rafmacalaba/datause-encoder-data huggingface dataset:
Heckman, J., E. Vytlacil (2006), “Econometric Evaluation of Social Programs”, chapter in the _Handbook of Econometrics_ , Volume 6 **,** J. Heckman and E. Leamer (eds), North Holland: Amsterdam, forthcoming.

- Imbens, G., K. Hirano and G. Ridder (2003) “Efficient Estimation of Average Treatment Effects using the Estimated Propensity Score”, _Econometrica_ 71, 1161-1189.

- Jalan, Jyotsna, and Martin Ravallion (2003) “Does Piped Water Reduce Diarrhea for Children in Rural India?” _Journal of Econometrics,_ (112) :153–73.

- Mistiaen, J. A., B. Ozler, T. Razafimanantena, and J. Razafindravonona. (2002), “Putting Welfare on the Map in Madagascar.” Technical report, African RegionWorking Paper no. 34, World Bank, Washington, DC.

- Ravallion, M. (2006) “Evaluating Anti-Poverty Programs”, Policy Research Working Paper no. 3625, The World Bank, Washington D.C.

- Ross, J. (1997) “Cost-effectiveness of the Nutrition Communication Project in Mali.” SARA Project (AED): Washington, D.C.

- Ruel, M.T. and P. Menon (2002) “Child feeding practices are associated with child nritional status in Latin America: Innovative uses of the Demographic and Health Surveys”, _Journal of Nutrition_ 132, 1180-1187.

- Ruel, M.T., Levin, C.E., Armar-Klemesu, M., Maxwell, D.G. and S.S. Morris (1999) “Good care practices mitigate the negative effects of poverty and low maternal schooling on children's nutritional status: evidence from Accra **”,** _World Development_ 27 (11): 19932009

- Salehi, M., S.M. Kimiagar, M. Shahbazi, Y. Mehrabi and A.A. Kolahi (2004) “Assessing the impact of nutrition education on growth indices of Iranian nomadic children: an application of modified beliefs, attitudes, subjective-norms and enabling-factors model”, _British Journal of Nutrition_ , 91, 779-87.

- Save the Children (2003) “Thin on Ground: Questioning the evidence behind World-Bank funded community nutrition projects in Bangladesh, Ethiopia and Uganda”, London, UK.

- Shaikh, A., M.Simonsen, Vytlacil, E., and N. Yildiz (2005) “On the Identification of Misspecified Propensity Scores”, mimeo, Columbia University, Department of Economics.

- Shrimpton R. et al (2005) “Worldwide Timing of Growth Faltering: Implications for Nutritional Interventions”, _Pediatrics_ , 107(5).

- Thomas, D., Strauss, J., and M., Henriques (1991) “How does mother’s education affect child height?”, _Journal of Human Resources_ , 26.2:183-211

- USAID (1988) “Growth Monitoring and Nutrition Education: Impact Evaluation of an Effective Applied Nutrition Program in the Dominican Republic”, CRS/CARITAS, 1983–1986. Washington, D.C.

- World Health Organization (2003) _Global Strategy for Infant and Young Child Feeding,_ http://www.who.int/nut/documents/gs_infant_feeding_text_eng.pdf

- The World Bank (2006) _Repositioning Nutrition as Central for Development. A Strategy for Large-Scale Action._

19
# 1. Introduction

Measuring poverty trends is a complex exercise that requires data on household welfare that can be compared over time. Limited availability of household-level data and changes, or improvements, to the methodology used to measure household consumption can affect the comparability of survey-based poverty estimates. These issues arise frequently in poverty measurement exercises (Deaton, 2005; Beegle et al. 2012) and can severely limit the availability of evidence needed to guide policies for reducing poverty.

