rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2**

# **Trends in Developing Countries' Merchandise Trade**

# Annual Rates of Growth and Trade / GDP(in %)

||**Exports**<br>(1989-1997)|**Imports**<br>**(1989-1997)**|**Total Trade /**<br>**GDP**<br>**(1989)**|**Total Trade /**<br>**GDP**<br>**(1996)**|
|---|---|---|---|---|
|42 Developing countries|10.2%|12.2%|36.2%|43.4%|
|HighIncome(4)|8.9%|10.1%|87.3%|83.9%|
|Middle Income (23)|11.0%|13.9%|30.8%|41.3%|
|Low Income (15)|9.4%|9.9%|24.0%|36.1%|
|Latin America and the Caribbean (13)|_10.5%_|15.8%|22.2%|27.3%|
|Asia and Pacific (12)|11.0%|11.4%|53.3%|64.4%|
|Sub-Sahara Africa(12)|4.5%|7.1%|45.3%|52.7%|
|*Europe, Middle-East and North Africa_(5)_|9.5%|11.2%|30.7%|37.4%|
|All Developing countries|9.5%|10.4%|38.3%|**44.0%**|

Explanation:

* Exports plus Imports relative to GDP.

**High Income** (H), Middle Income (M) and **Low Income** (L): see World Bank Classification of Economies (1996) **Latin America and the Caribbean :** Argentina (M), Bolivia (M), Brazil (M), Chile (M). Colombia (M), Costa Rica (M), Dominican Republic (M), El Salvador (M), Mexico (M), Paraguay (M), Peru (M), Uruguay (M), Venezuela (M).

**Asia and Pacific:** Bangladesh (L), Fiji (M), Hong Kong China (H), India (L), Indonesia (M), Korea (H), Malaysia (M), Pakistan (L), Philippines (M), Singapore (H), Sri Lanka (L), Thailand (M).

**Sub-Sahara Africa :** Benin (L), Cameroon (L), Cote d'lvoire (L), Ghana (L), Kenya (L), Mauritius (M), Nigeria (L), Senegal (L), South Africa (M), Uganda (L), Zambia (L). Zimbabwe (L).

**Europe, Middle- East and North Africa** : Cyprus (H), Egypt (M), Morocco (M), Tunisia (M), Turkey (M).

_Source: WTO, 1998, WorldBank, 1998; IMF. 1998._ The table also shows that both exports and imports grew the slowest in the. group of countries in Sub Sahara Africa, whose performance during this period was actually even worse than that of the LDCs.

Finally, Table 3 looks at the trade performance of the 42 countries by regional group in **terms** of their composition of trade as between manufactures and non-manufactures. The table highlights the strong expansion of manufactures exports in Latin America over the period; but also, somewhat surprisingly, among the Sub-Saharan countries, although the latter group was starting from a low base.

> 3The words "countries" and "economies" are used interchangeably in this study-although certain WTO members, for example, Hong Kong (China) are not sovereign states. The largest developing economies by trade value excluded from this analysis are China, Chinese Taipei and Saudi Arabia which are not members of the WTO.

7
I POLICY RESEARCH WORKING PAPER 2981

# Abstract

As part the Local Lcvel Institutions study of local life in villages in rural Indonesia informationi was gathered on sampled household's participation in social activities. We classified the reported activities into four distinct types of social activity: _sociability, nietworks, social organtizations,_ and _village governmiientt_ organizations. Respondenits were also asked about questions about their village government: whether they were _iniformed_ about village funds and projects, if they _participated_ in village decisions, if they expressed _voice_ about village problems, and if they thought the village government was _responisive_ to local problems. Several findings emerge regarding the relationship between the social variables and the governanice activities. Not surprisinigly, an individual houselhold's involvemnent with the village government organizations tends to increase their own reports of positive voice, participation, and information. In contrast, the data suggest a negative spillover on other houselholds. There is a strong "chilling" effect of one houselhold's participation in village government organizations on the voice, participation, and information of other households in the same village. The net effect of engagement in village government organizations is generally negative, while the net effect of membership in social organizations is more often associated with good governance outcomes. These findings indicate that existing social organizations have a potentially important role to play in enhancing the performance of government institutions in Indonesia and in the evolution of good governance more generally.

