rafmacalaba/datause-encoder-data huggingface dataset:
They are thus unable to exert much influence on either the pattern of economic growth or, indirectly, on poverty alleviation.

Second, the impression gathered from field visits to various provinces is that very few of the investment projects forwarded after feasibility studies are included in the plan. Moreover. the repair and maintenance expenditures approved are only a fraction of those requested (and needed). The criteria determining which projects are selected are obscure.

Table 12 Per Capita Revenues and Expenditures - Targets and Estimates

|||PerCapitR|cevcnues- 199|||PerCapiuE|xpendicurcs 199|3<br>1|
|---|---|---|---|---|---|---|---|---|
||TarScEs<br>(Th.<br>dongs)|Latest<br>Estimates<br>(h<br>Dongs)|Increasc in<br>Estitnaes<br>over<br>Targets<br>Cmb<br>Dones)|Percen-<br>tacg<br>Increase|Targets<br>(Th.<br>Dongs)|Latest<br>Estinate<br>(Tb.<br>Dongs)|Increase in<br>Estimates<br>ovcr<br>Targets<br>(Th-<br>Dongs)|Percen-<br>tage<br>Inasc|
|High inctimn<br>provinces|656.43|758-10|101.67|15.5|178.53|230.2|51.67|28.9|
|Middle income<br>prov mnce%|95.43|111.19|15.76|_16.5_|95.65|122-45|26.80|28.0|
|**L,wInictmnic**<br>**provinces**|**54.53**|69.92|15.39|282|104.73|135.76|31.03|29.6|
|**All Ponvinces**|27S.60|323-89|45.29|16.3|**126.54**|162.99|36.45|288|

Source: 1. State Planning Committee

# 2.Ministry of Finance. Government of Viet Nam

Third, as noted above, the norms used for determining expenditure allocations between different provinces could be improved by obtaining better information on unit costs and by targeting them more specifically (e.g. on students, rather than on population).

31
# Public Debt Reporting in Developing Countries

Diego Rivetti (World Bank)<sup>∗</sup> Originally published in the Policy Research Working Paper Series on _January 2022._ This version is updated on _June 2023._ To obtain the originally published version, please email prwp@worldbank.org.

JEL Classification Codes: F34, H63 Keywords: Debt transparency, Public Debt Management, Public Debt Statistics

* I am grateful to Monika Anna Matyja (WB consultant) for excellent research assistance.  Doerte Doemeland, Lars Jessen, Luca Bandiera (WB), Mike Seiferling (University College London), David Mihalyi (National Resource Governance Institute), Gabor Piski (UNCTAD) and Veronica Frisancho (Inter-American Development Bank) also provided helpful comments and suggestions.
26 tional price and its domestic price at the port of Colombo: where _pRoW,k_ is the 2019 price of crop _k_ in the RoW, and _pColombo,k_ is its 2019 “benchmark price” at the port of Colombo. This estimation approach selects between the ratio of international price to domestic price and its reciprocal the one that is larger than unity.<sup>63</sup> Thus, we implicitly assume that an international price that is larger ( _smaller_ ) than its domestic price reveals a crop’s status as an export ( _import_ ) crop. The magnitude of such price gap reveals the severity of the barriers faced by the crop’s exporters ( _importers_ ). Our estimated _B_<sup>ˆ</sup> _k_ are displayed in Table III.

# **VI.A.3 Production Function Parameters**

Since our paper focuses on agriculture, it is important to find context-appropriate values for the parameters _{γk_<sup>_f, γ_</sup> _k_<sup>_l, γ_</sup> _k_<sup>_n}k_that govern Cobb-Douglas agricultural production function (1).Leveraging</sup> 2022 NFS data on fertilizer intensity, we estimate the fertilizer coefficient _{γk_<sup>_f}k_of each crop</sup><sup>_k_as:</sup> where _Mk_<sup>_f,LKA_</sup> is annual fertilizer expenditure by Sri Lankan farmers for production of crop _k_ (in USD), _p_<sup>_LKA_</sup> _k_ is the price of crop _k_ in Sri Lanka (in USD/kg), and _Q_<sup>_LKA_</sup> _k_ is Sri Lanka’s annual production of crop _k_ (in kg).<sup>64</sup> The resulting estimates are displayed on Table III. The remaining Cobb-Douglas coefficients ( _γk_<sup>_l_,</sup><sup>_γ_</sup> _k_<sup>_n_) must add to (1</sup><sup>_−γ_</sup> _k_<sup>_f_) for each crop</sup><sup>_k_. We apportion this residual</sup> value to land and labor using data from Sri Lanka’s IO matrix on the labor share of value added: where _CEMPs_ is total employee compensation in sector _s_ , _V As_ is total value added (at factor cost prices) in sector _s_ , and _s_ ( _k_ ) is the sector to which crop _k_ belongs. The land coefficient _γk_<sup>_l_is then</sup> computed as: _γk_<sup>_l_= 1</sup><sup>_−γ_</sup> _k_<sup>_f−γ_</sup> _k_<sup>_n_.</sup>