This paper considers this issue in the context of Nigeria and seeks to understand the country’s poverty dynamics for the period between 2009 and 2019. Nigeria contains the largest number of extreme poor – that is, those living below the international poverty line of US$1.90 per person per day – in Sub-Saharan Africa, the world’s poorest region (World Bank, 2020). As such, understanding poverty trends in the country is crucial to inform policy at the country, regional, and global levels. Moreover, Nigeria has faced a series of compounding negative shocks to households’ well-being: even prior to the COVID-19 pandemic, the country suffered frequent conflict and climate shocks, as well as a deep recession following the collapse of global oil prices 2016. These crises are likely to have affected Nigeria’s progress towards poverty reduction.

Despite its high levels of poverty and its importance for regional and global poverty reduction, the household data needed to measure poverty have not been collected frequently in Nigeria over the past decade. In 2020, the Nigerian National Bureau of Statistics (NBS) released the first official poverty estimates in the country in almost a decade, calculated using data from the 2018/19 Nigerian Living Standards Survey (NLSS; see NBS 2020). The 2018/19 estimates show that 39.1 percent of Nigerians lived below the international poverty line of US$1.90 per person per day, corresponding to 78.5 million people (Castaneda et al., 2020).<sup>2</sup> While the 2018/19 estimates provide a crucial “snapshot” of the state of poverty just before the COVID-19 crisis, they do not provide information on poverty _trends_ in Nigeria. Prior to the 2018/19 NLSS, the last official poverty estimates for Nigeria came from the 2009/10 Harmonized Nigerian Living Standards Survey (HNLSS), but given crucial differences in the methodology used to collect

> 2 Poverty measured at the US$1.90 poverty line using World Development Indicators for population data (available at <u>https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG). This estimate differs from the population</u> estimate used by the Nigerian NBS (see Data section and Table 16 for a comparison). Poverty headcount rates are expressed at the international poverty line of US$1.90 per person per day unless otherwise stated.

2
- ALKIRE, SABINA and SANTOS, MARIA EMMA (2010). “Acute Multidimensional Poverty: A New Index for Developing Countries”. _Working Paper 038_ , OPHI.

- ANGULO, ROBERTO (2010). “Indice<sup> ́</sup> multidimensional de pobreza para Colombia”. In: _Seminario Internacional ”Colombia en las nuevas tendencias de medici ́on de la pobreza y la igualdad de oportunidades”_ , .

- APABLAZA, M.; OCAMPO, J.P. and G., YALONETZKY (2010). “Decomposing Changes in Multidimensional Poverty in 10 Countries”. Mimeo, Oxford Poverty and Human Development Initiative.

- APABLAZA, M. and YALONETZKY, G. (2013). “Decomposing Multidimensional Poverty Dynamics”. _Working Paper 101_ , Young Lives.

- AZEVEDO, JOAO PEDRO; INCHAUSTE, GABRIELA; OLIVIERI, SERGIO; SAAVEDRA, JAIME and WINKLER, HERNAN (2013a). “Is Labor Income Responsible for Poverty Reduction? A Decomposition Approach”. _Policy Research Working Paper 6414_ , The World Bank..

- AZEVEDO, JOAO PEDRO; INCHAUSTE, GABRIELA and SANFELICE, VIVIANE (2013b). “Decomposing the Recent Inequality Decline in Latin America”. _Policy Research Working Paper 6715_ , The World Bank.

- AZEVEDO, JOAO PEDRO; NGUYEN, MINH CONG and SANFELICE, VIVIANE (2012a). “ADECOMP: Stata module to estimate Shapley Decomposition by Components of a Welfare Measure”. Statistical Software Components, Boston College Department of Economics.

- AZEVEDO, JOAO PEDRO; SANFELICE, VIVIANE and NGUYEN, MINH CONG (2012b). “Shapley Decomposition by Components of a Welfare Aggregate”. Mimeo, he World Bank.

- BARROS, RICARDO; DE CARVALHO, MIRELA; FRANCO, SAMUEL and MENDOC ̧ A, ROSANNE (2006). “Uma An ́alise das Principais Causas da Queda Recente na Desigualdade de Renda Brasileira”. _Revista Econˆomica_ , **8(1)** , pp. 117–147.

