rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: OLS estimates on number of hours worked in previous week

||(1)|(2)|(3)|
|---|---|---|---|
|Agriculture|-16.51***||-16.06***|
||(0.220)||(0.368)|
|Average reg. road quality||-0.995***|0.444|
|||(0.242)|(0.379)|
|Poor reg. road quality||-0.653**|-2.902***|
|||(0.318)|(0.561)|
|Agriculture x Average reg. road quality|||-1.717***|
||||(0.438)|
|Agriculture x Poor reg. road quality|||3.533***|
||||(0.638)|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Geographic controls|Yes|Yes|Yes|
|Region-year FE|Yes|Yes|Yes|
|Survey month FE|Yes|Yes|Yes|
|Const.|14.47|0.87|14.95|
|Observations|37,245|37,245|37,245|
|R-squared|0.372|0.213|0.375|

Individual controls include: _gender, age, age squared, hh head, marital status, education categories_ . HH controls include: _family size, number of children, family member abroad as an immigrant._ Geographic controls include: _log distance to market, log distance to kindergarten, log distance to health center_ . Robust standard errors in parentheses clustered on household level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 21
respect to the public-private capital ratio, 2, is set equal to 015, close to the value used in Agénor (2011). There is not much evidence regarding the elasticity with respect to time allocated by mothers, 3; we use a value of 07, which is close to the elasticity of child human capital with respect to “time spent developing the human capital of children” by adults imposed by Moe (1998) for a middle-income country, 066.

Regarding health status and productivity, the degree of intergenerational persistence  is set equal to 05, a value that is relatively close to the implicit value used by Osang and Sarkar (2008), 045. The elasticity of child health status with respect to public health services, 1−, is set equal to the same value as theirs, 055. By implication, the elasticity with respect to mothers’ rearing time, , is equal to 045. There are no good benchmarks in the literature to guide the choice of the elasticity of health status in adulthood with respect to the ratio of human capital stocks, , so it is set initially at a relatively low value, 02; some sensitivity analysis is conducted subsequently. Similarly, there is not much information regarding the elasticity with respect to total spending (family plus government cash transfer) per child, , so we set it initially to 06 and perform sensitivity analysis. The elasticity of both male and female productivity with respect to health status is set at  = 08, which is consistent with some of the crosscountry econometric estimates reported in Cole and Neumeyer (2006).

Regarding the government, the effective tax rate on output,  , is calculated by multiplying the average ratio of tax revenues to GDP given in WDI for the years 2004-07, 163 percent, divided (to match the model’s definition) by the average share of labor income for developing countries estimated by Guerriero (2012, Appendix E), 06.<sup>27</sup> Thus,  = 239 percent. The initial share of government spending on health, , is based on the average estimate from WDI for the period 2002-09 and is set at 0052. The initial share of government spending on education, , is also based on the average estimate from WDI for the years 2004-09, which gives 0156. The initial share of government investment on infrastructure, , is set at 0056. This is obtained by multiplying the share of public investment in GDP, estimated by Paiva (2010) at 0021 on average over the past decade, by the inverse of the share of government spending

> 27The estimate used is the corrected measure LS4 proposed by Guerriero, which accounts for the self employed.

24
# **Figure 7. Guatemala: Additional Explanatory Variables of Growth, 1950-2002**

- (a) GDP and Capital Stock (5 percent depreciation), Billions of 1958 Quetzals

- (b) Annual Growth of GDP and Capital Stock (in percent)

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>50 55 60 65 70 75 80 85 90 95 00<br>GDP Capital stock<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>50 55 60 65 70 75 80 85 90 95 00<br>GDP growth Growth capital sto ck<br><!-- End of picture text -->

- (c) Participation of Primary, Secondary and Tertiary Education in Labor Force (share)

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>50 55 60 65 70 75 80 85 90 95 00<br>Primary Secondary Tertiary<br><!-- End of picture text -->

- (d) Life Expectancy at Birth (y ears)

<!-- Start of picture text --> 70<br>65<br>60<br>55<br>50<br>45<br>50 55 60 65 70 75 80 85 90 95 00<br>Life expectanc y<br><!-- End of picture text -->

- (e) Imported Capital Goods/Investment and Military Expenditure/GDP (shares)

- (f) Annual Growth of Terms of Trade and Trade Openness (in percent)

<!-- Start of picture text --> 0.025<br>0.020<br>0.015<br>0.010<br>0.4<br>0.005<br>0.3<br>0.2<br>0.1<br>50 55 60 65 70 75 80 85 90 95 00<br>Imp. capital goods/investment Military expenditure/GDP<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br>10<br>40<br>0<br>30<br>-10<br>20<br>10 -20<br>0<br>-10<br>-20<br>50 55 60 65 70 75 80 85 90 95 00<br>Growth trade volume/GDP Growth terms of trade<br><!-- End of picture text --> _Data sources:_ See Appendix.

