rafmacalaba/datause-encoder-data huggingface dataset:
or promissory notes issued by enterprises, banks or the government. Nonpayments grew rapidly between 1995 and 1998 and became entrenched as the most common way for enterprises to conduct business—in effect becoming a new form of industrial organization. This happened roughly in the following sequence: Initially, nonpayments was a survival response by enterprises. Those in heavy industry or the old military-industry complex were apt to have the highest share of sales in the form of barter and those selling fast-moving consumer goods the lowest, as confirmed by enterprise surveys.

Subsequently, nonpayments grew spectacularly over 1995-98 coinciding with the high real interest rates and real appreciation of the ruble engendered by the stabilization program; the analysis in Commander and Mumssen (1999) suggests that this was more than coincidence; the high real interest rates _caused_ a distinct preference for barter and other forms of NCS.

Interestingly enough, the government itself became a primary instigator. Over the 1995 – mid 1998 disinflation, NCS accounted for as much as 50 percent of spending by regional governments, while money surrogates and tax offsets averaged over 20 percent for federal government non-interest spending. The government’s example was quickly emulated by enterprises, giving them an excuse to deliberately run up tax arrears that could be settled at a lower cost through offsets (which incorporated tax forgiveness through the use of inflated prices when taxes were paid in kind). This was a major factor legitimizing tax arrears and contributing to the persistent shortfall of cash taxes over 1996-98.

Nonpayments morphed into an entrenched habit when profitable, cash-rich enterprises joined the bandwagon. They ‘gamed’ the system, running up tax arrears which could then be settled at a discount in kind; bought monetary surrogates from struggling enterprises—which had received these from the government in exchange for their unsaleable goods—at a discount, then

> 11 The discussion here is based mainly on Pinto, Drebentsov and Morozov (2000a, b).

9
# **Table 1: Descriptive** **<u>Statistics – choice of sample</u>** <u>.</u>

||_National_|_Urban_|_Rural_|_Women_|_Men_|_Formal_|_Informal_|
|---|---|---|---|---|---|---|---|
|_Public sector (% of total employment)_|8.9|16.9|1.5|8.4|9.1|47.8|0.9|
|_Agriculture (% of total employment)_|41.6|5.5|75.4|59.7|36.2|4.8|49.2|
|_Sample as % of total Labor Force_|50.5|75.6|22.6|36.3|55.8|46.7|48.1|

Source: Author’s own estimates using the 2015 MLFS.

# _<u>Estimating the worker’s type</u>_

As mentioned above, the model assumes that self-employed individuals earn the value of their type, _y_ i. In turn, a worker type is, thus, assumed to be a function of a set of observable characteristics, so that _yi = Zi Υ_ . We can then write the earnings equation for self-employed workers using a standard Mincerian equation where _v ~ N (0, σv)_ is an individual’s idiosyncratic shock that affects income<sup>9</sup> : Since the model assumes that self-employed individuals are a selected sample (they prefer to take self-employment opportunities rather than available offers for formal/informal job opportunities), we correct for this possible selection bias using a standard Heckman correction.<sup>10</sup>

# _<u>Estimating the Productivity Functions</u>_

The distributions _Gj (x|y)_ , with _j_ ∈ {Formal; Informal}; are assumed to be drawn from a logistic distribution with a mean equal to _y + kj_ , where _kj_ is a form of unobserved individual-specific heterogeneity that can influence the mean of the _x_ distribution. It is assumed that _kj_ is a Bernoullidistributed random variable. Recovering the productivity distributions requires a four step procedure:

- a. Estimate the part of observed wages, _wj j_ ∈ {Formal; Informal}, that is not due to the worker’s type. This is: _wj - yhat_ . To estimate _yhat_ we use the coefficients _Υ_ of the determinants used in the recovery of _y_ from the self-employed.

- b. Recover the part of this residual that is explainable by unobserved characteristics, and use this to characterize the distribution of the heterogeneity component _kj_ .

- c. Recover the value of the draw that is added to the mean _yhat + kj_ ; and

- d. Calculate the distribution of these draws.

The result of these calculations is a distribution (centered around _y_ or _y +kj_ ) of means for the draws from the _x_ distribution. Subtracting this mean from the observed wage gives an estimator of the value of the draw from the _x_ distribution.

