rafmacalaba/datause-encoder-data huggingface dataset:
lower in most countries than it was 20 years ago. The declines in this ratio have been considerable, with declines of 25% to 50% in many countries between their historic peak and the levels observed in 2005. Taken literally, a 25% decline in this ratio, a decline that is quite common across developing countries, implies that a given dollar collected in taxes for secondary schooling from each working-age person could pay for a 33% increase in school pending per youth in 2005, compared to the year of the peak ratio. These ratios are on the decline in almost every developing country, implying that from this perspective the economic circumstances for investing in youth are steadily improving.

# **Conclusions**

This paper has documented the demography of youth in developing countries. Using the medium variant projections of the United Nations Population Division, it is clear that the youth population – the population aged 12-24 – in most developing countries is the largest it has ever been. In a number of countries the youth population is the largest it will be for many decades, and plausibly the largest it will ever be. Four broad patterns describe the timing of the peak in the youth population. A few countries, notably Thailand and China, already experienced the peak in their youth population before 2000, and are on a clear downward trajectory. A large second group, including numerous Latin American countries and East Asian countries, is experiencing a peak between 2000 and 2010. A third group, characterized by somewhat slower fertility decline than the countries in the first two groups, is projected to reach a peak youth population between 2010 and 2030. A fourth group of countries, including most of Africa as well as some South Asian countries, is projected to continue with growth of its youth population until beyond 2030. These countries tend to be the poorest countries in the world, and have been late to experience declines in fertility.

22
Cooper and Haltiwanger (2006). The labor supply side is characterized by a rational expectations optimization problem of workers facing mobility costs and time-varying idiosyncratic shocks. The labor demand side is characterized by the rational expectations intertemporal prot maximization problem of rms facing costs for adjusting their capital stock and time-varying technology shocks. To deal with trade shocks, our model features multiple sectors. To deal with general equilibrium eects and labor market responses, we endogenize equilibrium wages across sectors.<sup>3</sup> We t our model to plant-level panel data and household survey data from Argentina for the 1994-2001. We use the rm-level data to identify the technology and capital adjustment cost parameters that dene labor demand. We use the panel component of the household survey data to identify the labor mobility costs parameters. We recover the structural parameters that characterize the frictions faced by both workers and rms. A major feature of our estimation strategy is the joint estimation of these parameters: rms internalize workers decisions when choosing investment and workers internalize rm decisions when choosing sector of employment. Finally, we use the estimated parameters to compute counterfactual stationary adjustments of investment, capital, labor allocations and wage distributions across sectors following a cut in taris. We use these counterfactual adjustments to carefully assess the implication of imperfect capital adjustment when the economy responds to trade shocks.

We focus on taris on textiles, a major import sector in Argentina that enjoyed signicant tari protection during the 1994-2001 period. In the benchmark simulation, we work with the full elimination of an initial tari on Textiles of 19.4 percent. This reduces textile prices and decreases protability in the sector. Capital gradually declines, as textile rms disinvest, and employment gradually declines, as workers are displaced. The capital stock decreases by 9.41 percent initially and by 25.47 percent in the new steady state. Employment decreases by 2.91 percent initially and by 4.64 percent in the new steady state. These are sizeable impacts.

At the time of the tari cut, the nominal wage in the textile sector goes down in proportion to the initial price decline. Lower textile prices imply a decline in the price index that reduces the cost of living. However, the real wage in textiles decreases on impact by 15.04 percent. Because of the dynamic adjustment of capital and labor during the transition, real wages continue declining gradually. In the new steady state, real wages are 20.32 percent lower than in the initial equilibrium.

> 3This feature is shared by the trade model of Artuc, Chaudhuri and McLaren (2010) but it is a major dierence with the capital adjustment costs models of Cooper and Haltiwanger (2006) and Bloom (2009) in which wages are exogenous.

