rafmacalaba/datause-encoder-data huggingface dataset:
What has a significant positive impact, however, is participation in the National Rural Employment Guarantee Scheme (NREGS), launched in 2006. So are the impacts due to IKP or to NREGS? Disentangling IKP and NREGS effects is complex: IKP influences NREGS participation, as we will see below, and NREGS participation enhances income and expenditures. Essentially, NREGS indeed contributes to the changes in expenditures for IKP participants, but so does IKP.

To see this, we check whether IKP had an impact where NREGS was not relevant. First, we exploit the fact that NREGS was expanded across survey districts in phases. Table 11 presents total, food, and education expenditure DIDs for poorest and poor households in districts where NREGS started in February 2006 (Phase 1), September 2007 (Phase 2), and April 2008 (Phase 3). The DIDs for total expenditures for poorest households were higher where NREGS had been active longer: Rs. 122 in Phase 1 districts, Rs. 79 in Phase 2, but still a positive if not significant Rs. 64 in Phase 3 districts, where NREGS had been operating for a few months at most at the time of the 2008 survey. The DID for education expenditures were significantly positive for some categories also where NREGS had been operating for shorter lengths of time.

**Table 11: DIDs for monthly per capita expenditures, early vs. never joiners, by NREGS phasing,** **<u>2004 Rs.</u>**

|_Poorest_|_Total exp_|_Food exp_|_Educ exp_|
|---|---|---|---|
|Phase 1 (NREGS since Feb 2006)|121.8**|48.3**|8.4|
|Phase 2 (NREGS since Sept 2007)|78.7|20.5|18.9**|
|Phase3 (NREGSsince Apr 2008)|63.8|9.9|24.9|
|_Poor_||||
|Phase 1 (NREGS since Feb 2006)|12.1**|15.9**|28.4**|
|Phase 2 (NREGS since Sept 2007)|-2.9|65.5|22.1|
|Phase 3(NREGS since Apr 2008)|19.5|53.7|100.9*|

Note: Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

Another way to isolate the impact of IKP is to look at DIDs separately who did and did not benefit from NREGS. Among those who benefited from NREGS, IKP participants had positive expenditure DIDs, significant for education; among those who did not benefit, DID are still positive for poorest participants, if not significant, and education spending is significant for poor households. Thus, IKP appears to have had a direct positive impact for the poorest and for education expenditures over and above the impact of NREGS.

**Table 12: DIDs for monthly per capita expenditures, early joiners vs. never joiners, by NREGS** **<u>participation, 2004 Rs.</u>**

||_Total exp_|_Food exp_|_Educ exp_|
|---|---|---|---|
|_Poorest_||||
|Households benefiting from NREGS|68|33|39*|
|Households not benefitingfromNREGS|50|10|11|
|_Poor_||||
|Households benefiting from NREGS|157**|20|38**|
|Households not benefitingfromNREGS|-40.6|-13.6|12.2**|

Note: Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

14
## **B Which Variables Account for Growth in Mean Welfare?**

Which variables in the model account for the bulk of the change in poverty? While this question is difficult to answer directly, indirect evidence is available by decomposing the growth in log per capita consumption predicted by the model. A natural framework for better understanding the contribution of individual variables is the classic Oaxaca-Blinder decomposition (Oaxaca (1973), Blinder (1973)). This technique decomposes the mean difference across two groups into a portion explained by differences in endowments and a portion due to differences in returns. In this case, the two groups are the households in the 2011-12 survey and the households in the 2014-15 survey. We decompose the difference between the model’s mean predicted per capita consumption in 2014-15 and 2011-12. Because the means from each year are generated by predictions from the same model, none of the difference is attributable to changes in the coefficients (returns), and all of the change is due to the mean of the predictor variables (endowments). These changes can easily be decomposed into the portion due to each individual predictor variable, which helps to identify the variables that account for the largest changes in the model.

Table 9 displays the results. Each cell represents the percentage of the total change in mean log per capita consumption attributable to each variable. The time trend alone, for example, explains 83 percent of the increase in urban areas and 80 percent in rural areas. Notably, transport expenditure declined in real terms during this period. This slowed the predicted increase in per capita consumption, particularly in urban areas. The rainfall variables contributed to a relatively large share of the increase in urban areas. However, the total increase in welfare was much smaller in urban areas, meaning that in absolute terms rainfall’s contribution was similar in urban and rural areas. As might be expected, changes in the means of livelihood categories contribute a significant amount in urban areas, but much less in rural areas. The results overall indicate that the time trend is a key driver of predicted change in the model. However, transport expenditure plays a significant role, as does the increase in service expenditure in rural areas. Finally, rainfall contributes a significant amount in urban areas, as do changes in the types of jobs held by workers.

# **V Robustness Checks**

We conduct three checks to assess the robustness of our model. First, we calculate elasticities and semi-elasticities to see what the models’ predictions imply about the change in extreme poverty with respect to real GDP growth. Second, we examine 17
the network. Similarly, having a larger number of connections will be better than having just a few, and so on.