This paper-a product of the Environm11ent and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to study local level institutions. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please cotitactAnju Sachdeva, room MC8- 112, telephone 202-458-2717, fax 202-522-1666, email address asachdeva@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.wvorldbank.org. The authors may be contacted at valatas((iworldbank.org, lant_pritchett@ksg.harvard.edu, or anna w@uclink.berkeley.edu. March 2003. (49 pages)

> _hrlc 1'ocvIci Reseirch Working Paper Series dissemiinates the findings of wvork in progress to encoutrage tie exchange of ideas abonit development issues. An ohjective of the series is to get the findings otit quiickly, eveni tf the presenitations are less than fully polished. Tbe papers carry the nanies of the authors and should be cited accordinkgly. The findings, interpretations, and co,icltsionis expressed in this paper are entirely those of the atithors. They dro niot niecessarily represent the viei of the World Bank, its Executive Directors, or the coiintries thev represeiit._

Produced by the Research Advisorv Staff
5 a similar result is obtained by interacting the election variable with the corruption index (negative and significant sign), and in column 6, we combine the interaction between the existence of a regulator and the corruption index with that of the election variable with the corruption index, and still obtain the same results for both.

Overall the results in Table 9 give additional evidence of the fact that strong regulatory mechanisms are specially important when the institutional quality is low. In this context, experienced and independent regulators are likely to act as barriers against political opportunism.

## 3.6 Robustness Checks

In our model, government-led renegotiations are both Pareto improving and rent shifting ones. Empirically, our sample contains a small number of renegotiations that are considered to be the result of a joint call. As discussed in GLS, this may both reflect the fact that the renegotiation was desired by both parties or that there was not a clear case to attribute the call, as with some Mexican road projects. In any case, it can be argued that joint calls are close in nature to Pareto improving government calls, so Table 10 (see Appendix) presents robustness checks using as a dependent variable the dummy variable indicating whether there is a renegotiation initiated by the government or by both parties. Overall, the main results are unchanged. Some noticeable include:

- The marginal effect of private financing is stronger and up to 10%.

- The significance of the lagged election variable is reinforced. In this case, it is significant at the 1 or 5% level in all cases and its marginal effect is around 2%.

- The existence of a bidding process becomes weakly significant (positive and significant at 10% level with a marginal effect near 3%).

# 4 Conclusion and Policy Implications

We have built a model of renegotiation of concession contracts in which the principal, the government, initiates both Pareto improving and rent-shifting renegotiations. Moreover, we have tested its predictions with a sample of 307 Latin American transport and water concessions in the 1990s.

35
tives. The data on violent events is used as a control variable in our econometric model. We also use population data, based on the 2008 census, in order to scale the number of ex-combatants in each colline.

Official Demobilization Registers. The National Commission for Demobilization, Reinsertion, and Reintegration in Burundi provided us with registers of ex-combatants by colline and faction, along with their sex, age, military rank, colline of origin and of return, as well as the date of their demobilization. The registers contain precise information about each demobilized ex-combatant as well as the exact number of demobilized soldiers in each colline. The large variation in the number of demobilized ex-combatants per colline allow us to identify the spillovers of the DDR program in Burundi.

# _3.2. Identification Strategy_

Our identification strategy is based on a lagged dependent variable model with province fixed effects. The lagged dependent variable model should be preferred to the difference-indifferences model when the assumption “that the most important omitted variables are timeinvariant doesn’t seem plausible” (Angrist and Pischke, 2008). The particular histories of civilian and ex-combatants households motivate an estimation strategy that controls for lagged dependent variables directly and dispenses with households fixed effects. Furthermore, when autocorrelation of outcomes is low or when data is imprecisely measured, as is the case for many economic variables such as household incomes and expenditures (McKenzie, 2012), controlling for the lagged dependent variable is more powerful than either employing the differencein-differences estimator or the single difference estimator using only the follow-up data. Intuitively, “when the baseline data have little predictive power for future outcomes, it is inefficient to fully correct for baseline imbalances between treatment and control groups” (McKenzie, 2012). Because the coefficients of correlation correlation between our outcomes of interest in 2006 and in 2010 are low,<sup>14</sup> we include the lagged dependent variable as a control variable in the regressions.

