rafmacalaba/datause-encoder-data huggingface dataset:
the Global Runoff Data Centre (GRDC 2007). The GRDC defines 405 major world river basins (also based on the HYDRO1k analyses) and we restrict our analysis to areas within these major basins.<sup>10</sup> Our main unit of observation intersects the river subbasins defined by HYDRO1k with international borders. Most subbasins are within a single country. A number of subbasins, however, are split by country borders; these subbasin-country areas are treated in the database as two or more separate observations. Table 1 describes the distribution of subbasin-country areas, subbasins, and major river basins across continents in 2005.<sup>11</sup> To construct the dependent variables, we placed dams in subbasin-country areas using the Global Reservoir and Dams (GRanD) dataset, a newly-available data set that provides geocoding for 6,862 of the world’s largest dams and reservoirs (Lehner _et al._ 2011). GRanD includes all dams with reservoirs that have storage capacity greater than .1 km<sup>3</sup> and a number of dams with smaller reservoirs. We use a total of 4,696 (just over two-thirds) of the dams described in the GRanD data because we must restrict our analysis to dams in major river basins. GRanD provides some information on the characteristics of the dams that we can take into account in our analysis. For example, GRanD classifies dams by use and reports total reservoir capacity and dam height. Table 2 reports the main use category for all the dams in GRanD. The most frequent use is irrigation, followed by hydroelectricity; 10 We can only use 383 of the 405 GRDC basins because HYDRO1k data are not available for the 22 basins on the Australian mainland (although one GRDC basin in Tasmania has HYDRO1k data and is included). In addition, we drop the subbasin-country areas in the Lake Chad basin because this basin has multiple inland mouths, making the Pfafstetter system inadequate to the task of defining upstream-downstream relationships.

11 The country borders are those in effect in 2005 according to CShapes (Weidmann et al. 2010), which reports country borders over time. We used CShapes because we aspired to use fixed effects equations that identified the effects of free-riding in dam placement through changes in country borders. However, analysis of the data found only 2 sets of border changes that could be used for this purpose: the breakup of the Soviet Union and the separation of Namibia from South Africa. In our data, no dams were built in any of the areas that experienced a change in status, so the panel data analysis is not reported.

7
poorest 15 countries is $1.67, for the richest 15 it is 20 times higher at $27 a day. The slope of the regression line is 0.485 (White s.e.=0.020).<sup>20</sup> The overall elasticity (using a log-log regression) is 0.863 (s.e.=0.027). (If we use the median instead of the mean, the slope is 0.564 (0.017) and the elasticity is 0.816 (0.026).) It is also notable that there is little sign of a “flat” segment at low means; the relationship is positive from the lower bound.

It might be argued that the true causal relationship is not as strong as Figure 2 suggests. Three concerns can be noted. First, the fact that some of the national lines are strongly relative lines is likely to be biasing the relationship. However, the relationship is still evident if one drops the OECD countries, though the slope falls slightly, to 0.454 (s.e.=0.039) while the elasticity falls to 0.773 (0.044). (Using the median instead for the non-OECD countries, the slope is 0.559 (0.042) and the elasticity is 0.740 (0.040).) Second, a bias due to correlated measurement errors in the mean and poverty line might remain given that the national lines for developing countries are often calibrated to survey data (though the direction of bias is ambiguous in theory, noting that there is also the usual attenuation bias). For example, one method of setting national poverty lines identifies the poverty line as the total consumption expenditure level at which pre-determined food energy requirements are met in expectation.<sup>21</sup> Then, for fixed requirements, over (under) estimation of total expenditure will lead to an over (under) estimation of the poverty line. This is also likely using food Engel curves to set the non-food component of the poverty line. Acknowledging this concern, as a further check we used per capita private consumption expenditure (PCE) from the national accounts as the instrumental variable (IV) for the survey mean, under the assumption that the measurement errors in these two data sources are uncorrelated. That assumption can be questioned, although it should be noted that the national accounts in most developing countries are not calibrated to household surveys. (Consumption is generally derived as a residual after subtracting recorded sources of domestic absorption at the commodity level.) The IV estimate of the slope is 0.471 (0.026) for the full sample and 0.425 (0.043) for the non-OECD sub-sample. Using log PCE as the IV the estimated elasticity is 0.844 (0.030) and 0.744 (0.051) for the nonOECD sample. So (again) this does not suggest there is anything but a small bias in the relationship seen in the raw data in Figure 2.

