rafmacalaba/datause-encoder-data huggingface dataset:
Typically, a transfer must be fairly substantial in order to justify the cost of physically connecting water systems. Whenever possible, it makes economic sense to utilize the existing water courses to implement water transfers. Barring extreme circumstances, it is useful for the analyst to think in terms of a set of relatively independent market regions defined by water basin, rather than envisioning an entire region or nation as a viable water market.

In this context, it is clear why the Colorado Big Thompson project in Colorado is usually cited as an ideal, and certainly one of the very few,large scale water market in operation today. Large, centralized stored water supplies are most amenable to water marketing because transportation and return flow issues are minimized. CBT project facilities connect three major watersheds with a highly centralized stored water supply.

In the same vein, expanding water markets nay require additional physical linkages. The Northern Colorado Water Conservancy District, (the agency that operates CBT water) has conducted a long term study of ways to accommodate uneven growth in regional water demand. The result is a proposal to develop additional infrastructure and to expand integrated (centralized) water treatment (NCWCD,1991). Shah (1991) also sees infrastructure as key to the development of water markets in India.

Based on the discussion above, it is clear that many state and federal agencies have the opportunity to provide leadership in water marketing. Large scale water supplies are typically controlled by these agencies. Unfortunately, many of these same agencies employ policies that explicitly prohibit gains from trade via water transfers. Policy makers should consider institutional innovations that allow efficient transfers. Transactions Costs The term "transaction cost" refers to the monetary expense of completing a particular transaction. In this case the transaction is the transfer of a water right. Typical transaction cosrs incurred in such a transfer include the cost of negotiation with the other water user(s) involved and the costs of fulfilling legal and/or administrative costs associated with completing the transfer.

Rather than being a minor consideration in water transfers, transaction costs have proven to be a. major element. They are often substantial enough to delay or prevent water transfers altogether. Transaction costs have been cited as the reason why more water transfers do not take place, even when large differences in the value of water in alternative uses exist (Young, 1986; Ingram and Oggins, 1990). Ingram and Oggins(1990) note that a transfer of 15,000 acre feet of water in Nevada required 15 months of 16
42

|Country|_Changesinf _<br>|_ramework_<br>|Ownership**links** <br>|betweenbank<br>_Ch_<br>|sandcorpor<br>_angesinown_<br>l|ations<br>_ershipstructure_<br>Cif|A|Athldb|Banks'andnon-<br>financials'capital<br>adequacypositions|
|---|---|---|---|---|---|---|---|---|---|
||Banks allowedto<br>ownnon-financials|Non-financials<br>allowedtoown<br>financials|Nationalization<br>(shlmeofassets held<br>bystate-owned)|Privatization|Saeto<br>foreigners|oncentratono<br>ownership<br>controlamong<br>top15fanmilies|ssets<br>carvedout|ssesey<br>stateasshareof<br>GDP||
|Hungary<br>(1991-95)|Nochanges|No changes|Recapitalizationin<br>1991-94through<br>exchangeofbonds<br>forNPLs resultedin<br>nationalizationsuch<br>that outof44banks<br>inoperationin<br>1995,17were<br>majoritystate-<br>ownedand5had<br>minority state<br>ownerhip.**3'**<br>Previously, bank<br>shareholders<br>included state-<br>ownedenterprises.|Government<br>implemented<br>bank<br>privatization<br>ineamest<br>startingin<br>1995.Byyear<br>end,about<br>threefourths<br>ofthebanks<br>weremajority<br>private.|Majority<br>stakein held<br>byforeign<br>investorsin7<br>largest<br>commercial<br>banksasof<br>1999.|Notapplicable|Not<br>applicable|Asof1998,state<br>heldminority<br>stakesin2 banks<br>and100%<br>ownershipofI<br>bank(due to<br>recapitalization<br>in1997-98),<br>equivalentto<br>0.3%ofGDP.|AverageCARsofthelarge<br>mostlyprivatized banks<br>improvedto13% in1998.<br>Averageinmedium private<br>(denovo)bankswas17%in<br>1998.**"**|
|Norway<br>(1987-93)|Nochanges|Nochanges|By1991,<br>recapitalization<br>resultedinthe<br>government'ssole<br>ownershipor<br>rajority<br>shareholdingofthe<br>threelargest<br>commercialbanks<br>whichaccounted for<br>85%ofall<br>commercial bank<br>assets.Inthemid-<br>1990s,the<br>government<br>expressed its<br>intentionto reduce<br>itsshareholdingto<br>50%/**o** fthetwo<br>largestbanksbyfall<br>1997and toatleast<br>onethirdthereafter.|In1995,two<br>yearsafter<br>resolutionof<br>thecrisis,the<br>govenment<br>soldthethird<br>largestbank.|Thesaleof<br>thethird<br>largestbank<br>wasmostly<br>toforeign<br>investors.|Notapplicable|Not<br>applicable|Asof1998, the<br>statemaintained<br>61%and51%<br>stakeinthetwo<br>largestbanksthat<br>accounted for<br>40%and 30%/**o** f<br>total commercial<br>banklending.3|AverageCARsof<br>commercialbanksincreased<br>from7%in1981-85to<br>10.6%in1993.Bymid-<br>1990,banks'capital<br>positionswere considered<br>healthyandsufficientto<br>absorblosses.|

"Recapitalization effectively renationalized the banks with the MOF receiving 75% of shares of the banidng system<sup>(Borish,Ding and Noel1996a)</sup> **32** World Bank 1999b.

