rafmacalaba/datause-encoder-data huggingface dataset:
**S**

**Table 2: Share In world trade of envirommentaliy s2nsitive goods, 1970-90**

|Rsgiona/countrier|Share inw<br>1970|orldimportu<br>1990|Por mfrence:<br>Shtrein worl<br>1970|<br>dexporu<br>1990|
|---|---|---|---|---|
|**Industr1**|**_78.2_**|72.9|_P._3|81.1|
|**AuAtxi**|1.3|1.v|1.3|2.0|
|Finland|2.0|2.1|2.1|2.4|
|**_Norway_**|1.9|1.5|1.9|1.7|
|**Sweden**|3.9|3.0|4.0|3.4|
|**Germany**|11.7|12.1|12.1|13.3|
|<br>**Japan**|7.8|5.3|3.0|6.0|
|US|11.2|_8.9_|11.6|10.1|

**Soure:** United **Nations** COMTRADE data **base.**

Changes in trade shares among industrial countries in the sensitive goods are diverse. There are both gainers and losers amcng them. Given that most industrial countries introduced higher environmental standards over the past two decades, the diversity in trade performance in the sensitive goods indicates that other factors are likely to have been more important than environmental standards. Austria, Finland, and Germany have increased their market share in the sensitive goods Austria nearly doubled its share between 1970 and 1990 (Table 2). As mentioned earlier Austria and Finland also had a good overall performance of competitiveness measured by their increased/maintained world market share in world exports of manufactures or total trade (Annex table 2). In both countries, overall competitiveness and that in the sensitive goods improved more than the average for industrial countries despite the high shares of the sensitive goods in total exports and increasing environmental expenditures in Austria (see below). Germany increased its market share of sensitive goods, despite losses in worid markets for manufactures and increases in environmental expenditures (see below) during the 80s.

Japan, Norway, Sweden and the US lost markets in the environmentally sensitive goods between 1970-90. Norway, Sweden and the IJnited States have also lost market shares in both total exports (except Norway because of oil) aid in manuf:cturcs s-ggesting poor overall
# **1. Introduction**

The number of international migrants has been increasing over time. In parallel with that, the participation of women in international labor migration has also been rising. In 2005, the share of female migrants in the world’s migrant stock was close to half (World Bank 2008a). Following an upward, global trend in international migration, Indonesia is now one of the largest migrant labor exporting countries in the world. International migration from Indonesia has been strongly dominated by women. According to official records, the number of documented female migrant workers accounted for approximately 80 percent of Indonesia’s migrant workers in 2007 (Figure 1).

The rise in migration has also contributed to the increase in global remittance flows. Recorded remittances sent home to developing countries were nearly US$ 222 billion in 2006. About 17 percent of global remittances flow back to the East Asia and Pacific region, including Indonesia. According to the IMF Balance of Payments data, remittances to Indonesia totaled approximately US$5.7 billion in 2006 (World Bank, 2008b). Although the amount of remittances appears small relative to total GDP (US$364.5 billion), these inflows may be quite significant in the specific regions of the migrants’ origin.

While international migrant work has become an increasingly important part of the Indonesian economy, not much research has been done to understand to what extent migration and remittances may affect the livelihoods of the migrant’s origin household. The impacts on adult and child labor supply as well as human capital accumulation can manifest through the income effects of remittances, the impacts on work incentives, exposure to new information, and consequences of family disconnects. Previous literature has shown evidence of impacts on the above outcomes in various developing countries (Adams 2010), though the gender dimension of the impacts has been less explored. The existing research on international migration from Indonesia mostly focuses on issues related to the financial literacy and vulnerability of migrant workers. Such research to date is largely based on qualitative assessments and anecdotal evidence rather than rigorous quantitative analysis.

This paper fills the gap by quantitatively investigating the development impact of international migration and remittances in Indonesia, particularly how the migrant’s 2
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# **Climate Change, Irrigation, and Israeli Agriculture: Will Warming Be Harmful?**

## **Introduction**

The eastern Mediterranean region, like the rest of the world, is expected to undergo changes in rainfall patterns and temperature over the next several decades due to Global Climate Change (Houghton et al 2001). Climate models for the region predict an increase in winter temperature combined with changes in rainfall amount and distribution (Ben-Gai et al., 1998). According to agronomic research, these climatic changes are likely to affect agricultural production (Gitay et al 2001). This study evaluates the economic impact of climate change on Israeli agriculture.

