rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text -->
-<br>JQQ-jQ  Qij,-)F,<br>Manager-only:  _  -4  - . (A2)<br>Q ,j,t  i,Q,t  i,jj,t<br>I1Q,j,t  Qi'j,t-1PP  1<br>Investor-only:  _  j,-I  -.  (A3)<br>Qi, j,t  I,jtjt<br><!-- End of picture text -->

This follows the separation of<sup>the manager-onlyand investor-onlylevelsin our analysisof</sup> bivariate momentum and contagion<sup>trading.</sup>

# Results

Tables **Al-A3** present **OLS** estimates<sup>of themodelsinequations</sup><sup>**(Al),(A2),**and</sup> **(A3).** At the whole-fund level<sup>(Table</sup><sup>**Al),**thefullsample generatessignificant positive</sup> coefficients on all of the first<sup>threevariables.Thus, momentumand contagiontrading are</sup> robust to moving from bivariate<sup>measures tomultivariatemeasures, and includingcontrols</sup> for the overall local and **U.S.**<sup>markets. Interestingly, the local-marketcontrolisnever</sup> significant. The U.S.-market<sup>control,incontrast,isquitesignificant,andnegative.This</sup> squares with past empirical work<sup>showingthat</sup><sup>**U.S.**investorstend tochaseemerging</sup> markets when returns at home<sup>arelow. When thesampleissplit into crisisand non-crisis</sup> sub-periods, we find that contagion<sup>tradingis largely a crisis-period phenomenon.</sup> Tables **A2** and **A3** present<sup>resultsforthe manager-only andinvestor-only</sup> regressions, respectively. At<sup>the managerlevel,wefindsignificantpositive momentum</sup> trading (both lag zero and lag one),<sup>andsignificantcontagiontrading withrespecttothe</sup> **U.S.** market, but no evidence of<sup>contagiontradingwithrespect to otherLatinAmerican</sup> markets **(j33),** except in times of<sup>crisis.Our investor-levelresultstell a distinctly different</sup> story. Once we control for the local<sup>indexreturn, we find that investorsdo notengagein</sup> stock-specific momentum trading.<sup>Thisis not surprising:onewould not expect investorsto</sup> respond to individual stocks, but<sup>tothemarketasawhole.Theydorespondstrongly,</sup> however, to the contemporaneous local-index<sup>return. And theyalso respond strongly within</sup>

**23**
8. Jalan, Jyotsna and Martin Ravallion, 2002, “Geographic Poverty Traps? A Micro Model of Consumption Growth in Rural China,” Journal of Applied Econometrics, Vol. 17, p. 329-346.

9. Kanbur, R and H. Rapoport, 2005, “Migration Selectivity and the Evolution of Spatial Inequality,” Journal of Economic Geography, Vol. 5(1), p. 43-57.

10. Kanbur, R and A. Venables, 2005, Spatial Inequality and Development, Oxford University Press: New York.

11. Machado, J.A.F. and J. Mata, 2005, “Counter-factual Decomposition of Changes in Wage Distributions using Quantile Regression,” Journal of Applied Econometrics, Vol. 20 (4), p.445-465.

12. Narayan, A, and N. Yoshida, 2007, “Trends and Patterns of Poverty in Bangladesh in Recent Years,” World Bank, mimeo.

13. Nguyen, B.T, J.W. Albrecht, S,. B. Vroman, and M. D. Westbrook, 2007, “A Quantile Regression Decomposition of Urban-rural Inequality in Vietnam,” Journal of Development Economics, Vol. 83, p. 466-490.

14. Overman, H.G, P. Rice and A. J. Venables, 2007, “Economic Linkages across Space,” CEP Discussion Paper No. 805.

15. Ravallion, M and Q. Wodon, 1999,“Poor Areas or Poor People?” Journal of Regional Sciences, Vol. 39(4), p. 689-711.

16. Ravallion, Martin, 2005, “Externalities in Rural Development: Evidence for China,” in Ravi Kanbur and Anthony Venables eds. Spatial Inequality and Development, Oxford University Press, New York.

17. Ravallion, Martin and Jyotsna Jalan, 1999, “China’s Lagging Poor Areas,” American Economic Review Papers and Proceedings, Vol. 89(2), pp. 301-305.