The remaining parameters to calibrate the model need to be estimated, recovered from the data, or withdrawn from the labor law. We estimate directly from the MLFS the shares of population in each employment state (unemployment, formal employment, informal employment and self-

> 9 Our Mincerian estimations control for strata, region, gender, age, age squared, level of education and sector of employment (agriculture/primary, manufacturing/secondary, services/tertiary).

> 10 Estimates are available upon request.

7
whether the individual has social security coverage or not, and the last one is the size of the employing firm or establishment (firms with fewer than 5 employees are often assumed to be informal). In addition to these measures of informality, we also consider other aspects of employment quality to describe employment trajectories post graduation such as: full wages, time employment, whether the individual is a wage worker or not, employed in the public sector, or employed in the agriculture sector. To complete the picture, we also consider the quantity of employment, namely whether an individual is employed and the number of hours worked per week.

We use Labor Force Surveys (LFS) between 2006 and 2019 to construct the employment outcomes.<sup>3</sup> Labor Force Surveys were first conducted in Egypt in 1957 by the Central Agency for Public Mobilization and Statistics ”CAPMAS”, on a quarterly, biannual, and annual basis. In 2006, CAPMAS improved the questionnaire by re-ordering existing questions and creating new ones to better capture labor market conditions.<sup>4</sup> The Economic Research Forum (ERF) cleaned and harmonized the data as a part of a project that started in 2009, in which major efforts were devoted to facilitate micro data access and use in Arab countries (OAMDIA (2018b)).

In our main model we use a labor market indicator (the regional rate of employment) that is constructed directly from the 2006 through 2019 LFS survey rounds. Therefore we must restrict the estimation sample to individuals who exited the schooling system during those years. The indicators used in our second model (youth employment rate, GDP growth, oil and wheat prices) are only available at the national level but cover 1990 onwards. In that case we can include all individuals who graduated after 1990. Besides 3Data were obtained from the Economic Research Forum: OAMDIA (2019a), OAMDIA (2019b), OAMDIA (2019c), OAMDIA (2018a), OAMDIA (2018b), OAMDIA (2016a), OAMDIA (2016b), OAMDIA (2016c), OAMDIA (2017), OAMDIA (2018c), OAMDIA (2018d), OAMDIA (2019d), OAMDIA (2021a), OAMDIA (2021b).

> 4In 2008, the representativity of the sample was improved at the governorate level. This implies that indicators constructed at the governorate level may contain measurement error in the 2006 and 2007 rounds. We check the robustness of our results to excluding these two rounds.

4
Table 2. Ten top constraints to doing business, by region – 2014* <u>(percentage of firms ranking each factor as a major obstacle)</u>

|||**Regio**<br>|**ns**<br>|
|---|---|---|---|
||**NC**|**NE NW**|**SE SS SW**|
|Electricity|56|59 45|52 50 51|
|Corruption|48|51 46|46 29 46|
|Access to finance|25|25 21|39 23 49|
|Political instability|28|52 28|35 5 12|
|Tax rate|17|52 12|29 28 19|
|Tax administration|17|53 8|24 15 14|
|Transport|18|27 14|17 7 20|
|Crime|9|16 7|18 13 2|
|Licensing and Permits|10|8 10|19 5 6|
|Courts|2|10 6|11 7 1|

Source: Authors’ calculation based on Enterprise Surveys Nigeria 2014. Notes. This table reports the percentage of firms that Rank each factor as a major or severe obstacle to doing business. Note: NC=North Central; NE=North East; NW=North West; SE=South East; SS=South South; SW=South West.

While the perceptions described above indicate an apparent improvement in the business climate over time, the percentage of sales lost due to each of the factors, specifically bribes, security, crime, and power outages increases over the same period. In 2014 Nigerian firms reported an average total cost due to these four factors equivalent to 40 percent of sales. These costs are larger for young, exporting, and manufacturing firms (Figures 5 to 7) and can be interpreted as fixed costs arising from the business climate.