According to this in accordance with this approach, we propose to estimate the following equation:

> 14The coefficient of correlation between the logarithm of total consumption per adult equivalent in 2006 and 2010 is 0.12. For other dependent variables - the logarithm of consumption expenditures per AE, the logarithm of consumption from stocks per AE, the logarithm of nonfood expenditures and the logarithm of tropical livestock units - coefficients of correlation between 2006 and 2010 are equal to 0.19, 0.10, 0.18 and 0.28 respectively.

11
Tanzania. _Unpublished manuscript:_ https://editorialexpress.com/cgibin/conference/download.cgi?db_name=CSAE2016&paper_id=1018.

- Canning, David, Sangeeta Raja, and Abdo S Yazbeck. 2015. Africa's Demographic Transition: Dividend Or Disaster?

- Chay, Kenneth Y and Michael Greenstone. 2003. The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession. _Quarterly Journal of Economics_ 118 (3): 1121-1167.

- Chetty, Raj, Nathaniel Hendren, and Lawrence F Katz. 2016. The effects of exposure to better neighborhoods on children: New evidence from the Moving to Opportunity experiment. _Am Econ Rev_ 106 (4): 855-902.

- Currie, Janet and Douglas Almond. 2011. Human capital development before age five. _Handbook of Labor Economics_ 4:1315-1486.

- Dahl, Gordon B and Lance Lochner. 2012. The impact of family income on child achievement: Evidence from the earned income tax credit. _Am Econ Rev_ 102 (5): 1927-1956.

- Duflo, Esther. 2012. Women Empowerment and Economic Development. _Journal of Economic Literature_ 50 (4): 1051-1079.

- Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2015. Education, HIV, and Early Fertility: Experimental Evidence from Kenya. _Am Econ Rev_ 105 (9): 2757-97.

- Fan, Jianqing. 1992. Design-adaptive nonparametric regression. _Journal of the American Statistical Association_ 87 (420): 998-1004.

- Field, Erica and Attila Ambrus. 2008. Early marriage, age of menarche, and female schooling attainment in Bangladesh. _Journal of Political Economy_ 116 (5): 881-930.

- Filmer, Deon and Norbert Schady. 2014. The Medium-Term Effects of Scholarships in a LowIncome Country. _Journal of Human Resources_ 49 (3): 663-694.

- Fiszbein, Ariel, Norbert Rüdiger Schady, and Francisco H G Ferreira. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty_ .

http://public.eblib.com/choice/publicfullrecord.aspx?p=459451.

- Foster, Andrew and Nizam Khan. 2000. Equilibrating the marriage market in a rapidly growing population: Evidence from rural Bangladesh. _Unpublished manuscript_ : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.536.2227&rep=rep1&type=pdf.

- _Gender Equality and Development._ 2011. Gender Equality and Development

- Gertler, Paul J, Sebastian W Martinez, and Marta Rubio-Codina. 2012. Investing Cash Transfers to Raise Long-Term Living Standards. _American Economic Journal: Applied Economics_ 4 (1): 164-192.

- Gladstone, M J, G A Lancaster, A P Jones, K Maleta, E Mtitimila, P Ashorn, and R L Smyth. 2008. Can Western developmental screening tools be modified for use in a rural Malawian setting? _Archives of Disease in Childhood_ 93 (1): 23-29.

Glennerster, Rachel. 2013. Empowering Girls. Available at: _http://www.3ieimpact.org/media/filer_public/2013/07/19/girls_empowerment_in_banglade_ 39
head _(kepala desa)_ and local executive council (LKMD) to a designated official for each group (RT) and sub-group (RW) of households.

Third, in the rhetoric of the 1979 law the new _desa_ organizations were a means of channeling a "bottom up" expression of the popular will, and the law created mechanisms whereby villagers would participate in the planning process and express their development needs. The general perception among villagers and those who worked in rural areas was that reality did not match the rhetoric: the _desa_ organizations operated "top down." The _desa_ apparatus were widely perceived as a means of co-opting and controlling all social forces at both the national and local levels and of delivering the programs and development priorities determined at the center.