> 20 All standard errors of regression coefficients in this paper are corrected for a general form of heteroscedasticity using White’s (1980) method.

> 21 For a review of the methods used to set national poverty lines see Ravallion (2012).

10
|||**Particulate**|**Sulphur**|**Carbon**|**Nitrogen**|**Hydrocarb**|**Volatile organic**|
|---|---|---|---|---|---|---|---|
|||**matter**|**dioxide**|**monoxide**|**oxide**|**ons**|**components**|
|||**PM**|**SO2**|**CO**|**NOx**|**CnHm**|**VOC**|
|F|Construction|-|-|-|-|-|-|
|G|Wholesale and retail trade; repair of motor<br>vehicles, motorcycles and personal and household<br>goods|||||||
|H|Hotels and restaurants|-|-|-|-|-|-|
|I_cmn|Post and telecommunications|-|-|-|-|-|-|
|I_trn|Land transport; transport via pipelines; Water<br>transport; Air transport; Supporting and auxiliary<br>transport activities; activities of travel agencies|222,40|86,69|439,20|183,10|1 258,30|123,00|
|J|Financial intermediation|-|-|-|-|-|-|
|LO|Public administration and defense; compulsory<br>social security; Other community, social and<br>personal service activities|7,00|3,40|9,70|1,60|33,10|3,40|
|K|Real estate, renting and business activities|20,97|87,10|168,01|45,14|13,20|49,38|
|M|Education|-|-|-|-|-|-|
|N|Health and social work|-|-|-|-|-|-|

_Source:_ SUST-RUS. 2012(a), 2012 (b).

96
7 1950), apparently after visual inspection of thc residuals suggested that the estimation was unstable in that year.<sup>5</sup> Breaks in the trend of the series were also disregarded. The next section undertakes a formal analysis of the role of structural breaks in reducing the power of unit-root tests, and implements a procedure to deternine the existence of breaks in commedity prices.

# 3. Testing for Structural Breaks in Commodity Prices

As mentioned in the introduction, an important drawback of the standard methodology utilized to assess the long-run trends of commodity prices is its reliance on parametric unit-root tests to determine the existence of non-stationary components in the series. These tests present two types of problems: first, they usually have low power against close compctitors in finite samples (Rudebusch, 1993) and, second, they are particularly sensitive to the presence of structurl breaks (Hendry and Neale, 1991). The lack of power, even in the absence of breaks, arises from the fact that "any test whether a continuous parameter _0_ is equal to some 00 has arbitrarily low power against alternatives 0-e in finitc samples" (Cochrane, 1991). In other words, when confronted with models which are close to the unit-root case, say an AR(1) model with an autoregressive parameter of 0.96, the test will tend to reject the null hypothesis of a unit root in fewer cases than when the autoregressive parameter is 0.85. In addition, structural breaks further reduce the power of the tests: as noted by Perron (1989), a stationary model with a level shift can easily be taken for an integrated process. In fact, Perron shows that several economic series usually regarded as containing a unit root (eg, GDP, interest rates, etc) actually could correspond to stationary models with shifts in the level, the trend or both.<sup>6</sup> I Cuddington (1992) also introduced a break in the case of oil in 1974, a conunodity not included in our study.

