations**

6.1. The health impact of extreme weather variables We find that most of the included socio-environmental variables become insignificant when country and climate zone dummy variables are included. In contrast, across both model specifications, the estimated impact of the key weather variables on health remains robust. This is as we would expect given the exogeneity of the weather events.

Moreover, the results confirm our expectations about the health impact of climate variables. Extreme weather events have a significant impact on the incidence of diarrheal disease and the incidence of weight for height malnutrition. The effects are much less evident on the long-term indicators such as the incidence of height for age malnutrition or the under-five mortality rate.

The estimated climate effects on the short-term health indicators, including incidence of diarrheal disease and weight-for-height malnutrition using the parsimonious model specification are presented in Table 4 and the full model is summarized in Table A. The height-for-age and under-five mortality results are also presented in Table A. The regression estimates from the OLS and RE method are of similar magnitude in both cases.

While the pathways through which climate variability and extreme weather conditions affect disease incidence and nutrition are complex and dependent on specific disease etiology and local socio-environmental conditions, our results provide strong evidence on the causal effect of climate conditions on health outcomes in the public health environments under study. Our disease incidence data are an average measure over the year, and it is likely that with more time-specific disease incidence data matched with the monthly rainfall and temperature we would find even stronger impacts.

The results show significant health impacts of extreme weather events in the case of both extreme temperature and excess rainfall in Sub-Saharan Africa. The effects are seen both on diarrheal incidence and weight for height malnutrition, our two short-term indicators of health. We would expect that there would be similar effects on both measures, given the close relationship between diarrhea and weight for height malnutrition.

11