During Soeharto's New Order era, the leacdership of the provincial and district _(kabupaten)_ governments was appointed by the Ministry of Home Affairs and was dominated by retired (and active duty) military officers. Even though there were local elections the _desa_ leaders had to be approved by and reported to this structure<sup>8</sup> . As the first LLI Study showed, at the local level often a very narrow group controls the _desa_ government apparatus in a way that does not always reflect a broad community consensus (Evers 2000).

The resignation of Soeharto in May 1998 put in motion three linked but distinct changes. First, there were (generally) free and fair general elections for the national and 8 The motivations for creating this structure are well beyond the scope of this paper but: (a) since its birth Indonesia has experienced centrifugal pressures in various regions and the armed forces (from which the New Order leadership emerged) has always considered itself a bulwark of nationalism and stressed the need for central control, (b) without apportioning responsibility, the New Order (Soeharto) government was unquestionably born in social chaos and brutal local violence, an experience no one was anxious to repeat, and (c) the government in this period was "developmentalist authoritarian," anxious to deliver on the concrete benefits of "economic 8
# **APPENDIX 2: THE MAMS DATABASE**

As noted in Section 3, the database for a typical MAMS application includes a wide range of data for its base-year – a social accounting matrix (SAM); stocks for production factors (including one or more types of labor and capital), population, and school enrollment; indicators for selected MDGs and the educational system – as well as a set of elasticities (for production, consumption, trade, and human development relationships), and projections into the future (for growth in GDP at factor cost and the evolution of disaggregated MDG and education indicators and determinants). We will here outline the contents of the current database and how it was used, emphasizing parameters related to the rural-urban disaggregation and the MDGs.

Like other CGE models, MAMS is calibrated so that its base-year solution exactly replicates the base-year SAM. MAMS is also calibrated dynamically to replicate projected GDP growth and, in this application, projections for a set of HD outcomes, conditional on the simulated evolution of variables influencing these outcomes. Much of the inputs required to construct such a database are available from national and international databases, which in recent years have become richer and more easily accessed in electronic form. However, in some areas, data and knowledge are incomplete or unsettled, requiring the analyst to apply judgment and consult with country and subject specialists. In this context, calibration to base-year and projected data (especially for HD outcomes) provides an important means of eliminating many sources of errors in data and model structure as well as of ensuring that simulation results are consistent with available evidence.

Table A2.1 shows the basic disaggregation of the current database. The main inputs into the construction of a LIC SAM with a matching disaggregation were (a) an existing detailed SAM for a LIC (Uganda); (b) a macro SAM for a median LIC during the period 2005-2009 – Table A2.2 shows the corresponding macro SAM for 2013; and (c) median data for LICs on sector shares in value added. The median macro SAM is for the most part based on medians of the averages of GDP shares for individual LICs during the period 2005-2009, for some cells complemented by data in the original Uganda data set. For the estimation of a balanced SAM that draws on the original LIC SAM and replicates the macro SAM and other structural data, an entropy program was applied (Robinson _et al_ . 2001).

28
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Benchmarking power indicators**

||Unit|Resource-rich<br>countries|Bots|wana|Middle-income<br>countries|
|---|---|---|---|---|---|
|||Mid-2000s|2006|2008|Late-2000s|
|Installed power generation capacity|MW/mil. people|43|132|132|796.2|
|Power consumption|kWH/capita|206|1,476|1,504|4,473|
|Power outages|Day/year|14.5|1.7||5.6|
|Firms’ reliance on own generator|% consumption||||0.5|
|Firms’ value lost due to power outages|% sales|6.99|1.40||0.8|
|Access to electricity|% population|46.05|22|50|59.9|
|Urban access to electricity|% population|79.41|45|47|83.7|
|Rural access to electricity|% population|28.03|9|44|33.4|
|Growth access to electricity|% population/year|2.38|—|—||
|Revenue collection|% billings|81.07|98|100|99.9|
|Distribution losses|% production|25.80|10.0|10.2|15.7|
|Cost recovery|% total cost|53.94|96|87||
|Total hidden costs as % of revenue|%|168.29|6.4|14.6||
|||Botswana<br>Mid-2000s<br>Late|<br>p<br>2000s|Countries with<br>redominantly<br>thermal<br>generation|Other<br>developing<br>regions|
|Power tariff (residential at 75 kWh)|U.S. cents|5.2|5.2|15.71||
|Power tariff (commercial at 900 kWh)|U.S. cents|||18.83|5.0–10.0|
|Power tariff (industrial at 50,000 kWh)|U.S. cents|||14.24||

