rafmacalaba/datause-encoder-data huggingface dataset:
The reporting practices of a country (or product) are therefore represented in the distribution of these variables. The shape of this distribution can therefore easily reveal several types of reporting problems.

An important aim of this paper is to develop measures that summarize these distributions and (in conjunction with other discrepancy measurements like the CIF/FOB ratio) indicate the type of reporting problem present. To this end, we define, evaluate, and assess a rich set of statistical measures derived from the DI .

The presented list of indicators below are statistical measures that summarize the properties of the Discrepancy Index distribution. They can be applied to any of the six variables outlined above, depending on the specific goal of the analysis. For simplicity, they are presented in the context of DIa , a variable that describes the import reporting practices of a fixed country across its imported products and trading partners. For each country a, we calculate the following:

**Average DI** - The average DIa of all transactions where country A is the importer. As previously discussed, because of differences in valuation methods the average DIa should ideally be slightly below 0, indicating that, on average, the reported imports were higher than the corresponding reported exports.

**Median DI** - The median DIa of all transactions. Ideally, the median DI should be (slightly) below 0, indicating that for most transactions where country A appears as an importer, the reported trade value for the import was higher than that for the export.

**First Quintile DI:** the value of the 25th percentile of the DIa distribution. It should be less than 0 but tends toward -1 for countries with a lot of orphan imports.

**Third Quintile   DI** - the value of the 75th percentile of the DIa distribution. Ideally, should be slightly below 0, but is above 0 for most countries due to the influence of lost exports.

**Standard deviation of DI** - The standard deviation of the DIa distribution. Ideally, it should not be high, indicating more consistent reporting across transactions.

16
-3-

Unfortunately there has been virtually no empirical research in this area: part of the reason being that the analysis of sequentially repeated, multi-unit auctions presents substantial theoretical and empirical difficulties. The aim of this paper is to build on an earlier institutional and statistical paper on the SSA auctions (Aron and Elbadawi, 1994), and estimate models for the micro-deterninants of the auction rate, based on a simple underlying model synthesized from auction theoretical literature, using weekly foreign exchange auction data from four Sub-Saharan African countries, Zambia, Uganda, Ghana and Nigeria. The empirical section of this paper constitutes a novel approach to empirical modelling in auction research: in the context of a dynamic model for these repered, sequential auctions, we employ more recent econometric techniques of cointegration for efficiert hypothesis testing in the presence of the regime shifts (structural breaks) which characterize these liberalizadion episodes in SSA.5 We use this empirical model to evaluate some micro-economic design and management features of the foreign exchange auctions through testing policy propositions motivated by auction theory. We hope to shed some light on the causes of success and failure of the auction regimes in SSA, and provide a few policy lessons for improved design and conduct of auctions at the micro-level.

This study is partly historical, but also has substantial current relevance. Two of the countries we examined at the time of analysis had ongoing auctions (Nigeria and Uganda), auctions were introduced in Ethiopia in 1993, while more recently the question of introducing an auction to disburse foreign aid has been mooted in Zambia.' Secondly, there is an increasing tendency to use auctions for the marketpricing of credit and import quotas in Latin American and Eastern European countries. This trend appears to be moving to SSA, so that many of the lessons from the foreign exchange auctions, which take explicit account of the structural characteristics of these countries, will be transferable.

The structure of the paper is as follows. Section 2 discusses some analytical issues and gives an outline of a benchmark model of the auction equilibrium selling rate. Data features and the empirical methodology are presented in section 3. The model is estimated in section 4 and various hypotheses are tested to clarify policy questions, using weekly auction data from Ghana, Nigeria, Zambia and Uganda. Finally, section 5 concludes with policy lessons from SSA.

