rafmacalaba/datause-encoder-data huggingface dataset:
|P057602|2003|2003|4/15/2003|3 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P057602|2003|2004|3/18/2004|6 Closed|Yemen,Republic of|
|P057602|2003|2005|4/14/2005|9 Closed|Yemen,Republic of|
|P057602|2003|2006|11/4/2005|10 Closed|Yemen,Republic of|
|P057602|2003|2006|4/28/2006|11 Closed|Yemen,Republic of|
|P057602|2003|2007|11/9/2006|12 Closed|Yemen,Republic of|
|P057602|2003|2007|6/25/2007|13 Closed|Yemen,Republic of|
|P057602|2003|2008|9/17/2007|14 Closed|Yemen,Republic of|
|P057602|2003|2008|5/2/2008|15 Closed|Yemen,Republic of|
|P057602|2003|2009|12/31/2008|16 Closed|Yemen,Republic of|
|P057602|2003|2009|6/30/2009|17 Closed|Yemen,Republic of|
|P057602|2003|2010|10/17/2009|18 Closed|Yemen,Republic of|
|P057602|2003|2010|5/11/2010|19 Closed|Yemen,Republic of|
|P057602|2003|2011|5/28/2011|20 Closed|Yemen,Republic of|
|P057624|1999|1999||2 Closed|Philippines|
|P057649|2001|2001||0 Closed|Brazil|
|P057649|2001|2002|6/21/2002|2 Closed|Brazil|
|P057649|2001|2003|5/21/2003|5 Closed|Brazil|
|P057649|2001|2004|6/23/2004|8 Closed|Brazil|
|P057649|2001|2005|4/29/2005|10 Closed|Brazil|
|P057653|2002|2002||0 Closed|Brazil|
|P057653|2002|2003|6/6/2003|2 Closed|Brazil|
|P057653|2002|2004|6/4/2004|6 Closed|Brazil|
|P057653|2002|2005|3/3/2005|9 Closed|Brazil|
|P057653|2002|2006|12/5/2005|10 Closed|Brazil|
|P057653|2002|2006|6/21/2006|11 Closed|Brazil|
|P057653|2002|2007|10/2/2006|12 Closed|Brazil|
|P057653|2002|2007|5/15/2007|13 Closed|Brazil|
|P057653|2002|2008|12/3/2007|14 Closed|Brazil|
|P057653|2002|2008|6/27/2008|15 Closed|Brazil|
|P057665|2002|2002|6/7/2002|1 Closed|Brazil|
|P057665|2002|2003|6/13/2003|3 Closed|Brazil|
|P057665|2002|2004|6/4/2004|5 Closed|Brazil|
|P057665|2002|2005|4/26/2005|7 Closed|Brazil|
|P057665|2002|2005|6/7/2005|8 Closed|Brazil|
|P057665|2002|2006|12/9/2005|9 Closed|Brazil|
|P057665|2002|2007|12/22/2006|10 Closed|Brazil|
|P057665|2002|2007|6/13/2007|11 Closed|Brazil|
|P057665|2002|2008|8/14/2007|12 Closed|Brazil|
|P057665|2002|2020|4/7/2020|13 Closed|Brazil|
|P057692|2002|2002|6/27/2002|2 Closed|Colombia|
|P057692|2002|2003|6/11/2003|4 Closed|Colombia|
|P057692|2002|2004|6/18/2004|6 Closed|Colombia|
|P057704|1998|1998||1 Closed|Egypt,Arab Republic o|
|P057704|1998|1999||3 Closed|Egypt,Arab Republic o|
|P057704|1998|2000|5/25/1999|3 Closed|Egypt,Arab Republic o|
|P057731|2001|2001|6/29/2001|1 Closed|Philippines|
**Figure B27: Tea**

<!-- Start of picture text --> Log scale<br>2.5<br>Log price Permanent component<br>2.0<br>1.5<br>1.0<br>0.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.75<br>Medium-term cycle Transitory component<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 68 —_
# _Appendix 4: Performance indicators in the East Caribbean States_<sup>_63_</sup>

# **Statistical Table** 5 **Results focus indicator and components (East Caribbean)**

||Geared to<br>organization's<br>objectives|Efficiency|merit based reward R<br>& punishment|esults focus|
|---|---|---|---|---|
|Antigua and Barbuda|6.0|3.6|0.7|3.6|
|Grenada|6.7|4.9|1.7|5.1|
|St. Kitts and Nevis|7.4|5.4|2.0|5.2|
|St. Lucia|6.9|4.1|1.3|_5.0_|
|St. Vincent And Grenadines|6.9|4.5|1.2|5.2|
|EastCaribbean|.~~~~~~~~~~~~~~~~~~~~<br>6.8|~~~~~~~~~~~~~~~~~~~~~<br>4.4|~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>1.3|~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>4.9|

