rafmacalaba/datause-encoder-data huggingface dataset:
6 length of an average representative of the species.' At this stage, we have not obtained a satisfactory measure of "charisma", although we have received many creative suggestions.7 2) Another possible component of existence value is the degree to which a species is considered to be a "higher form of life". In many contexts, it seems obvious that human beings care about other people in proportion to the degree to which they can identify with them. We might believe that this feeling extends to "higher forms of life" as well. We are not suggesting that this is an ideal ethical criterion to use; in fact, we are making no normative judgement at all. Instead, we want to recognize that if people do actually make distinctions among species in this way, then it will necessarily be a component of existence value.' To test for the possible role of such a component, we have divided the data set into the five broad classes of vertebrates: mammal, bird, reptile, amphibian and fish. In the regressions of the following sections, we include dummy variables for each of these classes **to** see if current policy discriminates among them.

3) Since we also may have existence value for "biodiversity" as a whole, some measure of the amount that a species adds to this diversity should play a role in deciding how much to spend on it. As a measure of such added diversity, we might use a species' taxonomic distinctiveness, or difference, from other species.9 Other things equal, **the more** unique a species is, as measured by distance from its closest living relative, the more 'Lowe et.al. (1990) **and** Mosely (1992) give **fairly** precise length ranges for all species on **tie** U.S. endangered species lisL For non-listed species, we consulted several standard biological references to obtain **length** estimates. A complete listing of these sources is contaned **in** the bibliography. **In** some cases, we **were** not able to obtain a published length **for** a species and it was **necessary** to form an estimate by **using** data from closely related species.

'Among the suggestions are: eye-size or eye-body ratio, number of times the animal's name appears in children's books or in articles in **_The New_** _York_ **_Tumes,_** space devoted to the animal in **zoDs, and** subjective charsma ratings from an as yet unperformed psychology expedment. Our judgement at this time is that none of these **measures** would be usefil enough to justify their inclusion.

**'It** is also possible to interpret this type of existence value as **an** example of a **_moral_** value, as in the previous section. Such a theme is developed in Nash (1989). We commenut on the implications **of** this interpretation later in the paper. For now, however, we think of the 'higher form of **life** effect as an anthropocentric value that can explicitly be placed inside a cost-benefit analysis.

9Tbis theme is developed more fully in Weitzman (1992) and (1993).
For example, the model implies that rebel activity will be higher in areas with high levels of pre-existing grievance, a high level of assets thal can be taxed easily, limited altemative economic opportunities as proxied by a lack of infiastructure, and availability of a large pool of uneducated potential recruits. Also, since individuals' investment decisions are affected by the presence of civil strife, our model predicts that communities with higher levels of civil strife will be characterized by lower investment and a lower level of non-agricultural business startups, factors thlat are often assumed to be crucial for broader economic growth.

These predictions are tested with community-level and household level data from Uganda spanning the 1992-2000 period which include information en the incidence of civil strife (as well as physical violence and theft) and possible determinants of these phenomena at the cormmunity level. This allows to probe for determinants of these phenomena and, by using the panel eleraent of the data, changes over time. Replicating the cross-sectional regressions regarding incidence of civil strife performed in most of the cross-country literature confirms the basic validity of the model. Lack of human capital, presence of taxable and immobile wealth, infrastructure access, and to some extent ethnic fractionalization, emerge as the main factors contributing to higher levels of civil strife. Use of the panel element in a subset of the communities, and exploration of factors leading to increases in the incidence of civil strife, confirmTs the validity of this finding but also points towards strong persistence of conflict over time, a clear conflictreducing impact of govemrment provision of infrastructure, and a strong role of households' perceptions. This overall finding, as well as the quantitative magnitude of the estimated coefficients, suggest that, somewhat contrary to traditional models that focus on "greed" as a key determinant of civil strife, lack of economic development is a key factor that increases the incidence of civil strife, contrary to what is found for theft and physical violence. Government policies to raise the opportunity cost of labor e.g. by improving education and infrastructure woulid have a clear negative impact on the propensity towards civil strife, in addition to their direct economic benefits.