Figure 5. Costs of crime, power outage, security, and bribe, by age category <!-- Start of picture text --> 27<br>18 17<br>15 15<br>9 8 9 9 8 10<br>6<br>5 4 5 5 6<br>3 2 3 3 3 3 2<br>2008 2014 2008 2014 2008 2014<br>Young(0-5) Mature(6-15) Old(15+)<br>Bribe Costs of security<br>Costs of crime Costs of power outages<br>Source: Authors' calculation based on Enterprise Surveys<br>30<br>20<br>Costs(% of sales) 10<br>0<br><!-- End of picture text --> 8
Looking at Table 2, we find that the choice of survey approach primarily affects reporting of exclusive as opposed to joint ownership, as well as joint rights to sell/bequeath (with the exception of women household heads, where the survey approach primarily affects exclusive rights, likely because their households have fewer adult household members).

**Figure 1.  Bundles of Ownership/Rights, by Survey Approach (T1/IHS4 vs T5/IHPS) and Gender Reported versus Economic Ownership Right to Bequeath and/or Sell**

**Right to Bequeath and/or Sell** ( **Among Reported/Economic Owners)**

# <u>Notes:</u>

- (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The estimates are weighted by the response weight. The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

- (2) In T5/IHPS, the parcel-level question on economic ownership was, by design, asked conditional on having been identified as a reported owner.

- (3) The rights-related variables are defined irrespective of the reported need to obtain consent/permission from anyone – a topic that the IHPS collected additional information on. Less than two percent of individuals across surveys had rights to bequeath/sell if they were neither reported nor economic owners.

Specifically, the business-as-usual approach significantly increases exclusive reported ownership among men overall (columns 7-8), and women heads of household (columns 1-2). Within the business-as-usual approach, nearly all (95 percent) of women household heads responded for themselves. The business-as-usual approach also raises exclusive economic ownership for men, as well as non-household head women, but results in lower exclusive economic ownership for women heads. For women heads in particular, Figure 2 (focused on own-reporting) also shows wider positive effects of the business-as-usual approach on exclusive reported ownership among younger groups less than 50 years, while the negative effects on exclusive economic ownership tend to be focused on older women aged 50-60 (about 15 percent of the sample). Under the business-as-usual approach, joint rights to sell/bequeath are also significantly higher for both men and women (the latter driven by women non-heads). In Section 3, we explore further what may be driving these 13
# ABSTRACT

In addressing water scarcity and increased population pressures<sup>many countries are adopting</sup> water-pricing mechanisms as their primary means to regulate<sup>irrigation water consumption.</sup> "Getting prices right" is seen as a desirable way to<sup>allocate water efficiently, but how to</sup> accomplish this remains a debatable issue. Water pricing methods<sup>are sensitive to the physical,</sup> social, institutional and political setting in each location.<sup>It is therefore necessary, when</sup> assessing the costs and benefits of a particular irrigation<sup>project, to cater the pricing method</sup> accordingly.

This paper surveys current and past views on the many aspects<sup>of irrigation services and pricing.</sup> The result will be useful in developing a comprehensive guideline<sup>for water policy practitioners</sup> as they address the growing demand f,or these services<sup>and need to allocate scarce water</sup> resources efficiently. This survey is organized to illustrate efficient<sup>pricing methods followed by</sup> alternatives to market failure and considerations of income distribution,<sup>water institutions and</sup> political economies of irrigation water pricing. Existing irrigation<sup>case studies, data sources, and</sup> methodologies are compared and referenced.

iv
can be available for oil bearing trees, of which more than 14 million hectares is from forest land alone (Table 7).

**Table 6: Land productivities of oil-bearing trees**

||Unit|Ja|tropha|Xanthoceras<br>sorbifolia|Pistacia<br>chinensis|
|---|---|---|---|---|---|
|Land<br>productivity<br>of<br>feedstock|Tonne feedstock/ha.|1.5-3<sup>a</sup>|6-9<sup>b</sup>|1.5-5<sup>c</sup>|7.5<sup>d</sup>|
|Oil content|%|35-40|35-40|35-45|30-46|
|Assumed<br>conversion<br>efficiency|tonne<br>(feedstock)<br>/<br>tonne (biodiesel)|<br>3|3|3|3|
|Land<br>productivity<br>of<br>biodiesel|tonne biodiesel/ha.|0.5-1|2-3|0.5-1.67|2.5|
|Total forest land area in<br>2009|Million ha.|||30.38<sup>e</sup>||
|Total oil-bearing tree base<br>by 2010|kha.|||200<sup>f</sup>||

Notes: a. 3-5 years after forestation in southwest area, source from Zhang, Lv & Lv, et al., 2008; b. 3-5 years after forestation in Hainan, source from Zhang, Lv & Lv, et al., 2008; c. 5 years after forestation, source from Zhang, Lv & Lv, et al., 2008; e. source from Xie, et al.2011 c. data from the newly 7<sup>th</sup> forest resource survey from 2004 to 2008 by State Forestry Administration (SFA, 2009); d. data from Wang (2011).