# **Statistical Table 6 Accountability indicator and components (East Caribbean)**

||Enforceme|nt of regul|ations in|_<br>_<br>**CD**<br>18-~<br>~<br>|A^<br>**0**<br>~<br>O<br>8<br>'~~~~~|<br>w<br>|Accounta<br>~~8|bility to<br>e~<br>g"|Accounta<br>~~~~~~~~~~~~bility<br>~~<br>from<br>previous<br>columns|
|---|---|---|---|---|---|---|---|---|---|
|Antigua and<br>Barbuda|5.4|7.7|4.6|5.6|5.8|5.0|4.4|6.2|5.4|
|Grenada|4.7|8.1|6.0|7.4|6.5|4.5|4.6|7.3|5.7|
|St. Kitts and<br>Nevis|6.1|9.2|7.4|5.7|7.1|5.9|4.6|7.3|6.2|
|St. Lucia|_5.1_|7.9|_5.5_|6.5|6.3|5.9|4.4|7.2|5.9|
|St. Vincent<br>And<br>Grenadines|4.8|7.6|5.4|4.5|5.6|_5.5_|4.1|7.4|5.7|
|East Caribbean|<br>_5.1_|8.0|5.7|6.1|6.2|5.3|4.4|6.2|5.5|

# **Statistical Table** 7 **Employee morale indicator and components (East Caribbean)**

|Country|Employee morale|
|---|---|
|Antigua and Barbuda|8.0|
|Grenada|7.8|
|St. Kitts and Nevis|8.5|
|St. Lucia|7.6|
|St. Vincent And Grenadines|8.2|
|East Caribbean region|8.0|

63 See footnote 48.

_54_
budgets of the Eurozone periphery countries.<sup>5</sup> Moreover, strong growth in the periphery does not seem to be associated with a boost to domestic demand from growing real wages. Although real wage growth in the periphery accelerated after the introduction of the common currency, this only put a floor to the declining trend of labor income in proportion to nominal GDP since the early 1990s (Figure 4d). The decline in labor income share is also characteristic of the Eurozone core, with the difference, however, that the downward trend continued until the 2008-09 crisis as a result of wage restraint adopted in several countries, most notably in Germany. Overall, in both the periphery and the core nominal wage growth lagged behind GDP growth.

# **Figure 4: Stylized facts of growth in the Eurozone core and periphery (1990-2011)**

<!-- Start of picture text --> (a) Real GDP growth (in % per year) (b) Labor productivity growth (3-year moving average)<br>6 4<br>4 3<br>2<br>2<br>0<br>1<br>-2<br>0<br>-4<br>-6 -1<br>Moving Average Core<br>Average Core Average Periphery<br>Moving Average Periphery<br>(c) Primary fiscal balance (in % of GDP) (d) Ratio of total labor cost to nominal GDP<br>5 0.74<br>0.72<br>0<br>0.7<br>-5 0.68<br>0.66<br>-10<br>0.64<br>-15 0.62<br>Average Core Average Periphery Average Core Average Periphery<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: WDI and OECD Economic Outlook databases.

> 5 This partly reflects the collapse of tax revenues linked to pre-crisis booming sectors such as construction but also the support provided to ailing banks in some periphery countries. It also partly reflects the fiscal fragilities of those periphery countries which used reduced interest payments on debt and buoyant tax revenues to expand public spending, thus leaving their budgets vulnerable to a loss of revenue as public expenditure was difficult to cut on short notice (see also IMF, 2011).

8
observation in the sample.<sup>12</sup> With this outlier detection technique, we remove outliers throughout the entire distribution, but mostly concentrated on the left- and right-hand tail.

Given the numerous data quality issues outlined above, we consider the second avenue of outlier removal, the more restrictive technique, most appropriate for our purposes and hence use the multivariate outlier removal technique to obtain the _final data set_ . All other data sets are contained for robustness check purposes and to illustrate the data processing only (cf. Table 1). Price estimations and predictions are only performed on the _final data set_ .

# Data Sets Illustrating Data Processing

Table 1 summarizes the property price data of the five economies for the different stages of the data processing. In the _original_ and _truncated_ data set, the means of price, size, and per square meter price are (much) greater than their medians as the distribution is positively (and in some cases strongly) skewed by outliers.