3 As of 1998, GDP = NOK 1100 billion and deposit money bank assets = NOK<sup>1030</sup> billion;<sup>government's holdingsof the two largest banks=NOK 9195 million.</sup>
2 There is a need for better indicators over time of the trade- and welfare-reducing effects of price-distorting policies than the existing weighted average NRA/CTE (or PSE/CSE) estimates for the farm sector, because the averaging process hides the fact that distortions vary across industries within the sector. This is especially problematic in cases where NRAs have opposite signs, as when trade taxes apply to both imports and exports, or when dual exchange rates operate. In those cases the NRA may be close to zero even though the trade- and welfarereducing effects of those interventions could be substantial.

Recent theoretical literature provides partial equilibrium indicators of the trade- and welfare-reducing effects of import policies. The purpose of this paper is to draw on that literature in order to develop indexes to capture global distortions to agricultural incentives that are based directly on estimates of NRAs and CTEs. We then exploit the Agricultural Distortion database recently compiled by the World Bank to generate the first set of estimates of consistent indexes for the agricultural sector for both developing and high-income countries over the past half century.

The new World Bank global panel dataset contains comparable estimates of annual NRAs and CTEs for a wide range of agricultural products for around 75 countries that together account for all but one-tenth of the world’s population, GDP and agricultural production (Anderson and Valenzuela 2008). Applying our indexes to these data takes us much closer to understanding the true trade and welfare effects of policies without needing a formal model of global markets or even price elasticity estimates.

The remainder of the paper is structured as follows. After a brief literature review the next section presents the theory for estimating welfare- and trade-reduction index numbers in the import-competing sub-sector. This is extended to cover the exportables sub-sector in the following section. The World Bank’s Agricultural Distortions database is then discussed,
<!-- Start of picture text --> 10 8 6 4 2 0 -2 -4 -6 -8 -10 G.D.P. (H&P) Right Scale 10 8 6 4 2 0 -2 -4 -6 -8 -10 G.D.P. (H&P) Right Scale<br>(except in the case of the<br>5<br>F (H&P) F (H&P)<br>From U<br>From O.L.F.<br>6b. Job creation<br>I (H&P) I (H&P)<br>S.E. (H&P) S.E. (H&P)<br>12 10 8 6 4 2 0 -2 -4 -6 -8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6<br>10 8 6 4 2 0 -2 -4 -6 -8 -10 G.D.P. (H&P) Right Scale 10 8 6 4 2 0 -2 -4 -6 -8 -10 G.D.P. (H&P) Right Scale<br>Figure 6. Job Creation and Destruction<br>Towards U F (H&P) F (H&P)<br>Towards O.L.F.<br>6a. Job destruction I (H&P) I (H&P)<br>S.E. (H&P) S.E. (H&P)<br>2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0<br>Note: Creation/Destruction rates inferred from the continuous time transition matrix for each period using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in Annex 1. Computations are based on 10.000 Monte Carlo replications. The series have been averaged per quarter, de-trended using a HP filter with smoothing parameter 10 panels titled as “In Levels”) and smoothed using a 3 quarter moving average smoother. OLF=Out of the Labor Force, U=Unemployment rate, I=Informal Salaried, SE=Informal Self-employed, F=Formal Sector.<br>2002 2002<br>2001 2001<br>2000 2000<br>1999 1999<br>1998 1998<br>1997 1997<br>1996 1996<br>1995 1995<br>1994 1994<br>1993 1993<br>1992 1992<br>1991 1991<br>1990 1990<br>1989 1989<br>1988 1988<br>1987 1987<br>1986 1986<br>1985<br>1985<br>1984 1984<br>1983 1983<br>2002 2002<br>2001 2001<br>2000 2000<br>1999 1999<br>1998 1998<br>1997 1997<br>1996 1996<br>1995 1995<br>1994 1994<br>1993 1993<br>1992 1992<br>1991 1991<br>1990 1990<br>1989 1989<br>1988 1988<br>1987 1987<br>1986 1986<br>1985 1985<br>1984 1984<br>1983 1983<br><!-- End of picture text -->
a long tradition of resettling humanitarian migrants through its Humanitarian Programme— which is the world’s second largest resettlement program managed in collaboration with the United Nations High Commissioner for Refugees (UNHCR) (behind only the United States). However, the living conditions of humanitarian migrants are not comparable to the general population. In particular, the refugee employment rate is around 23%, with a gap of up to 55 percentage points compared to the citizens’ employment rate. This gap is around half and five times higher than the corresponding figures for the U.K. and the U.S., respectively (Brell et al., 2020). Australia is also the only country that mandates immigration detention for all “unlawful” arrivals, including those seeking protection as refugees. Therefore, understanding the effect of mental health on refugee labor outcomes is vital for designing refugee policies not only in Australia but also in other richer countries facing a large influx of refugees.