We augment Equation 1 by now including these network measures. Implementation is for the same indicators as reported in the baseline estimates reported in Table 10 and includes country, sector, year and firm size dummies. Since the network variables are defined only for connected firms, all non-connected firms would be excluded from this analysis. As such, we replace all network values with zero for all non-connected firms as well as including a dummy for being connected in all specifications. The table below shows the different specifications we estimate.

Network estimations

|Variable:|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|SOE|✓|✓|✓|✓|✓|✓|
|Connect|✓|✓|✓|✓|✓|✓|
|Big Island (BI)|✓|✓||✓|||
|Has Betweenness||✓||||✓|
|Log Degree|||✓|✓|||
|BI _×_ Log Degree||||✓|||
|Politician|||||✓|✓|
|Politician _×_ Has Between.||||||✓|
|Constant|✓|✓|✓|✓|✓|✓|

For example, the estimation of column (1) in equation form is as follows: So, it is the same as Equation (1) with the Big Island network variable added to it. Similarly, for the other five columns of the table. Note that these correspond exactly to columns 1 to 7 in Tables 11-15.<sup>19</sup> The idea behind this set of specifications is to consider the network properties as different layers of the connection. The first layer is simply being connected (examined in equation 1). Then, it is being connected but also being in the Big Island (column (1) in the network specifications). Further the ‘location’ or centrality matters, so that the betweenness or the

> 19Country-specific estimates are reported in Appendix Tables A2-A6.

19
**<u>Table 3 Determinants of assets – all countries</u>**

||_FDI_|_Portfolio_|_Debt_|_Total_|
|---|---|---|---|---|
|Sovereign wealth fund|1.105***|-0.129|0.0435|–0.149|
||(0.395)|(0.440)|(0.132)|(0.120)|
|Ln(Credit)|0.705***|1.424***|0.109***|0.108***|
||(0.124)|(0.140)|(0.0411)|(0.0374)|
|Intermediate exchange rate|–0.156|–1.794***|–0.0166|–0.143**|
||(0.225)|(0.259)|(0.0737)|(0.0669)|
|Oil|1.029|4.343***|2.296***|2.376***|
||(0.903)|(0.560)|(0.843)|(0.661)|
|Constant|–2.716***|–4.962***|3.373***|3.952***|
||(0.883)|(0.813)|(0.640)|(0.509)|
|Year dummies|Yes|Yes|Yes|Yes|
|Observations|626|634|626|626|
|Number of countries|21|21|21|21|

_Note_ : Standard errors in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

An intermediate exchange rate does not have a significant association with FDI or debt; it has a detrimental effect on total assets and portfolio investment, because it increases uncertainty. This is interpreted by the fact that oil-exporting countries are transparently adopting fixed exchange rate regimes supported by the comfortable current account surpluses.

The oil dummy is positive and significant for portfolio and debt assets; it is not statistically significant for FDI. Thus, oil exporters in the region tend to invest more in debt assets and less in FDI (Elbadawi, Soto, and Zaki 2018).

Table 4 shows the results on the determinants of assets for oil-exporting countries. Three findings are worth noting. First, the coefficient for having an SWF is largest for FDI, followed by the coefficient for debt. This finding suggests that GCC countries are more risk averse and tend to invest in less risky assets, namely debt. Second, financial development matters most for FDI, followed by portfolio and debt assets. This result confirms the finding of Vermeulen and de Haan (2014) that the more financially developed a country is, the more likely it will be to invest in risky portfolio assets rather than debt.

