rafmacalaba/datause-encoder-data huggingface dataset:
-4-

workers above the poverty level or to attract and retain skilled manpower. Real remunerations are significantly affected by the rate of inflation and the regularity and nature of salary adjustments. Declines in real wages are often cushioned by elaborate allowances or non-wage benefit structures that in some countries have become an increasingly important part of the overall compensation package. A pervasive irony afflicts many countries; i.e., the overall salary bill is too high while wages are too low. This dilemma is the product of years of trade-offs, given fiscal constraints, in favor of hiring growing numbers of employees at diminishing salaries. Wage erosion is particularly a problem where there exists an alternative market for public servants, either through a better paying parastatal sector, a domestic private sector with higher salaries and benefits or an international market to which highly skilled public servants may migrate.

- (iv) Wage comgression. In many of the cases under examination, increasing compression of the ratio of top to bottom salaries has been identified as a serious constraint on governments' ability to attract and retain qualified personnel at the middle and higher levels. Wage compression is partially a function of expansive employment policies as discussed above, but in some countries it may also derive from regime preferences for egalitarian salary structures. This, in turn, may hinge on the ideological character of the regime or the degree to which its political support is drawn trom the lower socioeconomic strata.4

# B. LENDING INSTRUMENTS

The Bank's principal instrument for promoting public employment and pay reform has been some form of policy-based lending, principally through Structural Adjustment Loans. Between 1981 and 1987, twenty-three SALs had public employment and pay components. Conditionality relating to public employment and pay was also included in Reconstruction Import Credits and one Imports Program Credit. Thirteen Technical Assistance Loans with public employment and pay components were directly linked to SALs. An additional five free-standing TALs also included significant public employment and pay components. (Table 6 classifies pay and employment operations by region and lending instrument.) Bank promotion of public employment and pay reforms has also occurred through Country Economic and Sector Work. Some of this work took the form of sector studies in anticipation of specific Bank projects (e.g., diagnostic studies in Mauritania and Senegal). Other CESW dealing with public employment and pay issues was carried out through the preparation of country economic memoranda. In several instances, coverage of these issues in a Country Economic Memorandum (CEM) preceded or was accompanied by

- i/ Recent Bank research has discussed these problems for African cases. See, especially, David Lindauer, Oey Meesook, and Parita Subsaeng, Government Wage Policy in Africa: Summary of Findings and Policy Issues, CPD Discussion Paper No. 1986-24, April 1986.
capitalized in the value of a dwelling unit. Hedonic analysis of house prices or rents conditional on service access will therefore yield estimates of the contribution of individual housing unit characteristics and thus the willingness-to-pay (WTP) of residents for those characteristics. In contrast to the standard hedonics and WTP literature, we use a spatial framework that allows us to measure both direct effects and externality spillovers from upgrading by neighbors. We illustrate this approach in a valuation of water services in two Indian cities, Bangalore and Bhopal, and compare the results with those obtained from a standard WTP questionnaire. The results suggest that standard hedonic analysis considerably underestimates the benefits from service upgrading. The higher spatial econometric estimates are surprisingly consistent with households’ expressed WTP. Finally, we present a policy analysis based on simulation of different upgrade scenarios that is based on predicted values for individual observations in our household survey rather than on marginal willingness-to-pay.

The remainder of the paper is structured as follows. The next section discusses the use of hedonic models for valuation purposes. Section III describes the estimation strategy. The data used in this analysis and estimation results are described in Sections IV and V. We derive the marginal willingness to pay for improved water availability in Section VI and present a policy analysis using a simulation exercise in Section VII. Section VIII concludes.

# **II. Hedonic Analysis of Housing Characteristics and Public Services**

Valuation of public services and other housing attributes has been addressed through methods such as contingent valuation (Whittington 2002), conjoint and discrete choice analysis (Earnhart 2002) and hedonic specifications (Malpezzi 2002). In hedonic models, dwelling unit prices represent the sum of expenditures on a bundle of characteristics that can be priced separately. If _z_ =( _z_ 1,..., _z_ n) is a set of characteristics of the home, the price of the home is determined by some hedonic function, _p_ ( _z_ ), according to prevailing market clearing conditions. The set of characteristics that determine home values consists of structural attributes of the home itself, such as the floor area, lot size, and construction material, as well as the availability of public services such as clean water supply or electricity. Such models have been used in developing countries to determine the optimum housing characteristics for low income groups (Follain and Jimenez, 1985) and for valuation of access to specific services (North and Griffin, 1993; Crane et al., 1997; Oliveira and De Morais, 2000, and Knight et al. 2004 among others).
# **<u>Appendix table A6: Sample frame compared to national statistics</u>**

