rafmacalaba/datause-encoder-data huggingface dataset:
26 method focuses attention on the micro implications of a change in income, however, it still requires projections of changes in sectoral income which may require a macromodel.

Kanbur's method assumes that within-sector income distributions do not change. Thus if a worker moves from non-traded to traded production he takes on the income distribution characteristics of that sector (that is, poverty is not based on the individual's characteristics). A second assumption is that policy changes are assumed to enter additively or multiplicatively on all incomes in order to calculate the derivatives. A third assumption is that only wage earners are poor, so profit income can be ignored in the derivation of expenditure switching elasticities (relative price changes).

Kanbur (1988) applies this microdata method to Cote d'Ivoire. A poverty profile of Cote d'Ivoire is analysed using an LSMS survey for 1985. Per capita expenditure is used to create poverty indices by geographic region and by socioeconomic group. Elasticities of poverty with respect to aggregate income are calculated for the different groups. Cote d'Ivoire has a CGE already built and it is used to simulate alternative policies, obtain the change in real household income, and apply the elasticities to obtain poverty changes by socioeconomic group. The use of a CGE is not central to his method, but some projections of sectoral growth rates are needed.

The results from the dependent economy model described in section 2.1 has led Kanbur (1989b) to suggest the following data analysis, which requires survey data. A number of income profiles could be constructed along the following lines. For the very short run effect, the distribution of entrepreneurial income by production sector could be calculated. For the short run, income distribution among the unemployed, by sector, would be needed. This is because in the short
liabilities increase. The assets increase because the central government acquires a portfolio of government guaranteed bonds. The liabilities increase due to larger issuance of government securities. This increases transparency (the debt is now included in the sovereign balance sheet) and allows the related risks to be better included in the consolidated risk management of the central government; ‐Risk indicators from SOEs (transactions, liabilities, duration of assets and liabilities before and after swaps, etc.) are being collected quarterly and used as an input to assess the risks of the sovereign balance sheet.

# **Box 5.**

**Three examples of transactions designed to address risk exposure across the sovereign balance sheet in Uruguay**

Amante _et al._ (2018, forthcoming) describe and analyze different strategies implemented by Uruguayan authorities to reduce financial balance sheet vulnerabilities in the consolidated public sector, based on the identification of exposures under a sovereign ALM balance framework. In the case of Uruguay, the public‐sector balance sheet comprises the central government, the central bank, the four major non‐financial SOEs, and the state‐owned insurance bank.

‐ (i) <u>Liability management operations in managing FX risks during periods of high capital inflows:</u> From late 2010, Uruguay experienced strong capital inflows, and by 2015 the central bank had accumulated increasing levels of foreign‐currency reserves, sterilized by short‐term CB securities. Reserves were well above prudential metrics, and the CB was facing balance sheet pressures from the cost‐of‐carry of reserves. In turn, the government was keen to pursue its debt de‐dollarization strategy.

In July 2015, the central government and the central bank launched a joint operation of issuance and exchange of public securities which resulted in improving the ALM of the individual balance sheets of both the central government and central bank. In the case of the latter, a reduction in USD assets and local‐currency liabilities reduced the balance sheet mismatches and cost of carry. The central government was also able to reduce its currency mismatches by operating on both sides of its balance sheet: funding itself in local currency, and simultaneously increasing its dollar assets by cashing‐in the exchanged securities from the central bank.