6Ardeni and Wright (1992) and Reinhart and Wickham (1994) fmd evidence of a secular decline in aggregate indices of commodity prices, using Harvey's structural decomposition technique, which does not need stationarity assumptions. Their results, however, depend crucially on the absence of structural breaks, as acknowledged by the authors.
quality entrepreneurs. Low quality entrepreneurs are also likely to start with smaller exports, so as to extend a small share of the fixed entry cost. In Table 1, we report results from a Probit regression of exit after one year on the log value of exports during the initial entry, controlling for crop, market and year (Column 1) –results from a similar regression using OLS are reported in Column 2. We find a robust negative relationship, indicating that a ten percent larger entry is associated with about a 1 percent lower likelihood of exit. However, we know that many of these exits occur after the initial shipment. Therefore, firms may all start with similar size shipments, with some firms exiting after one shipment while others continue. This would generate a negative relationship between size and exit in the annual data, but only because firms that exit have fewer shipments. To control for this possibility, we also regress exit on the log value of the initial shipment exported by each firm (Column 3) –results from a similar regression using OLS are reported in Column 4.<sup>21</sup> We find that a ten percent larger initial shipment is associated with a 0.3 percent lower chance of exiting the market after the first year. The smaller coefficient suggests that part of what is driving the coefficient at the annual level is variation in the number of shipments.

Frequent entry and exit imply that the sunk costs to entry into exporting are not large. In addition to the high number of entries and exits observed in the data, additional evidence of the presence of small sunk costs (for the entry into exporting) is the observed pattern of re-entry of some firms in our sample. Not all firms enter and exit exporting only once. There are 194 firms (almost 10% of the total number of firms, excluding individuals, in our sample) that reenter after a few years (see also Annex 1). This is not consistent with very high sunk costs on entry.

> 21 We also tried Logit and results are similar, not reported.

26
We find that skewness and kurtosis of returns are similar between global and simulated global funds. For example, according to the largest number of funds (longest time series) simulations, the skewness of global fund returns is -0.69 (-0.81) whereas that of the simulated global funds is -0.84 (-0.94) or -0.70 (-0.74), depending on whether the variance is minimized or expected returns are maximized. The kurtosis of global funds is 10.69 for the largest number of funds simulation and 12.85 for the longest available sample simulation. The kurtosis of the simulated global funds is 10.04 (11.96) when the variance is minimized and 7.74 (8.60) when expected returns are maximized, using the largest number of funds (longest available sample) simulations. Overall, the evidence suggests that, despite the differences in the mean and variances reported above, higher moments of the distribution of returns are not considerably different across global funds and simulated global funds. If anything, the kurtosis estimates are lower for the simulated global funds than for global funds, indicating that global funds do not appear to be robust portfolios as their distribution of returns has fatter tails.

We now consider the ability of global funds to move away from turbulence-hit countries or regions, and thus actually avoid realized risks. Given the limited information on portfolio holdings at a high frequency, we focus the analysis on the incidence of negative returns during turbulent times. For instance, conditional on large negative returns on the MSCI Emerging Market Index (our proxy for turmoil periods), we compare the realized returns of both actual global funds and simulated global funds.<sup>38</sup> The results reported in Table 16 show that their performances are typically not statistically different from each other. For example, the average return of global funds is -3.42% per week (p.w.) when the MSCI Emerging Market Index falls more than 10% in one week, while the average return of the simulated global funds is -3.45% (-3.70%) p.w. when minimizing the variance (maximizing expected returns) for the largest number of funds. Therefore, global funds do not seem to avoid large losses if compared to specialized funds.<sup>39</sup>

> 38 The evidence reported here considers only weekly returns. The results are similar if monthly returns are analyzed. Results are available upon request.

> 39 A shift of the simulated global funds towards actual global funds and away from specialized funds does not seem to be driving these results. Portfolio weights on actual global funds are generally stable in periods in which the MSCI Emerging Market Index falls significantly. Moreover, this stability in portfolio weights

33
research would focus on the importance of group size and group strength on nepotistic preferences.

