rafmacalaba/datause-encoder-data huggingface dataset:
Bobonis, G. and Finan, F. (2007) “Endogenous Peer Effects in School Participation”. Paper presented at the Population Association of America Conference, April, New York. Cipollone, Piero and Rosolia, Alfonso (2007). Social Interactions in High School: Lessons from an Earthquake. _American Economic Review_ (forthcoming).

- Evans, William, Oates, Wallace and Schawal, R. (1992). **‘** Measuring Peer Group Effect’, _Journal of Political Economy_ , Vol. 100, No. 5, pp. 966-991.

Geen, A.V., Ahmed, K.M. and Madajewicz, M. (2005) “The Lethal Water Wells of Bangladesh”, _New York Times_ , July 30.

- Cattaneo, Alejandra and Lalive, Rafael (2006) “Social Interactions and Schooling Decisions,” C.E.P.R. Discussion Papers No. 5816.

- Manski, C. (1993). ‘Identification of Endogenous Social Effects: The Reflection Problem’, _Review of Economic Studies_ , Vol. 60, No. 3, pp. 531-542.

- _______  (2000). ‘Economic Analysis of Social Interactions’, _Journal of Economic Perspectives_ , 2000, Vol. 14, No. 3, pp. 115-136.

Rohde, D. (2005) “The Lethal Water Wells of Bangladesh”, _New York Times_ , July 17.

- Soetevent, A.R. (2006) “Empirics of the Identification of Social Interactions; An Evaluation of the Approaches and Their Results,” _Journal of Economic Surveys_ , Blackwell Publishing, Vol. 20(2), pp. 193-228.

- Wasserman, G.A., X. Liu, Faruque Parvez, Habibul Ahsan, Pam Factor-Litvak, Alexander van Geen, Vesna Slavkovich, Nancy J. LoIacono, Zhongqi Cheng, Iftikhar Hussain, Hassina Momotaj, and J. H. Graziano (2004) “Water Arsenic Exposure and Children's Intellectual Function in Araihazar, Bangladesh, _Environmental Health Perspectives_ 112(13): 1329-1333.

7
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

|**Focus area**|**IV. Regulation, Governance, and Institutions**<br>**Indicator**|**Data Source**<br>**(e.g. UK)**|
|---|---|---|
|? Establishment of<br>regulator|**? Start date of regulator (year)**|Questionnaire to<br>regulator and<br>interview|
|? Network regulation<br>and price control|**? T regulation (ROR, PCAP, other)**<br>**? D regulation (ROR, PCAP, other)**<br>? Length of price control period (years)<br>**? Are there quality of service targets? (no/yes – type)**||
|? Transmission network<br>access and system<br>operation|**? Is third-party access to T networks? (no/yes - rTPA, nTPA)**<br>? Is TSO independent of network operator? (no/yes)||
|? Market structure<br>regulation|**? Are there restrictions on vertical integration? (no/yes)**<br>**? Are there restrictions on horizontal integration? (no/yes)**||
|? Subsidies|**? Are there cross-subsidies between residential, industrial and**<br>**service users? (no/yes - type)**<br>**? Are there government-funded subsidies? (no/yes - type)**||
|? Regulatory budget and<br>resources|**? Annual budget size of regulator (amount)**<br>? Source of regulatory funds (government, levies on companies,<br>customer levies, other)<br>? Total and professional staff (no.)||
|? Regulator appointment<br>and dismissal|**? Who appoints the regulator? (president, prime minister,**<br>**parliament)**<br>? How long is the regulator’s term? (fixed term – years, unlimited_<br>? Can the regulator be re-appointed? (no/yes)<br>**? Which authority can dismiss the regulator (president, prime**<br>**minister, minister, parliament)**||
|? Regulatory decisions|? Who makes the final regulatory decisions? (a single head, board,<br>other)||
|? Regional regulators|? Are there regional regulators? (no/yes)||
|? Consultation and<br>transparency|**? Is there an open consultation process prior to decisions?**<br>**(no/yes)**<br>? Are regulatory decisions open to the public? (no/yes)<br>**? Does the regulator publish hearings, decisions, and**<br>**explanations? (no/yes)**<br>? Does the regulator have a website? (no/yes)<br>? Are consultation papers on the website? (no/yes)||
|? Appeal|**? Can the regulator’s decisions be appealed? (no, yes – to whom)**||
|? Regulatory discretion<br>and mandate|**? Can the regulator decide on prices? (no/yes)**<br>? Can the regulator decide award of G, T, and D permits/licences?<br>(no/yes)<br>? Is the regulator mandated to protect customers? (no/yes)<br>? Does the regulator have mandate and rules concerning<br>disconnection for non-payment? (no/yes)<br>? Are there other formal (government, consumer groups, etc.) bodies<br>in charge of customer protection? (no/yes – who)||

22
UCM also generates margins of error around the estimate of governance that reflect this imperfection. These estimates of governance and the associated margins of error are the core output of the WGI. In the remainder of this section, we spell out the methodology in further detail.