<u>(ii) Addressing the lack of FX hedging instruments in the local market to support SOEs management of risk exposure:</u> The lack of FX hedging instruments in the local market has forced entities in Uruguay to look for solutions to risk management exposures through cross‐sectoral natural hedges within the 24
|employment?|**h, 2013-2015**|Main program<br>(4)|0.289|0.138|0.061<br>e reference distributions|
|---|---|---|---|---|---|
|aggregate|**t. growt**|program<br>|||aggregat|
|or fow of|**tion:**<br>**gate emp**|nd-driven <br>(3)|0.219|0.194|0.057<br>aining and<br>ings.|
|the stock|**distribu**<br>**Aggre**|Dema|||ogram tr<br>row head|
|ining to|**ference **<br>**on**|program<br>(2)|.033|.035|.004<br>cross pr<br>cated in|
|tra|**Re**<br>**cti**|ain|0|0|0<br>D) a<br> indi|
|m match|**crossse**|ram<br>M|||ns (SS<br>riables|
|n progra|**empt. **|iven prog<br>1)|050|057|006<br> deviatio<br>gorical va|
|the demand-drive|**2013**|Demand-dr<br>ross:<br>(|on<br>0.|0.|n<br>0.<br>e sum of squared<br>dings) for the cate|
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|Table||Focal <br>Distrib|3-digit|Dtate|State-o<br>Notes: Table pr<br>(indicated in col|

41
**Table 2. Validated CRIs (‘red flags’) and their definitions for Bulgarian public procurement data**

|**Indicator name**|**Indicator definition**|
|---|---|
|Single bidder contract|0 = more than one bid received<br>1 = one bid received|
|Call for tenders publication|0 = call for tenders advertised<br>1 = call for tenders not advertised|
|Procedure type|0 = open procedure<br>0.5 = negotiated/accelerated procedures<br>1 = non-open procedure type (for example, direct contracting)<br>(for detailed definition see Annex 3)|
|Length of submission period|Number of days between publication of call for tenders and submission<br>deadline<br>0 = from 12 to 183 days<br>0.5 = from 7 to 11 days<br>1 = from 1 to 6 days|
|Length of decision period|Number of calendar days between submission deadline and announcing of<br>contract award<br>0 = from 9 to 365 days<br>0.5 = from 5 to 8 days<br>1 = from 1 to 4 days|
|Buyer’s dependence|Shows the contracting authorities’ contract share rewarded to the same<br>supplier in a given year|

**Single bidding** indicates that a given tender only had one bidder during the procurement process, hence there was no competition for the contract. The lack of competition is one of the main signs of corruption in the public procurement system. Single bidding can be easily extracted from the data and has the key qualities necessary for an adequate indicator (Abdou, Czibik, Tóth, and Fazekas 2021).

**The length of the submission period** is the time difference between the first publication date of the contract notice and the deadline until which suppliers can submit their bids (bidding deadline). A sufficiently short submission period could indicate corruption, and we find a strong connection between the length of the period and the likelihood of single bidding.

Not publishing a **call for tenders** in the official journal makes it less likely that eligible bidders notice the bidding opportunity and bid.<sup>4</sup> In addition, not publishing a call for tenders in the official journal weakens competition, allowing the issuer to more easily award contracts repeatedly to the same company (Fazekas, Tóth, and King 2016).

**Procedure type** indicates the type of procedure by which the tender will be contracted. Non-open procedures, which are less transparent and competitive, create more opportunities to limit the range of bids received and to exclude bids. Additionally, they create more opportunities for issuers to repeatedly award contracts to the same company.

> 4 Some tenders do not have a call for tenders' publication despite a legal requirement, hence this indicator captures the actual and not the legally prescribed publishing practices of public buyers.

7
**figure B.  Completed fertility rates according to women’s and their partner’s level of education in Turkey**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>Less than primary completed men<br>3<br>Primary completed men<br>2 Secondary completed men<br>1 University completed men<br>0<br>Less than Primary Secondary University<br>primary completed completed completed<br>completed women women women<br>women<br>women's education<br>7<br>6<br>5<br>4<br>Less than primary completed women<br>3 Primary completed women<br>Secondary completed women<br>2<br>University completed women<br>1<br>0<br>Less than Primary Secondary University<br>primary completed men completed men completed men<br>completed men<br>men's education<br><!-- End of picture text --> Source: Census ( i-pums) 2000, women cohort 1950 26
# **Introduction**