We analyze rich data on refugees from the _Building a New Life in Australia_ (BNLA) longitudinal survey, which is also the largest survey of humanitarian migrants in Australia.<sup>2</sup> We investigate the causal impact of mental illness on labor market outcomes for refugees, employing an instrumental variable model with individual fixed effects that allows us to address various econometric issues such as endogeneity and reverse causality (i.e., while a mentally healthier refugee is more likely to be employed, being unemployed may deteriorate their mental health status). We instrument for mental health with the interaction term between past trauma exposure and a year dummy variable indicating the time period after arrival. This choice of instrument is motivated by insights from a large number of studies in the epidemiological literature that show a strong relationship between pre-displacement trauma and post-displacement mental health (Fazel et al., 2012) and that trauma-related mental illness tends to diminish over time (Steel et al., 2002; Moya, 2018).

> 2 We use the term humanitarian migrants and refugees interchangeably in this paper given most respondents of the BNLA survey are refugees (more than 70%).

3
whether the firm has sufficient resources to “smooth things over.” Indeed, Chinese firms are able to reduce their effective tax burdens and obtain better government services when they spend more money entertaining government officials (Cai, Fang, and Xu 2011). The reported enforcement of various government regulations such as the number of days to obtain various permits differs greatly among firms in the same country, according to the Enterprise Survey, and a significant share of firms in countries tend to enjoy much better government services than what is warranted under de jure regulations and laws (Hallward-Driemeier and Pritchett 2015). Thus, since enforcement is better controlled for in the case of de facto BE indicators, the de facto indicator of the same theme of the BE (say, access to finance) should have stronger predictive power for firm performance than de jure indicators.

Furthermore, the effects of the de jure DB indicators on firm performance likely depend on contract enforcement. DB indicators reflect what the laws say about proper procedures and implied costs. A good BE aspect on paper (as reflected in _Doing Business_ ), without strong enforcement, would not translate into real benefits to firms in terms of performance. Indeed, the recent literature on state capacity emphasizes that the same rules and regulations have vastly different effects depending on state capacity, including the key element of contract enforcement (Besley and Perssons 2009, 2010). As an example, when obtaining a title for urban land, Chinese firms that do not expect strong enforcement (that is, firms without political connections) did not actually increase investment, Miao et al. (2019) find. The BE effects would thus likely be effective only when enforcement is also strong. Of the three sets of BE measures, the _Doing Business_ series are de jure, and their effectiveness thus depends crucially on contract enforcement. We expect that the _Doing Business_ indicators should have stronger effects when contract enforcement is better, especially for those BE elements that require strong government enforcement.

Thus, our _enforcement hypothesis_ is as follows: The de facto indicator of the same theme of the BE (say, access to finance) should have stronger predictive power for firm performance than de jure indicators. The influence of de jure indicators on firm performance depends on contract enforcement, and the influence is stronger in countries with better contract enforcement.

Does the BE exert the same influence on domestic firms and foreign firms? There are good reasons why they might respond to the same aspect of the BE differently. Foreign firms, for instance, have better access to finance (from headquarters or foreign owners) and to advanced technology. Furthermore, they likely have stronger bargaining power with respect to the 5
at lease once since they took their first job. The formality status is therefore generally stable and equivalent to the formality of their last job. Moreover, since hardly any Syrians have formal jobs, this is equivalent to comparing each group of informal workers to formal Jordanian workers. Nonwage workers are included as a separate category in the regression, but we do not show their results given their limited sample size.

