rafmacalaba/datause-encoder-data huggingface dataset:
**Tables 5: Communes with highest estimated stunting rates**

|Rank||Highest rate|||Highest number||
|---|---|---|---|---|---|---|
||Commune<br>ID|DISTRICT|Stunting<br>rate|Commune<br>ID|DISTRICT|Estimated<br>number of<br>stunted<br>children|
|1|118370|Antsirabe II|59.80|101005|Antananarivo<br>Renivohitra|192,859|
|2|118310|Antsirabe II|59.33|102039|Antananarivo<br>Avaradrano|144,928|
|3|118170|Antsirabe II|59.29|118010|Antsirabe II|141,220|
|4|118332|Antsirabe II|59.14|101006|Antananarivo<br>Renivohitra|107,916|
|5|118290|Antsirabe II|59.13|102079|Antananarivo<br>Avaradrano|106,032|
|6|118350|Antsirabe II|59.09|310030|Toamasina II|98,555|
|7|110292|Ambatolampy|59.07|102319|Antananarivo<br>Avaradrano|89,019|
|8|118250|Antsirabe II|58.95|310010|Toamasina II|65,808|
|9|118130|Antsirabe II|58.93|103172|Ambohidratrimo|54,678|
|10|118190|Antsirabe II|58.78|118331|Antsirabe II|54,101|

Source: Authors’ estimations Interplay between poverty and stunting and categorization of hidden hunger We have used an innovative method to create a bivariate map that integrate poverty and stunting data derived from the applications of SAE. This map ranks communes based on their combined levels of poverty and stunting, offering valuable insights into their socio-economic and nutritional status. Our visualization includes two maps: one that combines poverty rates and stunting rates (Figure 5), and another that combines the number of stunted children with the number of impoverished households (Figure 6). In this visual representation, communes shaded in a deep purple hue indicate regions where both poverty and stunting rates (or numbers) are high, as one would expect. Conversely, communes shaded in gray represent areas where both stunting, and poverty rates (and numbers) are low. The remaining seven color shades provide a spectrum of options for tailored policy interventions, addressing the unique nutritional and poverty challenges that different communes are facing.

The maps presented in Figure 5 serve as valuable tools for nutrition practitioners to refine their intervention strategies. For instance, in communes where stunting is prevalent, but poverty is not a significant issue, practitioners should incorporate elements that address behavioral changes in their interventions, helping households overcome non-financial barriers preventing them from adopting nutritious diets. In communes where both poverty and stunting rates are high, policy makers can employ traditional stunting prevention interventions. For the remaining communes where stunting rates are low, and poverty levels vary from high to low, policymakers can implement conventional poverty reduction measures or choose to simply provide reminders on good nutritional practices, respectively.

14 | P a g e
homicide victims are products of domestic violence. Looking at the United States, in 2011, 61 percent of female homicide victims were wives or intimate acquaintances of their killers.<sup>2</sup> The situation is perhaps worse in developing countries that tend to have weaker institutions and limited enforcement mechanisms.

A substantial literature exists on the drivers of overall adult mortality rates (see for example, Cutler et al. 2006 and Kavanagh 2015). In contrast, there is hardly any research on the determinants of women’s mortality rates, let alone the impact of domestic violence legislation on women’s mortality rates. Furthermore, the literature on legal institutions has mainly focused on labor market outcomes for women (see for example, Amin and Islam 2015, Weichselbaumer and Winter-Ebmer, 2007). In this study, we attempt to fill this gap in the literature by exploring the relationship between the presence of domestic violence legislation and the women-to-men adult mortality rate. Our choice of women’s mortality rate relative to men as an outcome variable has a distinct advantage of washing away spurious correlations due to broader factors responsible for improving the mortality rates of both women and men. We utilize recent data from the Women, Business, and the Law 2016 report (World Bank, 2015). The relationship between domestic violence legislation and women’s mortality rates is in much need of empirical validation as there is both a good argument for and against any effect being uncovered. On the one hand, the specific mention of domestic violence legislation may reduce women’s mortality through various channels outlined in the conceptual framework (section 2). On the other hand, there may be little enforcement of such legislation thereby debilitating any impact the law may have on the prevalence of domestic violence.