Environmental risk factors account for about one-fifth of the total burden of disease in low-income countries according to recent estimates (World Bank, 2001). WHO (2002) reports that among the 10 identified leading mortality risks in highmortality developing countries, unsafe water, sanitation and hygiene ranked second, while indoor smoke from solid fuels ranked fourth. A number of econometric studies using household survey data from low income countries also find significant relationships between environmental factors and child morbidity and mortality (e.g., Wolfe and Behrman, 1982; Lee, et al. 1997). Such evidence suggests that public investments in health infrastructure can improve health outcomes, particularly child survival prospects.

In this paper, we evaluate alternative empirical methodologies for estimating the impact of environmental factors on child mortality in micro-data. Our primary question is whether taking into account cause of death using a competing risk model affects conclusions about the effectiveness of policy interventions. Such information is critical for prioritizing public investments in order to maximize the health benefit for given resources, particularly in the context of achieving the targets set by the Millennium Development Goals (MDG) on child mortality and environment. To our knowledge, no previous econometric study of child mortality has explicitly modeled cause of death. Indeed, distinguishing child deaths by cause may seem unwarranted. After all, the objective of policy should be to prevent child deaths from _any_ cause; as long as a child’s life is saved, it should be a matter of indifference whether the averted death would have been the result of diarrhea disease or respiratory infection.<sup>2</sup> By this reasoning, it is sufficient to analyze the determinants of _all-cause_ mortality, which only involves estimating a simple child survival-time (or hazard) model.

Nevertheless, there are a couple reasons why accounting for cause of death using a competing risks framework might be advantageous. First, this type of survival model is more flexible than an all-cause model and might, therefore, give more accurate estimates of environmental impacts. Second, separate estimates of the effect of environmental factors by cause of death may provide a way to detect the presence of confounding

> 2 This is not to deny the _epidemiological_ interest in the particular pathways by which an intervention prevents a death.

2
other hand, were more frustrated and skeptical that contracting was worth the time and effort (Nellis, 1989). Neither France nor Senegal ever resolved the problem of conflicting objectives, but France had the resources to compensate the enterprise, at least in part, when the government interfered. In Senegal, by contrast, the solution was often to simply slash the promised aid without adjusting the enterprise’s responsibilities. It is not surprising that the Senegalese managers became cynical about the advantages of negotiating a plan, or felt little responsibility for complying with their part of the bargain.

# **Corporatization**

There are few studies from developing countries of the effects of corporatization even though it appears to have been a reasonably popular reform.<sup>8</sup> One reason is the difficulty of getting good financial data on government departments to compare with the data from their corporatized successors. Another reason is that corporatization was often accompanied by some form of performance contract, which makes it difficult to disentangle their effects. The few reports on the subject cite several examples where corporatization is thought to have improved performance but also include warnings that the reform can take many years to implement and that the transformation is often “incomplete” in as much as the firm is still unable to lay off excess employees or charge realistic prices (World Bank, 1994, pp. 40-41; Estache, 1994).

These cautions are confirmed by the experience of industrialized countries, where corporatization has been more closely studied. The Britain’s experience is particularly interesting because its nationalized industries enjoyed many of the corporate governance provisions recently recommended by the OECD for SOEs. The nationalization statutes of 1945 to 1951 gave the boards of directors of British SOEs substantial independence from ministerial interference, a concession apparently made to help win private sector support for the state takeover of the industries.<sup>9</sup> Ministers could appoint the board members and set their salaries, but could dismiss them only for certain restricted reasons.

> 8 One of the few quantitative studies of corporatization alone (without a performance contract) examines 18 vegetable oil firms in Pakistan whose performance improved after they were moved from provincial government control to a national corporation.  Whether the improvement was due to the shift from provincial to national control or from a government department to a corporate form is unclear (Shaikh, 1988).