3
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To overcome these limitations, we also analyze a generalized form of the NDG method, in which different sections of the income distribution are assumed to grow at different rates, following a specific and ad-hoc algorithm. This approach attempts to capture the heterogeneity of growth across individuals or households by assuming that quantile-specific contributions to growth during a known-data period remain similar during the unknown-data period. This assumption is an extension of the “share of incremental income” definition of pro-poor growth studied by White and Anderson (2001). The authors explain that if the share of total income growth (Yt −Yt−1) that corresponds to the income growth of the poor (YtP −Yt−1P ) from time t−1 to t is higher than from t−2 to t−1, the economy can be classified as pro-poor. Instead of studying the contribution of the poor population, which varies over time, we analyze the contribution of each quantile to total growth and assume stability over time. We refer to this method as Quantile Growth Contribution (QGC).

Two main assumptions are required under QGC. First, the method assumes that people with similar welfare aggregates in one period will perform similarly in the next period. That is, we expect that, after ranking the population by quantiles based on welfare, two households within the same quantile in period _t_ will belong to the same quantile in period t+ 1. Second, the method assumes that the economy performs today as it did in the past.

Data requirements needed to implement all these three methods are fairly reasonable and therefore they can be applied in our setting: to nowcast poverty estimates in many countries and years and in a consistent manner. In addition, all these methods have real-life applications and have been extensively used to nowcast and forecast poverty estimates. For instance, the World Bank uses the NDG method to extrapolate poverty estimates from the latest surveys available (Yoshida et al. 2014), while it uses the PE method to produce poverty forecasts based on per capita GDP forecasts (World Bank 2015; Yoshida et al. 2014).

# **3 Analytical assessment of measuring poverty in real time**

# _3.1 Poverty measurement in the world and in LAC_

Since the early 1990s, the World Bank has measured global poverty based on a comparable total household per capita welfare measure and an international poverty line, both expressed in the same purchasing power in all countries of the world (Ferreira et al. 2016). In the particular case of LAC, income is the proxy for welfare most commonly used. Even when consumption is usually preferred 7
degree contingent on congestion. These results are in line with the conjecture that firms in the non-tradable sector are more dependent on local consumer markets, while firms in the tradable sectors rely less on local markets and employ workers who are better able to locate close to their workplaces and/or afford faster transport services.

**Table 4: The Moderating Effect of Urban mobility and Congestion on the Relationship between Urban Density and Labor Productivity in Tradables – Mundlak Estimation**

|**Tradability indicator**|**Manufacturing**|**Exporter**|**Foreign-owned firm**|
|---|---|---|---|
|**Panel A: Lack of Mobility**||**Three-way interaction**||
|Urban density (ln)|0.04 (0.10)|0.06 (0.10)|0.07 (0.10)|
|Lack of mobility|0.01 (0.27)|-0.04 (0.27)|-0.02 (0.27)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.13 (0.06)**|0.16 (0.08)**|
|Urban density (ln) * Lack of mobility|-1.63 (0.48)***|-1.25 (0.45)***|-1.19 (0.45)***|
|Tradability indicator (0/1) * Lack of<br>mobility|0.04 (0.10)|0.48 (0.13)***|0.56 (0.18)***|
|Urban density (ln) * Tradability indicator *<br>Lack of mobility|0.65 (0.22)***|0.71 (0.33)**|0.65 (0.42)|
|**Panel B: Lack of Uncongested Travel**<br>**Speed**||**Three-way interaction**||
|Urban density (ln)|0.00 (0.10)|0.12 (0.09)|0.13 (0.09)|
|Lack of uncongested travel speed|-0.06 (0.37)|-0.06 (0.35)|-0.01 (0.36)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.15 (0.06)**|0.22 (0.07)***|
|Urban density (ln) * Lack of uncongested<br>travel speed|-1.34 (0.57)**|-1.00 (0.53)*|-0.98 (0.53)*|
|Tradability indicator (0/1) * Lack of<br>uncongested travel speed|0.11 (0.12)|0.60 (0.15)***|0.42 (0.20)**|
|Urban density (ln) * Tradability indicator) *<br>Lack of uncongested travel speed|0.38 (0.28)|0.35 (0.42)|0.22 (0.48)|
|**Panel C: Congestion**||**Three-way interaction**||
|Urban density (ln)|-0.07 (0.11)|0.03 (0.10)|0.04 (0.10)|
|Congestion|0.54 (0.62)|0.20 (0.60)|0.13 (0.60)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.23 (0.07)***|0.23 (0.07)***|
|Urban density (ln) * Congestion|-4.10 (1.19)***|-2.85 (1.13)**|-2.74 (1.14)**|
|Tradability indicator (0/1) * Congestion|-0.42 (0.22)*|0.13 (0.32)|0.57 (0.36)|
|Urban density (ln) *|2.14 (0.49)***|1.79 (0.73)**|1.66 (0.85)*|
|<br>Congestion * Tradabilityindicator||||
|Standard errors in parentheses. *p<0.10; **<br>centered. All models include the firm-level c<br>and time fixed effects. Common samples,N|p<0.05; ***p<0.01. Varia<br>ontrols from Table 1, Mu<br>=38,526.|bles included in the interact<br>ndlak correction, and countr|ions have all been mean-<br>y correction, and industry|