There are different approaches in the literature to evaluate the impacts of climate conditions and agriculture. The agro-economic approach developed by Adams et al. (1989; 1995) begins with agronomic models that predict how climate change will affect yields of specific crops. Mathematical programming is then used to predict which crops farmers will want to plant and what will happen to aggregate production and prices. This approach captures adaptation behaviors including crop switching, but only in a partial and arbitrary fashion. By contrast, the Ricardian approach includes all adaptation behaviors implicitly in the model.

We rely on the Ricardian method (Mendelsohn Nordhaus and Shaw 1994) (MNS) to measure the economic impacts of climate change on Israeli agriculture. Annual net revenues are regressed on climate, soils, and other socio-economic control variables. By using net revenues and not individual crop yields, we allow farmers to adapt to climate change by choosing different crops, crop mixes, technologies and management practices under different climate conditions.

Previous work on adaptation to climate change in agriculture suggests that there are a variety of adaptation measures that can be initiated at the private and public levels (see review of literature in Kurukulasuriya and Rosenthal 2003).It became evident that in order to address the complicated nature of climate change impact on the agricultural sector, a joint private-public and dynamic adaptation is needed (Mendelsohn 1999). As such, technological development and knowhow measures related to various aspects of the production process, including irrigation, where water is available, could be considered.

2
10 TABLE 2.3 SeneaL libm atl bduial **Produc** (1976-100)

||1978a|1981k|1983i|198 <sup>b</sup>|<br>1986b|
|---|---|---|---|---|---|
|Mining|95.9|107.4|843|1126|107.7|
|Food Production|91.6|88.5|109.7|1032|99.6|
|Textiles, Clothing<br>and Leather|101.0|110A|139.9|133.9|<br>123.0|
|Wood Products|111.0|127.1|133.0|119.0|<br>1002|
|Paper|113.6|120.6|1375|120.1|<br>130.5|
|chemicals|1042|85.5|73.1|77.9|_51.9_|
|Construction<br>mnaterials|103A|94.7|128.6|94.1|83.8|
|Machineiy and<br>Equipment|110.6|61.8|93.6|78.1|41.0|
|Electricity and<br>Water|1215.~-r'|134.6|148.7|_155.0_|166A|
|General Index with-<br>out oil processing|<br>106.8|108.2|111A|119.6|1125|
|General ladcx|982|972|107.1|107.6|99.6|

Source: aDirection de la Statistique, Indice de la Production Industielle, in World Bank, SenegaL An Economy Under Adu stment 1987, p. 96. bnMP, Senega - Statistical Anna; 1988.

to 90 percent) and escalated sharply for finished goods. Quantitative import restrictions existed for about 160 commodities, with significant effects on domestic market prices. The average level of protection in the domestic market was uneven and high, probably equal to at least 50 percent of the cost, insurance, and freight (CIF) value of competing world products (World Bank, 1984, p. 69). The gradual lowering of tariffs and diminution of quantitative restrictions since the mid 1980s has led
Underlying this measure is an assumption that political autonomy is more meaningful if regions can finance their expenditures out of their own revenues, hence establishing a degree of independence from the center. Regions that spend a lot out of central government transfers are less independent in that they are uncertain if they can fund their future spending deficits, since from year to year the center might adjust the transfer amount.