- FERREIRA, FRANCISCO H. G. and LUGO, MARIA ANA (2013). “Multidimensional Poverty Analysis: Looking for a Middle Ground”. _World Bank Research Observer_ , **28(2)** , pp. 220–235.

25
# **Table  B.4. The list of selected variables in Lasso and Elastic Net models, Viet Nam**

||**LASS**|**O**|**Elastic**|**Net**|
|---|---|---|---|---|
||**Non-standardized**|**Standardized**|**Non-standardized**|**Standardized**|
|Head`s age|0.003|0.039|0.003|0.039|
|Head`s ethnicity|-0.125|-0.046|-0.125|-0.046|
|Primary education<br>|0.037<br>|0.016<br>|0.036<br>|0.016<br>|
|Lower secondary education|0.051|0.023|0.050|0.023|
|Upper secondary education|0.121|0.043|0.120|0.043|
|College|0.220|0.055|0.219|0.055|
|Household size|-0.151|-0.236|-0.150|-0.236|
|Dependency Ratio|-0.087|-0.060|-0.087|-0.060|
|Gender Ratio|-0.015|-0.013|-0.015|-0.013|
|Household Head worked for a wage, salary, or commission in the last 12 months|-0.029|-0.010|-0.029|-0.010|
|<br>Household Head engaged in casual/ganyu labor in the last 12 months|-0.001|0.000|-0.001|-0.000|
|Household Head employed in industry 1|-0.018|-0.007|-0.018|-0.007|
|Household Head employed in industry 2|0.036|0.012|0.035|0.012|
|Household Head employed in industry 3|0.028|0.004|0.028|0.004|
|Household Head employed in industry 5|0.033|0.003|0.032|0.003|
|Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|-0.007|-0.004|-0.007|-0.004|
|Proportion of adult males that worked in farm in the last 12 months|-0.019|-0.009|-0.019|-0.009|
|Proportion of adult males that worked in industry 1|-0.008|-0.003|-0.008|-0.003|
|Proportion of adult males that worked in industry 4|0.012|0.003|0.012|0.003|
|Proportion of adult males that worked in industry 5|0.019|0.002|0.019|0.001|
|log of residential area|0.196|0.113|0.196|0.112|
|Roof: cement|0.017|0.007|0.017|0.007|
|Wall: bricks|0.029|0.012|0.029|0.012|
|Wall:cement|0.051|0.007|0.050|0.007|
|Improved water source|0.006|0.017|0.006|0.017|
|Improved toilet source|0.029|0.049|0.029|0.049|
|Lighting source - Electricity|-0.108|-0.015|-0.107|-0.015|
|<br>Dwelling_cookfuel|0.016|0.006|0.016|0.006|
|Household owns a car|0.625|0.080|0.624|0.080|
|Household owns a motorbike|0.094|0.035|0.094|0.035|
|Household owns a bicycle|-0.048|-0.024|-0.048|-0.024|
|Household owns a DVD|0.060|0.030|0.060|0.030|
|Household owns a TV|0.054|0.014|0.054|0.014|
|Household owns a computer|0.182|0.072|0.182|0.072|
|<br>Household owns a refrigerator|0.124|0.061|0.125|0.061|
|<br>Household owns a sewing machine|0.063|0.013|0.063|0.013|
|<br>Household owns an electric/gas cooker|0.122|0.042|0.122|0.042|
|Anyone in the household cultivate any plot|-0.072|-0.036|-0.072|-0.036|
|Anyone in the household earn revenues from husbandry, hunting, trapping and dome|-0.023|-0.012|-0.023|-0.012|
|Household obtains goat/sheep|0.027|0.002|0.026|0.002|

71
overhang means that the full formula, without exceptions, would result in very small cuts in average applied rates.