22
Most of the surveys of farm production and input data are for the 2002–2003 agricultural year<sup>5</sup> . In this paper, the analysis is undertaken at the farm level. Plot specific data on crops grown is summarized to obtain the suite of crops grown throughout the year. The full dataset revealed 130 distinct combinations of crops. However, some of the combinations were rare, with only a handful of observations. We only examine crop alternatives where there are at least 100 observations. We are restricted to analyzing this subset of the data given that the district specific climate and soil variables place a limit on the number of covariates that can be accommodated in the analytical framework.<sup>6</sup> We therefore do not analyze very rare crop selections. Our primary purpose is to investigate how climate change is likely to affect the crop choice that the majority of farmers make and subsequently how that crop selection affects farm earnings.<sup>7</sup> Using this restricted dataset, the mean and median district-level yield price of each crop is also estimated (see Table 3).

Data on climate are from two sources. Long-term temperature data come from US Department of Defense satellites. The Defense Department satellites pass over every location on earth between 6am and 6pm every day. They are equipped with sensors that detect microwaves that can pass through clouds and detect surface temperature (Weng & Grody 1998). Precipitation data come from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation. The mean annual temperature and precipitation for each country in the sample is shown in Figure 1.

Although monthly climate measures were available, individual months are highly correlated with neighboring months. Previous research indicates it is useful to aggregate monthly data into seasons (Mendelsohn et al. 2004). However, it is not self-evident how to cluster monthly temperatures into a limited set of seasonal measurements. We explored several ways of defining three-month average seasons, starting with November, December and January for winter. Comparing the results, we found that defining winter in the northern hemisphere as the average of November, December and January provided the most robust results for Africa. This

> 5 Data from Cameroon, Ethiopia, Kenya and Zimbabwe were collected in 2003–2004.

> 6 There are in total 394 districts in the sample. This places a restriction on the number of observations that can be in the model, given the district specific variables that we use to analyze the climate sensitivity of crops.

> 7 The results of this research are under way.

8
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31 Table S. 8: Within-cohort weight gain regressions using National Family Health Survey data

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Men|Women|Ev. m. wom.|Wom. ex.1998|Men|Women|Ev. m. wom.|Wom. ex.1998|
|year|0.613***|0.549***|0.538***|0.607***|0.682***|0.536***|0.521***|0.608***|
||(0.009)|(0.003)|(0.003)|(0.004)|(0.008)|(0.003)|(0.003)|(0.004)|
|Cohort FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Height control|No|No|No|No|Yes|Yes|Yes|Yes|
|Observations|180,794|892,509|694,387|811,964|180,794|892,509|694,387|811,964|
|R-squared|0.154|0.159|0.140|0.158|0.331|0.257|0.237|0.264|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Dependent variable is weight in kilograms. Results are the year coefficient from a regression of sample weight on state dummies and cohort (birth-year) fixed effects, so the reported coefficient on (survey) year captures the linear (annual) time-trend in weight gain within cohort. Columns 5-8 include height as a control to (partially) capture attrition related to nutritional status. Columns 3 and 7 restrict the female sample to only ever married women, making the 2005 and 2015 survey rounds comparable to the 1998 survey round. Columns 4 and 8 exclude the 1998 survey round so the estimated trend is more directly comparable to the one for men, who only have anthropometric data starting in 2005.
Chinese exports to the United States will continue to erode the region's exports to the U.S. market.