Although we propose an economic measure of sovereignty, our analysis is likely to apply also to different concepts of decentralization or autonomy.<sup>33</sup> While economic decentralization (income and asset redistribution and regional fiscal decentralization) and political decentralization are conceptually distinct, they are highly correlated.<sup>34</sup> This is confirmed by simple bivariate correlations. Using data from the Polity III database, we coded three binary variables denoting the level of centralization (federal, semi-federal, and centralized) and correlated each indicator with an economic decentralization index – the size of regional expenditures as a share of total government expenditures (this index is compiled by Fisman and Gatti (2000), using data from the International Monetary Fund’s _Government Finance Statistics_ ). Economic decentralization data are averaged for each country for the period 1980-95, and political decentralization is taken for the year 1994. The Pearson correlation between a binary variable identifying all federal systems<sup>35</sup> and a variable denoting economically decentralized countries (identified as those countries with a decentralization level above the sample mean) is positive and highly significant (Pearson <sup>2</sup> (1) = 33 On decentralization measures and concepts, see Fisman and Gatti (2002) and Treisman (1997). The results we present are substantively the same for federal systems and for unitary systems with some degree of regional fiscal independence. Our analysis could be extended to different political measures of sovereignty, such as a region’s freedom to choose its own secondary school curriculum, its freedom to use local languages in teaching, and so on. Another fairly common measure of sovereignty demand is the vote share of ethno-regional parties (see De Winter and Tursan 1998, Sorens 2005, Gordin 2001). But voting could be strategic and that measure limits analysis to electoral democracies with developed regional parties. Party strength as a measure of ethnopolitical interest in autonomy is also mostly applicable to advanced industrialized countries (see Levi and Hechter 1985).

34 See Diaz-Cayeros (n.d.); Treisman (1997). Economic decentralization is also positively correlated with the level of democracy (see Garrett and Rodden 2003).

35 With semi-federal and centralized systems combined coded as 0, and federal systems as defined in the Polity III database.

19
- 15 -

|Obs.|548|548|548|
|---|---|---|---|
|No. of district||||
|dummies|3|3|3|
|R-squared|0.674|0.670|0.675|
|F-statistic|76.31|74.82|70.99|

**<u>Table 3. Spatial autoregressive estimation results</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln_MIN_|-0.402|(0.131) ***|||-0.329|(0.141) **|
|ln_JAI_|||0.122|(0.061) **|0.093|(0.067)|
|ln_POPDEN_|0.044|(0.030)|0.034|(0.030)|0.036|(0.030)|
|ln_CSB_|-0.037|(0.028)|<sup>-0.034</sup>|<sup>(0.028)</sup>|<sup>-0.033</sup>|<sup>(0.028)</sup>|
|_VUL_|-0.024|(0.008) ***|-0.022|(0.008) ***|-0.024|(0.008) ***|
|_RD_|1.8208|0.562 ***|1.8744|0.5598 ***|1.7532|0.5632 ***|
|_ALLWEARD_|0.189|(0.070) ***|0.171|(0.070) **|0.180|(0.070) **|
|_RICE_|-0.151|(0.159)|<sup>-0.220</sup>|<sup>(0.158)</sup>|<sup>-0.184</sup>|<sup>(0.160)</sup>|
|_AMENITY_|1.271|(0.787) *|1.707|(0.782) **|1.283|(0.786) *|
|_TAXIBE_|0.187|(0.087) **|0.169|(0.087) *|0.182|(0.087) **|
|_SUBU_|-0.034|(0.058)|<sup>-0.039</sup>|<sup>(0.059)</sup>|<sup>-0.041</sup>|<sup>(0.059)</sup>|
|Constant|1.509|(2.051)|<sup>-12.508</sup>|<sup>(2.634)***</sup>|1.254|(2.216)|
|Obs.|548||548||548||
|No. of district|||||||
|dummies|3||3||3||
|Wald chi2|181.15||164.45||184.97||
|Spatial parameters:|||||||
|_λ_|0.801|(0.193) ***|2.010|(0.260) ***|0.783|(0.210) ***|
|_ρ_|2.772|(0.123)***|2.416|(0.054)***|2.786|(0.125)***|