**Annex Table 3. Implications of the Modalities for applied agricultural tariffs, %**

||Base|Formula|Formulaplus flexibility|
|---|---|---|---|
|Australia NZ|2.5|1.5|1.9|
|Argentina|4.2|4.1|4.2|
|Bangladesh|16.5|16.4|16.5|
|Brazil<br>|4.8<br>|4.7<br>|4.8<br>|
|Canada|10.7|5.1|8.6|
|China|7.8|5.3|7.5|
|EU-27|15.2|6.2|9.6|
|Korea and Taiwan Pr.|27.8|18.5|27.1|
|HK & Singapore|0.2|0.2|0.2|
|India|59.2|54.6|59.2|
|Japan|29.8|14.0|20.4|
|Cambodia|16.5|12.7|16.5|
|Mexico|3.9|3.3|3.9|
|M.E. & N. Africa|15.5|12.9|15.5|
|Nigeria|24.0|24.0|24.0|
|Pakistan|20.9|20.7|20.9|
|Selected LDCs|9.7|9.7|9.7|
|South Africa|6.7|6.1|6.7|
|Sri Lanka|18.4|16.2|18.4|
|Thailand|20.6|15.3|19.6|
|Turkey|13.6|11.2|13.4|
|United States of America|4.8|2.1|3.0|
|**World Bank Classification**||||
|All countries|14.6|9.0|11.9|
|High income countries|15.0|7.5|11.0|
|Developing countries, non LDC|13.7|11.8|13.6|
|LDCs|12.5|12.2|12.5|
|**WTO Classification**||||
|Developed WTO|15.1|6.9|10.2|
|Developing WTO non LDCs|14.0|11.4|13.9|
|_Normal Developing WTO_|15.1|12.3|15.0|
|_RAM WTO_|10.1|6.7|9.8|
|_SVE WTO_|14.0|12.5|14.0|

The flexibilities for particular countries and commodities more than halve the estimated worldwide cut in tariffs, from 5.6% with country flexibilities to 2.7%. Interestingly, it is in the industrial countries that the tariff cut is reduced the most by flexibilities—with the tariff after incorporating the effects of sensitive products declining from 7.5 percentage points to 4 percentage points. In developing non-LDCs, these flexibilities reduce the cut from 1.5% to 0.1 percentage points—a large proportional reduction in the cut than for high income countries, but a smaller one in percentage point terms. The flexibilities have much less impact in percentage point terms because they are used in a context where the formula requires much small cuts in protection.

19
as high as in the SOE sector. However, the job destruction rate in the private<sup>sector is</sup> also much higher than in the public sector (7 and 2 percent, respectively).

**Table 9 Job creation and job destruction by sector, 2001** (as percent of total employment)

|Sector|Job<br>creation|Job<br>destruction|Job<br>turnover|Employment<br>growth|Job<br>reallocation|Shamreplen<br>employment|
|---|---|---|---|---|---|---|
|Ownership|||||||
|Public|||||||
|Firms|2.2|<br>1.9|4.1|.0.2|3.8|27.5|
|Institutions|3.1|4.5|7.6|<br>-1.4|6.2|25.9|
|Private|6.2|<br>7.1|13.3|<br>-0.9|12.4|22.1|
|Mixed|3.0|<br>6.7|9.7|<br>-3.7|5.9|24.5|
|Size(employment)|||||||
|-50|6.9|<br>6.0|13.0|<br>0.9|12.1|13.7|
|51- 250|3.8|<br>5.7|9.5|<br>-1.8|7.7|38.0|
|251+|2.2|<br>4.0|6.2|<br>-1.7|4.5|48.3|
|Activity|||||||
|Agriculture|2.4|<br>6.0|8.4|<br>-3.6|4.8|3.8|
|<br>Non-agricultural business sector|4.1|6.0|10.1|-1.9|8.2|67.8|
|<br>Non-businesssector|2.2|<br>2.1|4.3|<br>0.1|4.2|28.4|
|Firmstatusa)|||||||
|Businessstart-ups|1.7|<br>na|na|<br>na|na|na|
|<br>Continuingfirms|8.1|5.2|13.2|<br>18.4|31.7|na|
|Closures|na|<br>0.5|na|<br>na|na|na|

na = not applicable Notes: Data refer to establishments defined by economic activity, rather than<sup>firms. Continuing firmsonly.</sup> a) Data on firm status come from a different source than the other data and<sup>are not directlycomparable.</sup> Source: Central Bureau of Statistics, FINA, Authores calculations.