2
|Clarity of instructions||88.58|88.58|83.19|74.24|68.91|68.91|
|---|---|---|---|---|---|---|---|
|5 = Always clear||(75.81)|(75.81)|(76.00)|(74.58)|(74.77)|(74.77)|
|Treatment X|||42.15***|||42.15***|21.460|
|Decision number|||(12.84)|||(12.84)|(18.07)|
|Treatment X||||111.3||110.3|110.3|
|Risk preference||||(111.9)||(109.7)|(109.7)|
|Treatment X|||||2.377**|2.373**|0.741|
|Religious preference|||||(1.02)|(1.02)|(1.34)|
|Religious preference X|||||||-0.007|
|Decision number|||||||(0.08)|
|Treatment X|||||||0.251*|
|Religious preference X D|ecision number||||||(0.1)|
|Constant|3004.2***|3498.5***|3635.5***|3559.8***|3303.1***|3501.2***|3495.7***|
||(80.47)|(958.40)|(959.30)|(960.50)|(943.50)|(946.40)|(948.30)|
|Within R2|0.391|0.391|0.396|0.391|0.391|0.396|0.399|
|Between R2|0.028|0.127|0.127|0.134|0.168|0.176|0.176|
|Overall R2|0.315|0.335|0.339|0.337|0.344|0.350|0.351|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level.
Page 3

# GHARXER ONE THE RECORD OF CO?hRCIAL BANK LENDING

Commercial bank lending to Sub-Saharan Africa over the past thirty years reflects to a large extent the changes in economic performance in various countries and the vagaries in external conditions that affected this performance or provided unusual opportunities for business. In the period 1960-1970, when commodity prices were strong and per capita incomes were rapidly rising, commercial bank lending was negligible. Between 1970 and the early 1980s, due to strong fluctuations in commodity prices and the need for petrodollar recycling, commercial lending increased exponentially; from less thani half a billion dollars in 1970 to a stock of $13 billion in long-term loans, on the strength of annual net flows peaking at nearly $3 billion in 1980. Since that time the level of stocks and flows has moved in response to a combination of factors, including new lending as a function of the perceived creditworthiness of individual borrowing countries or entities and the viability of particular forms of security for loans; the effect of a depreciated U.S. dollar on the value of debt stocks; the volume of debt reschedulings and debt service performance; and the development of activity in the secondary debt markets.

# A. STATISTICAL OVERVIEU

Unless otherwise stated, the data presented in the Statistical Annex and discussed below are drawn from the World Bank Debt Tables (WDTs). The aggregate statistics for the past few years are considerably overstated, since C6te d'Ivoire's private sector is shown to have received some $900 million per year in commercial bank medium-term loan disbursements in 1985-88, a significant portion of all new lending to the region. Since this statistic represents 10 percent of C6te d'Ivoire's GNP, it bears little relation to actual lending activities and must be heavily discounted.

Table 1 summarizes the WDTs' estimates of aggregate commercial bank long-term loan activity as measured by the following definitions: Lona-term loans: (a) commercial bank loans with original maturities of more than one year and (b) formally rescheduled interest arrears on both long-term and short-term loans.

Debt stocks: outstanding principal amounts on long-term loans.

Disbursements: actual drawings on loan commitments.

Principal reDavments: actual amounts of principal paid in foreign currency, goods or services during the year specified.

nterest paymnents: actual amounts of interest paid during the year specified.

Net_flows: disbursements minus principal repayments.
transportation may well be endogenous with factors that simultaneously lower the cost of participating in the migrant labor market.

We next add individual, household, and village level covariates, summarized in Table 8 and with additional detail in Appendix Table A.5. As covariates are added to equation (2), we find the share of migrants in the village workforce consistently has a negative, statistically significant effect on high school enrollment (Table 7, columns 1 through 5).<sup>5</sup> We first reproduce the IVGMM results shown in column (5) of Table 6, in which we include only province-year and village fixed effects, and show that a 1 percentage point increase in the village migrant share is associated with a 2.4 percentage point decrease in the probability a middle school graduate will enroll in high school the following year. As the migrant share of the village labor force increases, and presumably the size and quality of the network improves, the net return to migrating and the opportunity cost of staying in school rises enough that we observe a substantial decline in probability of high school enrollment.

To account for time varying village economic conditions, we initially add time-varying village controls to equation (2) (Table 7, column 2). While none of the village level coefficients differ significantly from zero, the inclusion of these variables is associated with an increase in the first stage _F_ -statistic on the instruments to 4.4. Importantly, the inclusion of these variables does not change the point estimate of the effect of migration on high school enrollment.

Individual and parental characteristics may also affect the decision to attend high school by contributing to differences in levels of household wealth or family preferences for education. We initially add gender, an indicator variable for whether the child is first born, and an indicator variable for households in which the first born child was male (Table 7, column 3). From these coefficient estimates, it is apparent that girls with older male siblings will be less likely to attend high school. The gender preference holds even after including additional family characteristics, but the positive effects of being first-born become less important and insignificant.

We next add parental characteristics that reflect innate ability, proxy for wealth, and the ability to migrate, and continue to find that migrant opportunities negatively affect high school enrollment (Table 7, columns 4 and 5). Both the father’s and mother’s years of schooling positively affect an individual’s likelihood of attending high school (column 4). The number of 5First stage regressions for Table 7 can be found in Appendix Table A.7.

