rafmacalaba/datause-encoder-data huggingface dataset:
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33 time-sensitive variables (how long in office, in majority, etc.); electoral rule variables (closed list, plurality/PR); and the ideological orientation of parties. They were resolved largely by establishing and refining the conventions of data collection, such as when a ruler was credited with taking office (in former colonies, upon internal self-government rather than independence) or what exactly constituted an electoral rule (whatever rule governs the majority of seats). There were, besides, extensive consultations on resolution of ambiguities. The results of these discussions are all reflected either in the documentation sheet, in the form of rules that were followed for coding, or in the notes embedded in the corresponding cells in the Excel spreadsheet.

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Electoral rules were more difficult to capture than other institutional variables, and were often not available in the handbooks. For the later period (1985-1995) we took most of our data from the website of the International Parliamentary Union, which reports rules for 1998. When this data matched the institutions described in 1975-1984, we assumed that no changes had been made and filled in the intervening years. In the event that a system changed and then switched back, this introduces errors. Since this assumption was made only when institutions from 1984 matched those in 1998, these cases are limited to very stable democracies. In the case where IPU-1998 did not match earlier data, the IPU data-was only inputted for 1995, and blanks were left for remaining years.

There were a number of alternative sources of data that could have been used. Prominent among these is the Statesman’s Year Book edited currently by Brian Hunter. This is a valuable source, but we rejected it in favor of the above sources for several reasons (recalling that the addition of a single source to the compilation process implied at least four personmonths of additional work). First, although Statesman’s level of detail is excellent in Europe
was obtained retrospectively for the time period starting in 1980. Similar information was obtained on the occurrence of shocks, households' responses to these shocks and their coping strategies.<sup>5</sup> To obtain information on key village characteristics, a village level questionnaire was administered to village leaders, with a significant part of the information being obtained from village books.

# **3.2 Household and village characteristics**

_Income size and composition:_ The households included in the sample are very similar with regard to total or per capita earned income, defined as the sum of wage income and profits (excluding family labor) from agriculture and non-agricultural enterprises. As illustrated in table 1, this figure amounts to between Y 1116 in Guizhou and Y 1468 in Hunan, clearly identifying all of the households in the sample as very poor (table 1).6 Despite some diversification of income sources, agricultural income, i.e. profits from crop and livestock production, still accounts for more than two thirds of earned income, varying from less than 69% in Guizhou to more than 74% in Yunnan. Other characteristics for households in the sample are fairly similar. For example, Guizhou has the largest family size, with 4.5 members per family, compared to 4.13 in Hunan and 4.22 in Yunnan. Village characteristics, as reported in table 2, point not only towards differences in overall size but also to differences in population growth. The mean annual rate of population growth (from 1985 to 2000) was 1.1% in Guizhou and Yunnan, but only 0.4% in Hunan. At the same time, the villages in the sample have by now virtually universal access to electricity.

_Inter-generational links:_ Although overall levels of earned income and household characteristics are similar across provinces, the composition of such income and the extent to which households are able to complement it with remittances differs across provinces. The survey indicates that, for the majority of households, remittances from farmly members who had migrated out and inter-generational transfers are of great importance. For those who receive them, remittances increase non-earned income by more than 50%. With 44% percent of households receiving remittances in the aggregate, regional differences are considerable; the share of households receiving remittances ranges from almost two thirds in Hunan to one third in Yunnan. On the other hand, providing resources for household members who have left the household, mainly for education, constitutes a significant burden - almost two thirds of households provide support for off-spring and for those who do so, the mean amount of support given is equal to 25% of earned income.

## **3.3 Land rights arrangements and preferences**

> **5 Obtaining** this retrospective information proved fairly easy **in** practice

> **6** Even though Hunan **is** much ncher than the other two provinces, **limiting** the sample to the mountaineous part **in** the west of the province **implies** that per capita incomes are very similar

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<!-- Start of picture text --> Rest of EMDE Sub-Saharan Africa<br>110 Projects — Total Investment: US $57.4B (2015) 17 Projects — Total Investment: US $5.2B (2015)<br>Debt Public Equity Debt<br>4.0% 1.5% Bilateral 20<br><!-- End of picture text --> Source: World Bank PPI Database — 7 Feb 2017 "Note: Debt categories sum to 100%, Data excludes telecom, divestitures, and merchants.
snowfall) are observed at most of the same stations over the same period (Dimri and Dash, 2011). In Tibetan Plateau, upward trends were noted in the southern and northern regions while downward trends were observed in the central region for most extreme precipitation indices (You et al., 2008).