> 2 See for example, Violence Policy Center (2013).

3
import prices and import values for the United States and India, respectively.<sup>2</sup> Bown and Crowley (2006) show that imposition of AD duties also affects the prices of the targeted countries’ products in alternative destinations. Studies examining the pricing effect of AD duties also consider the impact of events (filing, decision, and termination) during the determination period of AD investigations into the pricing decisions of foreign firms (Staiger and Wolak 1994; Krupp and Polard 1996). Moreover, Prusa (1992) focuses on how AD cases might lead to collusion between domestic and foreign firms. We contribute to this literature through the innovative use of four-dimensional customs data (firms, products, destinations, and years) to determine the effects of AD duties and the threat of their enforcement on export prices.

Our retaliation analysis broadly fits into the literature that investigates retaliatory incentives in AD activity. Many existing studies propose a number of political and strategic considerations to explain the proliferation of AD duties even though the ideal AD case aims to hinder unfair competition in the international market. One proposed explanation is that countries tend to retaliate against countries by which they were previously targeted in AD investigations (Miranda et al. 1998; Prusa and Skeath 2002; Francois and Niels 2004; Feinberg and Reynolds 2006). These studies suggest that this tit-for-tat strategy in AD actions has created an “AD club,” which consists of countries that have implemented and actively utilize AD law. Blonigen and Bown (2003), however, provide another perspective on the debate. They suggest that the worldwide spread of AD regimes may dampen AD activity because countries become able to retaliate when they begin enforcing these laws. Concentrating on AD activity in the United States, these authors show that the risk of a potential reaction from the target country decreases the probability of an AD petition from U.S. industries as well as decreasing the likelihood of an affirmative AD decision from the U.S. AD agency. Whether retaliation encourages or dampens 3
Fuller, B. 1985. Raising School Quality in D0velopinx Countries: **What** Investments Boost Learning? Washingtont World Bank, Education and Training Series, *EDT17.

- Government of Peru. 1981. Censos Nacionales. Tomo I, II. Limas Oficina Nacional de Estadistica y Censo.

- Government of Peru. 1982. Desigualidades en el Acceso a la Escuela. Limat Ministerio de Educacion.

- Grilichee, Z. and W. H. Mason. 1972. 'Education, Income and Ability'. Journal of Political Economy, 80 (2): pp. 74-103.

- Gronau, R. 1977. 'Leisure, Home Production and Work." Journal of Political Economy. 85:1099-124.

- Grootaert, C. and A. M. Arriagada. 1986. The Peruviar .iving Standards Surveys An Annotated Questionnaire. Washington, D.C.: The World Bank, July.

- Heckman, J.J. 1979. 'Sample Selection as a Specification Error." Econometrica, 68s 200-207.

- Heyneman, S. P. and W. A. Loxley. 1983. 'The Effect of Primary-School Quality on Academic Achievement Across Twenty-Nine High-and Low-Income Countries.' American Journal of Sociology, 88 (6): 1162-94.

- King, E. and L. Lillard. 1983. Determinants of Schooling Attainment and Enrollment Rates in the Philippines. Santa Monica: The Rand Corporation.

- Klng, S. 1986. 'Does Education Pay in the Labor Market?" Womens' Labor Force Particigation, Occupation and Earnings Peru. The World Bank.

- Lockheed, M. and A. Komenan. 1988. 'School Effects on Student Achievement in Africas The Case of Nigeria and Swaziland." Washington, D.C.s World Bank, Working Paper Series #71.

- Mincer, J. 1974. Schooling, Esperience, and Earnings. New Yorks Columbia University Press.