20
# **Table F.1. Conflict and food insecurity, alternative lag structure**

|||||Even<br>|t type<br>|Pe<br>|rpetratorty<br>|pe<br>|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Conflict events|0.080***<br>(0.021)|0.071***<br>(0.020)|0.070***<br>(0.019)||||||
|Conflict events property||||0.126***<br>(0.036)|||||
|Conflict events violence|||||0.119***<br>(0.042)||||
|Insurgents|||||<br>|0.080**<br>(0.032)|||
|Bandits/criminal|||||||0.282***<br>(0.080)||
|Pastoralists/nomads||||||||0.021<br>(0.019)|
|Observations|3,550|3,457|3,457|3,457|3,457|3,457|3,457|3,457|
|R-squared|0.014|0.031|0.041|0.040|0.038|0.040|0.041|0.038|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the CSI. Data used are from the six visits of the GHS and telephone survey for conflict. The conflict recall is one year. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xxi
Figure 3: Real Consequences in **the U.S. of** Permanent **U.S.** Deficit Reduction **Using Alternative** Instruments <!-- Start of picture text --> U.S. Real GDP U.S. Private Consumption<br>0.3 5 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1.2<br>0.5 4~~~~~~~~~~~~~~~~0<br>j~~~~~~~~~~~~~~ ~ ~ ~ ~~ ~~~~~~~~~~~~~~~ i} -^- - -s -- -¢-fg .- - - -' - -- - -- --<br>0j~ ~ ~ ~ ~ ~ ~~~~~~~~~~~_~ ~ ~<br>lto, ISSs IS90 2ogo 2Z OW0 20002 W ZW 2001 2010_ 19S4d2N ins 1995 20 t00 lDlO _^- 201C 1<br>Yescr Year<br>U.S. Private Investment U.S. Trade Balance<br>- ._ lIl4 till 1ii 000 20220406 20081 2010 2012 19411 912000 2002 =04 2M0 2006 2010 2012<br>Year Year<br><!-- End of picture text -->
question of whether women are "poorer", or in some sense deprived relative to men, as discussed by Appleton and Collier (PSP). Behavioral responses within households can also alter assessments of policy impacts at the household level; for example, in their study in PSP, Ravallion and Datt show how cross-gender effects in time allocation _within_ the household have bearing on the net benefits from workfare.

Our understanding of certain dynamic issues in the incidence of public spending has also been greatly constrained by data limitations. The analysis of government program impact has been for the most part static. Yet, in developing countries (particularly in rural settings) and in transition economies, a key concern pertains to the variability in living standards to which the poor-in particular-are prone. The performance of public spending programs in providing effective safety nets that lower risk and income variability, protecting households from uninsurable risks and shocks for which insurance markets do not exist, is clearly of considerable importance. Evaluations based on a single cross-section may not be very informative about impacts on living standards. As shown in benefit incidence analyses for Indonesia and Malaysia's health and education sectors (respectively van de Walle, and Hammer and others in PSP), analysis of two cross-sections or more reveals how marginal changes in public expenditures are distributed ignoring all behavioral incentive effects. Panel data, which follow households over time, present a promising new avenue for combining determination of both dynamic and behavioral effects. As yet, few studies have applied this approach to examining the distributional performance of public programs over time. One exception uses a Hungarian panel to examine how well the social safety net performed both in protecting families from falling into poverty and in promoting the poor out of poverty during a period of the early transition (van de Walle and others 1994). The collection and setting up of panels are becoming more common, which will hopefully make the exploration of dynamic issues easier. This is a ripe area for future research.

20
# **SUMMARY**

Foreign direct investment in the developing world experienced a rapid growth in recent years, rendering it one the most important financial flows to developing countries as a whole One of the driving forces behind this renewed interest by private investors in developing country economies is the privatization of 3tate-owned en:erprises, which expanded rapidly over the last years in many developing countries. In order to gain a better understanding of the relationship between privatizations and foreign investment, a database was constructed for privatization transactions worldwide for the period 1988-92. The data were compiled based on information from a wide variety of public sources such as newspapers and journals, country and regional departments as well as residence missions of the World Bank, and privatization groups and ministries within the individual countries. The database ',omprises about 1,100 transactions and is available from the author upon request.