While more robust statistics such as median and the interquartile range (IQR) stay relatively consistent across data sets, the standard deviation and mean drop significantly from the _original_ data set to the _final_ data set. This pattern remains constant throughout the five countries and provides some suggestion that the outlier removal process, while comprehensive on distance metrics, does not significantly alter the balance of the right and left tail and the order of the distribution.

In Albania, the difference in standard deviation between the _original data set_ and the _final data set_ is stark despite the relatively low number of outliers being removed. In Morocco, the _right censored data set_ is the same as _the truncated data set_ as the maximum price listed in Numbeo is smaller than the maximum price in the truncated data set, hence, no right censoring is applied here. In South Africa, the _truncated data set_ – particularly if compared to the other four countries – excludes a relatively larger share of data entry errors and potential rental data. This might be attributed to large farms being included for sale on the website we used for South Africa (property24.com). In Costa Rica, the multivariate outlier removal technique detected particularly high-end, luxurious properties.

> 12

> We are applying the _smultiv_ command in Stata, which has consistently proved to outperform _mcd_ , an alternative robust estimator for outlier detection (e.g., Verardi & McCathie 2012.).

16
campaigns of the EPRDF.

## [Insert Figure 1 about here]

We, therefore, exploit the coincidence of the boundaries between the military campaigns and the carrying out of the 1997 land redistribution in the Amhara region as a natural experiment. This allows us to address endogeneity concerns, since empirically evaluating these effects without any exogenous variation would have made it diffi cult to distinguish the effects of economic change on population change from the effects of population change on economic change.

# 2.3 Some theoretical considerations

We argue that the unanticipated change in the modus operandi of land governance closed down the possibility of getting access to land via family size. This affected fertility decisions. We represent these insights in a simple model. We presume that children provide a net economic benefit to a household.<sup>13</sup> Dasgupta (1993) argues that such an assumption is more valid in poor than in rich countries because productivity is less tied to human capital. One channel may be that children earn more than they consume at earlier ages. Other channels may be that they provide old-age support or help diversify risk. To illustrate the mechanism at work for expositional purposes, we link household size and land ownership to household wealth. A household’s wealth W depends on its land holdings, denoted by k, and children, denoted by r, and is given by a continuous function W (k, r). We assume that it is strictly increasing and concave in both arguments. Raising children generates economic benefits, but is also costly. The cost function is given by a strictly convex function, c(r). The objective of the household is thus to choose r in order to maximize the net function Now assume that land is redistributed according to household size. Land holdings then positively depend on the number of children. Assuming that they are given by the function k(r), the net benefit function can be re-written as The condition is then Note that the term<sup><u>dW</u></sup> dk<sup>k′(r)isstrictlypositive.Itcapturesthefactthatan</sup> increase in household size increases landholdings, which ultimately leads to more

> 13 This view departs from the classical literature studying the determinants of fertility, in which children generate utility as a consumption good and/or altruism (Barro, 1974; Barro and Becker, 1989; Becker, 1960; Burbidge, 1983; Eckstein and Wolpin, 1985; Razin and BenZion, 1975).

8
and Yao, 2020; Gazeaud, Mvukiyehe and Sterck, 2023). Other potential confounding factors include trade liberalization, climate change and a rural roads program.<sup>4</sup> To establish causality between migration and land rental contracts, we employ an instrumental variable strategy relying on arguably exogenous migration “pull” factors. Our main explanatory variable is the share of the high-skilled in total migrant outflows: lacking information on land ownership of migrants, we use this measure to capture the out-migration of the landholding classes. We instrument for it by using a shift-share strategy (Card, 2001; Anelli et al., 2023; Madhok et al., 2024). One unique feature of our paper is that the data allows us exploit migration networks to instrument for _internal_ migration using this strategy.