_-21-_
Table 6: Impact of the decree on decision phase (flexible trends)

||(1)<br>Duration<br>of decision<br>stage (in<br>days)|(2)<br>Hazard<br>ratio -<br>fnishing<br>decision<br>stage|(3)<br>Likelihood<br>of decision<br>completion<br>in 1 month|(4)<br>Number of<br>decision<br>stage<br>hearings|(5)<br>Decision<br>stage<br>likelihood<br>of being<br>heard|
|---|---|---|---|---|---|
|Entered after decree|8.628|1.094|-0.040|0.027|0.037|
|application (_β_)|(9.489)|(0.096)|(0.048)|(0.379)|(0.025)|
|P-value 6pWCBoot|0.594||0.704|0.934|0.328|
|Chamber FE x Trend|Yes|Yes|Yes|Yes|Yes|
|Calendar month FEs|Yes|Yes|Yes|Yes|Yes|
|Without adj. period|Yes|Yes|Yes|Yes|Yes|
|Pre-mean|63.119||0.499|2.599|0.774|
|Pre-sd|82.701||0.500|3.399|0.246|
|R-Squared|0.073||0.173|0.033|0.386|
|Observations|3608|3844|3844|3844|3844|

<u>Notes:</u> Estimates of case-level impacts of the reform on decision-stage proceedings. See Notes on Table 3. 3844 observations for decision stage outcomes, except col 1 (censoring).

Table 7: Impact of the decree on quality of decisions (flexible trends)

||(1)<br>Pre-trial<br>insufcient|(2)<br>Decision<br>postponed|(3)<br>Number of<br>articles|(4)<br>Decision<br>length|(5)<br>Appeal|
|---|---|---|---|---|---|
|Entered after decree|0.008|-0.009|-0.189|-0.092|0.023|
|application (_β_)|(0.042)|(0.028)|(0.134)|(0.205)|(0.052)|
|P-value 6pWCBoot|0.890|0.808|0.463|0.815|0.562|
|Chamber FE x Trend|Yes|Yes|Yes|Yes|Yes|
|Calendar month FEs|Yes|Yes|Yes|Yes|Yes|
|Without adj. period|Yes|Yes|Yes|Yes|Yes|
|Pre-mean|0.118|0.055|2.838|5.538|0.536|
|Pre-sd|0.323|0.228|1.591|2.555|0.499|
|R-Squared|0.025|0.051|0.013|0.041|0.046|
|Observations|3832|3832|2742|2741|2742|

<u>Notes:</u> Estimates of case-level impacts of the reform on the quality of pre-trial and deliberation proceedings. See Notes on Table 3. 3844 observations for decisoin stage outcomes, except cols 1-2 (censoring as only for cases with at least one decision stage hearing), and cols 3-5 (missing outcomes).

45
In recent years there has been growing experience with the development of “poverty maps” that combine household survey with population census data, so as to impute income or consumption to each household in the census.<sup>3</sup> The resulting household-level estimates can then be aggregated into welfare indices at different levels of geographic aggregation, and have been found to yield fairly reliable estimates of welfare at the local level. In an attempt to inform the design of policies under the INDH, the HCP developed a poverty map based on census data from 2004 and household survey data from 2002, applying a methodology developed by Elbers, Lanjouw and Lanjouw (2003) under the auspices of the World Bank. Such a poverty map can be used to assess the potential gains in targeting efficiency from geographic targeting.