Using limited data from the Bhutanese and Nepalese Himalayas, Caesar et al. (2011) observe a statistically significant upward trend in R95n index (annual total precipitation when rainfall is >95th percentile). The Bhutanese and Nepalese Himalayas rate of increase in the R95n index is 82.3 mm/decade compared to 4.68 mm/decade global average and 22.66 mm/decade average for the Indian Ocean region (Caesar et al., 2011). Additionally, comparing remotely sensed precipitation data across all regions, Brookhagen (2010) finds that the Himalaya has almost twice as many extreme events as the Ganges Plain or the Tibetan Plateau, regardless of rainfall amount.

In summary, temperature related climatic extremes appear to be increasing across the Himalayas. General increasing trends also exist in precipitation related climatic extremes in Nepal, Bhutan, southern and northern Tibetan Plateau.

# **2.4 Retreating glaciers, shrinking glacial extent, expanding glacial lakes, and negative mass balance**

# **_Glacier retreat and areal shrinkage_**

Several studies involving field based observations, satellite imagery, and repeat photography have shown that a majority of Himalayan glaciers are retreating. A notable exception is the Karakorum region where some glaciers have shown advancement (Hewitt, 2005). Hewitt (2011) recently outlined important climatic conditions that make Karakoram glaciers different from the rest of the Himalayas. Notable among them include, orographic conditions that enhance precipitation in the source area, an all-year accumulation regime, concentration and role of avalanche, and ablation buffering due to thick debris cover (Hewitt, 2011).

Pandey et al. (2011) analyzed satellite images of 26 glaciers of the western Indian Himalaya from 1975, 1989, 1992, 2001, and 2007, and found all glaciers were retreating (19751989/1992, 1989/1992-2001, and 2001-2007). Pandey et al. (2011) note fluctuating retreat rates 5
noted above, in 2008, to circumvent the constraints of the legal framework in obtaining external funding, CFPA microfinance registered itself as a ‘company-like institution.’ After this transformation, CFPA was allowed to receive loans from commercial banks. To capture changes in portfolio quality and average loan size associated with this switch, we include the variable _Year 2008 and after_ as a control variable. It is a binary variable that equals one in years 2008, 2009, and 2010. Lastly, to control for social and economic differences across branches, we include _Annual income per capita of rural households_ measured at the county level.

It is possible that work experience prior to joining CFPA Microfinance influenced loan officers’ lending behavior. To explore this issue, we estimate the following alternative specification: _Yijt = a0 +a1*(EXPi × GOVi) + a2*(EXPi × FARMi) + a2*EXPit + a3*INCOME +eijt (2)_ In addition to the variables that are included in equation (1), in equation (2) we include two interaction terms ( _Months as a CFPA loan officer × GOV_ and _Months as a CFPA loan officer × FARM_ ) _._ The coefficients _a1_ and _a2_ measure the additional effect of CFPA experience for loan officers with government or farming backgrounds relative to all other CFPA loan officers.

A number of time-invariant unobservable factors could also be correlated with the independent variables of interest. To account for those factors, we exploit the longitudinal nature of our data and include a set of regressions that are estimated with loan officer fixed effects (columns 2 and 3 in Table 6; columns 2, 3, 5 and 6 in Table 7). Those fixed effects are intended, therefore, to summarize an officer’s suitability for the job based on characteristics that we do not necessarily observe.<sup>20</sup> As such,

> based on CFPA data. That said, our regressions do rely on explanatory variables that can be seen as proxies for those concepts. For example, the revenue derived from a loan is tied closely to the likelihood that it is repaid. At the most basic level, loan officers’ selection of applicants that are highly likely to be creditworthy contributes to high repayment rates among loan recipients. Within the pool of applicants deemed creditworthy, the interest rate and maturity of the loan further account for potential risk and provide additional information about the amount and timing of repayment. Regarding costs, many of the administrative costs incurred to support CFPA lending are pooled across loan officers, and thus it is not possible to ascribe them to an individual officer or a loan. Officers’ salaries would be easier to allocate, but we lack the necessary data. We argue, however, that these salaries are likely to fall within a relatively tight band and, in any event, the loan officer fixed effects regressions help account for any differences in salary costs across officers.