> 9 The architect of nationalization, Herbert Morrison, imagined an “arm’s length” relationship, explaining that “no self respecting board would tolerate political interference” (Redwood and Hatch, 1982, p. 24).

25
**Table 6: Trade gap, tariff rates and differentiated products. Controlling for country-year fixed effects** **<u>and 6-digit product fixed effects.</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||||_Trad_<br>|_e Gap_<br>|||
|Tariff|0.008|0.002|0.003|0.007|0.001|0.002|
||(0.001)***|(0.001)**|(0.001)***|(0.001)***|(0.001)|(0.001)|
|Tariff*Conservative||0.013|||0.014||
|Dummy||(0.002)***|||(0.002)***||
|Tariff*Liberal|||0.012|||0.013|
|Dummy|||(0.002)***|||(0.002)***|
|Agricultural products|Included|Included|Included|Excluded|Excluded|Excluded|
|Observations|132408|132408|132408|127893|127893|127893|
|Adjusted R-squared|0.6|0.6|0.6|0.6|0.6|0.6|

All regressions include country-year and 6-digit product fixed effects as well as a constant. Robust standard errors are listed in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

As another robustness check, we estimate a model in first differences. This will allow us to eliminate the time-invariant effects specific to a particular product imported by a particular country. To control for importing country-specific time trends, e.g., an improvement in the quality of the customs services over time, we include importing-country fixed effects. Our estimating equation takes the following form: Δ _trade_ gapcpt_ = γ 0 +γ 1 Δ _tariff cpt_ +γ 2 Δ _tariff cpt_ * _differentiated_ dummy p_ + λ _c_ + ε _cpt_ Again, the estimation results confirm our earlier findings (see Table 7). The interaction term is positive and statistically significant for both the liberal and the conservative definition of differentiated products. The coefficient on tariff level, however, loses its significance.

16
<!-- Start of picture text --> Figure 14: Reform 4 - Minimum Pension at Age 65 with Claw Back Rate of 0%<br>Earnings = 100% Average Earnings=50% Average<br>4 2<br>3<br>2<br>1 High probability of formal sector 1<br>work<br>0<br>-0.07 -0.05 -0.03 -0.01 0.01<br>-1<br>0<br>-2 -0.05 -0.03 -0.01 0.01 0.03 0.05<br>-3<br>-4 -1<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Note: Comparison is relative to Reform 3 (minimum pension at age 65 with claw-back rate of 100%)..<br>Source: Simulation model.<br>Change in Retirement Age (# of Years) Change in Retirement Age (# of Years)<br><!-- End of picture text --> Our next simulations look at the effect of matching contributions (Reforms 5 and 6) which have been suggested as a promising instrument to expand pension coverage to the informal sector (see Palacios and Robalino, 2009). In essence, the program involves matching part of the contributions made by employees (and in this case the employer) as an incentive to promote enrollment and contributions, and thus help finance an adequate pension at retirement for individuals with low savings capacity.

In our example we look at two matching levels: 75% (Reform 5) and 225% (Reform 6). In both cases we allow for two retirement ages, minimum 55 and minimum 65. The analysis is only applied to low income workers. Most high income workers here are not eligible for the minimum pension guarantee and therefore would not be eligible for matching contributions either.

The results are summarized in **Figure 15** . The two panels give the changes in contribution densities and retirement ages for the two matching levels relative to the case of the minimum pension guarantee at age 65 (Reform 3). In the figures the circles correspond to the case with a minimum retirement age at 55 and the squares to the case with a minimum retirement of 65 years. In the second panel we have also included a case where individuals can retire after age 55 but before age 65, as long as they have a pension that is above or equal to the minimum pension (see triangles).

We observe that in all cases the matching increases contribution densities. The most significant effects, however, are seen when there are no restrictions on the retirement age. Contribution densities in that case can increase, on average, by up to 30 percentage points depending on preferences. The tradeoff, however, is a reduction in the retirement age that can decline by up to 10 years. In essence, individuals who before delayed retirement to benefit from the minimum pension offered at age 65 now are able to retire early and benefit from the matching.