This paper asks how much the high degree of spatial disaggregation offered by the Moroccan poverty map can help to improve targeting schemes aimed at reducing poverty in the country. The paper builds on the earlier analysis in Ravallion (1993) who finds that spatial disaggregation to the broad regional level in Indonesia – the lowest level at which household survey data provide reliable estimates of poverty – improves targeting, but only to a modest extent. Similarly, in a companion study to the present one, Elbers, Fujii, Lanjouw, Özler, and Yin (2007) find that fine geographic targeting offers significant benefits over broad targeting in Ecuador, Madagascar and Cambodia.. As in Ravallion (1993) and Elbers et al (2007), we consider here the distribution of a hypothetical budget to the population of Morocco. We assume that we have no information about the poverty status of this population other than the geographic location of residence and the level of poverty in each location. As a benchmark case we make the extreme assumption of no knowledge whatsoever about the spatial distribution of poverty – in which case our given budget is distributed uniformly to the entire

> 3 See Elbers, Lanjouw and Lanjouw (2002, 2003) and Demombynes, Elbers, Lanjouw, Lanjouw, Mistiaen and Ozler (2002).

3
13 considered as own revenue (Table 4). This new variable is illustrated in the upper part of Figure 1. This measures takes into account total fiscal autonomy. For example, if one country has considerable proportion of non-tax revenue, like the Czech Republic, and another has a considerable proportion of tax autonomy, like the Slovak Republic, partial measures will show a different level of tax autonomy while overall both countries have a similar level of fiscal autonomy.

_Figure 2 :_ **_Subnational share (%) of governments revenues (1999)_** _10 European transition countries_ <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>BGR CZE EST HUN LVA LTU POL ROM SVK SVN<br>Own revenue ratio Dependent revenue ratio<br><!-- End of picture text --> Source : OECD, Fiscal Design Across Levels of Government, 2001, 2002.

It is important to know how much subnational governments are in control of their own revenues, but it is essential to keep in mind that the size of subnational governments also matters. It is agreed that subnational shares of government expenditure or revenue are not the best approximations of decentralization, but this does not mean that they are irrelevant. For example, subnational governments in the Slovak Republic have a very high level of control over their revenues (see Figure 1). One could think of a high degree of decentralization. However, subnational governments in the Slovak Republic represent a small proportion of aggregate government revenues as shown in Figure 2. Hence subnational governments in the Slovak Republic have a high degree of autonomy over a relatively small share of revenue. In this case, should the Slovak Republic be classified as more or less decentralized?
Table S4. Balance sheet composition during government debt booms

||World|Advanced<br>economies|EMDEs|
|---|---|---|---|
|Short-term government debt, initial|3.5|4.5|2.7|
|(Percent of GDP)|[1.6, 6.2]|[1.9, 7.0]|[1.6, 6.2]|
|Short-term government debt, changes|0.2|1.0|0.1**|
|(Percentage points of GDP)|[0.0, 0.8]|[0.3, 1.6]|[-0.1, 0.6]|
|Foreign currency government debt, initial|14.4|0.6|40.9***|
|(Percent of government debt)|[0.6, 40.8]|[0.1, 3.9]|[27.0, 61.5]|
|Foreign currency government debt, changes|0.0|0.0|0.3|
|(Percentage points of government debt)|[-0.2, 0.5]|[-0.1, 0.0]|[-0.2, 0.7]|
|Nonresident-held government debt, initial|39.0|40.8|35.1|
|(Percent of government debt)|[20.0, 53.8]|[16.4, 54.2]|[22.3, 53.8]|
|Nonresident-held government debt, changes|0.3|0.4|0.3|
|(Percentage points of government debt)|[-0.6, 1.2]|[-0.5, 1.2]|[-0.9, 1.2]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of indicators on balance sheet composition of debt across all government debt booms in respective country groups. Initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. ** and *** denote that medians are statistically significantly different between advanced economies and EMDEs at the 5 percent and 1 percent levels, respectively.