> 5 Regime shifts and unit-root are common features of these high firquency SSA data (Aron and Elbadawi, 1994). 6Further, a numnber of Eastern European countnies employ foreign exchange auctions (eg. Romania and Khazakstan) where stuctral characteristics may not be too different in some cases to SSA countries.
The basic specification that we used to test the'effect of rainy-day funds on the volatility of expenditures is the following: where Volexp is the volatility of expenditures in state s at time t; _Volinc_ is the volatility of gross state product (GSP) in state s at time t; _Rainy_ is the rainy-day fund balance in state s at the end of year t-1; and co are state fixed effects. The above variables are in million 1988 dollars.

**Data:** The data are drawn from a number of different sources. State rainy-day fund balances, and expenditures were obtained from several issues of the _Fiscal Survey of States_ and _State Expenditure Report_ published by the National Association of State Budget Officers (NASBO). Data on the characteristics of rainy-day funds<sup>2</sup> were obtained from NASBO (1999), Wagner (1999), and Knight and Levinson (1999a) and from the departments of finance of some states.

**Results.** Table 3 shows the regression results-for (1), using data from the 1985 to 2000 period. We find that a dollar in the rainy-day fund balance decreases the volatility of social sector expenditures by about 34 cents. By contrast, column (3) of Table 3 shows that rainy-day fund balances do not have any effect on non-social sector expenditures. This implies that rainy-day funds are effective in reducing the volatility of the social sector expenditures but are ineffective as an overall budget stabilization fund. This result is consistent with Sobel and Holcombe (1996), Wagner and Sobel (2001), and Gonzalez (2002), which as mentioned found that rainy-day fimds do not reduce the volatility of aggregate spending. Also, column (1) of Table 3 shows that states with higher volatility of income have a higher volatility in social sector expenditures.

Most of the regression results depicted in columns (1 and 3) of Table 3 are statistically significant at a 90 percent level of confidence. We tried a variation on

> **2** See Table 2.

7
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## **Table A13. Depth of fertilizer shortfalls**

# **<u>Log fertilizer shortfall (kg/ha)</u>**

||(1)<br>Ethiopia|
|---|---|
|Distance to nearest market|-0.00144<br>(0.00225)|
|Distance to nearest road|-0.000776<br>(0.00846)|
|Distance to nearest population center|0.00144<br>(0.00690)|
|Wealth quintile|-0.137**<br>(0.0567)|
|Constant|5.761***|
||(0.240)|
|Observations|218|
|R-squared|0.044|

Note: Results from OLS regression of the amount of additional fertilizer that could not be acquired on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

34
17 I=IWBEARUG VERSS FUSALY PlA NRNG

# Tabl&2. RISKS OF

||E<br>|stimated A<br>per 100.0|nnual De<br>00 women|aths|
|---|---|---|---|---|
||Deve<br>Coun<br>|loped<br>tries<br>|Deve<br>Coun<br>|loping<br>tries<br>|
||Age<br><35|Age<br>35+|Age<br><35|Age<br>35+|
|Maternal deaths with<br>no contraception|10|27|60|160|
|Deaths from side effects<br>of oral contraceptives<br>among nonsmokers|1|23|1|23|
|Deaths ftom side effects<br>of oral contraceptives<br>among smokers|7|85|7|85|
|Deaths from side effects<br>of IUDs|1|2|2|4|
|Deaths from 'side effects"<br>of condoms|<br>0|0|0|0|

Source: Pooulation Regorts, 1984.
- 13 -

1981).<sup>7</sup> In the region, IMF (2020) applies the fixed-effects method to estimate the impact of transport infrastructure under Nurly Zhol on firm revenues and profits in Kazakhstan. However, the fixed-effects model may lead to biased estimates if there are any time-variant individual effects that are correlated with the residuals.

25. The generalized method-of-moments (GMM) estimator is more efficient than the fixedeffects estimator. In this paper, the system GMM using instruments from both levels and differences (e.g., Roodman, 2009). The traditional difference GMM approach (Arellano and Bond, 1991) where only differences lagged differences are used as instruments tends to poorly perform particularly when the time series persistence is high in a dependent variable. The system GMM can generate more instruments under the assumption that their changes are uncollated with the individual fixed effects and better control for the endogeneity and autocorrelation issues simultaneously. The reliability and validity of instruments will be examined by ex post statistical tests, such as the Hansen overidentifying restriction tests.