The study shows that during the period 1988-92, developing country governments earned almost IJS$62 billion in revenues from the sale of state-owned assets. About a third of these revenues came from foreign sources in the form of portfolio equity and foreign direct investment. Measured in sales volume, the largest privatizers are located in Latin America, which accounts for 66 percent of total privatizations in the developing world. European countries, including the Eastern European economies, account for 17 percent, followed by East Asia with another 13 percent. The degree of foreign participa n differs substantially among countries, but the Eastern European nations clearly show the most substantial invoivement of foreign investors compared to other large privatizers, primarily due to the fundamental lack of domestic investment finance. But besides being a direct source of foreign exchange, privatizations also serve as an important vehicle in attracting additional foreign investments, not directly obtained from h..e sale of state-owned assets to foreigners. An econometric analysis shows that the relative size of the privatization program as well as the degree of openness to foreigners are important determinants of foreign direct investment inflows. In fact, for the analyzed sample of countries, it turns out that each dollar in privatization revenue generates an additional 38 cents in new foreign direct investment inflows, and that a one percentage increase in foreign participation adds another 50 cents. The analysis also shows that privatizations in the financial sector and infrastructure have a strongly positive effect on foreign investors, while privatizations in manufacturing or services do not appear to attract foreigners to any significant extent beyond the initial sale of the state-owned enterprises themselves. Thus, developing countries can use privatizations as a vehicle to attract additional foreign investment in two ways: directly through the sale of assets to foreign investors as well as by improving the general economic environment such that it appears more profitable for foreigners to engage in ventures in these countries.

2
|**STUDY**|FINDINGS|
|---|---|
|TradePolicyForum,<br>PECC'<sup>99</sup>|*<br>SimpleaveragetariffsforAPEC have falleninthe1995-<br>98periodbutexperiencedanincreasein1999.Tariffs<br>declinedfrom about11percenttoabout7percentduring<br>thistime.|
||*The increaseintariffsin1999representsachange in<br>approachdue toUruguayRoundcommitments.Countries<br>beganapplyingtariffs to agriculturalproducts. Before this<br>time,agriculturalproductswerelistedundernon-tariff<br>measures.Therefore,theincreaseintariffsin1999 isnot<br>seenasanincreaseinprotectionism.|
||*Hightariffsremaininthe followingareas:food,<br>beveragesandtobacco;agricultureandhunting; textiles;<br>and fishingsectors.Peaktariffsareusedinareaswherea<br>country does nothaveacomparativeadvantage.|
||*Thestandard deviationintariffsdeclined fromnearly30<br>percenttounder10percent between1995and1999.The<br>reportstates,"lowerdispersionimplylessdistortionsand<br>thus gainsinwelfare."|

Between 1995 ancd 2000, the average tariff rate for APEC members decreased from 12 percent to 8 percent.200 Figure 3.2 shows a country-by-country picture of significant reductions in tariff levels.

61
# 1 Introduction

Sharecropping, among the archetypical incentive problems, has fascinated economists since Adam Smith (see, e.g., Laffont and Martimort, 2001). Despite the continued high prevalence of sharecropping in much of the developing world and a vast theoretical literature seeking to explain it, there have only been scattered attempts to link observed contractual forms in agriculture to speci fi c models of the principal-agent relationship (recently, see Ackerberg and Botticini, 2002; Laffont and Matoussi, 1995; Pandey, 2004).

Most empirical attention has instead focused on the sharecropper-productivity debate; the question of whether tenant effort is indeed prohibitively costly to monitor, thereby leading to moral hazard (cf., Stiglitz, 1974), or whether underprovision of tenant effort is obviated by landlord supervision (as argued by, e.g., Johnson, 1950). While Shaban’s (1987) landmark study appeared to vindicate the moral hazard view, evidence for the ‘superiority’ of owner-cultivation or fi xed rental over sharecropping is, on the whole, inconclusive (Otsuka, et al., 1992; Binswanger, et al., 1995).<sup>1</sup> In this paper, we argue that heterogeneity in supervision costs has important implications for both of these strands of the empirical literature on land tenancy. To show this, we develop a simple model in which tenant effort is observable and enforceable, but at a cost to the landlord. Tenants vary in their ex-ante wealth, landlords in their cost of supervision, and equilibrium in the tenancy market is achieved through a process of matching along these two dimensions. The model yields two insights: First, otherwise identical share-tenants can receive different levels of supervision and, consequently, have different productivities. Second, a landlord’s decisions regarding the intensity of supervision and the type of incentive contract to offer are driven (in a fairly restricted way) by his cost of supervision.

Our empirical work, which uses a large and detailed micro-level data set from rural Pakistan, begins by estimating the average yield differential between sharecropped and owner-cultivated plots cultivated by the same household. We are able to state with high con fi dence that this differential is small, a conclusion supported by evidence on family labor allocation. Next, using unique information on monitoring frequency collected directly from tenants, we demonstrate that yields on plots cultivated by ‘unsupervised’ tenants

> 1 In Laffont and Mattousi’s (1995) study of tenancy in a Tunisian village, for example, sharecropper productivity (evaluated at mean tenure duration) is higher than that of owner/renters. On the other hand, recent evidence from a tenancy reform in West Bengal (Banerjee, et al., 2002) shows that a modest reallocation of property rights in favor of share-tenants, falling far short of outright ownership, led to a dramatic yield increase, much greater than any found in farm-household level data.

1