-43-
# **Figure 3.2. Spillovers from China**

_Since investment in China accounts for a large share of domestic output and is import‐intensive, its investment growth slowdown has weighed on output growth, both domestically and in other EMDEs._

## **A. Import intensity of China’s investment, exports and B. Share of investment in China’s GDP consumption, 2014**

<!-- Start of picture text --> Percent<br>20<br>15<br>10<br>5<br>0<br>Private Investment Export<br>Consumption<br><!-- End of picture text -->

**C. Response of EMDE output growth to a decline in China’s investment, export and output growth**

<!-- Start of picture text --> Percent of GDP<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2010 2015<br><!-- End of picture text -->

**D. Contribution of China’s investment and non‐ investment movements to commodity‐exporting EMDE growth**

<!-- Start of picture text --> Percentage point Percentage point<br>16-84 percent confidence bands Median 3<br>0.2<br>2<br>-0.2<br>1<br>-0.6 0<br>-1.0 -1<br>-1.4 -2<br>Other<br>-1.8 -3 China's growth excluding investment<br>Investment Export GDP Investment Export GDP -4 China's investmentActual<br>Commodity exporters Commodity importers<br>-5<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics, International Monetary Fund, Oxford Economics, World Input Output Database, World Bank estimates.

C. Cumulative impulse response of weighted average EMDE output growth after 1 year to a 1 percentage point decline in growth in real investment, real exports, and real GDP in China. Investment spillovers based on a Bayesian vector autoregression of world GDP growth (excluding China), the U.S. 10‐year sovereign bond yield, JP Morgan’s EMBI index, growth in the non‐investment component of China’s real GDP, China’s real investment growth, and real GDP growth in the spillover destination group. Oil price is exogenous. Exports and real GDP replace real investment in models that estimate spillovers from exports and output. Sample includes 18 EMDEs from 1998Q1‐2016Q2. Blue bars denote 16th‐84th percentile confidence interval, red dots denote median of posterior distribution.

D. Historical contribution of China’s investment and non‐investment growth based on model used for Figure C. Line denotes unweighted average demeaned GDP growth.

54
population, so the impact on their income is affected more by the relatively lower increase in the returns to capital.<sup>14</sup> Skilled labor is more evenly distributed across income deciles, reflecting the fact that government employees such as researchers and teachers often receive very low wages, and that retirees living on a pension were often skilled workers.<sup>15</sup> Rural households typically gain less than urban households. This is due to the fact that rural households have less education and are therefore classified as less skilled than urban workers in the same income group, and unskilled wages do not increase as much as skilled worker wages.

_Global Free Trade and the Doha Agenda Scenarios._ We see from the results in **c** olumn (4) that the impact of global free trade, removal of export subsidies and reduction in domestic support yields very little difference in the results across deciles. Household welfare in all deciles increases by an amount between 0.1 percent and 0.3 percent of consumption. That is, the results for all deciles falls within 0.1 percent of the weighted average for the entire population (shown previously in table 5) of 0.2 percent welfare increase as a percent of consumption. Similar results apply to the Doha scenario shown in column 5—the weighted average estimated welfare gains for virtually all deciles is within 0.1 percent of the estimated weighted average for the entire population in the respective scenario. The standard deviations for households within the deciles is 0.2 percent of consumption for all but one group.

> 14  Household income in Russia exceeds household consumption for almost all households. The reason is that Russian has a large current account surplus. Consistency between the macro balances and the household data in construction of the Social Accounting Matrix implies that household factor income must be larger than household consumption for most households to allow for the transfer of capital to foreigners as well as to pay for investment. It follows that the change in factor income as a percent of consumption will be larger than the change in factor income as a percent of household income.

> 15 An individual is classified as skilled if he or she has any education post-high school. We defined skills at the individual level. We define labor and capital shares individually, and then aggregated factor shares within the household.