**Table 7: Land resources distribution and potential for developing oil-bearing trees**

|Province|Main Oil Species|Area of<br>Available<br>Land|Available<br>forest land|Biodiesel<br>potential on<br>marginal land|In which, potential<br>on available forest<br>land|
|---|---|---|---|---|---|
|U|nit|Milli|on ha.|Mill|ion tonne|
|South<br>China<br>and<br>Southwest|Jatropha,<br>euphorbia<br>tirucalli, etc.|2|1|1-4|0.5-2|
|North China, Central<br>China and East China|Chinese pisache, tung<br>tree,<br>varnish<br>tree,<br>sapium sebiferum,<br>Idesia,<br>euphorbia<br>tirucalli|4|3|2-8|1.5-6|
|Northwest, North of<br>North China and west<br>of Northeast|xanthoceras sorbifolia,<br>Chinese pisache, robur|30|10|15-60|5-20|
|Total||36|14|18-72|7-32|

Note: Calculated based on the assumed land productivity as 0.5-2 tonne biodiesel/ha.

Source: Lv, et al.2008 12
**Table 3:** Internally Calibrated Parameters

|Parameters|Target|C|alibrated Val|ues|
|---|---|---|---|---|
|||1988|1996|2006|
| ̄_B_|Local government debt|0.0128|0.114|0.116|
| ̄_D_|Central government debt|0.3|0.585|0.315|
|_θ_|Central-local spending ratio|0.789|0.653|0.515|
|_ε_|Central-to-local transfer|0.014|0.014*|0.014*|

Note: Debt and transfer levels are normalized by national GDP. *We assume _ε_ for 1996 and 2006 is the same as 1988.

**Table 4:** Targeted and Untargeted Moments

||19|88|19|96|2|006|
|---|---|---|---|---|---|---|
|Moments|Data|Model|Data|Model|Data|Model|
||Ta|rgeted Mo|ments||||
|Local government debt|0.062|0.062|0.130|0.130|0.122|0.122|
|Central government debt|0.338|0.337|0.615|0.614|0.336|0.336|
|Central-local spending ratio|3.63|3.63|1.85|1.85|1.05|1.05|
|Central-to-local transfer|0.038|0.039|0.077|0.080*|0.056|0.055*|
||Unt|argeted M|oments||||
|Local spending|0.060|0.062|0.125|0.129|0.166|0.162|
|Central spending|0.217|0.224|0.231|0.239|0.174|0.170|
|Vertical fscal imbalance|32.2|34.0|52.2|53.1|21.3|21.1|
|Local interest payment|0.0037|0.0032|0.0085|0.0066|0.0030|0.0062|
|Central interest payment|0.026|0.017|0.043|0.031|0.013|0.017|

Note: All variables except central-local spending ratio and vertical fiscal imbalance are normalized by national GDP. Out of the untargeted moments, there is one degree of freedom, given the targeted moments and the central and local governments’ budget constraints. Additionally for 1996 and 2006, because _ε_ is assumed the same as 1988, there is one more degree of freedom(*).

spending ratio; a higher _ε_ increases the distortion of transfers, raises total transfer, and leads to higher central and local government debt levels.<sup>40</sup> Table 3 reports these internally calibrated parameters, and Table 4 presents the targeted and untargeted moments. We match the targeted moments well. In particular, the calibrated model captures three properties in the data: debt levels increased from 1988 to 1996 and fell from 1996 to 2006; total transfer also increased during the first period and fell during the second period; and central-local spending ratio fell during both periods. For the untargeted moments, the model also does reasonably well in matching untargeted moments in the calibrated years.

**Degree of freedom.** Out of the untargeted moments in Table 4, there is one degree of freedom,

> 40We also tried an alternative calibration strategy where we set _ε_ = 1 and jointly calibrated the share of unconstrained regions _μ_ (instead of taking it directly from data) with the other parameters. _ε_ = 1 means _τ_ is fully redistributive and distortionary, while a smaller _μ_ means that a smaller share of regions are subject to the distortionary effect of _τ_ , and so the aggregate distortion is limited. As such, we can loosely match data moments of total transfer and central debt using _ε_ = 1 and a very small _μ_ . However, we find that because of the large distortionary effect of _τ_ when _ε_ = 1, unconstrained regions’ debt in the model is implausibly high—about 10 times higher than the level observed in the data.

