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22
other major cities of 38 to 115,000 residents, and 26 districts (most of which have between 25,000 and 65,000 residents). The persistence of unemployment is suggested by the fact that the correlation between end-of-year unemployment rates in 1993 and 2002 across 33 NUTS 3 regions is 0.77. The correlation in year-on-year unemployment rates varies from 0.925 to 0.991 in the 1995-2002 period.

Appendix Figure 9 and Appendix Tables 12 and 13 show that overall disparities in local unemployment increased sharply until 1994, fell between 1994 and 1998, and rose again in 1999-2001. These trends were driven by trends in districts with above-average unemployment. This pattern holds true whether or not weights for the size of the local labor force are taken into account. The picture does not change either when the 7 major cities of Latvia are included in the adjacent districts. Appendix Figure 9 also charts the evolution of the “unemployment Gini index”, which is calculated in the same way as the usual “income Gini” and measures inter-regional inequity of distribution of unemployed persons in country’s labor force (ignoring inequity within districts in question). Recent values of Gini (0.31) are quite close to Latvia’s income Gini (0.34 in 2001). This Gini value suggests a much more uneven distribution of unemployment than, say, in Lithuania. According to this measure, the Lithuanian unemployment Gini is about 0.20.

Persistence in wages has been even stronger than that of unemployment. Year-on-year wage correlations across districts vary between 0.954 and 0.992. The correlation for 1992-2002 is 0.92. The overall level of disparities, as measured by Gini or weighted coefficients of variation, fell in 1993-1995. Since then, inter-district wage disparities appear to have remained stable. This trend is shown in the left bottom panel of Appendix Figure 9.

Still, non-weighted measures of inter-regional wage disparities, which do not account for variation in the number of workers in regions, show that such differences have been declining since 1996. In other words, the position of small low-income districts is gradually improving. This is shown in the right bottom panel of Appendix Figure 9. The wage advantage of Riga and some other major cities has been shaping the trend. Disparities between average wages of NUTS4 regions account for approximately 25% of overall inequality of in the wage distribution of Latvia.

The next section analyzes labor market dynamics in Latvia over recent years, focusing on flows between employment, unemployment, and inactivity; determinants of exit from unemployment; job search methods of unemployed; and the evolution of relative wages.

19
|**-Table 4.6b:**<br>**H**<br>**In**|**HDI**<br>**DI-M**<br>**come**|**-Income and**|**HDI-Health**<br>**1991 2000**|**HDI-M**<br>**Health**||
|---|---|---|---|---|---|
||**2000**|**Growth (%)**<br>**1991-2000**||**2000**|**Growth (%)**<br>**1991-2000**|
|Distrito Federal|0.842|5.5|Rio Grande do Sul|0.769|3.4|
|São Paulo|0.790|3.5|Santa Catarina|0.762|4.1|
|Rio de Janeiro|0.779|6.8|Distrito Federal|0.756|3.4|
|Rio Grande do Sul|0.755|7.7|São Paulo|0.753|4.5|
|Santa Catarina|0.750|10.2|Paraná|0.743|5.6|
|Paraná|0.736|8.9|Minas Gerais|0.736|6.2|
|Mato Grosso|0.719|8.8|Rio de Janeiro|0.727|6.1|
|Espírito Santo|0.719|10.4|Espírito Santo|0.726|6.7|
|Mato Grosso do Sul|0.718|6.7|Goiás|0.726|4.8|
|Goiás|0.718|8.0|Mato Grosso do Sul|0.724|5.1|
|Minas Gerais|0.711|9.5|Mato Grosso|0.722|5.4|
|Rondônia|0.683|10.2|**Pará**|**0.718**|**5.8**|
|Roraima|0.682|-6.0|Ceará|0.709|13.0|
|Amapá|0.666|2.4|Amapá|0.707|6.3|
|Pará|0.643|7.4|Piauí|0.706|7.4|
|Acre|0.640|5.4|Amazonas|0.704|6.9|
|Rio Grande do Norte|0.636|10.0|Tocantins|0.703|7.0|
|Amazonas|0.634|-0.7|Roraima|0.699|6.9|
|Tocantins|0.633|9.0|Rio Grande do Norte|0.690|9.0|
|**Pará**|**0.629**|**4.6**|Paraíba|0.687|9.3|
|Sergipe|0.623|7.0|Acre|0.679|7.6|
|Bahia|0.620|8.4|Bahia|0.675|9.6|
|Ceará|0.616|9.7|Rondonia|0.672|8.3|
|Paraíba|0.610|11.3|Sergipe|0.668|9.6|
|Alagoas|0.597|7.7|Pará|0.663|10.6|
|Piauí|0.584|12.6|Maranhão|0.644|12.0|
|Maranhão|0.558|10.4|Alagoas|0.597|16.2|
|_Source_: UNDP.||||||