33
Table 2: Policy levers that affect the know gap

||(1)<br>Know|
|---|---|
|_‡_<br>Target|0<br>(.)|
|Certified Midwife|-0.191<sup>_∗_</sup><br>(0.101)|
|Registered Nurse|-0.120<br>(0.0974)|
|Associate Degree Nurse|-0.0394<br>(0.148)|
|Bachelors of Science Nurse|-0.0700<br>(0.0879)|
|Medical Doctor|-0.0270<br>(0.0839)|
|Other|-0.101<br>(0.146)|
|Constant|0.508<sup>_∗∗∗_</sup><br>(0.0839)|
|Observations|767|
|Adjusted_R_<br>2|0.057|

Clustered standard errors in parentheses. _∗ p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Note that the category “Physician Assistant” was chosen as the reference category for cadre in column 1 because it has the highest number of observations. It requires the second most years of education and training, after MDs.

_‡_ Target is a constant, so the regression produces only a constant.

20
sample. The error term, , is assumed to be of a truncated normal distribution, with mean zero and truncated at ii ), such that remains non-negative. θθ JJ jj=1 jj iijj ii −(γγ+ ∑ δδ ZZ μμ Technical efficiency and the parameters from Equation 3 are estimated jointly using maximum log likelihood. The model, which substitutes Equations 4 and 5 into Equation 3, is estimated four times for each of the MAPS-based and AFSIS-based soil suitability measures: (i) including all plots and controlling for soil suitability with the inclusion of the distance measure from suitability class S1; (ii) including only plots classified as highly suitable (S1); (iii) including only plots classified as moderately suitable (S2); and, (iv) including only plots classified as marginally suitable (S3). Analysis is implemented on a sub-sample basis, rather than in a single model with suitability indicators, as it allows for the estimation of suitability-class specific production frontiers. Technical efficiency (TE) scores are computed based on the conditional distribution of given , following Battese and Coelli (1988), whereby the technical efficiency on plot _i_ is defined as: μμ εε (6) T ii<sup>= TT[exp (−μμ</sup> ii<sup>)|εε</sup> ii<sup>]</sup>

The technical efficiency scores are then used to compute potential production and productivity for the given level of inputs.<sup>15</sup>

# **5. Results**

# **5.1.Maize-Specific Suitability Measures**

The fuzzy set membership method described above was implemented using both the MAPS plotlevel soil samples and the AFSIS geospatially-derived soil data. The results of the maize-specific soil suitability classification exercise are summarized in Table 3. Using the MAPS plot-level soil samples as the basis for classification, 13 percent of plots are considered highly suitable, the majority (75 percent) considered moderately suitable, and the remaining 12 percent of plots considered only marginally suitable. Note that classification into a specific group does not suggest that the plot-level soil properties fit that category in full. Rather, they are most closely aligned with that class relative to the other classes. No plots were classified as not at all suitable, in line with expectations as these are all maize-growing plots.

> 15 Potential output is computed as: observed output/ technical efficiency score.

14
# **TABLE OF CONTENTS**

|Acknowledgments ....................................................................................................................... i|
|---|
|Introduction..................................................................................................................................1|
|The PMR Methodology ...............................................................................................................2|
|Choosing Benchmarks .................................................................................................................6|
|The Romanian Context................................................................................................................7|
|Benchmarking Production Market Policies in Romania..............................................................8|
|Inward Oriented Policies....................................................................................................12|
|Outward Oriented Policies.................................................................................................22|
|Conclusion: The Need to Focus on Implementation, Compliance and Enforcement................27|
|References..................................................................................................................................31|

|**ANNEXES**|
|---|
|Annex I. Comparisons with Full Sample.......................................................................................33|
|Annex II. Detailed Results of PMR Survey for Romania..............................................................41|