- Hoock, P. and J. Leslie. 1986. 'Childhood Malnutrition and Schooling in the Terai Region of Nepal.* Journal of Development Economics, 120: 33-52.

- Moock, P. and R. Bellow. 1988. Vocational and Technical Education in Peru. Washington, D.C.: The World Bank. Draft.

- Paulston, R. 1972. Society, Schools and Progress in Peru. Pergamon Press.

- Rosenzweig, M. R. and R. Evenson. 1977. 'Fertility, Schooling and the Economic Contribution of Children in Rural Indias An Econometric Analysis.' Econometrica, 45 (July)s 1065-1080.

48
- 35 -

# VI* SUMMARY AND CONCLUSIONS

60. This paper **has** presented evidence on the reaction of 20 commodity futures price. to news in announcements about money supply (MH), inflation indices (CPI and PPI), and real activity indices (industrial production annd unemployment) and to shocks from the foreign exchange and credit markets. For macroeconomic variables (money stock, CPI, PPI, the unwmployment rate and the industrial production index) about which announcements are made periodically, survey data were used to separate the announcements into expected and unexpected components--with the latter measuring news. For other macroeconomic variables, whose values are realized on financial and credit markets (exchange rates and interest rates), autoregressions were used to model their daily behavior and the residuals from these autoregressions were taken to be exogeneous shocks to commodity markets.

61. It was found that careful consideration must be given to the stage of the ..lsiness cycle when analyzing the impact of news on commodity prices. The reaction to news about other macroeconomic variables as well as to economic activity variables themselves appears to be affected by the stage of the business cycle. Most of the significant commodity price reactions were in the period 10/01/86-12/31/87 when the economy was moving out of a local recession. It is not clear why this is so; several possible reasons have been presented. It is a question which warrants further investigation. News about real activity initiated a response in commodity prices virtually only when the economy was moving out of a local recession. The impact of exchange rate and interest rate shocks on commodity prices were found to be significant.
_________________________________________________________________________________

|Country|Year of<br>|Labor p<br>|roductivity premia<br>|No. of<br>|No. of<br>|
|---|---|---|---|---|---|
|___________________|start<br>____________|of expo<br>[p-valu<br>__________|rt-starters (percent)<br>e]<br>___________________|observ.<br>_____________|starters<br>________|
|West Germany|1998|3.82|[0.10]|8,057|334|
||1999<br>|2.32<br>|[0.35]<br>|7,869<br>|352<br>|
||2000|2.90|[0.21]|8,722|362|
||2001|5.65|[0.02]|8,465|338|
||2002|3.80|[0.11]|8,575|337|
||2003|9.54|[0.00]|8,413|669|
||2004|0.42|[0.87]|7,744|296|
|East Germany|1998|1.84|[0.67]|2,272|103|
||1999|-1.30|[0.78]|2,345|117|
||2000|5.95|[0.19]|2,590|94|
||2001|-2.34|[0.60]|2,628|103|
||2002|7.14|[0.14]|2,651|126|
||2003|7.30|[0.04]|2,632|185|
||2004|4.06|[0.40]|2,490|109|
|Italy|1992|18.72|[0.00]|2,967|353|
||1993|21.01|[0.00]|2,855|353|
||1994|13.84|[0.00]|2,455|261|
||1995|21.47|[0.00]|2,380|249|
||1996|21.03|[0.00]|2,150|283|
||1997|10.66|[0.02]|1,641|144|
|Republic of Ireland|1994|15.04|[0.22]|306|26|
||1995|4.86|[0.71]|308|24|
||1996|2.31|[0.83]|319|32|
||1997|15.30|[0.22]|314|32|
||1998|-6.17|[0.67]|315|22|
||1999|9.08|[0.26]|326|30|
||2000|-7.94|[0.28]|321|31|
||2001|24.14|[0.09]|318|31|
||2002|17.58|<br>[0.22]|327|20|
||2003|14.58|[0.24]|334|26|
||2004|-6.89|[0.64]|296|9|
|Slovenia|1997|11.97|[0.01]|69|6|
||1998|-3.83|[0.88]|85|7|
||1999|-16.71|[0.36]|100|18|
||2000<br>|4.03<br>|[0.66]<br>|95<br>|15<br>|
||2001|-6.50|[0.62]|90|10|
||2002|22.10|[0.21]|84|14|
|Spain|1993|16.71|[0.07]|282|32|
||1994|8.62|[0.47]|268|29|
||1995|23.54|[0.06]|236|22|
||1996|20.85|<br>[0.09]|234|28|
||1997|27.73|[0.07]|220|35|
||1998|8.59|[0.60]|178|24|
||1999|27.29|<br>[0.28]|173|10|