# **VI.A.4 Land Distribution Parameters**

The model’s parameters that govern land distributions ( _{μi_ , _σLi_<sup>2</sup><sup>_}i_) are estimated by leveraging the</sup> properties of the log-normal. We use 2016 HIES data to estimate _σ_ ˆ _Li_ as the (weighted) standard

> 63 _<u>pRoW,k</u>_ As ratios _pColombo,k_<sup>and</sup><sup>_<u>pColombo</u>_</sup> _pRoW,k_<sup>_<u>,k</u>_</sup> are each other’s reciprocals, only one of them can be larger than unity.

> 64Fertilizer use _Mk_<sup>_f,LKA_</sup> is computed at post-subsidy fertilizer prices and includes the three main types of fertilizer used: urea, muriate of potash (MOP), and triple superphosphate (TSP).
forecasts over rolling windows.

In both graphs, mean risk sharing displays a cycle: a slight initial decline, which is reversed in the windows that start in the late 1970s, and then a steady increase beginning around the windows that start in the mid-1980s. Closer inspection reveals that in the top graph the mean estimate obtained in the ...nal window lies outside the 95-percent con...dence region of the initial window, suggesting that over the entire sample period there has been a statistically signi...cant increase in the average degree of consumption risk sharing, at least when the calculation of the latter is based on income forecasts that allow for common factors. Examining the individual-country risk sharing parameter estimates we ...nd that a majority of countries (33 when using the common factor-inclusive forecasts, and 27 when using the AR1 forecasts) exhibit higher coe¢ cients in the ...nal estimation window than in the initial one. Further, for 16 countries (15 when using the AR1 forecasts) the increase in the risk sharing parameter is statistically signi...cant at least at the 10 percent level. At the other end, 10 countries (or eight, when using AR1 income growth forecasts) show statistically signi...cant declines in risk sharing between the initial and ...nal estimation windows.

Figure 2 plots the time path of the average risk sharing coe¢ cients separately for industrial and developing countries, along with their respective two standard-error bands, for the same two sets of permanent income innovations as in the previous ...gure. Both panels reveal a clear contrast between the mean estimates of the two country groups. The mean for industrial countries displays a rising trend over time, particularly marked in the top panel, which is based on the permanent income forecasts inclusive of common factors. Indeed, this rising trend of average risk sharing among rich countries is the force behind the trend increase in overall mean risk sharing found in Figure 1. More formally, in both panels the 95-percent con...dence region for the industrial country mean corresponding to the ...nal window lies fully outside that obtained in the initial window. In other words, over the period of analysis there has been a highly signi...cant rise in mean risk sharing among industrial countries. In contrast, there has been no discernible trend among developing countries. While the ...gure 21
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17
proxied individuals, are consistent with Kilic et al. as they compare the two survey methods with both annual and weekly recall.

Finally, the findings in our paper have implications for a range of labor linked papers, especially those in rural settings that rely on similarly constructed data.<sup>5</sup> Large, primarily descriptive literatures currently exist on rural labor, gender and age differences in labor contributions, and rural income diversification. Our results suggest that all these estimates could be meaningfully impacted by reliance on long recall-based data and further distorted by the use of proxy reporting.

We discuss our survey experiment and data in Section 2 and empirical strategy in Section 3. Results are presented in Section 4, followed by a brief discussion in Section 5, and conclusion in Section 6.

# **2. Data**

Our survey experiment is built on a sample of households included in a separate study, the MwAPATA Instituteʼs Malawi Rural Agricultural Livelihood Survey (MRALS), conducted inperson in the fourth quarter of 2019. Their original sample is representative of farm households in rural areas of eight selected districts and used a multitopic household survey to collect information on demographics, health, socio-economic status, time use, and agricultural production from a sample of 3,259 households (Muyanga et al. 2020).<sup>6</sup>

# _2.1 Sample and survey structure_

Our analysis uses a sample of 701 households for whom a complete set of four quarterly phoneinterviews were successfully completed. Starting with the MRALS sample of 3,259 households only 2,435 households had contact phone numbers and were therefore eligible for inclusion in our phone-based survey. Stratifying by region, 1,505 eligible households were randomly selected to be included in our study. 1,020 of these households were successfully contacted and included in Round 1 and ultimately, 701 households were successfully reached for the full set of interviews.