19
that flag supply or demand shocks that may translate into drastic changes in food prices, can help design and implement ex-ante adaptive mitigation responses to protect the well-being of vulnerable households, particularly net food buyers. Social safety nets, a natural policy option to alleviate the unequal effects of inflation and other shocks, are also susceptible to inflation heterogeneity. In many countries, benefit levels are indexed to overall consumer prices. This means that the value of public transfers falls in real terms at times when the benefits are needed the most to offset the uneven impact of inflation on the poor. A similar logic applies to the incidence of indexing pensions or minimum wages to core inflation measures.

Finally, our results show that heterogeneity in realized inflation has implications for the measurement of poverty and income inequality and the formulation of policies to address them. Broadly speaking, poverty and inequality in real incomes are expected to increase when inflation for the bottom deciles is higher than the overall CPI or the inflation experience of individuals from the upper parts of the distribution. The evidence for Turkey in the year following the exchange rate crisis that started in the middle of 2018 and pushed the inflation rate to the highest level in the last decade illustrates this point. Using the inflation realization of the 1st decile (20.6%) to more accurately account for the changes in prices –as opposed to the CPI (19.9%)– results in 209,000 extra individuals living below the poverty line in 2019. In fact, not adjusting the World Bank’s upper-middle-income -poverty line ($5.50 2011 PPP) to take into consideration the inflation experience of the bottom deciles underestimates the incidence of poverty in Turkey in every single year between 2011 and 2020. As evidenced in this paper, accurate estimation of the actual purchasing capacity of households matters not only for the identification of the poor, but also for the effective and efficient design of poverty alleviation policies.

23
**<u>Table 6: Ordered probit regression for land rental market participation</u>**

|**Re**<br>|**ntal markets**<br>|**Sa**<br>|**les markets**<br>|
|---|---|---|---|
|**1**|**2**|**3**|**4**|
|Total land owned<br>0.1150***<br>(0.0360)|0.1060***<br>(0.0375)|-0.1074***<br>(0.0334)|-0.1091***<br>(0.0337)|
|Agricultural ability|0.5882***<br>(0.1911)||0.0630<br>(0.2056)|
|No. of family members on farm<br>0.1322***|0.1356***|-0.0299|-0.0313|
|(0.0398)|(0.0391)|(0.0496)|(0.0501)|
|Head‟s age (years)<br>-0.0118***|-0.0136***|-0.0065*|-0.0066*|
|(0.0039)|(0.0039)|(0.0037)|(0.0038)|
|Head‟s education (years)<br>-0.0077|-0.0115|-0.0116|-0.0119|
|(0.0140)|(0.0139)|(0.0138)|(0.0137)|
|Total value of animals<br>0.0571***|0.0492***|-0.0304***|-0.0315***|
|(0.0121)|(0.0126)|(0.0109)|(0.0112)|
|Non-farm income share<br>-0.2478|-0.0696|0.1351|0.1494|
|(0.1798)|(0.1838)|(0.1869)|(0.1983)|
|Income share from remittances<br>-0.5650***|-0.3805*|0.3125*|0.3313*|
|(0.1983)|(0.2089)|(0.1822)|(0.1992)|
|Community-level migration share<br>-0.0193***|-0.0169**|-0.0092|-0.0089|
|(0.0071)|(0.0073)|(0.0073)|(0.0073)|
|**Lower Bound (rent out/sale to autarky)**||||
|Deed from 1991<br>0.9418***|1.0119***|0.4458|0.4292|
|(comm.. share for sale)<br>(0.3435)|(0.3476)|(0.5890)|(0.5926)|
|Land inherited<br>-0.1290|-0.0913|0.0013|0.0043|
|(0.1998)|(0.1964)|(0.2739)|(0.2756)|
|Community distance to bank<br>-0.0098|-0.0103|-0.0173*|-0.0173*|
|(0.0086)|(0.0085)|(0.0101)|(0.0101)|
|**Upper Bound (autarky to rent in/purchase)**||||
|Share of land with ‟91 deed<br>-0.3162|-0.2684|-0.4278|-0.4478|
|in the community<br>(0.4446)|(0.4455)|(0.7159)|(0.7166)|
|<br>Distance to bank<br>0.0106**|0.0105**|0.0162|0.0161|
|(0.0046)|(0.0048)|(0.0106)|(0.0106)|
|No. of observations<br>1796|1796|1796|1796|

Note: Constants as well as district dummies are not reported. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 26
# **3. METHODOLOGY**

Tanzania’s response to the type of global maize production heterogeneity delineated in the five case-study years must be understood through mechanisms that can control for the substantial year-toyear changes in the global economy, and the frequent changes in Tanzanian maize trade policy that have occurred over the 1971-2001 period. In order to understand the poverty implications of the Tanzania’s responses, the mechanism must also account for the changes in prices and factor incomes that will occur across multiple markets, and other general equilibrium (GE) effects. For example, the Tanzanian maize sector employs about 30% of agricultural labor, or 13% of all workers in the economy. Shocks that affect the demand for labor in the maize sector thus have implications for all industries. Since we are interested in the sensitivity of poverty changes to climate volatility under the different maize trade regimes, we also need to track the impact on non-farm earnings as well as the consumption of goods and services. These variables will be affected directly by the shocks to the maize sector. However, given the importance of maize in the economy, they will also be affected indirectly through changes in other commodity prices and on the changes in wages, all determined in general equilibrium.