S (2) iitt ii tt 1tt ii tt 2tt ii tt iitt yy = αα + γγ + ββ ∗HH + ββ JJSS ∗HH + εε S : _dummy for Syrian whose last job was private informal, 0 otherwise._ ii _: dummy for Jordanian whose last job was private informal, 0 otherwise._ ii JJSS In the third, fourth and fifth models we limit the sample to Syrians. We use an in-camp indicator to test the effect of living in camp among Syrians (3), a permit indicator for examining the effect of holding a work permit (4), we then account for both variables in the same model (5). (3) iitt ii tt 1tt ii tt iitt yy = αα + γγ + ββ CC ∗HH + εε : takes 1 if Syrian living in camp, omitted category is Syrian not living in camp ii CC (4) iitt ii tt 1tt ii tt iitt yy = αα + γγ + ββ PP ∗HH + εε : takes 1 if Syrian holding work permit, omitted category is Syrian not holding permit ii PP (5) iitt ii tt 1tt ii tt 2tt ii tt iitt yy = αα + γγ + ββ CC ∗HH + ββ PP ∗HH + εε Finally, using the full sample we include two dummy variables for being a Syrian who lives in camp or being a Syrian who does not live in camp to have a closer look at these categories in comparison to Jordanians. In this model we also control for formality of the last job. (6) iitt ii tt 1tt ii tt 2tt ii tt 3tt ii tt iitt yy = αα + γγ + ββ SSCC ∗HH + ββ SSnnCC ∗HH +ββ EE ∗HH + εε 20
(as argued earlier) but would also make the existing stock of private capital more productive. An important issue in this context, however, is the existence and magnitude of congestion costs, which imply that the productivity gains associated with a greater stock of public capital may diminish over time because the services produced by this stock are over-used. As discussed later, this may be a particularly relevant consideration for public infrastructure (such as roads, for instance) which tends to get overcrowded due to the lack of alternatives.

The evidence linking public investment to private capital formation and growth has grown significantly in the past few years. For instance, Milbourne, Otto, and Voss (2003), using an extended version of the Solow-Swan neoclassical model, find evidence of a positive conditional correlation between public investment and economic growth in a sample of 74 industrial and developing countries. Aschauer and Lachler (1998), using cross-country growth regressions for a group of 46 developing countries, find that public capital contributes significantly to productivity — growth, as long as it is financed by lower current government spending as opposed to a higher level of public debt (which may signal higher current and future taxation, or a future increase in the cost of borrowing). Ahmed and Miller (2000), using a sample of 39 industrial and developing countries for the period 1975-84, find that expenditure on social security and welfare reduces private investment (through the crowding-out effects alluded to earlier) in both groups of countries, whereas expenditure on transport and communication raises aggregate investment in developing countries. Bose, Haque, and Osborn (2003), using panel data for 30 developing countries and an econometric methodology that explicitly accounts for the government budget constraint and possible biases arising from omitted variables, find that the share of government capital expenditure in GDP is positively and significantly related to income growth per capita, whereas current expenditure is insignificant.

Belloc and Vertova (2004), using a vector error-correction approach, find a complementarity relationship between public and private investment, and positive effect of investment on output in 6 out of 7 HIPC countries. In a study of eight Latin American countries during the period 1980-95, Ramirez (2000) also finds that public 6
few states.<sup>14</sup> The increase in Boko Haram’s activity in the North East might have fostered differences in farmers returns to diversification. In a nutshell, these events could have pushed low-skilled farmers to diversify into non-farm activities already occupied by more skilled ones and resulting in very limited gains for them.

In the presence of heterogeneous responses to diversification on which individuals act (select into diversification), a variety of “effects” of diversification can be defined and conventional intuitions about instrumental variable estimators break down (Carneiro, Heckman, and Vytlacil 2000). A more detailed discussion of the inconsistency of the OLS and traditional IV estimators of the effect of diversification on the outcome of interest is presented further in this analysis.

For all these reasons, we take a step forward in our analysis and focus on modeling the major constraints to the identification of the effect of diversification on Nigerian farmers’ wellbeing, which are: 1) Missing counterfactual (Blundell and Dias, 2009): we only observe per capita expenditure in one or the other state (diversified/non-diversified), not both simultaneously for each farmer.

2) Selection into diversification (decision to diversify is endogenous): farmers may self-select into diversification based on unobservables. These same unobservables may, in turn, have an impact on per capita expenditures so that the error terms in the per capita expenditure and selection equation can be correlated.

3) Essential Heterogeneity: agents base their decision to diversify on these unobservables as different farmers might benefit differently from diversification. The gap in per capita expenditures between diversifying and non-diversifying for a given farmer are individualspecific and depend on different returns to both observables (human capital; gender; physical constraints; etc.) and unobservables (ability; entrepreneurship; family background; shocks). Most importantly, since the returns to undobservables can differ across the distribution and can change over time as consequence of a shock as we document henceforth, using a panel Fixed Effect estimation leads to loose crucial information. The comparison of the two surveys taken separately and the use of non-parametric techniques captures rather than disregard this heterogeneity.

> 14The full escalation of the insurgency, with mass displacement and heavy fighting between insurgents and Nigerian Army starts de facto in June 2013, few months after our data collection. Proof that, during 2013, households were only marginally affected by the conflict is that in the third round of the panel survey (2015/2016), most of the households residing in Borno and Adamawa states visited in 2013 could not be re-interviewed due to the poor security situation.

11