> 20 As a robustness check, we also ran regressions that used county fixed effects, and found results very similar to

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nature of the search data. Here, we take several different approaches. First, we apply a simple exponential smoothing model with additive errors to the data. Following the notation of Makridakis et al. as used in Hyndman et al. [2002], this model can be written We choose to fix _α_ = _._ 5. Results typical of this smoothing can be seen in figures 3 and 4. We include both the forecastable part of the series and the unsystematic “surprise” part of the series.

We also tried smoothing the results by applying the Christiano-Fitzgerald (CF) bandpass filter [Christiano and Fitzgerald, 2003]. The CF filter starts from the (false) assumption that the underlying data obeys a unit root process. Using this assumption, the CF filter provides an approximation to an optimal band-pass filter as follows where _Bj_ =<sup>sin(</sup><sup>_<u>jb</u>_</sup><sup><u>)</u></sup> _πj_<sup>_−_sin(</sup><sup>_<u>ja</u>_</sup><sup><u>)</u></sup> _, j ≥_ 1 and _B_ 0 =<sup>_<u>b−</u>_</sup> _π_<sup>_<u>a</u>_,</sup><sup>_a_=</sup><sup><u>2</u></sup> _p_<sup>_<u>π</u>_</sup> _u_<sup>,</sup><sup>_b_=</sup><sup><u>2</u></sup> _p_<sup>_<u>π</u>_</sup> _l_<sup>,</sup><sup>_B_ ̃</sup><sup>_k_=</sup><sup>_−frac_12</sup><sup>_B_0</sup><sup>_−_</sup> � _j_<sup>=1</sup><sup>_k−_1</sup><sup>_Bj_.Theparameters</sup><sup>_pu_and</sup><sup>_pl_denotethecut-offsforthecyclesforthehigh</sup> and low frequency elements, respectively. We remove all stochastic cycles at a periodicity lower than 3 months and higher than 12 months. This has the effect of both smoothing the series and removing long-term seasonality. The results of applying the CF filter to our two selected series can be seen in figures 5 and 6.

One notable advantage of techniques such as exponential smoothing and the CF filter is that they provide us with real-time estimates at the ends of our series so that we do not need to truncate our observed series at the beginning or the end as would be necessary if we used a simple moving averages, seasonal differences, or, another filter such as the Baxter-King.<sup>3</sup>

> 3 Of course, we could estimate a model and forecast and backcast then apply a filter that truncates, using these extra data points. However, this is another form of uncertainty that we would like to avoid introducing. Instead, we prefer to use only the information we have.

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To answer such questions we require information on a range of characteristics **not** just at one point in t<sup>4</sup> rme but across the entire life-cycle. No such source of longitudinal data exists. Evei. where longitudinal surveys have been carried out (Medical Research Council 'Dou"las cohort'; National Child Development Survey; OPCS Longitudinal Survey, Michigan Pane! Study of Income Dynamics (Duncan, 1984, Elder, 1935)) the inforrn.ition is onlv for periods witlin the life course, not complete life histories. EconowLists have frequently attempted **to** estima'e lifetime profiles or functions using a range of econ metric and simulation techniques (e.g. Blinder, 174; Lillard, 1977). While such approaches have shed light on particular aspects of life"ime profiles, they have all failed to a greater or lesser extent to capture the enormous degree of Onange in the circumstances of individuals over time. For example, plotting the lifetime earnings profile of married me,, fails to take into account that very few men stay cc,nstantly married and constantly in the labour force for their entire lives. It is precisely these changes in marita, and employment statuses that are of particular importance in determrining an individual's ability to accumulate pension entitlements over their working life. Ignoring the degree of change in personal circumstances when attempting to provide a picture of lifetime welfare is thus a critical omission.