We apply our framework to the analysis of the potential impact that MFA quota elimination might have on exports to the United States. We use available estimates of the export tariff equivalent of the quotas and our estimated elasticities of substitution to understand the implications of the ensuing relative price changes. According to [Usitc02], the export tariff equivalent of the quota for Chinese apparel sales to the United States were approximately equal to 21 percent. In estimating elasticities in section 4, we assumed all Chinese apparel exports were subject to the export tariff equivalent rate, in addition to the usual duties applied in the United States. With the elasticities in hand, we then ask what the impact of eliminating the export tariff equivalent would be.

Columns 1 and 2 of Table 11 present our forecasts of the impact of quota elimination on US imports. Chinese exports increase by an impressive 40.3 percent, paralleled by falls everywhere else; as a result, US imports grow by a modest 2.2 percent. Latin America is undeniably affected but our forecasts are smaller than the common perception seems to be ---between 2 and 3 percent, except for South America, where it drops by 0.5 percent. Table 11 also shows what our forecasts imply for the change (in percentage points) in each region's share of the US market. China's share rises by 5.8 points, Latin America's falls by 1.7, whereas the rest of the world accounts for the balance.

In order to assess whether our estimates are reasonable, we adopt an alternative strategy to measure the impact of removing quotas on each region's market participation. We employ a difference-in-difference approach in which we compare the change in market shares from 2000 to 2003 in tariff lines that had import quota removed in 2002 (the treatment group), with those in tariff lines that had quotas eliminated in 2005 (the control group). Specifically, let _Shareirt_ stand for region _r_ ’s share of US imports of good _i_ (measured at the 10-digit harmonized system tariff line level) during period _t_ ∈{ 2000, 2003 } . All goods that had quotas removed on January 2002, are _i_ defined as belonging to the treatment group. The control group consists of all _i_ that had quotas removed on January 1st, 2005. We believe the latter is a better control group than using all apparel goods ―regardless of when they had their quotas removed, if they 16
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**Table 5. Expected replacement rates**

<u>(annuity as a percentage of last wage)</u>

||Average|Std. Dev.|P5|P50|P95|
|---|---|---|---|---|---|
|Balanced International|51%|20%|26%|48%|89%|
|Base Scenario|72%|41%|27%|62%|154%|
|BalancedLocal|46%|18%|23%|43%|81%|
|Stylized Chilean Fund|57%|26%|27%|51%|106%|
|Default Chile|53%|21%|27%|49%|93%|
|Stylized Peruvian Fund|68%|38%|27%|60%|141%|
|StylizedEstonian Fund|53%|25%|24%|48%|104%|
|Annuity Immunization|81%|65%|23%|63%|197%|
|Conservative|29%|6%|21%|29%|40%|
|Aggressive|81%|55%|27%|66%|175%|

Source: Authors’ calculations While the PDF provides a useful indicator for evaluating the preferences of the contributors, the utility function provides a more precise indicator of the individual preferences. We proceed to calculate the value of the utility function (equation 9) for each of the investment strategies for different values of the risk aversion parameter (��. As shown in Table 5, the aggressive strategy provides the highest value of the expected utility for different values of the risk aversion parameter.

It is interesting to compare, for example, utility achieved by the Annuity Immunization strategy and the Stylized Peruvian Fund strategy. While the first one provides higher expected utility for low levels of risk aversion, the Stylized Peruvian Fund provides higher expected utility for higher levels of risk aversion. This is basically a consequence of the higher volatility if the returns in the Annuity Immunization strategy.

The main problem with simply using the specified utility function is the fact that it is unable to capture potential tail risk in the probability distribution of the asset prices and consequently is unable to capture the annuitization risk. The recent financial crisis has once again has been a reminder that the probability distribution of equity prices may approximate more a curve with “fat tails.” Despite showing higher utility at higher levels of risk aversion, the Stylized Peruvian fund strategy brings individuals to retirement age with an equity allocation of 45 percent of the assets under management, compared with 10 percent in the case of the annuity immunization strategy.

However, there are no easy solutions. While the annuity immunization strategy has the advantage of reducing the annuitization risk, it implies that individuals would have to live with high levels of volatility in the value of their portfolios during their working life. As shown in the third column of Table 6, the annuity immunization strategy has the highest standard deviation among the ten investment strategies analyzed.

28
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