_Source:_ Eberhard and others 2008. Derived from AICD electricity database (www.infrastructureafrica.org/aicd/tools/data **).** _Note:_ kWh = kilowatt-hour; MW = megawatts. — **=** Not available.

The Botswana Power Corporation (BPC) has achieved relatively high efficiency in the power distribution sector. Botswana was able to secure for many years relatively inexpensive power purchase prices in the past importing most of its power from South Africa. While South Africa was able to provide power, Botswana’s good governance practices allowed it to build up a national power utility with full operational autonomy and the country established sound power regulation policies and incentives. This scheme of buying power to South Africa and focusing in increasing access and quality of distribution, worked well for years until the generation crisis hit South Africa and the power supply from Eskom was reduced significantly. This external shock made it evident that Botswana needed to secure its own generation capacity and could no longer postpone the needed generation and transmission investments.

24
17

# **Figure 1**

## **months of salary**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>< 1 3 5 10 20 Max<br>Years of service<br>1996 law 2000 law 2003 law<br><!-- End of picture text --> Source: LP3E FE UNPAD and GIAT, 2004
# **4 A theoretical model of trade statistics discrepancies**

# **4.1 Components of trade statistics discrepancies**

In this section we describe the theoretical model that guided our econometric analysis. Define the following variables related to country _i_ ’s imports from country _j_ :

- _I_<sup>_A_</sup> _ij_ Actual imports: the true amount imported

- _I_<sup>_R_</sup> _ij_ Reported imports: the amount published in a country’s annual trade statistics

- _I_<sup>_UR_</sup> _ij_ Unreported imports: the amount that is unreported due to illegal activities like smuggling and underinvoicing

_I_<sup>_M_</sup> _ij_ Measurement error: errors in measuring or recording imports.

The following identity holds: This says that reported imports ( _I_<sup>_R_</sup> _ij_<sup>) can differ from actual imports (</sup><sup>_IA_</sup> _ij_<sup>) for two reasons,</sup> measurement error ( _I_<sup>_M_</sup> _ij_<sup>) and unreporting (</sup><sup>_IUR_</sup> _ij_<sup>). Note that we define the latter as an intentional,</sup> illegal act. _I_<sup>_M_</sup> _ij_<sup>can have any sign: measurement error can result in reported imports being</sup> understated or overstated. The other variables are always nonnegative.

The corresponding variables for country _j_ ’s reported exports to country _i_ are _E_<sup>_A_</sup> _ij_ Actual exports _E_<sup>_R_</sup> _ij_ Reported exports _E_<sup>_UR_</sup> _ij_ Unreported exports _E_<sup>_M_</sup> _ij_ Errors in measuring exports (including recording errors) Like unreported imports, unreported exports refer to quantities affected by illegal trade practices. The identity for exports is Taking the difference between the two identities, we obtain: This expression indicates that an observed discrepancy between reported imports and reported exports can be disaggregated into three components: a discrepancy in the actual trade flow ( _I_<sup>_A_</sup> _ij_<sup>–</sup> _E_<sup>_A_</sup> _ij_<sup>), a discrepancy due to measurement error (</sup><sup>_IM_</sup> _ij_<sup>–</sup><sup>_EM_</sup> _ij_<sup>), and a discrepancy due to unreported</sup> imports or exports ( _I_<sup>_UR_</sup> _ij_<sup>–</sup><sup>_EUR_</sup> _ij_<sup>).</sup> 10