24
This sectoral breakdown of investigations has changed over time. Between 1987 and 1992, 95% of all investigations concerned industrial supplies.<sup>52</sup> Hence the need to conduct antidumping investigations with the greatest possible objectivity, since duties could affect the competitiveness of other domestic industries using such products.

This concern led the UPCI to create new administrative mechanisms to mitigate the effect of antidumping duties on some industries or prevent too great an impact on the consumer. This particular issue is discussed in Section IV.

# **3. Safeguard Cases: Almost Non-Existent?**

To date, the UPCI has only conducted three safeguard investigations and has applied (bilateral) safeguard measures in only one occasion. Analysts have been suspicious for quite a long time of this absence of safeguard investigations in Mexico, assuming that in some cases the Mexican government was resorting to antidumping measures when safeguards should have been applied.

Mexican authorities systematically dismissed all safeguard requests filed before them on the grounds that such measures might introduce distortions in the manufacturing sector and, most especially, because of the compensations Mexico would have to pay its trade partners.

The UPCI’s _Informe de Labores 1991-1996_ indicated that several safeguard consultations were requested, but there were only three cases where an investigation was formally requested: pork products, shoes and fish flour. The report stated that many of the safeguard cases filed “were settled by resorting to other types of measures.”<sup>53</sup> In 1993, the National Commission of Hog Farming (CNP, in Spanish) filed the first formal request for safeguard measures, but it was dismissed on the grounds that it did not provide sufficient evidence. In 1994, the National Chamber of the Shoe Industry, and the Chambers of the Shoe Industries in the states of Guanajuato and Jalisco filed the second formal request for safeguard measures against imports of all types of shoes —with the exclusion of parts not

> 52 Reyes de la Torre (1995), p. 389.

> 53 SECOFI (1997), _Informe de labores de la UPCI, 1991-1996_ , p. 93.

23
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||Long-run growth|=Detficit-driven|Difference|
|---|---|---|---|
||(1)|(2)|(1) - (2)|
|Initial tax level|23.7|23.3|0.4**|
|Sizeoftaxchange|0.34|0.22|0.12|
# **_Table F3b: Heterogeneous Impact of COVID-19 School Closures by Women’s Perceived Norms (Conservative versus Liberal Societies)_**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||||Tim|e Spent on(<br>|Hoursper d<br>|ay)...<br>||
||Business<br>(1=Yes;|closed<br>0=No)|Paid|Work|Care and<br>Respon<br>(S|Domestic<br>sibilities<br>elf)|Care and<br>Respon<br>(Spo|Domestic<br>sibilities<br>use)|
||_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19=1|0.110|0.041|0.397|-1.794|3.136***|0.389|1.869**|-1.013|
||[0.09]|[0.06]|[1.47]|[1.10]|[1.19]|[0.63]|[0.93]|[0.94]|
|Women's Community Norm (0-1)|-0.023|0.056|-0.656|0.363|2.183|3.413***|4.216***|-2.253**|
||[0.07]|[0.06]|[1.61]|[1.33]|[1.48]|[0.77]|[0.92]|[1.09]|
|School closure due to COVID-19*Women's Community Norm|-0.117|-0.067|-0.335|3.277*|-3.564**|-0.290|-2.707**|2.308|
||[0.13]|[0.08]|[2.18]|[1.71]|[1.75]|[0.95]|[1.38]|[1.43]|
|GDP per capita (Ln)|0.004|-0.025***|0.159|0.380***|0.108|-0.081|-0.425***|0.031|
||[0.01]|[0.00]|[0.11]|[0.09]|[0.09]|[0.05]|[0.08]|[0.07]|
|Constant|-0.035|0.174**|7.421***|5.070***|1.169|1.934**|4.677***|6.133***|
||[0.10]|[0.07]|[1.50]|[1.27]|[1.23]|[0.77]|[0.96]|[1.07]|
|Observations|11692|23578|6794|13759|11692|23578|8806|17960|
|Adjusted R-squared|0.10|0.09|0.07|0.09|0.14|0.04|0.06|0.07|
|<br>Mean|0.14|0.13|6.83|7.95|6.38|4.09|3.60|6.21|
|Sample<br>Notes:* significant at 10% level, ** significant at 5% level, *** significa|FEMALE<br>nt at 1% level.|MALE<br>|FEMALE|MALE|FEMALE|MALE|FEMALE|MALE|

Notes:* significant at 10% level, ** significant at 5% level, *** significant at 1% level. [1] School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of survey wave. Women's Community Norm is the country-level aggregate of the perceived social norm as reported by men in the sample towards gender equality expressed as a proportion between 0 and 1. <mark>[2] OLS pooled regressions include Region of the world and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls</mark> (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents). Academic Break control is included in the regressions but not shown in the table.