# **Conclusion**

In this paper we have sketched out the channels through which culture interacts with corruption. We have argued that culture manifests itself through institutions and social norms. Experiments allow us to hold institutions constant in order to observe the impact of norms on behavior. A review of experimental studies on culture and corruption reveal several patterns. Barr and Serra (2010) find that bribery is related to country of origin in a one-shot game. Cameron et al. (2009) find evidence of variation in the propensity to engage in and punish bribery in the lab. Banuri and Eckel (2012a) also find that punishment of government officials varies due to norms of behavior, such that identical punishment institutions yield different corruption outcomes. The relationship between gender and corruption is also seemingly modified by culture, with western countries displaying a greater gender effect. We also observe nepotism in Israel, as opposed to discrimination in Belgium (Fershtman et al. 2005), and the propensity of Pakistanis to engage in costly nepotism as driven by risk preferences.

We have attempted to reconcile the findings of various lab studies. The discrepancy in results between the lab and the CPI may be due to unobserved variation in beliefs, or to uncontrolled differences in the effect of punishment across societies. Furthermore, the differences between one-shot and repeated bribery games are also open to further study. We hypothesize that the importance of norms for bribe offers and acceptances is reduced with repeated interactions. Finally, the extent to which individuals in different countries respond to externalities (and the type of externality they respond to) are also questions worthy of exploration.

Caution is advised in using corruption as a general term when conducting micro-level analyses. It is important to recognize different classes of corruption separately, and identify and prescribe remedies appropriately. In addition, it is important to standardize corruption games across different settings and cultures in order parse the reasons for the discrepancies. The papers discussed above utilize slightly different experimental protocols, making comparisons across studies difficult. Each paper has a valid reason for conducting their specific games with their modifications, but future research should strive to facilitate comparisons.

21
is much larger when care is required: relative to the reference group of “early” days in the control, parents seek care 14 (17) times more often when care is required and they receive the subsidy (full program) (Panel B, column (2)). By comparison, when care is not required, care is sought only 2.5 (3.6) times more often. The difference can also be seen clearly in Figure 2. Again, this suggests that an important barrier to needed health care is simply the cost of care. In this sample, even heavy subsidization of primary care did not lead to rampant overuse of care, while successfully encouraging needed care. However, underuse was far from eliminated. Indeed, there is little room to reduce overuse, given the low daily hazard of care when care is not required, but there is substantial room to decrease underuse further.

In contrast with the encouraging findings about subsidized care, we find little impact of the CHW treatment on conditional demand. The CHW effect on the hazard of care is almost exactly zero. While some of the signs in column (2) go in the right direction, estimated effect sizes are small, and the hazard rates plotted in Figure 2 look similar between the groups with and without the CHW. On average, the CHW do not appear to improve the allocation of care. Given the extremely low probability of early care seeking to begin with, it is perhaps not surprising that the CHW do not significantly decrease overuse; however, they also do not significantly increase care-seeking on care-required days. There is also no evidence of a complementarity between CHW and subsidies.

# **5.3 Discussion: CHW effects and health outcomes**

The results so far leave us with two questions: first, do CHW visits have any effect, and in particular, is there any evidence that subsidies and CHW can be complements? And second, are there longer-term health outcome improvements in response to the interventions? To answer these questions, we present some exploratory results on care-seeking behavior conditional on child age, and briefly discuss the relationship of our findings here with results from Dean and Sautmann (2023) regarding effects of the two interventions on prevention and health outcomes. The 2014 follow-up data collection reported in this paper focused on 29
# TABLES