28
Equation (3) will be the starting point for attempting to improve a household survey design.

In many surveys, when samplers have the possibility to group elements into clusters, they try to reduce the variance (3) by minimizing the components _within_ clusters 2 ; rewriting (3) in terms of relative variance and assuming that the same number of sample elements or households is selected from each PSU (i.e. 2ll _hc = h_ ). SS Hansen _et al._ (1953) and Cochran (1977) have shown that such relative variance could be expressed in terms of intra-class correlation _δ_ , which is related to the degree of homogeneity of elements/households within clusters. So, the aim is to reduce such intra-cluster correlation and consequently the variance (3).

However, in general, the construction of clusters cannot be modified easily when planning a household survey. In many countries, clusters represent the Census Enumeration Areas (EAs), in others they correspond to some geographical or administrative units, such as municipalities or counties. The _vicinity_ of households clustered in a PSU, therefore, is an essential condition for its implementation, since the corresponding survey costs are reduced and, consequently, clustering is preferred to simple random sampling. For all these reasons, the _within_ clusters component 2 of variance cannot be modified and, therefore, cannot be reduced. 2ll In such cases, samplers are able to do little in terms of SS _δ_ or variance _within_ clusters reduction. Alternatively, efforts have been made to try to reduce the effect of variance _between_ clusters 2 in each stratum _l_ . In practice, clusters or PSUs are ordered according to some external variables available in the frame list, which are 1ll SS in some way correlated with the phenomenon under investigation Y: this permits getting more homogenous clusters and then it permits a reduction in variance _between_ clusters 2 . 1ll SS To make the sampling design more practical, the selection of clusters is performed with systematic sampling of such ordered frame lists. According to Kish (1965) this type of design can be seen as an ( _implicit_ ) stratified sampling, where one single cluster or PSU is selected from each ( _implicit_ ) stratum.

7
or contractors (who are specifically banned under the MGNREGS stipulations), or simply by turning up at the worksite when it opened. However, there was a marked increase in the proportion demanding work between the two survey rounds, particularly among female workers, for whom the proportion rose from 16% to 34%. (For men it rose from 25% to 40%.) Awareness that access to employment under the scheme is a right for all rural households and not limited to specific groups or gender also increased between the two rounds, but is not yet universally known. In consequence, potential applicants can be excluded based on certain characteristics (e.g., widowhood, gender, old age, and disability) or a lack of documentation, such as having a Below Poverty Line (BPL) card which is used to identify target groups for many anti-poverty schemes in India but is expressly not required for participation in MGNREGS.

We ran various _ad hoc_ regressions of the incidence of correct answers on individual, household and village attributes.<sup>12</sup> Details of the results summarized below are available in Dutta et al. (2013). We find certain factors to be consistently important. At the individual and household level, one can think of these as falling roughly into two categories of attributes ─ namely those that enhance a person’s knowledge and understanding, and those that render the information more relevant and meaningful to those individuals. In the first category, higher levels of education and higher scores on the Pearlin scale are dependably significant attributes that help process and retain information. Political connections (measured by whether the household is related to elected panchayat officials) create opportunities to access the work that one may not have otherwise and in that sense will also help people take on board information that may concern them.

Being from the Mahadalit caste (typically the most disadvantaged),<sup>13</sup> from a household that has engaged in casual work before, or has suffered a shock, fall in the second category of attributes that make knowledge about BREGS vital to the household’s well-being. Male gender, given men’s responsibility as the main breadwinner, can also be seen to fit this category. Wealthier households (based on ownership of durable assets, housing quality, and land ownership) know less about the scheme, reflecting both that the scheme is less relevant to them,

> 12 Tests for selection bias with the standard Heckman method using probits for individual awareness questions suggested that the people who said that they did not know about BREGS are not systematically different from those who said they did.  The invest mills ratio was insignificant in all specifications.

> 13 In Bihar, Mahadalits, comprising the poorest and most disadvantaged among Scheduled Castes, have been notified as a separate sub-category by the state government.

9