> 5 See Dzanku (2020), Abay et al. (2019), Asfaw et al. (2019), Yeboah and Jayne (2018), Imai et al. (2015), Himanshu et al. (2013), Djurfeldt (2013), Haggblade et al. (2010), Ellis (1998), and Ellis and Freeman (2004) for some recent examples.

> 6 The selected districts include two in the Northern Region (Rumphi and Mzimba), four in the Central Region (Lilongwe Rural, Dowa, Kasungu, and Mchinji), and two in the Southern Region (Neno and Blantyre Rural).
Table S Variability and persistence of annual commodity prices, 1900-1987

|Coinmodity|CV|ARI|AR2|PER20|PER40|
|---|---|---|---|---|---|
|Bananas|0.17<br>|0.91<br>|0.82<br>|0.59<br>|0.52<br>|
|Cocoa|0.54<br>|0.83<br>|0.66<br>|0.29<br>|0.24<br>|
|Coffee|0.45|0.80|0.62<br>|0.17<br>|0.11<br>|
|Copper|0.38<br>|0.84<br>|0.64<br>|0.31<br>|0.22<br>|
|Cotton|0.35|0.88|0.68<br>|0.39|0.13<br>|
|Jute|0.33|0.71|0.45|0.19|0.09|
|Maize|0.38|0.76|0.53|0.19|0.10<br>|
|Palmoil|0.48|0.73|0.48|0.13|0.05<br>|
|Rice|0.36|0.83<br>|0.61<br>|0.18<br>|0.08<br>|
|Sugar|0.60|0.62|0.39<br>|0.11<br>|0.06<br>|
|Tea|0.26|0.78<br>|0.59<br>|0.37<br>|0.28<br>|
|TMI|0.42|0.90|0.76|0.43|0.18|
|Wheat|-<br>0.38|0.86|0.68|0.24|0.11|

Source: Deaton and Laroque (1989, Table 2).

Notes: CV is the coefficient of variation. ARI and AR2 are the first and second order autocorrelation coefficients of the deflated series of prices. PER20 and PER40 are the Cainpbell/Mankiw-Cochrane measures of persistence with window widths of 20 and 40 years.

50
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34
Figure 6, Panel A shows the evolution of short-term assets during the major crisis period in the sample that affected Chile, indicating the dates of some of the main events in international financial markets. The pattern of short-term asset holdings shows an increase from an average of 2% one week before the Asian crisis hit the Republic of Korea in November 1997 (with the downgrade of Korean debt) to more than 3% two weeks afterward, and remains high for the rest of this turbulent period. If anything, the evolution of short-term assets is more consistent with a flight-to-liquidity strategy than with the hoarding of liquidity to take advantage of fire-sale asset prices.

In principle, this pattern could be explained by a sudden inflow of capital into pension funds. Because it takes time for fund managers to find prudent long-term investments, in the short run the fund’s average maturity might decline (Ferson and Warther, 1996) if managers have more capital to invest. However, the net inflows PFAs received during this period were around the two-year mean, within two standard deviations of the mean.<sup>18</sup> Therefore, the patterns in Figure 6, Panel A, do not seem to be significantly affected by sudden inflows of cash.<sup>19</sup> Furthermore, to explore if the reaction of PFAs was different from that of mutual funds and insurance companies, we run difference-in-differences regressions around the 1998 Russian crisis (Figure 6, Panel

> 18 In unreported, more general results, we do find some evidence partly consistent with the effect documented by Ferson and Warther (1996). That is, in periods when pension funds receive large inflows, the allocation to short maturities increases more. Moreover, using the auction data we find that bids on short-term bonds tend to increase proportionally more (when net inflows are positive or above different means) than the bids on other bonds. However, this effect is small and managers do not change the type of bonds they demand in a significant way.

> 19 Because pension funds do not guarantee a certain rate of return, during a crisis all pension funds could be yielding a negative return, as indeed happened in 2008 when all funds with equity exposure suffered large losses. Therefore, they are unlikely to be perceived as a safe heaven and thus witness more inflows during crises. In fact, the flows into pension funds tend to be very stable, including during crises, because they are mandatory savings.

23
- 20 -

# II. METHODS **FOR** ESTIMATING STUMPACE VALUES

2.1 Market distortions due to taxes and subsidies, shortage of accurate, time-tested data, and confusion of terminology complicate the task of estimating stumpage values. These problems have<sup>to be taken into</sup> account when using the methodologies described in this section.

2.2 The main methods used to estimate fuelwood stumpage values are:

# (a) Market Approaches:

(i) Residual stumpage; (ii) Auctiun sale/tender;

# (b) Surrogate market approaches:

(i) Opportunity cost of gatherer's time; (ii) Differential land values;

- (c) Cost-based approaches:

(i) Alternative fuel substitution; (ii) Wood replacement cost.