[3] Sample is restricted to business owners who are married or living with a partner with any child dependents aged 18 years or younger in the household and is split by gender with FEMALE (Columns (1), (3), (5), and (7)) versus MALE (Columns (2), (4), (6), and (8)).

71
115 phenomenon is found in both Pfeffermann's and our data. Pfeffermann (1968) found that 17 percent of his sample occasionally earned some extra money from moonlighting, but in our sample the percentage is much lower--5 percent.<sup>2</sup> Lachaud's (1987) study of workers in state owned firms finds that 11.8 percent of these workers have at least one other source of income. These findings suggest that the extent of moonlighting by industrial workers has been decreasing over time, and that in the 1980s the phenomenon is more prevalent among workers in government owned rather than private firms.

Except in the case of young unmarried workers living in their parents' homes, it is rare to find more than one wage earner per household. As soon as job seekers find stable employment, they set up their own households. In response to our question regarding secondary earners in the household, only 7.7 percent of our sample indicated there were others in the home contributing to the family income. This answer may have been biased downward because the question also asked how much was being contributed by others in the family. However, it is interesting to note that only 12 percent of the women indicated that there was another person in the household contributing to the family income and that 7 percent of the men said their spouse or some other family member was contributing. Pfeffermann found that 2.2 percent of the men in his survey had wives who earned money. As with moonlighting, there appears to be a different pattern of household labor force participation in the case of public sector employees. In particular, Lachaud (1987) finds that about onethird of state enterprise workers in his sample live in households with two or more wage earners and that more than one-half of these secondary earners also work in the public sector.

In summary, neither our nor Pfeffermann's findings strongly support the industrialists' argument that increases in industrial wages do not translate directly into increases in the worker's consumption and standard of living. The cross sectional evidence for 1963 and 1986 indicates at best a weak positive relationship between the size of extended families and income. A simple linear regression of earnings and the size of extended family produces 2. This statistic may be another reflection of the deterioration in Senegars economic conditions.
|||**Site 5**||**Site 6**||**Site 7**|
|---|---|---|---|---|---|---|
|Scenario|Surge<br>Level<br>(m<br>PWD)|Attenuation<br>of surge<br>height from<br>the baseline<br>(cm)|Surge<br>Level<br>(m<br>PWD)|Attenuation<br>of surge<br>height from<br>the baseline<br>(cm)|Surge<br>Level<br>(m<br>PWD)|Attenuation<br>of surge<br>height from<br>the baseline<br>(cm)|
|50m_Sonneratia apetala_<br>+50m_Avicennia officinalis_<br>+500m_Ceriops decandra_|4.339|10.130|4.398|12.948|4.541|10.527|
|50m_Sonneratia apetala_<br>+50m_Avicennia officinalis_<br>+1000m_Ceriops decandra_|4.325|11.483|4.377|15.000|4.546|10.056|
|50m_Sonneratia apetala_<br>+50m_Avicennia_ _officinalis_<br>+2000m_Ceriops decandra_|4.297|14.329|4.350|17.752|4.481|16.476|

# <u>Reductions in water flow velocity from afforestation of mangrove species</u>

Study Area 1: Site 1 Water flow velocity of Sidr (2007): 0.624 m/s Study Area 1: Site 2 Water flow velocity of Sidr (2007): 0.593m/s Study Area 2: Site 3 Water flow velocity of Sidr (2007): 0.687m/s Study Area 2: Site 4 Water flow velocity of Sidr (2007): 0.651m/s <!-- Start of picture text --> Site 1 Site 2 Site 3 Site 4<br>85% 84% 85% 84% 50 m width of Sonneratia apetala at 7.5m spacing followed by<br>50 m width of Avicennia officinalis at 6m spacing<br>86% 86% 87% 87% 50 m width of Sonneratia apetala at 7.5m spacing followed by<br>50 m width of Avicennia officinalis at 4m spacing<br>85% 87% 85% 89% 50 m width of Sonneratia apetala at 5m spacing followed by<br>50 m width of Avicennia officinalis at 6m spacing<br>90% 91% 89% 92% 50 m width of Sonneratia apetala at 5m spacing followed by<br>50 m width of Avicennia officinalis at 4m spacing<br>29% 42% 29% 32%<br>50 m width of Avicennia officinalis at 6m spacing<br>32% 44% 30% 42%<br>50 m width of Avicennia officinalis at 4m spacing<br>31% 42% 29% 38%<br>50 m width of Sonneratia apetala at 7.5m spacing<br>34% 46% 33% 46%<br>50 m width of Sonneratia apetala at 5m spacing<br><!-- End of picture text --> 21
<!-- Start of picture text --> oe COL ——ARG —-— —CHL ———MEX —PER OECD?<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 [-T 10<br>0 0<br>: 0.1 0.6 1.1 1.6 2.1PJ2.6 3.1 3.6 4.1 4.6 5.1 5.6 .<br><!-- End of picture text -->
36 significant. Thus the stocks of unemployment and vacancies seem to capture most of the variation across voivodship types but not the variation over time.