14
Table S.4: Differences in Unit Costs across different Work Types

||EQ1|EQ2|
|---|---|---|
||(1)|(2)|
|_Preservation Works_|||
|Routine Maintenance|-4.289<sup>_∗∗∗_</sup><br>(0.143)|-4.279<sup>_∗∗∗_</sup><br>(0.169)|
|Grading|-6.396<sup>_∗∗∗_</sup><br>(0.352)|-6.333<sup>_∗∗∗_</sup><br>(0.378)|
|Gravel Resurfacing|-2.275<sup>_∗∗∗_</sup><br>(0.117)|-2.236<sup>_∗∗∗_</sup><br>(0.168)|
|Bituminous Pavement Preventive Treatment|-3.076<sup>_∗∗∗_</sup><br>(0.134)|-2.991<sup>_∗∗∗_</sup><br>(0.179)|
|Unsealed Preventive Treatment|-3.375<sup>_∗∗∗_</sup><br>(0.179)|-3.361<sup>_∗∗∗_</sup><br>(0.207)|
|Surface Treatment Resurfacing|-1.981<sup>_∗∗∗_</sup><br>(0.135)|-1.946<sup>_∗∗∗_</sup><br>(0.186)|
|Asphalt Mix Resurfacing|-.848<sup>_∗∗∗_</sup><br>(0.092)|-.904<sup>_∗∗∗_</sup><br>(0.153)|
|Strengthening|-.117<br>(0.085)|-.210<br>(0.162)|
|Concrete Pavement Restoration|1.000<br>(0.629)|1.071<sup>_∗∗_</sup><br>(0.522)|
|Reconstruction|0.073<br>(0.102)|-.002<br>(0.164)|
|_Development Works_|||
|Partial Widening and Reconstruction|0.258<br>(0.224)|0.343<sup>_∗∗_</sup><br>(0.156)|
|Widening|1.410<sup>_∗∗∗_</sup><br>(0.171)|1.360<sup>_∗∗∗_</sup><br>(0.233)|
|Widening and Reconstruction|1.539<sup>_∗∗∗_</sup><br>(0.142)|1.479<sup>_∗∗∗_</sup><br>(0.177)|
|Upgrading|0.271<sup>_∗∗_</sup><br>(0.118)|0.201<br>(0.166)|
|New 1L Road|-.349<sup>_∗∗_</sup><br>(0.148)|-.036<br>(0.203)|
|New 2L Highway|1.529<sup>_∗∗∗_</sup><br>(0.199)|1.404<sup>_∗∗∗_</sup><br>(0.257)|
|New 2L Highway|1.529<sup>_∗∗∗_</sup>|1.404<sup>_∗∗∗_</sup>|
||(0.199)|(0.257)|
|New 4L Highway|2.542<sup>_∗∗∗_</sup><br>(0.297)|2.487<sup>_∗∗∗_</sup><br>(0.326)|
|New 6L Highway|2.464<sup>_∗∗∗_</sup><br>(0.097)|2.374<sup>_∗∗∗_</sup><br>(0.191)|
|New 4L Expressway|2.824<sup>_∗∗∗_</sup><br>(0.072)|2.762<sup>_∗∗∗_</sup><br>(0.163)|
|New 6L Expressway|3.682<sup>_∗∗∗_</sup>|3.403<sup>_∗∗∗_</sup>|
||(0.127)|(0.234)|
|Obs.|3322|3322|
|_R_<sup>2</sup>|0.83|0.842|

_Notes_ : Omitted category is partial widening; contract characteristics and year dummies included in all models; column (2) adds region fixed effects and country7characteristics; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.
analysis. First, the marginal cost of irrigation water varies widely, including zero marginal cost, which allows for different water saving motivations. As we explain later, this variation can be regarded as a de facto natural experiment from the WUG perspective, which provides an ideal setting for our research. Second, we observe multiple paths of institutional change, including the case of maintaining the status quo even under volumetric pricing. Our data provide us with an opportunity to examine both institutional change and persistence. Third, our sample of WUGs is scattered over a large irrigation scheme and varies widely in terms of initial conditions. This variation allows us to examine how initial conditions affect the direction of institutional change.

Our three major findings are summarized as follows. First, a modest increase in a volumetric price of reservoir water induces institutional change to prevent each member’s overuse of water. Therefore, for institutional change to occur, the correct signal of resource scarcity must be transmitted to resource users, and the price can play an important role in signaling scarcity. Second, paths of change have multiple directions, depending on initial conditions (e.g., infrastructure conditions, land distribution equality, social capital, and the availability of alternative water sources). For example, redrawing the institutional boundary (the formation of new smaller group) is chosen when the bonding social capital among a smaller group’s possible members is high, while a change in institutional design (the appointment of a water manager) emerges when simple topography enables the manager to effectively supervise water distribution. Either change contributes to water savings. Third, when the volumetric price is set high enough, many farmers leave a WUG and start using private irrigation. This tendency is found to be associated with a higher probability that the remaining farmers do not undertake institutional change (i.e., uphold informal management of WUG) and that they do not save water. This may be due to the increased management difficulties (e.g., coordination of water allocation, supervision of water distribution, and maintenance of irrigation infrastructure) among the dispersed remaining farmers after the exit of many members. As a result, in spite of many farmers’ substitution of private irrigation for reservoir irrigation, we do not find evidence that the reservoir water is saved at high water price levels.

Our study contributes to two related areas of research: institutional change and common pool resource (CPR) management. As Pande and Udry (2005) noted, only a limited number of micro-level studies are available compared with the growing number of macro-level or historical studies on institutional change. Topics that micro-level studies have explored include the evolution of land rights institutions (McMillan et al. 2012; Otsuka and Place 2001; Pande and 3
# **Figure 4. Trends in the adoption of explicit deposit insurance by income level**

**Panel A: All income categories**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>High income Upper middle income Lower middle income Low income<br>1934 1962 1966 1969 1974 1977 1980 1983 1985 1987 1989 1992 1994 1996 1998 2000 2002<br>Number of countries<br><!-- End of picture text -->

**Panel B: High income category versus others**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>High income Middle and low income<br>1934 1962 1966 1969 1974 1977 1980 1983 1985 1987 1989 1992 1994 1996 1998 2000 2002<br>Number of countries<br><!-- End of picture text --> 18
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