A computable GE simulation approach is thus necessary, and we employ a modified version of the GTAP simulation model. In addition to allowing us to estimate the changes in consumer prices and earnings stemming from changes to agricultural productivity due to climate effects, this approach also allows us to examine the additional sensitivity of Tanzania’s economic responses to the historical maize production heterogeneity under alternative trade regimes, without including the additional uncertainties of a forecasting approach.

# **3.1 MODEL DESCRIPTION**

We begin with the GTAP Database Version 6 (Dimaranan, 2006) and use this with a modified version of the standard GTAP model (Hertel, 1997). Maize is included in the GTAP database and model parameters as a component of the ‘Coarse Grains’ composite commodity group. In Tanzania, maize accounts for more than 78% of the total output of ‘Coarse Grains’ and more than 98% of ‘Coarse Grains’ trade. As such, we consider the market and production structure of the Tanzanian ‘Coarse Grains’ sector to be a reasonable approximation of the Tanzanian maize sector. The global database has the additional advantage of reconciling the global input-output and trade data from a range of sources, and benchmarking them to a single representative year; 2001 in our particular case. The reconciled data may 10
to speci fi c countries for political reasons, and/or that certain products are more likely to bene fi t from tariff preferences, due for example to US domestic lobbying, will be controlled by country and industry dummies.

# 4 Empirical strategy

Our empirical strategy consists of estimating a stochastic version of equation (10). Because the endogenous variable, θ, is bound at 0 (no preference) and 1 (full preference) a Tobit estimator is necessary.

When aggregating tariff and trade data from the 8-digit HS level to the 3-digit ISIC level one may introduce some heteroskedasticity due to group aggregation if one believes that the equation to be estimated is determined at the 8-digit HS level and if the number of 8-digit HS tariff lines in each 3-digit ISIC industry is not the same. In order to correct for this potential heteroskedasticity problem, we follow a parametric correction suggested by Dickens (1990). It consists in estimating (10) and then running the error term against the inverse of the number of 8-digit HS tariff lines in each 3- digit ISIC industry. The constant of such a regression provides an estimate of the variance of the industry level component of the error term, whereas the coefficient in front of the inverse of the number of lines provides an estimate of the variance of the tariff line level component of the error term. To obtain asymptotically efficient estimates, one re-weighs each observation at the industry level using these variance estimates.<sup>19</sup> An important problem with the estimation of equation (10) is that the

> 19We alternatively provide estimates with no heteroskedasticity correction.

17
only allows comparisons of weight across cohorts, the availability of multiple NFHS rounds allows for estimation of average weight gain with age for adults _within cohort_ . In Appendix Table S.8, the average weight gain is estimated controlling for cohort (birth-year) fixed effects across survey rounds, with data availability allowing for measurement of average weight gain between 2005 and 2015 for men and women, and between 1998 and 2005 for ever married women. Estimates controlling for height are also reported, which may capture some non-random attrition within a cohort related to nutritional status. The data imply an average weight gain of between 0.5KG and 0.6KG per year within cohort, with a slightly higher rate between 2005-2015 than 1998-2005 for ever married women. A weight gain of 1 kilogram is associated with a caloric surplus of 7700 for adults ((St. Jeor and Stumbo 1999)), so the reported level of weight gain translates into between 10.5 (= 0 _._ 5 _∗_ 7700 _/_ 365) and 12.7 (= 0 _._ 6 _∗_ 7700 _/_ 365) surplus calories per day above TEE and the caloric cost of disease. This suggests that, on average, the Indian (adult) population has been in caloric surplus since at least 1998 and even more between 2005 and 2015, even as official NSS caloric intake estimates using 30-day recall plummeted during the 2000s to reach levels well below the TEE estimates. Given that the analysis above suggests a limited scope for such a large decrease in population TEE, and that most of the improvement in the disease environment occurred before this period, the overall evidence is consistent with the view espoused in (Smith 2015) that the steep decline in official caloric intake estimates beginning around 2000 is likely the result of substantial measurement error.