|<br>**Total**||<br>**Farm s**|**ize (ha)**||
|---|---|---|---|---|
||**<50**|**50-120**|**120-500**|**>500**|
|**Panel A: No. of farms and median/average**<br> <br>|**farm size**<br>||||
|_Total Form 29_<br>36,167|16,138|5,400|7,142|7,487|
|_Total SAR_<br>75,571|61,705|6,127|4,444|3,295|
|....Legal Entities %<br>20.8|9.0|58.4|76.3|97.1|
|FOPs %<br>11.2|9.3|28.2|21.1|2.0|
|Individuals %<br>68.0|81.7|13.4|2.7|0.8|
|Ratio SAR/F29<br>2.09|3.82|1.13|0.62|0.44|
|Median farm size, SAR<br>6.65|4.92|74 .99|206.60|1,307.9|
|Med. farm size, Form 29<br>68.57|24.00|77.09|241.00|1,298.6|
|Avg. farm size SAR<br>154.0|9.75|88.70|283.11|2,802.5|
|Avg. farm size Form 29<br>777.7|25.07|79.43|264.67|3,392.9|
|**Panel B: Area cult. (mn. ha)**|||||
|_Total Form 29_<br>27.85|0.40|0.43|1.89|25.14|
|_Total SAR_<br>11.64|0.60|0.54|1.26|9.23|
|....Legal Entities %<br>90.5|33.0|63.9|82.8|96.9|
|FOPs %<br>4.4|13.6|25.5|15.3|1.1|
|Individuals %<br>5.1|53.4|10.7|1.9|2.0|
|Ratio SAR/F29<br>0.42|1.49|1.27|0.67|0.37|
|**Panel C: Informality**|||||
|Area cult. 2020 (mn. ha)<br>45.73|||||
|F29 overlapping (mn. ha)<br>7.51|0.1|0.22|0.75|6.46|
|F29 non-overlap (mn. ha)<br>20.34|0.31|0.21|1.14|18.68|
|SAR only (mn. ha)<br>3.36|0.45|0.27|0.33|2.32|
|Informal (mn. ha)<br>14.51|||||

_Source:_ Computation based on data from the SAR, 2020 Form 29 data from SSSU, and crop maps based on satellite imagery for 2020, as discussed in the text. _Note:_ Region is not reported in the SAR for 1,629 farms cultivating a total of 39,735 ha. FOP = family-owned business; ha = hectare; mn = million; SAR = State Agrarian Register; SSSU = State Statistics Service of Ukraine.

37
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**Table  4.1: Bond Issuance Process in the Eurobond Market for New or Infrequent Issuers**

|_Date_|_Event_|_Responsibility_|
|---|---|---|
|_X_− 47 days|Mandate awarded|Issuer|
|_X_− 46 days|Document request list sent to issuer|Lead manager and<br>lawyers|
|_X_− 43 days|Information receivedreplying to thelist|Issuer|
|_X_− 40 days|All regulatory approvals and all relevant authorizations applied<br>for(intheissuer’shome country),if required|Issuer|
|_X_− 39 days|First draft of offering circular distributed to issuer and to lead<br>manager forcomment|Lead manager and<br>lawyers|
|_X_− 36 days|Initial comments on offering circular received from issuer and<br>leadmanager|Issuer|
|_X_− 33/34<br>days|Due diligence/verification meetings|Issuer and lead<br>manager|
|_X_− 31 days|Revised draft of offering circular distributed to all parties,<br>includingLuSE|Lead manager and<br>lawyers|
|_X_− 17 days|Preliminary comments received from LuSE and further<br>commentsreceivedfromallparties|Listing agent, lead<br>manager, andlawyers|
|_X_− 15 days|Revised draft of offering circular circulated; subscription<br>agreement and otherdocuments distributedfor review|Lead manager and<br>lawyers|
|_X_− 8 days|Further comments received from LuSE|Listing agent, lead<br>manager, andlawyers|
|_X_− 6 days|Further draft of offering circular circulated for final comments|Lead manager and<br>lawyers|
|_X_− 4 days|All regulatory approvals and authorizations to be in place; final<br>comments received from LuSE; draft subscription agreement<br>and other documents approved by all parties; final draft of<br>offering circulardistributedforsignoff|Issuer|
|_X_|Launch; commencement of road shows (could start earlier); any<br>preliminary offering circular to be printed and distributed;<br>documents distributed tomanagersfor review|Listing agent and lead<br>manager|
|_X_+ 3 days|LuSE to sign off; bringdown due diligence call; pricing and<br>allocation|Lead manager, issuer,<br>andlawyers|
|_X_+ 4 days|Signing of subscription agreement and other documents; printing<br>of finaloffering circular|Issuer, lead manager,<br>andlawyers|
|_X_+ 9 days<br>_Source_: Author|Closing of the issue; listing to take place<br>and his interviews with Euro market parishioners.|Issuer, lead manager,<br>andlawyers|