An alternative modelling option which endeavours to incorporate the diversity and constant change in the circumstances of individuals over time lies in dynamic microsimulation models. Microsimulation is the synthetic generation of data about social and economic 'micro' units. There are three major types of microsimulation models: static models; dynamic population models; and dynamic cohort models (Merz, 1991). The type of simriulation model which is applicable is depelndent on the question which needs to be .. ,swered.

Static models are used for estimating the immediate impact of policy changes by systematically varying certain behavioral relations and/or institutional conditions of a microdata base. Perhaps the best known examples of static models are those first develorned in the US such as the TRIM (TRansfer Income Model) (Sulvetta, 1976) which is used to investigate the impact of changes to payroll taxes and to both State 7
actions. This provides an empirical advantage, we can confidently assume that individual effort does not play a role on this outcome.

Height-for-age for toddlers is a _human opportunity_ . On the one hand, several studies have shown that taller children are more likely to show higher cognitive test scores and, as a result, higher labor earnings.<sup>2</sup> On the other hand, children’s height is a function of both nature and nurture. The height of a child can depend on genetic factors, like the height of his/her parents for example; but it can also depend on nutrition, access to medical services when the mother is pregnant, access to clean water, and many other potential environmental factors that can depend on parents’ choice and government interventions –which belong to the set of nurture variables.<sup>3</sup> Note that both nature and nurture variables make up our set of circumstances. But, although the DHS provides information on a broad number of these observable characteristics, many of these variables contain a big number of missing values, compromising the representativeness of the samples. Therefore, I include as circumstances variables that do not compromise the representativeness of our samples due to missing values. I include: age of the toddler (in months), gender (as a dummy), birth order (numerical), age of the mother (in years), height of the mother (in centimeters, as proxy for nature), educational attainment of the mother (in years), household wealth (the DHS’ index of wealth) and geographical location (urban or rural, as a dummy). These comprise a set of variables not much different than the set of variables that is used to analyze other human opportunities (Molina et al., 2013).<sup>4</sup> It is important to note that since the height of toddlers increases in variance with age and varies by sex, I use the World Health Organization’s growth charts (WHO, 2006) to create a standardized measure for height ( _x_ ), which corresponds to the equivalent height the child would have had if (s)he were a 24 months old female. More formally where _F_ is the distribution function of heights in the reference population for the age and sex group of an individual of age _a_ and gender _g_ ; _h_ is the actual height of that individual; _<u>a</u>_ = 24 months; and _<u>g</u>_ = _female_ .

> 2See Case and Paxson (2008), for example, for evidence and a discussion.

> 3See Aturupane et al. (2008) for a discussion.

> 4My full sample consists of 70,788 children. However, after dropping biological implausible values for height of my sample –following the WHO (2006) guidelines to identify biologically implausible values for height- and dropping missing values in my variables of interest, I lose 6% of my full sample. Information on sample sizes can be found at: https://sites.google.com/site/cfbalcazars/misc

4
# **Figure 4. Economies with investment growing below its long‐term average**

_The share of EMDEs with investment growth below its long‐term average has risen since 2012. The increase has been especially pronounced for commodity exporters._ <!-- Start of picture text --> A. EMDEs with investment growth below long‐term B. All economies and advanced economies with<br>average investment growth below long‐term average<br>Percent of countries Percent of countries<br>100 Commodity exporters 100 AEs World Share = 50<br>Commodity importers<br>80 Share = 50 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Oxford Economics; World Development Indicators, World Bank. A.B. Long‐term averages are country‐specific and refer to the period 1990‐2008. The world sample includes 157 economies. The AE sample includes 35 economies, and the EMDE sample includes 78 commodity exporters and 44 commodity importers.

# **Figure 5. Investment growth forecasts**

_Short‐term and long‐term forecasts for investment growth in EMDEs have declined steadily since 2010._

## **A. Next‐year forecasts in EMDEs**

## **B.  Forecasts: Five‐year ahead expectations**

<!-- Start of picture text --> Percent Percent EMDEs<br>12 GDP Investment 8 EMDE commodity importers<br>EMDE commodity exporters<br>EMDEs: 2015 actual<br>6<br>8<br>4<br>4<br>2<br>0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> A. Next‐year monthly Consensus Forecasts of investment and output growth. Unweighted averages across 18 EMDEs. Latest observation is October 2016.