# **4 Conclusion**

In this paper, population level Total Energy Expenditures (TEE) for India over the 1983-2012 period are quantified using a combination of time-use data, anthropometric data, and the NSS Schedule 10 employment survey. The main finding is that population TEE was fairly flat over this period, rising slightly until 2005 and then declining slightly only in the 2005-2012 period. This finding is driven by two main features of the data. First, household characteristics that tend to predict lower activity levels, such as education, expenditures, and industry/occupation, also tend to be associated with greater height and weight that imply offsetting increases in metabolic requirements. Second, even though the TEE estimates for adults in India fell 17
# **Figure 1:** Households with Access to Computers

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>%<br>KSACHENDL DEUKAZCANPOLUSAMLT AUSMYSFRAHRVJPN AZEROURUSKORBELEGY URYMARARGCHLTUN TURCRIBRAMEXECUDZA JAMPANCOLBOLPER VNMDOMBWAPRYPHLZAF THAIDNSLVHNDGHA PAKINDKENBGDETHRWA<br><!-- End of picture text --> Source: ITU data, author calculations.

28
# Table 4: Dynamic Estimates: Patents on R&D

|**Observations**|**Annual**<br>482|**Five Years**<br>105|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Countries**|46|43|||||||||||
|**Period**|1978-99|1985-99|**Ann**|**ual**|||||**Five **|**Years**|||
|||**Linear**||**E**|**xponential**|||**Linear**|||**Exponentia**|**l**|
||**Levels**|**WG**|**PSM**|**Levels**|**WG**|**PSM**|**OLS**|**WG**|**GMM**|**Levels**|**WG**|**PSM**|
|**Patents(-1)**|0.95 ***|0.62 ***|0.91 ***|0.32 ***|0.41 ***|0.45 ***|0.82 ***|0.26 ***|0.62 ***|0.46 ***|0.47|0.54 ***|
||(0.02)|(0.03)|(0.24)|(0.09)|(0.08)|(0.06)|(0.13)|(0.05)|(0.12)|(0.09)|(0.30)|(0.11)|
|**ln R&D**|0.03|0.12 ***|0.03|1.06 ***|2.64 ***|0.91 ***|0.18 ***|0.23 ***|0.37 **|0.91 ***|0.16|0.88 ***|
||(0.02)|(0.03)|(0.02)|(0.04)|(0.37)|(0.07)|(0.07)|(0.06)|(0.15)|(0.16)|(0.10)|(0.12)|
|**ln US trade**|0.02|0.13 **|0.03 *|0.19 **|-0.24|0.22 **|0.11 ***|-0.06 ***|0.14 **|0.29 ***|1.38 ***|0.28 ***|
||(0.01)|(0.06)|(0.02)|(0.10)|(0.19)|(0.12)|(0.02)|(0.02)|(0.05)|(0.06)|(0.25)|(0.08)|
|**Natural Resources**|0.00|-0.04 *|-0.01|0.09 ***|0.03 *|0.07 **|0.15|-0.19|0.01|0.01|-0.28 ***|0.01|
||(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.04)|(0.11)|(0.13)|(0.01)|(0.01)|(0.06)|(0.01)|
|**ln (Mean Patents)**|||0.04 ***|||0.10||||||0.06|
||||(0.02)|||(0.07)||||||(0.05)|
|**Long-Run Elast. of R&D**|0.71|0.33|**0.37**|1.06|2.64|**0.91**|1.00|0.31|**0.96**|0.91|0.16|**0.88**|
|**Short-Run Elast. of R&D**|0.03|0.12|0.03|0.71|1.56|0.50|0.18|0.23|0.21|0.50|0.09|0.40|
|**Sargan Test (p-value)**|||||||||0.63||||
|**First Order (p-value)**|||||||||0.24||||
|**Second Order (p-value)**|||||||||0.23||||

**Note: The dependent variable in the linear specifications is log Patents. We also include an dummy for those countries with no Patents. Robust standard errors were estimated in all the specifications. The dependent variable in the exponential specification is the count of Patents. The exponential models were computed using EXPEND (see Windmeijer 2000), whereas the linear models were estimated in Stata. For the PSM models we include two lags of the dependent variable in set of instruments.  The GMM system estimator used two lags of the variables and  first differences to instrument. All the specifications include time dummies.**

***Significant at 10%, ** Significant at 5%, *** Significant at 1%**

28