48
# Table 11: Program Impact on Child Participation in Farm Labor

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|0.024|0.01|0.033|0.04|0.014|0.06|-0.034|0.01|-0.111|
||[0.025]|[0.032]|[0.037]|[0.029]|[0.038]|[0.042]|[0.045]|[0.046]|[0.080]|
|Dry Rations|-0.045|n.a.|-0.089|-0.05|n.a.|-0.089|-0.044|n.a.|-0.118|
||[0.032]|n.a.|[0.037]**|[0.037]|n.a.|[0.042]**|[0.068]|n.a.|[0.084]|
|Follow-up|0.087|0.047|0.13|0.075|0.041|0.114|0.133|0.071|0.21|
||[0.017]***|[0.023]**|[0.026]***|[0.020]***|[0.027]|[0.029]***|[0.032]***|[0.032]**|[0.056]***|
|Girl|-0.178|||-0.176|||-0.188|||
||[0.012]***|||[0.014]***|||[0.022]***|||
|Constant|0.334|0.247|0.227|0.336|0.261|0.223|0.827|0.816|0.657|
||[0.022]***|[0.030]***|[0.027]***|[0.023]***|[0.033]***|[0.028]***|[0.033]***|[0.038]***|[0.045]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.26|0.29|0.24|0.25|0.27|0.23|0.23|0.19|0.21|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether child participated in any farm labor the week before the survey. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

34
# **Table A.17. The district fixed-effect RDD-DD regressions of individual-level outcomes using districts with the 2006 poverty rate** **<u>from 35%</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1021*|0.0561***|0.0270|0.5345*|0.3477**|0.3535***|-0.0080|1.2841***|
||(0.0569)|(0.0158)|(0.0546)|(0.3071)|(0.1461)|(0.1340)|(0.0448)|(0.4805)|
|(Poverty rate – 50) * Post-program|0.0066|-0.0060***|-0.0034|-0.0319|-0.0566***|-0.0535***|0.0075|-0.0467|
|period|(0.0059)|(0.0019)|(0.0044)|(0.0294)|(0.0210)|(0.0201)|(0.0049)|(0.0517)|
|Program * (Poverty rate – 50) * Post-|-0.0023|0.0049**|0.0021|-0.0039|0.0558**|0.0519**|-0.0102**|0.0504|
|program period|(0.0064)|(0.0019)|(0.0051)|(0.0325)|(0.0214)|(0.0205)|(0.0051)|(0.0546)|
|Age|0.0114***|0.0062***|0.0240***|0.2651***|-0.0143***|-0.0126***|0.1571***|0.3874***|
||(0.0021)|(0.0010)|(0.0012)|(0.0127)|(0.0033)|(0.0031)|(0.0093)|(0.0473)|
|Age squared|-0.0001***|-0.0001***|-0.0003***|-0.0037***|0.0004***|0.0004***|-0.0081***|-0.0197***|
||(0.0000)|(0.0000)|(0.0000)|(0.0002)|(0.0001)|(0.0001)|(0.0004)|(0.0021)|
|Male|-0.0822***|-0.0076**|0.0942***|1.0887***|-0.1543***|-0.1370***|0.0370***|0.1056***|
||(0.0075)|(0.0032)|(0.0057)|(0.0628)|(0.0233)|(0.0220)|(0.0105)|(0.0325)|
|Ethnic minorities (yes=1, Kinh=0)|0.1939***|-0.1021***|-0.0328*|-0.2361|-0.2387**|-0.2305**|-0.1293***|0.7992***|
||(0.0283)|(0.0113)|(0.0180)|(0.1705)|(0.1059)|(0.1048)|(0.0258)|(0.1114)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.4184***|0.0093|-0.3031***|-2.9807***|0.7855***|0.6951***|0.2662***|-1.1919***|
||(0.0455)|(0.0168)|(0.0275)|(0.2782)|(0.0797)|(0.0782)|(0.0530)|(0.2757)|
|Observations|118,369|118,369|118,369|118,369|82,402|82,402|50,645|50,645|
|R-squared|0.191|0.070|0.187|0.167|0.084|0.083|0.195|0.330|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