Table S5. Balance sheet composition during government debt booms (relative to nonboom years)

||t-3|t-2|t-1|t|t+1|t+2|t+3|t+4|t+5|
|---|---|---|---|---|---|---|---|---|---|
|Short-term government debt|-0.7|-1.5|0.0|-1.3|-1.2|-1.0|**-1.1**|-0.8|-0.3|
|(Percent of GDP)||||||||||
|Foreign currency government debt<br>(Percent of government debt)|1.6|1.8|0.0|-0.5|**-3.5**|**-2.4**|-1.5|-2.2|-1.5|
|Nonresident-held government debt<br>(Percent of government debt)|-2.7|-3.3|**-4.3**|**-4.2**|**-5.8**|**-4.7**|**-3.2**|-2.5|-1.0|

Note: Coefficients of estimations in which each indicator of balance sheet composition is regressed on a series of dummy variables for government debt booms started in year t, based on data for all countries. The numbers in bold indicate that the coefficients are statistically significant at least at the 10 percent level.

36
58 Table 11

||Argentina|||**Col**|**ombia**|||Mexico||
|---|---|---|---|---|---|---|---|---|---|
||Credit to Credit to<br>housing as construction as<br>hare of<br>share of**total**<br>total credit credit<br>Hou|sing Price|Index/CPI|Credit to<br>construction as<br>share of total<br>credit<br>Ho|using Pric|e Index/CPI|Credit to<br>construction and<br>housing as share<br>of total credit|Housing Pric|e Index/CPI|
|1984|(Av|erage)|(Growth)|(A|verage)|(Growth)|10,80|(Average)<br> <br>0.77|(Growth)|
|1985|||||||9.70|<br>0.71|<br>-7.79|
|1986|10.80|0.69|||||7.40|<br>0.69|<br>-2.82|
|1987|11.10|0.68|<br>-1.00||||6.80|<br>0.59|<br>-14.4|
|1988|13.10|0.71|<br>4.40||||11.10|<br>0.61|<br>3.3|
|1989*|16.20|0.64|<br>-9.90|27.99|0.91||11.50|<br>0.78|<br>27.87|
|1990**|<br>13.05<br>6.20|0.71|<br>10.60|28.49|0.90|<br>-1.10|13.50|<br>0.89|<br>14.1|
|991|14.20<br>6.50|0.82|<br>15.60|21.04|0.90|<br>0.00|13.60|<br>0.95|<br>6.7|
|1992|12.80<br>5.40|0.88|<br>6.80|20.55|0.89|<br>-1.11|12.10|<br>0.97|<br>2.11|
|1993|9.80<br>4.20|0.94|<br>7.00|24.46|0.92|<br>3.37|13.10|<br>0.98|<br>1.03|
|1994|10.30<br>4.20|0.98|<br>4.10|30.11|0.94|<br>2.17|13.40|<br>1.00|<br>2.0|
|1995|12.20<br>4.40|0.99|<br>1.60|30.35|0.96|<br>2.13|14.20|<br>0.96|<br>4.0|
|1996|12.70<br>4.10|0.99|<br>0.10|28.21|0.98|<br>2.08|13.40|<br>0.92|<br>-4.17|
|1997|13.00<br>3.60|0.98|<br>-1.10|22.69|0.99|<br>1.02|13.70|<br>0.92|<br>0.0|
|1998||||16.70|0.98|<br>-1.01||||
|1999|1|||18.82|0.97|<br>-1.02||||

Sources: Argentina: Boletin Estadistico del Banco Central de la Republica Argentina Colombia: Banco de la Republica. Mexico: INEGI.

Argentina: Housing credit from banks and savings institutions. Construction credit from financial institutions.

Colombia: Construction credit from savings and housing corporations.

* Argentina 1989 credit to housing is datum for Aug. '89 and it excludes foreign currency lending.

** Argentina 1990 credit to housing is datum for January '91 and it includes foreign currency mortgage lending.