Our approach, which uses district-to-district migration flows from the 2001 and 2011 Population Censuses, can be explained as follows. For a given migrant-sending (or, “source”) district, every other district in India is considered a potential “destination” district. We proxy for the attractiveness of each destination district by considering the total immigration it from all other districts (barring the source district). This is the shift component. The share component is the historical share of each destination district in the total migrants from the source district. The share-weighted shift yields a measure of the migration pull factor for the source district. We construct two instruments, one for high-skilled migrants and one for low-skilled migrants, and use these to predict high-skilled out-migrants as a percentage of total out-migrants in each district. This distinction by education level is unique and contributes to 4Trade liberalization exposed farmers to increased volatility, affecting crop choices (Allen and Atkin, 2022) and agricultural investments (Tian, Xia and Yang, 2021). Changes in rainfall patterns (associated with climate change) may have impacted agricultural wages and migration directly (Rosenzweig and Udry, 2014; Colmer, 2018; Brey and Hertweck, 2019; Kaur, 2019), or indirectly through a declining groundwater tables (Blakeslee, Fishman and Srinivasan, 2020). A rural roads construction program increased educational investment and migration (Adukai, Asher and Nosovad, 2020), while affecting agricultural technology and crop choice (Aggarwal, 2018; Shamdasani, 2021). Note, however, that trade liberalization and climate change increase risk and should push land tenancy towards _more_ sharecropping. Hence they are not a competing explanation for the observed trends, although they can be a threat to establishing causality.

5
average of the dependent variable among the regressors.<sup>4</sup> We estimate the model using a time-series cross-section dataset comprising 50 industrialized and developing countries over the period 1970-2007. We ...nd a cross-country average coe¢ cient of partial risk sharing around 0.42. In terms of the paper’s theoretical model, this means that the average country contributes less than half its income to the income pool. The estimates, however, vary systematically between industrial and developing countries, with the former exhibiting, on average, signi...cantly higher degrees of risk sharing than the latter (centered around 0.52 and 0.35, respectively). Re-estimating the model over rolling time samples, we ...nd evidence that the degree of risk sharing has been on the rise among industrial countries during most of the sample period, while for developing countries average risk sharing remained ‡at or even declined somewhat during the globalization period (although there seems to be a modest uptick at the end of the sample). This result is consistent with the view that the bene...ts from ...nancial globalization do not accrue evenly to all countries, and may prove elusive in particular for economies with relatively low levels of ...nancial development.<sup>5</sup> In the paper’s framework, these partial risk sharing coe¢ cient estimates can be interpreted as measuring agents’chosen contributions to the global income pool. However, the model is silent on the factors behind those choices. To gain further insight, we assume that they re‡ect cross-country variation in policies and institutions that help or hinder consumption risk sharing. Speci...cally, we re-estimate the model expressing the partial risk sharing coe¢ cient as a function of selected measures of ...nancial and trade openness, which are commonly viewed as re‡ecting the mechanisms through which risk sharing may be actually implemented.

While this adds further complication to an already-challenging nonlinear estimation problem, it yields fairly robust empirical results. The main conclusion is that international ...- nancial integration is a signi...cant factor behind the observed cross-country patterns of consumption risk sharing. The degree of ...nancial integration, as summarized by total foreign

> 4Moreover, even if we had written the estimating equation in terms of a weighted consumption average, the nonlinearity of the model would break the linear relation between right and left-hand side variables inherent to the linear regression.

> 5On the costs and bene...ts of ...nancial globalization, see Kose, Prassad, Rogo¤, and Wei (2006).

4
4 managed somewhat differently from thos in private sector pensions. Thus Table 2 indicates tnat until 1960, corporate equities -2yistituted only a negligible fraction of state and local plan assets, while government securities and corporate bonds were much more heavily favored than private plans. This pattern of asset holdings was in part motivated by state and loe"al government rules prohibiting pension managers from investing in what were perceived to be "risky" assets including equity, venture capital, and foreign holdings.

Though these strictures have been curtailed ocr the last decade, yields on public pension fund assets have frequently been low, with puv!ic plans earning rates of return substantially below those of other pooled funds, and of:en below leading market indices.<sup>2</sup> Table 3 demonstrates this for the eighteen year perio i 1968-86, and the pattern persists in more recent statistics. More recently, state and local pension plans reported annual returns averaging 11.13%, during a time when bonds rose by 15.5% and securities by 13.9% (Table 4).

One explanation for why public plan investment yields are often low is that they are operated according to principles different from those adopted in the private sector. Specifically, many public pensions are managed by staff which must respond to political as well as economic incentives and pressures. A typical public system is governed by a Board of Directors comprised of eight members, on average, with three elected members, three appointed members (often by the Governor), and two serving _ex officio_ (e.g. the State Treasurer, the superintendent of schools, etc). Those elected to public pension Boards are frequently active employees, which is quite uncomrnon in the private sector; in addition, in Olivia S. Mitchell - Public Pension Govemance and Performance
A Retrospective Analysis of the House Prices Macro-Relationship in the United States<sup>_∗_</sup> Ibrahim Ahamada<sup>_a_</sup> and Jose Luis Diaz Sanchez<sup>_b,c,d_</sup>