_<mark>Source</mark>_ <mark>: Author and his interviews with Euro market parishioners.</mark> _Note: X_ = launch date; LuSE = Luxembourg Stock Exchange (for example).

Aligning disclosure requirements with an actual investor profile by choosing the optimal offering method would shorten the issuance process substantially. Complying with disclosure requirements is most responsible for the long and uncertain issuance process period of corporate bonds (Figure 3.3). An issuance process period consists of the pre-launch period and the post-launch period. The former is mainly for producing disclosure documents and clearing them from the regulator, while the latter is for placing the securities offered. In an emerging market issue, the pre-launch period may take several months, whereas the placement period may take one to four weeks.

19
denoting whether or not in _t_ , block _i_ had entered the systematic registration program; i are time-varying covariates including population, built-up area and the interaction between the two; are block fixed XX effects; are time fixed effects; i is an error term; and 1, the coefficient of interest, is the difference ii αα in differences (DID) estimate of the short-term impact of systematic registration under LARP. ii λλ εε ββ To interpret the results from this regression, the fact that legal, regulatory, and institutional changes affected all parcels in the city implies that their impact, if any, will be subsumed by the _λt_ s. If policy change has a positive effect, 1 will thus provide a lower bound of the full impact of LARP, and if it has a negative effect, it will provide an upper bound. Although policy effects cannot be identified separately from the time ββ dummies, we can check for potential policy effects by including interactions between the policy dummy and the pre-reform stock of registered parcels i ) and the pre-reform (baseline) values of the outcome variable in question ( i ). Significant coefficients for the former would imply that policy reform made it (SS<sup>�</sup> possible to capitalize on parcels already registered, and for the latter would point to policy reform YY<sup>�</sup> facilitating convergence or divergence independent of efforts at systematic registration.

Formally, letting _P_ be an indicator for the post-2010 period when the Land Act became effective and LAA was established, we estimate was established, we estimate i 1S i 2 i 3 i i i (1) ii ii where, in addition to the variables defined above, YY = αα + ββ SS + ββ PP× SS<sup>�</sup> + ββi is the stock of registered parcels pre-reform, and PP× YY<sup>�</sup> + δδXX + λλ + εε i denotes initial conditions for i , i.e., either the stock of registered parcels with a female co-owner, the SS<sup>�</sup> share of built-up area in 2009, or the 5-year average of registered transfers or mortgages in 2005–09. The YY<sup>�</sup> YY new coefficients 2 and 3 thus indicate whether policy reforms affected outcomes conditional on the prereform stock of registered parcels or on initial conditions. If yes, more detailed analysis to identify the ββ ββ impacts of policy reforms over a longer period may be warranted.

Investigating long-term effects using the 2000–19 period adds two dimensions to this analysis.

In the first place, it allows us to test whether the impact of systematic registrations supported by LARP is different from that of sporadic registrations conducted independently before, after, or during the LARP rollout. To do so, we use the lagged stock of parcels registered by systematic and by sporadic processes where the lagged stock of sporadically registered parcels is instrumented by its second lag to allay concerns about endogeneity. As policy changes took effect in 2010, we assume that the effect of parcels registered sporadically after 2010 equals that of sporadic parcels before 2010 plus a policy shifter. By comparison, systematic initial registration under LARP only happened post-reform.