67
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 5.2. Spending on regional infrastructure as a percentage of national infrastructure spending**

<!-- Start of picture text --> Congo DR<br>Chad<br>Cameroon<br>Congo Rep<br>0% 50% 100% 150% 200% 250%<br>existing national infrastructure spending<br>Regional infrastructure spending needs as a % of<br><!-- End of picture text --> _Source:_ Derived from Carruthers and others 2009, Rosnes and Vennamo 2009 and Mayer and others 2009.

56
38

## **Table VIII. Probability of Democratization AND Autocratization by Recent Intervention Onset. Multinomial Logit Model, 1961–96**

||**Autocrat**<br>|**ization**<br>|**Democra**<br>|**tization**<br>|
|---|---|---|---|---|
|**Explanatory variables**|β<br>**(s.e.)**|**p-value**|β<br>**(s.e.)**|**p-value**|
|Democratic military interven-<br>tion|0.31<br>(0.44)|0.477|0.91<br>(0.31)|0.004**|
|Autocratic military interven-<br>tion|0.93<br>(0.38)|0.013**|0.33<br>(0.38)|0.387|
|Number of non-governmental<br>organizations,logged|–0.29<br>(0.086)|0.001***|0.39<br>(0.096)|<0.0005***|
|Post-Cold war period|–0.58<br>(0.27)|0.032*|0.79<br>(0.20)|<0.0005***|
|Polity2, lagged|0.14<br>(0.021)|<0.0005***|–0.14<br>(0.018)|<0.0005***|
|Polity2, lagged, squared|–0.044<br>(0.0038<br>)|<0.0005***|–0.033<br>(0.0035)|<0.0005***|
|Proximity of democratization|–0.23<br>(0.25)|0.367|0.35<br>(0.19)|0.069 (*)|
|Constant|–0.099<br>(0.46)||–4.79<br>(0.55)||
|N|4,840||||
|Loglikelihood|–1212.11||||
|Pseudo-R2|0.170||||

(*) Significant at 0.10 level

- Significant at 0.05 level

- ** Significant at 0.01 level

- *** Significant at 0.001 level

The model was estimated using robust standard errors. Significance levels refer to two-sided tests. ‘ref. gr.’ is the reference category of the respective dummy variable The analysis includes only countries that scored 8 or lower on the Polity2 scale prior to the observation.

The onset of democratic interventions is found to lead to subsequent democratizations just as in Table VI. We also find the onset of autocratic interventions to be followed by a high probability of autocratizations. We do not find democratic interventions to reduce the risk of subsequent autocratization, however, nor do autocratic interventions reduce the risk of democratization. The estimate for democratic interventions in the autocratization equation as well as that for autocratic interventions in the democratization equation are not statistically significant, and even have a positive sign.