# **VI. DISCUSSION**

32. One may be concerned about the robustness of the above estimation results. In particular, the definition of bus service access was an ad hoc assumption in the above. To address this issue, the accessibility to bus services was redefined using a different threshold, i.e., 500 meters. The results are shown in the first two columns of **Table 4** . The statistical significance associated with access to taxi-be routes disappeared, while the rest of the coefficients remain broadly unchanged. It can be understood to mean that the close proximity to taxi-be services is particularly important to local people. The 500-meter distance seems too long, suggesting that an extensive bus network needs to be developed to meet the people’s needs, which are reflected in the observed land values.
competitive elections or not).<sup>26</sup> We also use _Herfindahl Index of Government Concentration_ from the Database of Political Institutions. The variable measures political cohesiveness of government with lower values indicating more fragmented or less cohesive government (e.g. a coalition government with many parties). Unfortunately, since the DPI data are available only for the period 1975-2000 we lose a significant number of observations. The coefficients change too. Ethnic fractionalization becomes much more important (jumping from 2.7 to 4.3 Gini points) and despite that, so does the Africa dummy which now amounts to more than 13.5 Gini points, a value higher than that of the LAC dummy. The hypothesis of equality of the two dummies is easily accepted. In formulation **3** , we drop the average three-year lagged _Democracy_ variable and instrument contemporaneous _Democracy_ by its lagged value. The results are unchanged. In formulation **4** , we replace _Democracy_ variable with another variable that also reflects the level of democracy: _Party competitiveness_ similarly derived from PolityIV database and averaged over three years. _Party competitiveness_ measures the “extent to which non-elites are able to access institutional structures for political expression”, and ranges from 0 to 5 (definitions taken from the code book of Polity database by Jaggers, 1996).<sup>27</sup> The two variables are strongly correlated (above 0.9)<sup>28</sup> and the coefficients on the fractionalization variable and the Africa dummy are practically unchanged. _Party competitiveness_ performs much better than _Democracy_ and is significant and negative. We thus keep _Party competitiveness_ in all other formulations.

We then move to testing whether the slopes (in addition to the intercept) may also be different for African countries. We are mostly interested in how democracy and ethnolinguistic fractionalization affect inequality in Africa compared to the rest of the world. The first is clearly a political variable; the second may be thought of one (in the

> 26 DPI uses a set of clear rules to distinguish between political systems, such as presidential veto power, presidential appointment of ministers and dissolution of the parliament. Thus, the French system is classified as parliamentary, because the Prime Minister depends only on parliamentary majority and not on president’s will, and the Russian system as presidential since the Prime Minister is proposed by the President while the _Duma_ can only _reject_ him.

> 27 Available on the Internet at ftp://isere.colorado.edu/pub/datasets/polity3/polity3.codebook.

> 28 They are, however, not as strongly correlated in Africa. The correlation coefficient is 0.79.

18
_Short-run relationship or Error Correction Model (ECM)_ We are also interested in the short run relationship, which can be estimated by using the change in gross domestic savings as the dependent variable and the change in the other variables and their lags as the independent variables. We also add the lag of the residuals of the long run relationship to our independent variables. The coefficient of this latter variable will measure the speed of return to the long-run pattern after a unitary shock. This coefficient must be negative in order to ensure a return to the long run relationship.

**Table 5: Error Correction Model Specification**

|∆s = -0.012 + 0.49 g + 0.15 ∆open – 0.94 ECM-1<br>Dependa<br>∆(|nt variable<br>sr)|
|---|---|
|Independent variable<br>Coefficient|_t statistic_|
|constant<br>-0.012**|_-2.1_|
|∆(gdpc) = growth rate_g_of GDP per capita<br>0.49***|_13.5_|
|∆(OPEN)<br>0.15**|_2.0_|
|lag(ECM)<br>-0.94***|_-6.1_|
|Period : 2002q2-2011q2 ; N° observation=37 ; R = 0.96 ; Durbin Wat<br>***significant 1%;** significant 5%|son=1.96;|

As depicted in table 5, the coefficient of _lag(ECM)_ is negative and quite high in absolute terms ( _0.94_ ). This means that there is a strong catch up process to the long-run equilibrium. The short run coefficients are also important. For instance, the coefficient of _∆(gdpc)_ is about 0.5 which suggests a strong short run reaction to a shock but that shock effect disappears quickly due to the strong catch up process to the long-run equilibrium. We also notice a positive important and significant impact of _∆(OPEN)_ on savings. This may be due to the fact that an increase in the ∆Openness (i.e. acceleration in Openness) which may be due to an increase in the net exports (for instance, an increase in primary resources exports) leads to an increase in aggregate output and aggregate savings rate.