26. Assuming that agglomeration economies are influenced by transport connectivity and other factors, the following dynamic panel model is considered: where _Nit_ is the number of firms registered at location _i_ at year _t_ . Our unit of analysis is region or district. The level of spatial disaggregation is different across countries (see **Table 1** above). All time-invariant characteristics that are specific to location _i_ , such as local culture, language and politics, are assumed to be captured by the location-specific fixed-effects, _ui_ , and removed by the first difference transformation of the dynamic panel model. The systematic differences in definition and coverage of firm data across countries are also expected to be controlled by _ui_ .

27. To deal with the persistence of agglomeration economies, the lagged dependent variables are included on the right-hand side. As shown above, our dependent variable exhibits strong

> 7 As will be shown below, the dynamic panel model estimator is more suitable to our data where there is a strong first-order serial correlation in the first-difference residuals.
funds between September 2002 and June 2008. In addition to this monthly data set, we use a daily data set with portfolios of the universe of funds and PFAs in operation, which contains 201,288,833 observations for 62 funds between July 1996 and July 2008.<sup>10</sup> The daily data have the same fields included in the monthly database.

The data on Chilean insurance companies comprise monthly portfolio holdings from January 2002 to December 2008. The database contains 4,071,927 observations corresponding to the fixed-term assets of 36 insurance companies. Information on security type, maturity date, and currency, among others, are available in this data set.

# **4. Maturity Structure**

We describe the holdings of long-term assets by documenting in turn the maturity structure of mutual funds, pension funds, and insurance companies. For the case of mutual funds, Figure 1 plots the fraction of investments in fixed-term assets per year-tomaturity, both within each maturity range and accumulated. We construct the figure by determining at each point in time (each month) the term to maturity of each instrument in a mutual fund portfolio, measuring the fraction of the value of all assets invested at different terms to maturity, and then averaging these fractions across mutual funds and time.

_k_ Let _di_ , _t_ and _wi_ , _t_ denote the term to maturity of asset _i_ at time _t,_ and the share of fixed-term assets invested in asset _i_ at time _t_ by fund _k,_ respectively. The fraction of fund _k’_ s fixed-term assets with term to maturity _D_ is

> 10 The difference between the number of funds in the monthly and daily data sets is due to the extended period the daily data set covers.

13
- Myers, D., P. Peterson, D. Mayer, J. Chou, and W. Howell. 2000. "School Choice in New<sup>York</sup> City after Two Years: An Evaluation of the School Choice Scholarship<sup>Program."</sup> Mathematica Policy Research, Inc., Washington, D.C.

- Neal, D. 2002. "How Vouchers Could Change the Market for Education." _Journal of_<sup>_Economic_</sup> _Perspectives_ 16(4): 25-44.

- Patrinos, H. 2002a. "A Review of Demand-Side Financing Initiatives in<sup>Education."World Bank.</sup> Processed.

- . 2002b. "Private Education and Public Finance: The Netherlands as a Possible<sup>Model."</sup> National Center for the Study of Privatization in Education, Occasional Paper<sup>No. 59.</sup>

- Plank, D., and Sykes, G., editors. 2003. _Choosing Choice: School Choice Policies_<sup>_in_</sup> _International Perspective._ New York: Teachers' College Press<sup>(in press).</sup>

- Ritzen, J., J. van Dommelen, and J. De Vijlder. 1997. "School Finance and School Choice<sup>in the</sup> Netherlands." _Economics of Education Review_ 16(3): 329-35.

- Rodriguez, J. 1988. "School Achievement and Decentralization Policy: The Chilean<sup>Case."</sup> _Revista de Analisis Economico_ 3(1): 75-88.