# **What Kind of Democratization Follows Democratic Intervention?**

The analysis reported in Table VI shows that democratic interventions tend to be followed by changes toward democratic institutions. However, democratization was defined as a change of at least two units at the 21-point Polity2 scale. In addition to fairly marginal changes from any initial democracy level, these changes may include changes from very repressive autocracies to semi-
11 (v) The HES also suggests that urban poverty levels exceeded rural levels after 1981/82. This is surprising. The result is not confirmed by the alternative estimates based on the means from NA. All of these comparisons of urban and rural poverty levels may be, however, highly sensitive to the assumed cost-of-living differential (of about 50%) between sectors implicit in the BBS poverty lines for 1981/82. I have no basis for properly evaluating that assumption.

# III. Growth. Inequality, and Poverty

# Methodological Issues

There has been much debate about whether poverty is best alleviated by promoting economic growth, or by reducing economic inequalities. The level of poverty can be thought of as a function of mean consumption, the poverty line, and the inequality of consumption around that mean, as embodied in the Lorenz curve. An increase in the mean, holding the Lorenz curve and poverty line fixed, will unambiguously decrease any well behaved poverty measure, Following recent practice, I shall refer to such an increase in the mean as "distributionally neutral growth". Note that this requires an equiproportional. increase at all levels.

Certain shifts in the Lorenz curve will also reduce poverty, holding the rean constant. If the Lorenz curve shifts such that the share of any poor household increases, with the corresponding loss being incurred by any non- poor person, then the outcome is unambiguous poverty must fall. However, there is little one can say in general about the qualitative effect on poverty of a reduction in inequality;<sup>8</sup> for example, while a small transfer from
years. By 2019, it had increased to 14.2 years and is expected to continue increasing.<sup>3</sup> Together, the data points to the fact that there are increasingly more older Indonesians, and that they can expect to live longer on average.

**Figure 1: Estimated share of population by age, 1990-2100**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Year<br>Age 0-14 Age 15-64 Age 65+ Transition into aging society Transition into aged society<br>Share of population (%)<br>1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2053 2056 2059 2062 2065 2068 2071 2074 2077 2080 2083 2086 2089 2092 2095 2098<br><!-- End of picture text --> Source: UN World Population Prospects 2022

**Aging and the rising share of older persons raises policy challenges in a number of areas, including income security and employment.** At 36 percent, a large share of the elderly is either poor or vulnerable, a phenomenon that will only be exacerbated by rapid aging without adequate policy responses (Holmemo et al. 2020). Coverage of the elderly by existing social assistance programs is limited, thus increasing their vulnerability. At the same time, in 2023 only 18.2 million and 14.3 million workers – making up 13 and 10 percent of all workers respectively – were covered by old age savings ( _Jaminan Hari Tua_ , JHT) and pensions ( _Jaminan Pensiun,_ JP) respectively (Holmemo et al. 2020). Further, the estimated adequacy for covered workers is also low, as reflected by low projected replacement rates (Holmemo et al. 2020).<sup>4</sup> The vulnerability of older persons, as well as the lack of coverage and adequacy of social assistance and social insurance programs and rising life expectancy create the need for older persons to work longer.

# **Indonesia has already started addressing these concerns by gradually raising the retirement**

**age.** The government started increasing the retirement age in 2014, with an increase in retirement age by one year every three years. Between 2014 and 2022, the retirement age was raised from 55 to 58. This will continue until the retirement age reaches 65 by 2043 (OECD 2023). Aside from income security, this policy move also seeks to address the sustainability of the pension system (Holmemo et al. 2020). A study by Kudrna, Piggott and Poonpolkul (2021) models the impact of raising the retirement age on the economy, and finds positive effects on consumption, labor supply, and welfare of formal workers. Further, they find that the increase in the retirement age makes the contributory pension system more sustainable. Raising the retirement age and extending working

> 3 Life expectancy at age 65 was lower between 2020 and 2022 due to the COVID-19 pandemic.

> 4 Using the current contribution rate, Holmemo et al. (2020) estimate the future replacement rate by projecting the accumulated balance after 40 years of contributions plus interest, and converting it into an actuarially fair annuity. See Holmemo et al. (2020) for technical details of the estimation.

2