B. Each column shows five‐year‐ahead Consensus Forecasts as of the latest available month in the year denoted. Unweighted averages among 11 EMDE commodity importers and 10 EMDE commodity exporters. Diamond denotes average actual investment growth in 2015 for 21 EMDEs. Last observation is for October 2016.

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# **Table 7: Structure of investor base in MENA countries as a percent of total (as of December 2008)**

||**Egypt**|**Morocco**|**Tunisia**|**Jordan**<br>**(5)**|**Lebanon**|
|---|---|---|---|---|---|
|**Banks (1)**|55|23|33|81|62|
|**Public sector &pension funds(2)**|30|27|1||36|
|**Insurance companies**|3|12|0||0|
|**Mutualfunds (3)**|1|22|29|19|0|
|**Foreign investors**|10|1|0|0|1|
|**Others (4)**|1|15|37|0|1|
|**Total**|100|100|100|100|100|

Source: Ministries of Finance and staff calculations.

(1) State owned commercial banks are included (e.g. CDG in Morocco holding 9 percent of debt) (2) For Egypt, National Investment Bank of Egypt and Social Security; for Morocco CDG, for Tunisia and Lebanon the Central Bank.

- (3) Morocco: state-owned CDG manages 29 percent of industry's assets.

(4) Tunisia: holdings by individuals (5) Jordan: available information only reports holdings of banks and non-banks.

# **Table 8: Structure of investor base in selected Emerging Markets as a percent of total**

||**Brazil**|**Hungary**|**Mexico**|**Malaysia**|**Costa Rica**|
|---|---|---|---|---|---|
|**Banks**|38|<br>30|11|22|20|
|**Publicsector (1)**|0||0|45|50|
|**Insurancecompanies**|4||0|9|0|
|**Pension funds**|16|37|23|0||
|**Mutualfunds**|30|2|15|0|24|
|**Foreigninvestors**|10|27|12|12|6|
|**Others**|2|4|39|12|0|
|**Total**|100|100|100|100|100|

Source : Ministries of Finance and staff calculations Data for Brazil and Hungary, 2010. Data for Malaysia, Mexico and Costa Rica, 2009.

(1) For the case of Costa Rica and Malaysia Social Security funds are included. For Costa Rica, the state-owned insurance company holdings of public debt (3 percent) are included.

_Overview of non-bank financial institutions_

**Non-bank financial institutions are underdeveloped in the MENA region, only Morocco stands out as an exception.** Moroccan NBFIs account for 91 percent of GDP, compared with a

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Substituting (18) and (19) into (17) we obtain: (20) DE/E - DPh/Ph -- (w.(DP*m/P*m) + (l-w).(DP*x/P*x8) - - (w.Tm + (1-w).Sx) + z.Dts Given proper estimates for w and z, eqn.(20) tells the required changes in the ratio E/Ph required to recover equilibrium in the market for non-traded goods.

We have estimated the relation presented in (13) using annual data for Colombia for the period 1971-86. The results are as follows: Ln(Px/Ph) = 1.707 - 0.576.Ln(Pm/Px) + 0.0452.(TS/GDP) (T-Value) (39.6) (-3.39) (3.97) R2 Adj.:0.85 D.W.:1.47 S.E.:0.087 The estimation used TSLQ and the instruments where current and lagged Pm/px, Government Deficit ,and lagged Trade Trade Surplus to GDP ratio.

The first thing to note is the similarity of the coefficient on (TS/GDP) with those obtained in the regressions presented in Tables III.1 and III.2.

The estimated value of w of 0.576 implies that the reduction of 10% points in import duties should be accompanied by a 5.76% real devaluation, meaning that this is the amount that nominal devaluation must exceed the price of home goods for equilibrium to be restored. In this case, the real devaluation will be obtained either by nominal devaluation, fall in Ph or any combination between the two. In this case, our measure of Ph used was the consumer price index; other measure of Ph will probably yield a different value for w. If the measure of Ph used actually has into it traded goods, the initial nominal devaluation of 5.76% will not be able to obtain the required real devaluation as Ph(as measured) in itself will tend to increase by some fraction of this amount. Because of this measurement problem, it is better to specify the exchange rate adj1'scment in relation to the price index used as deflator in (20).

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