Figure 3 presents the dynamic simulation derived from our econometric specification. We can notice that our model fits quite well with reality. Using our short-run equilibrium, we managed to decompose the change in savings rate in term of the effect of each independent variable we retained in our specification. In Figure 4, the sum of the bars is almost equal (because of the residuals) to the actual change in savings rate for each quarter. As shown in our econometric models, we see that the change in GDP per capita is the main driver of the change in savings rate. The contribution of GDP per capita is presented by the dark blue bar. In terms of the effect on savings rate change, the GDP per capita is followed by the Openness of the country (orange bar). Although they have a significant effect on long run savings rate, the actual impact of REER and TDR are quite small compared to the previous variables. Those effects are presented by the yellow and light blue bars. Finally we can see the effect of the crisis thanks to the dark bar. That effect is very strong in the second quarter of 2009 but tends to disappear thereafter.

9
primary schooling. Both the urban/rural' and the outer Islards/Java differentials no doubt reflect the correlated differences in average incormes. Finally, proportions of dchool going kids increase the higher the par caFita expenditure quantile. The one exception is for the Java urban middle 40 percent who do better than the top 20 percent at the junior and senior secondary age levels. However, the difference in attendanca rates bet,een different consumption groups becomes far more pronounced for age groups 16 to 18 and 19 to 25 than at the 13 to 15 age level. At the prinmary school level, the disparity is small.

# _Changes in Incideiice_ **_Between_** _1978 and 1987_

Table 2 presents the proportions of all children in a series of subgroups defined by age, gender, and location who were going to school in 1978 and 1987. For example, 73 percent of all Indonesian female childrv aged 13-15 were at school in 1987. The 1978 results which are taken from Meesook (1984) are all in parentheses to make them easier to distinguish from the 1987 numbers.

In 1978, across all subgroups, a higher proportion of boys than girls attended school. Utilization was positively correlated with household per capita expenditures (as can be seen in the bottom panels) and the higher the level of education, the higher the differential between high and low expenditure groups. University education, for example, was almost exclusively received by better off urban households. Indeed, urban households everywhere tended to benefit mere from education facilities than rural ones (at levels beyond primary school), as did households in the Outer Islands in cor-parison to Javanese ones.

Turning now to the 1987 results, there are 3 main points to note. i) Level improvements are apparent at all ages and for all regions, gender, and expenditure groups. In other words, for each subgroup, the proportions of school going children have increased, and in some cases, quite dramatically.
# **Table 1 Socioeconomic Characteristics of the Survey Population in the OCC Survey (Percentages may not sum to 100% due to rounding, nonresponses, or the omitted category "other")**

|**Characteristics of the Survey Population**|**% of combined**<br>**survey popula-**<br>**tion **|**% of the NYC**<br>**survey popula-**<br>**tion **|**% of the LA**<br>**survey popula-**<br>**tion **|
|---|---|---|---|
|**Age**||||
|18to 29 years old|29.8|22.2|37.5|
|30 to 64years old|59.3|64.1|54.5|
|65or older|8.0|10.2|5.8|
|Male|45.7|44.2|47.3|
|**Highest completed level of education **||||
|Less than high school degree|37.8|36.1|39.5|
|High school degree or equivalent|38.0|44.0|32.0|
|More than high school|24.2|19.9|28.4|
|**Household composition **||||
|No children in household|40.7|45.0|36.4|
|One or two children|41.6|41.6|41.7|
|Three or more children|17.5|13.3|21.8|
|Other adults in household|81.6|75.0|88.2|
|**Housing status**||||
|Rent home|75.3|88.7|61.8|
|Own home|21.2|9.4|33.1|
|**Employment status & non-labor income**||||
|Workingfull orpart-time|57.7|54.7|60.7|
|Notworking|35.7|39.9|31.6|
|Social security, veteran,orpension benefits|8.9|11.8|5.9|
|Welfare, SSI,or food stamps|15.9|22.5|9.2|
|Nopersonal income inpastyear|8.6|7.7|9.5|
|**Household income in 1997**||||
|$15,000or below|24.2|33.6|14.8|
|$15,001 to$30,000|32.5|31.3|33.8|
|$30,001 to$45,000|24.6|18.7|30.6|
|More than$45,000|18.6|16.3|20.9|
|**Self-reported race & ethnicity**||||
|White non-Latino|7.9|5.6|10.3|
|Black non-Latino|33.1|44.0|22.2|
|Other non-Latino|3.6|4.7|2.5|
|Latino|53.4|43.6|63.3|
|**Banking status**||||
|No deposit account|36.9|42.2|31.6|

13