- Rouse, C. 1998. "Private School Vouchers and Student Achievement: An Evaluation of<sup>the</sup> Milwaukee Parental Choice Program." _The Quarterly Journal of Economic_<sup>113:553-602.</sup>

- Shavell, S. 1979. "Risk Sharing and Incentives in the Principal and Agent Relationship." _Bell Journal of Economics_ 10: 55-73.

- Smithfield Project. 1995. "Trading in Futures: The Nature of Choice in Educational Markets<sup>in</sup> New Zealand." Phase 1, Third Report. Wellington: Ministry of Education.

- Swanson, C., and B. Schneider. 1999. "Students on the Move: Residential and<sup>Educational</sup> Mobility in America's Schools." _Sociology of Education_ (72): 54-67.

- Walford, G. 1997. "Diversity, Choice and Selection in England and Wales." _Educational Administration Quarterly_ 33(2): 31-44.

- Walford, G. 2003. "School Choice and Educational Change in England and Wales."<sup>In D. Plank</sup> and G. Sykes, G., eds. _Choosing Choice: School Choice Policies in International Perspective._ New York: Teachers' College Press (in press).

- Witte, J. 2000. _The Market Approach to Education._ Princeton, NJ: Princeton University<sup>Press.</sup>

- Wolfe. 2003. _School Choice: The Moral Debate._ Princeton, NJ: Princeton University<sup>Press.</sup>

- World Bank. 2001. _Czech Republic: Enhancing the Prospects for Growth with Fiscal_<sup>_Stability._</sup> World Bank Country Study. Washington, D.C.

- World Bank 2000. _Bangladesh: Education Sector Review._ World Bank, Washington,<sup>D.C.</sup>

- World Bank. 1998. _C6te d'Ivoire: Education and Training Support Project._ World<sup>Bank,</sup> Washington, D.C.

- Wylie, C. 1998. "Can Vouchers Deliver Better Education? A Review of the Literature,<sup>with</sup> Special Reference to New Zealand." Wellington: New Zealand Council for Educational Research. Processed.

23
creation of broader groups with a stake in further financial deepening can help develop a dynamic process of financial sector reforms.

Research in these five areas will have to be supported by an array of new data and a variety of methodological approaches. Increased data availability has helped spur a rich research agenda on African finance over the past decade. Further advances will require expanding this data availability towards non-bank providers, such as equity funds, but also exploiting existing data sources better, including credit registry and central bank data sets. In addition to exploiting more extensive micro-level data sets, a variety of methodological approaches is called for. First, randomized experiments involving both households and micro- and small enterprises will shed light on specific technologies and products that can help overcome the barriers to financial inclusion in Africa. One of the challenges to overcome will be to include spill-over effects and thus move beyond partial equilibrium results to aggregate results. Second, further studies evaluating the effect of specific policy interventions can give insights into which policy reforms are most effective in enhancing sustainable financial deepening and positive real sector outcomes.

36
# **TABLE A1.  African Development Bank (ADB)**

## **A1: African Development Bank (ADB)**

http://www.afdb.org/ The African Development Bank (ADB) is a major development bank in Africa. Established in 1963 in order to promote economic and social development, the Bank has grown into a $33 billion, multinational development bank, with 52 African countries and 24 other shareholders.

The African Development Bank develops its own "Country Policy and Institutional Assessments" for its own client sample. Similarly to the World Bank's CPIA, the ADB Indicators annually assess the quality of African Development Bank borrowers’ policy and institutional performance in areas relevant to economic growth and poverty reduction. Data is publicly disclosed only on a limited basis.