# **INCOME INEQUALITY**

Part of the reason why the poverty indicators of Pará and those of the nation as a whole are worse than in other countries with similar per-capita incomes is because of income inequality. Both Brazil and Pará have an extremely unequal income distribution. In 2000, the Gini coefficient for Pará was 0.6, at par with the coefficient for the North region as a whole (Figure 4.8). Moreover, the income inequality has not changed during the last decade and remains stubbornly high. However, it is worth noting that international research shows that the more unequally income is distributed the less effective is economic growth in reducing poverty (Lustig et al 2001).

28
Gini index for the entire distribution decreased during the period considered from 60.01 in 2001 to 54.7 in 2009 (see contributions by Ferreira et al. 2008, World Bank 2012).

Therefore, it is interesting to examine how the perspective of opportunity inequality can add elements of knowledge to the analysis of two markedly different distributional dynamics.

We use the Bank of Italy’s “Survey on Household Income and Wealth” (SHIW) to assess the distributional impact of growth in Italy. In particular, we consider four of the most recent available waves to compare the 2002–06 growth episode with the 2006–10 episode. We use the “Pesquisa Nacional por Amostra de Domic ́ılios” (PNAD), provided by the Istituto Brazilero de Geograpia e Estatistica, to analyze growth in Brazil, and we focus on the 2002–05 growth episode against the 2005–08 episode.

As far as Italy is concerned, when we focus on each single growth episode, some relevant insights arise. For instance, when the 2002–06 growth period is considered, the standard GIC shows a clear progressive pattern, but this pattern is reversed when the individual OGIC is adopted. When the 2006–10 period is considered, the regressive pattern shown by both the individual OGIC and the type OGIC demonstrates that the burden of the economic crisis has been borne by the weak groups in the population. Important information can be gained when we compare the two periods. The first period dominates the second according to the GIC and the individual OGIC, but this dominance does not hold when the type OGIC is adopted. We suggest that these results may be interpreted as the consequence of differences in per capita income growth between regions and some structural changes introduced in the Italian labor market in the recent past.

With respect to Brazil, it is interesting to note that although the growth experienced by the individual outcome in 2002–05 appears considerable for the whole distribution (with the exception of the top 15%), the growth experienced in terms of opportunities is less prominent. Indeed, most of the types suffer a reduction in the value of the opportunity during the growth process.<sup>3</sup> In contrast, the 2005–08 growth episode appears to be beneficial for the whole population regardless of the focus of the analysis (whether outcome or opportunity). Our analysis shows that the 2005–08 growth process is not only generally progressive but that it also leads to a reduction in the IOp (progressive

> 3To obtain this conflict between type OGIC and GIC, it is necessary that rich individuals experiencing losses are spread across the majority of socioeconomic groups.

5
September of 1993. Table 9 depicts all of the events that pertain to flooding after 1988. As there are a large number of events pertaining to the Mekong, we arbitrarily limit our event enumeration to events that took place within a three year span of time after the last flood identified. In total we present 30 events. The majority of the events are cooperative in nature (with only one event considered conflictive in nature). One of the events also represents an international water treaty signed in the basin. Although only an anecdotal finding, we note that the various floods in the region have elicited a relatively high number of cooperative events among the riparians.