|TABLE 1:|TAXES PAID BY THE ENTERPRISE SECTOR...........................|4|
|---|---|---|
|TABLE2:|REAL INVENTORIES IN THE POLISH ENTERPniSE SECTOR ......<br>........|..<br>8|
|TABLE3:|PROFrrABILITY<br>AND THE INFLATION<br>BIAS.......<br>.............|10|
|TABLE4:|THE 1991COLLAPSE INPROFrrABILrTY.......<br>..............|13|
|TABLE_5:_|INVENTORY ACCUMULATION AND THE NATIONAL ACCOUNTS....<br>.|..<br>14|
|TABLE6:|TAXATION OF THEINvLAONBAs<br>**.**.**....**.**.**.<br>BA..... ..<br>..|_15_|
|TABLE 7:|DIRECT AND INDIRECT<br>IMPACr OP CHANaES IN VARIOUS<br>FACTORS<br>ON ENTERPRIsE SECTOR PROFITrrAB Y, IN 1990-91.18||
|TABLE 8:|ENTERPRISE TAXATION AND THE GOVERNMENT BUDGET....<br>......|<br>22|
|TABLEA.1:|THE RELATIONSHIP<br>BETWEEN HISTORICAL<br>COST PROPlT CASH FLOW<br>AND INVENTORIES.............................|28|

# FIGURES

FIGURE 1: PROFrTABILTY INTHE ENTERPRISE SECTOR, 1989-91 ..... ............ 6 FIlURE 2: DIVISION OF ENTERPRISE SECTOR SURPLUS, 1990 ..... ............. 16 FIlURE 3: DIVISION OP ENTERPRISE SECTOR SURPLUS, 1991 ..... ............. 16 FIaGRE A2. 1: GROSSED-UP INvENToRIEs IN MONTHS OP TURNOVER ..... ........... 33
- 8 -

Table 1. Summary of Project Description

||Vishnugad<br>Pipalkoti|Upper<br>Kotmale|Thac Mo|
|---|---|---|---|
|Country|India|Sri Lanka|Vietnam|
|Water source|Alaknanda<br>River|Kotmale<br>River|<br>Thac Mo<br>Reservoir|
|Catchment area (km2)|2,700|...|2,200|
|Project location|Lat. 30.3° N;|Lat. 7.0° N;|Lat. 12° N;|
||Lon. 79.3° E|Lon. 80.5° E|Lon. 107° E|
|New/extension project|New|New|Extension|
|Installed capacity (MW)|440|150|75|
|Annual generated energy (GWh) 1/|1,838|528|52|
|Facility utilization rate (%)|100|40|39|
|Availability of installed capacity (%)|95|95|92|
|90% firm monthly generation capacity (MW)||||
|Average|212|46|48|
|Maximum|440|107|75|
|Minimum|70|6|10|
|Average available water flows (m3/sec)|115|10|83|
|Net head (m)|205|473|90|
|Intended power generation operation|Base load (wet)<br>Peak load (dry)|Peak load|Peak load|
|Reservoir storage capacity (10<sup>3</sup>m3)|3,630|800|1,250,000|
|Relative to the average water flow|4 hours|1 day|6 months|
|Hydrological data availability|Jun 1974 to|<br>Oct 1951 to|Jan 1976 to|
||May2004|Sep1998|Dec2001|

Sources: Feasibility studies. 1/ The annual energy estimates are the original figures in the individual feasibility studies, which account for not only water resources availability but also other operational factors in the grid, including dams and other types of power plants.

Climate changes are likely to occur differently among project locations. According to the IPCC forecasts, around the project area of Northern India temperature is expected to increase by about 1–2oC in the next two decades (Table 2). In the fall and early winter, a warming may be relatively modest on the order of 1⁄2–1oC. Toward the 2050s an additional warming of about 2oC would be expected in this region. On the other hands, monthly precipitation would decrease by about 1⁄2–1 mm _per day_ in most months but might upsurge in the middle of the rainy season. As a result, precipitation would concentrate even more on the rainy season.

In Sri Lanka temperature is likely to increase evenly all the year around. Precipitation would concentrate on one of the current double-humps; more rainfall is expected from June to September, but the dry season would have less precipitation. Around the Thac Mo project site in Southern Vietnam, temperature would rise by 1⁄2–1 percent, especially in _winter_ . The area will experience increases in rainfall in most months, but the increments may be relatively small, compared with Northern India and Sri Lanka.