In the table below we list the variables included in each of the governance indicators. We use data from 2000, and from 2002-2005.

|**Table A1: Africa Developme**|**nt Bank(50 African**|**coun**|**tries)**|||||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Policies to improve efficiency of public sector|X|X|X|X|X|..|..|
|Budget Management|X|X|X|X|X|..|..|
|Efficiency of Public Expenditures|X|X|X|X|X|..|..|
|Management of public debt|X|X|X|X|X|..|..|
|**Regulatory Quality**||||||||
|Trade policy|X|X|X|X|X|..|..|
|Competitive environment|X|X|X|X|X|..|..|
|Labor Market Policies|X|X|X|X|X|..|..|
|**Rule of Law**||||||||
|Property rights|X|X|X|X|X|..|..|
|**Control of Corruption**||||||||
|Anti-corruption policies|..|X|X|X|X|..|..|
|Transparency / corruption|X|X|X|X|X|..|..|

46
Figure 3. **Average Output of High-Caste Participants** _Note._ Brackets indicate differences between treatments with 95% confidence based on the MannWhitney _U-_ test.

Figure 4 superimposes on Figure 3 the average L output by condition. All three blocks show that when caste is not revealed, the average output of H is almost the same as that of L. However, when caste is made public, the performance declines for L are generally steeper than those for H.

18
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# **Table B1c. Johannesburg home electricity**

|Percent<br>Division|Access rate|Take-up rate given<br>access|Coverage rate|Unserved population|Pure demand-side gap|Supply-side gap|Pure supply-side gap|Mixed demand and<br>supply-side gap|Proportion of deficit<br>attributable to demand-<br>side factors only|Proportion of deficit<br>attributable to supply-<br>side factors only|Proportion of deficit<br>attributable to both<br>supply and demand-<br>side factors|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Alexandra|100|80|80|20|20|0|0|0|100|0|0|
|Central Region|100|87|87|13|13|0|0|0|100|0|0|
|Diepkloof / Meadowlands|70|81|57|43|13|30|24|6|30|57|13|
|Diepsloot|75|89|67|33|8|25|22|3|24|67|8|
|Doornkop / Soweto|73|75|55|45|18|27|20|7|40|45|15|
|Ennerdale / Orange Farm|83|90|75|25|8|17|15|2|32|61|7|
|Johannesburg South|75|65|49|51|26|25|16|9|51|32|17|
|Midrand / Ivory Park|100|87|87|13|13|0|0|0|100|0|0|
|Northcliff|67|100|67|33|0|33|33|0|0|100|0|
|Roodepoort|55|100|55|45|0|45|45|0|0|100|0|
|Sandton / Rosebank|100|94|94|6|6|0|0|0|100|0|0|
|Simple average|82|86|70|30|11|18|16|2|52|42|5|
|Weighted average<br>**Table B2a. Nairobi piped**<br>Percent|84<br>**water**|84|71|29|13|16|13|3|55|38|7|
|Division|Access rate|Take-up rate given<br>access|Coverage rate|Unserved population|Pure demand-side gap|Supply-side gap|Pure supply-side gap|Mixed demand and<br>supply-side gap|Proportion of deficit<br>attributable to demand-<br>side factors only|Proportion of deficit<br>attributable to supply-<br>side factors only|Proportion of deficit<br>attributable to both<br>supply and demand-<br>side factors|
|Central|60|40|24|76|36|40|16|24|47|21|<br>32|
|Makadara|67|20|13|87|54|33|7|27|62|7|<br>31|
|Kasarani|100|23|23|77|77|0|0|0|100|0|<br>0|
|Embakasi|44|18|8|92|36|56|10|46|40|11|<br>50|
|Pumwani|100|24|24|76|76|0|0|0|100|0|<br>0|
|Westlands|67|57|38|62|29|33|19|14|46|31|<br>23|
|Dagoretti|59|36|21|79|38|41|15|26|48|19|<br>34|
|Kibera|71|16|11|89|60|29|5|25|67|5|<br>28|
|Simple average|71|29|20|80|51|29|9|20|64|12|<br>25|
|Weighted average|69|26|17|83|52|31|8|22|63|10|<br>27|

56