> _a_ International Monetary Fund _b_ Paris School of Economics

> _c_ University of Paris 1 Pantheon-Sorbonne _d_ The World Bank

This study provides empirical evidence on the strengthening of the impact of house prices on the US macroeconomy. The stability of the house prices macro-link is tested in a small-dimensional vector autoregressive model over the last fifty years. The estimated break-points are used to split the sample into different segments and a multivariate time series analysis is performed within subsamples. The paper finds a robust structural break in the mid-1980s. In addition, time series analysis across segments provides evidence that the effect of house prices, not only on private consumption, but also on economic activity, has intensified since the mid-1980s.

JEL Code: C32, E20, E44, R30. Keywords: House prices, mortgage market deregulation, vector autoregressive.

Sector Board: Financial Sector (FSE).

# **1. Introduction**

A decade ago, Alan Greenspan suggested in his testimony to the Joint Economic Committee of the US Congress (2002) that the strong appreciation of house prices after the crash of the stock market in 2001 might have helped save the US economy from a more serious recession, a point of view shared by most of the economists

> _∗_ The views expressed in this article are those of the authors and do not necessarily represent those of the International Monetary Fund or the World Bank. The authors would like to thank John Williams (editor of the _International Journal of Central Banking_ ), two anonymous referees, Joerg Breitung, Jean-Bernard Chatelain, Jordi Gali, Jean-Olivier Hairault, Juergen von Hagen, Aristomene Varoudakis, seminar participants at the Eurostat 6th Colloquium on Modern Tools for Business Cycle Analysis, the Bonn Graduate School of Economics Macro-Workshop, the Central Bank of Peru research seminar, the PSE Macro-Workshop and the IMF Institute research seminar for their helpful comments and suggestions. Authors e-mail: iahamada@imf.org and jdiazsanchez@worldbank.org.
Second, we account for potential differences in survey years. While multi-purpose HH surveys are conducted every 3-5 years, some countries have dated surveys, including Iraq (2012), Madagascar (2012), or Senegal (2011). Since then, individual HHs might have improved on one or more of the seven adequacy dimensions. Yet, literature suggests that housing conditions are “sticky”. For instance, according to the WEO, only seven million people in Sub-Saharan Africa have gained access to clean cooking since 2015 (IEA 2019). In order to test the “stickiness” for all housing conditions, we compare the _AHI_ of the most recent survey year available in HCES to an adjusted _AHI_ where we project every country’s _AHI_ to 2019 making use of available macro data discussed in the previous paragraphs. The results of this comparison are presented in Annex 6 along with the individual correlations of the four adequacy dimensions for which current macro-level data was available. Since the results exhibit great consistency of the housing conditions of the survey year and the most recently available macro-level year, we present the results of the _AHI_ that uses the original survey year without any adjustments to current date.

# **4.Results: The** **_Adequate Housing Index_ across and within Countries**

The _AHI_ provides the first micro-founded, comparative assessment of housing conditions. In the following sections we present the results for 64 emerging economies. The _AHI_ can be disaggregated along several dimensions, allowing to draw a nuanced picture of housing adequacy within and across countries. The aggregate _AHI_ represents the adequate housing deficit for each of these emerging economies. Since we are applying a global framework in denoting housing adequacy, the respective adequacy deficits – also if disaggregated – are comparable in magnitude within and between countries.

## **_4.1 AHI – Housing Adequacy across Countries_**

Across the countries covered, there is a large variance in terms of adequate housing deficits (Figure 1). On average, about 68 percent of houses are adequate across emerging economies. However, there are stark differences between countries. Within the sample, Sub-Saharan African countries have the lowest _AHI_ while countries in Europe & Central Asia as well as Latin America and the Caribbean have relatively high _AHI_ scores. In Niger, Chad, and Madagascar up to 73 percent of the existing housing stock is likely to exhibit an adequate housing deficit. In comparison, Argentina’s, and Costa Rica’s current housing stock consists of largely adequate houses. The difference between Argentina respectively Costa Rica (both 0.97) and Niger (0.27) is significant and large. Comparing the _strict AHI_ between these countries, a similar pattern emerges. In Costa Rica, 92 percent of houses are adequate on all seven adequacy dimensions, whereas in Niger virtually no house is adequate on all seven dimensions. In Niger, only about 53 percent of HHs have access to adequate water, 16 percent of HHs have access to improved sanitation, 2 percent of