Even though the regression is exploratory, the result has some credence as the coefficient estimates on vacancies, short-term and long-term unemployment are of the same order of magnitude as the estimates of Boeri (1994) who estimated a Polish matching function for the time period January 1992 to March 1993. The estimates imply that an increase in the number of short-term unemployed has a much larger effect on hirings than an increase in the number of long-term unemployed. Thus the two pools are not perfect substitutes in the matching process.<sup>34</sup> The strongly diverging elasticities also indicate that the previously presented estimates of job accession rates which were conditioned on duration of unemployment (cf. pp. 26-27) could be seriously biased. Like in OECD countries outflows from unemployment into employment seem to fall drammatically with duration of unemployment.

The most important result is the insignificance of the coefficient on the training variable. This result can be interpreted that after having ended a training course an unemployed person has not increased his/her effectiveness in search of a job, it can, however, also be interpreted that training participants among the unemployed have increased their search effectiveness but have, in the hiring process, then "crowded out" non-participants among the unemployed. While the results of this regression do not allow to discriminate between the two scenarios they do seem to imply that an increase in the (lagged) stock of training participants does not push up the flow from unemployment into regular employment. Whether Polish training courses have a too small element which enhances human capital or whether unemployed who have decisively enhanced their human capital just "jump the waiting queue" of those searching for a job<sup>35</sup> the overall net effect of Further Training and Re-Training on hirings seems to be zero.

The labour force survey evidence presented by G6ra, Socha and Sztanderska (1995) on the relative magnitude of labour market transition probabilities of training participants cannot be sensibly used to check our result. They establish that the

> 34This statement is equivalent to the introduction of the search effectiveness index c into the matching function. Because long-term unemployed are not perfect substitutes for short-term unemployed when it comes to hires the search effectiveness of the stock of unemployment falls as the proportion of long-term unemployed rises in the total stock. For a thorough discussion of the reasons **why** long-term unemployed are 'worse" job searchers see e.g. Layard, Nickell and Jackman (1991).

350f course, a combination of both is most likely.
larger level of spatial aggregation increases the estimated impact of the shock—although not significantly—without affecting statistical significance (table 4.6). In future work it would be interesting to explore much larger areas of aggregation if this is possible (i.e., if enough variation in the hazard is left and GPS data are available) to see if this affects the results.

**Table 4.6. Results for different areas of spatial aggregation of hazard data**

||**Maize(GPS**<br>|**coordinate c**<br>|**ountries only)**<br>|**2**<br>|**0 km radius**<br>||<br>|**50 km radius**<br>||
|---|---|---|---|---|---|---|---|---|---|
||**10km**|**20km**|**50km**|**Maize**|**Highland**|**Roots**|**Maize**|**Highland**|**Roots**|
|**Landed h**|**ouseholds**|||||||||
|Hazard|0.066***|0.075***|0.086***|0.055***|0.045***|0.152***|0.056***|0.045***|0.154***|
||[4.252]|[4.608]|[4.765]|[3.852]|[4.495]|[8.123]|[3.722]|[4.495]|[8.487]|
|**Landless**|**households**|||||||||
|Hazard|0.109***|0.112***|0.123***|0.043**|-0.005|0.166***|0.045**|-0.004|0.159***|
||[4.165]|[4.212]|[4.451]|[2.239]|[-0.214]|[3.698]|[2.276]|[-0.176]|[4.017]|
|Obs.|34,824|34,739|35,212|50,266|15,046|5,705|50,739|15,060|5,856|
|R-sq|0.138|0.137|0.133|0.362|0.265|0.175|0.357|0.260|0.177|

_Source:_ World Bank.