In regards to the flood events pertaining to the Zambezi, the BWI predicted a flood in 2007. Floods (of the 2007 magnitude) were also identified in 1989, 1993, 1998, 1999, 2000, 2001, and 2003. A flood was also identified in 2004 but of a slightly more severe magnitude than 2007. A look at the water related socio-political data as it pertains to events associated with the Zambezi River Basin reveals six cooperative (interestingly, rather than conflictive) events associated with these flood events. Table 10 presents these events from The Basins at Risk Database (TFDD, n.d.) following the first major flood reported by the BWI—1989. These cooperative events, with one event constituting an international water treaty, are (like the Mekong case) perhaps one indication that the severity of the floods led the parties to different forms of cooperative action.

# **CONCLUDING DISCUSSION**

The paper introduces an analytical framework to assess the impact of variability of river flow on international river basin treaty stability, which includes economic, social and political impacts. Policy makers can use these probabilities to assess mechanisms that impact treaties, thereby allowing institutions to develop mitigation to extreme events. The probability of non-compliance of a treaty obligation can help policy makers identify the relative risk and economic impacts. In addition, the estimated hydrological variability and flow probabilities provided by the BWI can be used to forewarn of future extreme events, thus minimizing their vulnerability and stress during periods of low or high water levels. Finally, we believe that the BWI provides appropriate and more accurate water variability data, compared to other predictive variables such 22
# **Table 3 Key Determinants of PPP Investment in Electricity Renewable Generation**

||**Two step (1)**|**Two step (2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**SECTO**|**RALGOVERNA**<br>|**NCE VARIAB**<br>|**LES**||
|**Introduction of feed-in tariffs**_FIT_<br>_First step inred_|0.803***<br>(0.143)|0.681***<br>(0.150)||0.509*<br>(0.318)|
|**ECONOM **|**YWIDEGOVER**<br>***|**NANCE VARI**<br>***|**ABLES**||
|**Control of Corruption**_wgi_cc_<br>_First step inred_|0.386<br>(0.088)|0.372<br>(0.088)|||
|**Degree of democracy****_Polity2_**<br>|0.023***<br>(0.008)|0.020**<br>(0.009)|||
|_First step inred_<br>**ENVIRONM**<br>|<br>**ENTALSUSTA**|<br>**INABILITY F**|**ACTORS**<br>*||
|**Price of oil**|||0.011<br>(0.006)||
|**Kyoto Protocol**||0.003***<br>(0001)|||
|**MA**|**CROECONOM**<br>|.<br>**IC CONTROL **<br>|<br>||
|**GDP**<br>_ln_GDP_|0.627***<br> (0.173)|0.541***<br>(0.174)|0.586***<br>(0.197)|0.628***<br>(0.192)|
|**Population**<br>_ln_Pop_|0.345***<br>(0.171)|0.196<br>(0.170)<br>|0.312<br>(0.213)|0.373*<br>(0.209)|
|**Constant**|-6.998***<br>(2.412)|-5.466**<br>(2.397)<br>|-5.370**<br>(2.345)|-5.109**<br>(2.378)|
|**N**|1184|1184|189|189|
|**Censored N**|1002|1002|||
|**Wald χ**<sup>**2**</sup>|32.85***|27.32***<br>|23.80***|22.87***|
|**Within R**<sup>**2**</sup>|||7.88|7.20|
|**Between R**<sup>**2**</sup>|||17.92|20.29|
|**Overall R**<sup>**2**</sup>|||16.72|15.85|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

**Some interesting results are worth highlighting. In what follows we focus on the different factors that affect entry and the level of investment in renewables.**

**First, better control of corruption and higher degree of political competition are crucial in attracting the entry of private investors, but they do not affect the extent of investment in the presence of substantial sharing of risks between the public and private sector.** The economy wide governance factors affect only the first step of the Heckman model, confirming the results of the probit model analyzed before. In other words, This suggest that the first generations of private investment in renewable-based energy have been developed on the basis of attractive feed-in tariffs and long term PPAs guaranteeing a fixed rate of return, through takeor-pay clauses and/or government guarantees, shifting most of the risks to the public sector.