12
- Klein, R. "Regulation and Catastrophe<sup>Insurance."InH.KunreutherandR. J.Roth,Sr.(eds.),</sup> _Paying the Price: The Status and Role of_<sup>_Insurance Against Natural Disasters inthe_</sup> _United States._ Washington D.C.: Joseph Henry<sup>Press,1998.</sup>

- Kunreuther, H, J. Meszarous, R. Hogarth,<sup>andM.Spranca."AmbiguityandUnderwriter</sup> Decision Processes." _J. of Economic Behavior_<sup>_and Organization_26(1995):337-352.</sup>

- Kunreuther, H. "Combining Insurance<sup>withHazardMitigationto Reduce Disaster Losses."</sup> _Natural Hazards Observer_ 17 (1993): 1-3.

- Kunreuther, H. "Mitigating Disaster Losses<sup>throughInsurance."</sup><sup>_J. of Risk and Uncertainty 12_</sup> (1996): 171-187.

- Kunreuther, H. "Limited Knowledge<sup>andInsuranceProtection."</sup><sup>_Public Policy_24 (1976):227-</sup> 261.

- Kunreuther, H. _Recovery From Natural Disasters:_<sup>_Insurance or Federal Aid?_Washington,DC:</sup> American Enterprise Institute for Public<sup>PolicyResearch,1973.</sup>

- Kunreuther, H. and P. Slovic. "Economics,<sup>Psychology,and ProtectiveBehavior."</sup><sup>_American_</sup> _Economic Review_ 68 (1978): 64-69.

- Lamm, R. M. Jr. "The Catastrophe Reinsurance<sup>Market:GyrationsandInnovationsamid Major</sup> Structural Transformation." _Bankers Trust_<sup>_Research._BankersTrust Company,New</sup> York, NY. February 3, 1997, pp 1-13.

- Leikin, H. L. And T. L. Hayes. "National<sup>FloodInsuranceProgram FloodInsurance Rate</sup> Review." Federal Insurance Administration,<sup>FederalEmergency ManagementAgency.</sup> October 31, 1996.

- Lewis, C. M. and K. C. Murdock. "The<sup>Role of Government Contractsin Discretionary</sup> Reinsurance Markets for Natural Disasters." _J._<sup>_of Risk and Insurance_63 (1996): 567-</sup> 597.

- Miranda, M. J. and J. W. Glauber. "Systemic<sup>Risk, Reinsurance,and the Failure of Crop</sup> Insurance Markets."Amer. _J. Agr. Econ._ 79 (1997):<sup>206-215.</sup>

- National Oceanic and Atmospheric Administration.<sup>http://www.nws.noaa.gov.November7,</sup> 1997.

- Natural Disaster Coalition. "America at<sup>Risk:TheGrowingThreat fromNatural Disasters."</sup> Washington, DC, undated.

- Noll, R. G. "The Complex Politics of Catastrophe<sup>Economics."</sup><sup>_J. of Risk and Uncertainty_12</sup> (1996): 141-146.

- Noonan, B. "A Catastrophe Waiting to<sup>Happen?"</sup><sup>_Best's Rev.,_February1994, pp.30-33.</sup>

- Priest, G. L. "The Government, the Market,<sup>andtheProblemof CatastrophicLoss."</sup><sup>_J. of Risk_</sup> _and Uncertainty_ 12 (1996): 219-237.

- Rejda, G. E. _Principles of Risk Management_<sup>_and Insurance._New York:Harder Collins College</sup> Publishers, 1995.

- Rettger, M.J. and R.N. Boisvert. "Flood<sup>Insuranceor Disaster Loans:An EconomicEvaluation."</sup> _Amer. J. Agr. Econ._ 61 (1979): 496-505.