_Note:_ Results are from a weighted regression such that each household is assigned its survey-specific population weight. Dependent variable is the log of per capita (or adult equivalent) consumption. The hazard variable used is the soil moisture index. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, agroecological zone indicators, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district. Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

# 4.3. Exploring food and nonfood consumption impacts separately

Four of the nine countries (Ethiopia, Malawi, Mozambique, and Niger) had separate aggregates of food and nonfood consumption in the data sets available for analysis. We conducted the same analysis separately on food and nonfood consumption for these countries to assess whether the main impact was on food consumption or whether households protected food consumption by reducing nonfood consumption; the latter result is found, for example, in Kochhar and Knippenberg (2023) using a similar method. Tables 4.7 and 4.8 present results for the maize and highland livelihood zones respectively. Results are shown using just the soil moisture variable. For both livelihood zones, the impacts of the hazard variables on food consumption are larger than impacts on nonfood consumption. Nonfood consumption is impacted only among landed households. Further work with data from additional countries would be informative in understanding whether this pattern holds.

25
# **Usus Fructus: Taking Account of Property Use Rights in Household Wealth Measurement**

G. D’Alessio,* A. Neri ** and I. Toma *

# **JEL Classification** : D23, D31, P14.

**Keywords** : property rights, use rights, usufruct, wealth distribution.

# **Index**

|1.|Introduction ............................................................................................................... 4|
|---|---|
|2.|Some general reflection on use rights ....................................................................... 4|
|3.|The data ..................................................................................................................... 6|
|4.|Taking account of use rights in Italy ......................................................................... 8|
|5.|Free residential houses in HFCS data ....................................................................... 9|
|6.|Conclusions ............................................................................................................. 10|
|Ap|pendix A: statistical tables ......................................................................................... 11|
|Ap|pendix B: SHIW empirical evidence on the value of use rights ................................ 17|
|Ref|erences ....................................................................................................................... 19|

_Usufructus est ius alienis rebus utendi fruendi, salva rerum substantia_ Roman law

* The World Bank, Development Data Group - Living Standards Measurement (DECLS Unit).

** Bank of Italy, Directorate General for Economics, Statistics and Research.
**Figure 1: Levels and trends for poverty between HBS and NPS in Tanzania Mainland**

<!-- Start of picture text --> 40<br>34.4<br>35<br>30 28.2<br>25<br>21.2<br>20 18.1<br>14.6<br>15<br>10<br>5<br>0<br>HBS 2007 HBS 2011/12 NPS 2008/09 NPS 2010/11 NPS 2012/13<br>Source : HBS 2007 and 2011/12 and NPS 2008/09, 2010/11 and 2012/13.<br>the basic needs poverty line<br>Percentage of population below<br><!-- End of picture text --> Discrepancies in poverty incidence and trends are observed at the regional level. As shown in Table 1, the poverty level in the rural areas that was estimated using the NPS data is two times lower than that estimated using the HBS data, while the poverty incidence in urban areas measured using NPS is over three times lower than that estimated using HBS. In addition, HBS suggests a decline in poverty in all regions, while NPS indicates a decline in poverty only in Dar es Salaam (and Zanzibar).

**Table 1: Poverty headcount by region (percent)**

|||**HBS**||**NPS**||
|---|---|---|---|---|---|
||**HBS 2007**|**HBS 2011/12**|**NPS 2008/09**|**NPS 2010/11**|**NPS 2012/13**|
|Tanzania|N/A|N/A|14.8|17.9|20.9|
|Tanzania Mainland|34.4|28.2|14.6|18.1|21.2|
|Rural|39.4|33.4|17.2|22.7|26.4|
|Urban|22.7|21.5|7.7|6.7|8.5|
|Dar es Salaam|14.1|4.0|1.0|1.4|0.6|
|Zanzibar|N/A|N/A|20.4|12.4|14.3|

_Source_ : HBS 2007 and 2011/12 and NPS 2008/09, 2010/11 and 2012/13.

# **B. Inequality trends**

NPS and HBS data indicate important differences in inequality levels and trends. Although HBS 2007 and NPS 2008/09 show similar inequality levels with a Gini index of real per capita monthly 4