**Second, the sectoral support scheme, provided by feed-in tariffs, instead, affect both the entry and the level of investment in renewable-based energy, though they become much less significant when it comes to**

21
include the rice price — which appears with the wrong sign but is only marginally signi fi cant — and elevation and population density — which are no longer signi fi cant. Comparing Tables 7 and 5, we fi nd that in the smaller NLSS 2002/3 dataset none of the anticipated consumption variables is statistically signi fi cant. Other results are as before.

## 4.4 Magnitude

To assess the relative magnitude of our results, we multiply coe ffi cients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more in fl uence the regressor has on the choice of a destination district.

We see that the most important regressors in terms of magnitude are travel time to the nearest road, elevation, language similarity, and the price of rice. Consumption variables have an e ff ect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an e ff ect on destination that corresponds to a third of the e ff ect of a one standard deviation in elevation — and one-sixth of a one standard deviation in distance from the nearest road. Income variables have a negligible e ff ect on migration decisions. These calculations con fi rm our earlier assessment.

# 5 Conclusion

Combining data from a household survey and an 11% census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong in fl uence on migrants’ choice of destination. These results con fi rm earlier work on the factors a ff ecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008).

29
difference of US$ 1,600 in foreign income reported by the husband and the wife was statistically significant at conventional levels. There was a smaller discrepancy in remittance reports, wherein wives underreported remittances by 12 percent or US$ 523 on average.<sup>13</sup> Aside from inaccurate recalls, a possible reason why wives underreport remittances is that they are reporting the amount received net of the fees deducted by the receiving agent (the survey did not query the couple about such fees at the receiving end). We show later that the underreporting of remittances is uncorrelated with the underreporting of income, which alleviates concerns about reporting biases.

We also compared our sample, labeled henceforth as the Qatar Study of Kerala Families (QSKF), with a representative household survey conducted between December 2010 and June 2011 in Kerala by the Center for Development Studies. The Kerala Migration Survey (KMS) 2011 covered 15,000 households in all fourteen districts of the state and collected data on characteristics of household members who migrated outside the state. The number of married, male migrants working in the Arabian Gulf countries (Qatar, United Arab Emirates, Saudi Arabia, Kuwait, Oman, and Bahrain) amounted to 1,778 individuals in the KMS 2011 dataset. Only the household (i.e., not the migrant) was interviewed for the KMS 2011, so their data is comparable to the responses of the wives in our sample. Appendix table A.1 provides the comparison of the summary statistics created from the recipient household’s reports from our QSKF survey and KMS sub-sample, which appear to be similar in terms of the average age, duration abroad, and annual migrant earnings.

> was US$ 6,660 and mean annual remittance sent was US$ 3,297, which is only about US$ 200 more than the corresponding numbers reported in the current sample. This does not suggest a pattern of overreporting financial information by the migrant.

> 13. We separately test and find that underreporting of remittance is uncorrelated with the migrant’s age, income, years married and duration in Qatar, suggestive of classical measurement error. We also find no statistically significant discrepancy in cross reports on observable characteristics known to both parties.

16
MA;,;=1+ Djs Petit Pit Tij,t 0 AlnMA;, = In(1+ Djs Piet —In(1 + Djsi Pert e—1) Aln MAje = (In(1 + DiyaPiet) — Ind + Lys Piet74-1) +(in(1 + Djei Pretigt—-1) — Ind + Dyai Pier Tit) = PB, roaa in MA; + Bp pop. In MA;
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29