- Rossi, P., J. Wright and E. Weber-Burdin.<sup>_Natural Hazards and Public Choice: The State and_</sup> _Local Politics of Hazard Mitigation._ New<sup>York: AcademicPress,1982.</sup>
<!-- Start of picture text --> 8.0%<br>6.0%<br>4.0%<br>0.0% As fattesteer fe<br>1961 1966 1971 \isys 9 86 1991 1996 {2001 2006\ 2¢11 2016<br>-2.0%<br>-4.0%<br>--TFP Growth PWT 9.0 —-30-year median (PWT9)<br>-6.0%<br>Source: PWT9<br><!-- End of picture text --> <!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>10%<br>0.5%<br>0.0%<br>1960 1968 1976 1984 1992 2000 2008<br>Source: PWT 9.0<br><!-- End of picture text -->
its average for October, while for Nampula the two months are similar. Consistent with this, NDVI peaks in March in Manica, while it peaks a month later in Nampula. Unlike in temperate countries, tropical Africa has a green harvest and some local food insecurity is alleviated just after the NDVI has reached its peak.<sup>16</sup> More pronounced within-country differences are evident closer to the equator. In Tanzania (Figure A 1-c), Kilimanjaro, in the Northern Highlands, exhibits a bimodal pattern. In contrast, Iringa and Mbeya, in the Southern Highlands, have unimodal harvest cycles.<sup>17</sup> We have chosen not to use deviations from NDVI as a measure of weather averages disturbances. This is because NDVI deviations may reflect deviations in vegetation unrelated to food. However, it is essential to have a broad understanding of what harvest cycles typically look like for each province, to more accurately estimate the impacts of weather disturbances during a child’s birth season. In what follows, we use this understanding, along with rainfall data, to develop measures of drought shocks.

# B. _Identification of Dry Spells_

A consensus is yet to emerge on best-practice approaches to measuring droughts (Trenberth et al. (2014)). Soil moisture, evapotranspiration and water levels are all important. However, there are significant challenges to developing comprehensive measures for tropical Sub-Saharan Africa and other data-scarce contexts. At the same time, there is little that anomalous rainfall the season affects disagreement during growing adversely crop productivity (Funk et al. (2019)). Consistent with our overall objective, here we develop a simple and transparent approach to measuring droughts. Other than the use of the NDVI to estimate harvest cycles, the only data we use are the CHIRPS Rainfall dataset (Funk et al. (2015)).

Our algorithm involves the following steps. First, we calculate the average and standard deviations of total rainfall (in mm) for every 10-day period or decad in a given month in any given province (i.e. decad x month x province).<sup>18</sup> Second, we calculate the standardized anomalies for every decad x month x province x year. Third, we exclude all decads that are not in the growing season. Fourth, we define a dry spell of length _n_ for a given decad, as having below average rainfall in that decad, as well as the _n −_ 1 preceding decads. In addition, at least half the decads in a dry spell are required to receive less than

> 16Expert, on the ground opinion (e.g. http://fews.net/southern-africa/mozambique) is broadly consistent with these observations (scroll down to look at the seasonal calendars for South-Central and Northern Mozambique).

> 17See, for example, http://fews.net/east-africa/tanzania.

> 18The length of the last decad in a month adjusts so that it just covers the month. Consequently, decads may be compared across years.

9
about 85-year old companies, with a more or less continuous distribution of firms started in the 1958 to 2005 period. Table 8 presents descriptive statistics for the firms in our survey.

# **V.2 Financing an SME unit**

Figure 3 shows the relative importance of the various alternative sources of funds at the startup (Panel A) and growth (Panel B) phases. In the start-up phase, family constitutes the “extremely important” source of funds for an overwhelming majority (over 85%) of the respondents. Trade credits come next in importance, representing an “extremely important” source of funds for 27% of the respondents. In comparison, bank loans from state – owned banks make up an “extremely important” source for 15%, and a “very important” source for about 17% of the firms surveyed. The role of bank financing seems to be somewhat more important in our sample survey than in the NSSO survey discussed in Section III above, though the role of family connections remains supreme as in the NSSO survey. In the growth phase too, most firms (86%) find family and friends to be the easiest and least expensive source of funds. Again, as in the start – up phase, trade credits continue to be the next most important source. For 52% of the respondents, trade credits are the easiest source of funds. The importance of trade credits for SME firms is consistent with the importance of trade credits that we noted in section III above based on our analysis of secondary sources of financial information.

Internal funds appear to constitute a crucial source of funds at the growth stage. 27% of the respondents said they invested between 75% and 100% of their net income in the business in the first year they made profits. Again, the importance of internal sources underlined by the surveys is consistent with our analysis based on secondary sources of financial information in section III above. There are, however, some evident differences in bank credit availability within our sample. In the growth stage, short-term bank credit was an easy and inexpensive source for 45% of the respondents, while long-term bank credit was similarly viewed by 33%. On the other hand, 22% 30