2.3 Stumpage values can be estimated using either financial or economic criteria. The basic methodology is the same for<sup>both. For</sup> simplicity, this section focuses on financial analysis, using examples of actual costs and prices. Key price adjustments fot economic analysis are noted in the text. In application, the level of dic',mposition for shadow pricing purposes will depend on the level of acciracy desired.<sup>Cood</sup> examples may be found in Cittinger (1982) and ot'er standard<sup>texts on</sup> project economic analysis.

# Market Approaches

# Residual Stumpage

2.4 One way to determine the stumpage fee for the various forest products is to work back from their market price through the wholesaler, transporter, producer, extractor, and tree cutter (or feller), allowing for local taxes and fees, rent, and profit margins. This<sup>will give a</sup> "residual stumpage fee" which sets a limit on what can be charged for<sup>the</sup> standing tree, taking into consideration how much of<sup>the tree is</sup> saleable. This analysis has been performed in Table 2.1 for charcoal production in Sudan at a specific distance (400 km) from the market. The table groups the costs under various headings and each line<sup>item may</sup> summarize many individual operations. For example, charcoal production costs include but are not limited to--tree cutting,<sup>preparation of the</sup> raw material, kiln building, firing, cooling, charcoal<sup>bagging, loading,</sup> etc.
**Table** I The composition of current expenditures. 1981, 1985, and **1990** (percent _of GDP)_

|_Economnic_<br>clossification<br>_(current expenditures)_|1981|_1985_|1990|
|---|---|---|---|
|Wages and salares|6.0|6.1|6.0|
|Teacher,' salaries|2.8|3,2|3.9|
|Total|8.8|9.3|9.9|
|Nonwage expenditures|8.5|6.7|6.3|
|Defense related|2.8|2.1|2.6|
|Nondefense related|5.7|4.6|3.7|
|Interest|2.4|**4.1**|5.4|
|Dornestc|**1.3**|2.6|3.6|
|Foreign|1.1|1.6|1.8|
|Transfers<br>to parastals|0.8|||
|Other||||
|Sectoral distribution||||
|(total expenditure)||||
|Education|6.1|5.5|6,4|
|Health|2.3|1,7|1.7|

Not available.

SoLuce: Statistical Abstracts, Economic Surveys (variou; **s** sues), IMF, and World Bank stalf estin es.

**Table** 2 Financing of budget deficits, 1981-91

(change in ratio)

|_Item_|_1981-84_|_I984-91_|
|---|---|---|
|Budget deficit/GDP|||
|(excluding grants)|-5.8|+4.5|
|Financing|||
|Grants/GDP|-0.1|+ 1.2|
|Foreign<br>borrowing/GDP|-4.7|+ 1.7|
|<br>Domestic financing/GDP|-1 .0|+ 1.5|
|Inflation (change in rate)&|-II. I|+4.7|
|Growth of real domestic credit|||
|(annual percentage)|3,4|5.6|
|Change in share of prvate sector<br>in total credit|-6.3|-2.8|

a. From December to December. Source: Statistical Abstracts, Ecnomic Surveys (various Is -es), IMF, and World Bank staff estimates 64
suggests that a hike in the minimum wage during a positive output gap period might be correlated with a temporary “cooling down” of the economy. The reverse is true in a period of below potential growth (2011-2016): a minimum wage hike corresponds to a reduction of the positive unemployment rate gap and, accordingly, to a decrease in the size of the slack in the economy (see Figure 15, right).

_Figure 15. Mean of unemployment rate gap during 2004 Q1-2008 Q3 (left) and 2011 Q1-2016 Q4 (right) within a period of three months before/after a minimum wage hike_ <!-- Start of picture text -->
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3<br>.2 .1<br>.1<br>.05<br>0<br>0<br>mean of unemployment gap -.1 mean of unemployment gap<br>-.2 -.05<br><!-- End of picture text -->

Source: NIS, NBR calculations Note: The time of the minimum wage hike is marked “0” on the x-axis Figure 16 shows similar conclusions for the youth (aged 15-24) unemployment rate gap, for the two periods mentioned above. In this case, given the prevalence of minimum wage contracts among young persons (which have less experience and skills), the effects described above are potentially stronger. In particular, for the negative output gap period between 2011 and 2016, the positive unemployment rate gap diminishes more suddenly and abruptly for youth (see Figure 16, right) than for total unemployment (see Figure 15, right).

_Figure 16. Mean of youth (aged 15-24) unemployment rate gap during 2004 Q1-2008 Q3 (left) and 2011 Q1-2016 Q4 (right) within a period of three months before/after a minimum wage hike_ 20