# 3.1 An Unobserved Components Model for Governance

For each of the six dimensions of governance discussed in Section 2, we assume that the relationship between the observed data and the unobserved level of governance is as follows:

## (1)

## y(c, k) = α(k) + β(k)(g(c) + ε(c, k))

where y(c, k) represents the observed data for country c on data source k ; g(c) is the unobserved level of governance in country c which we assume has zero mean and unit standard deviation across countries; ε(c, k) is the measurement error in data source k for country c which we assume has zero mean and variance σ<sup>2</sup> (k) ; and α(k) and β(k) are source-specific parameters that map the unobserved level of governance in the country into the observed data. To avoid notational clutter, we omit time subscripts, and our notation does not distinguish between the six dimensions of governance. In practice, we estimate the model in Equation (1) separately for each of the six dimensions of governance, in each of the time periods covered by the WGI.

The parameters α(k) and β(k) capture differences in the units used by different data sources to measure governance. For example, one data source might measure governance on a six-point scale, while another data source might measure governance on a ten-point scale. More subtly, one data source might adopt the convention of using its entire range of units to measure governance, while another data source might have its scores clustered in a narrower band than its underlying units would in principle allow. The parameters α(k) and β(k) capture these explicit and implicit differences in units across data sources.

The error term ε(c, k) captures two potential sources of measurement error in the observed data. First, each data source may have its own statistical imperfections, ranging from perceptions errors on the part of respondents (in the case of expert assessments) to simply sampling variation (in the case of survey responses). Second, even if a data source had none of this first type of measurement error, it might nevertheless be an imperfect proxy for the broader concept to which we have assigned it in the WGI. For example, questions about perceptions of corruption among elected officials, or corruption among unelected public servants, might both be imperfect proxies for the overall level of corruption in a country, even if each of these proxies were measured well individually. Both types of measurement 13
trade openness, as unproductive sectors are wiped out by foreign competition but the capital and labor attached to them fail to divert to more efficient uses.

We then present some cross-country empirical evidence on how the growth effect of openness depends on a variety of structural characteristics, including some that may be subject to reform. We build on the panel-data growth regressions presented in Dollar and Kraay (2004) and Loayza, Fajnzylber, and Calderón (2005). As these papers do, we use a GMM procedure that controls for endogeneity and unobserved country-specific factors in order to estimate the growth effect of openness, as well as those of other policy and nonpolicy variables. We, however, depart from those studies in that we interact the openness measure with proxies of, respectively, educational investment, financial depth, inflation stabilization, public infrastructure, governance, labor-market flexibility, and ease of firm entry and exit. Our objective for using this non-linear specification is to assess whether an increase in openness may have a growth effect that depends on country characteristics that, at least in principle, are subject to improvement through economic and institutional reforms. We find that the growth effect of openness is positive and economically significant if certain complementary reforms are undertaken. This quantitative assessment may contribute towards identifying the specific reforms that are most needed to complement a trade liberalization agenda.

The empirical growth literature offers some examples of non-linear specifications considering interaction effects. On the related topic of foreign direct investment, Borensztein, De Gregorio and Lee (1998) and Alfaro, Chanda, Kalemli-Ozcan, and Sayek (forthcoming) find that the growth effect of FDI is significantly positive only when the host country has, respectively, sufficiently high human capital and financial depth. Specifically in the analysis of growth effects of trade openness, an important antecedent of our work is the empirical study by Bolaky and Freund (2004). Using cross-country regressions in levels and changes of per capita GDP and controlling for simultaneity via external instruments, they find that trade opening promotes economic growth only in countries that are not excessively regulated. They argue that in highly regulated countries, growth does not accompany trade openness because resources are prevented from flowing to the most productive sectors and firms, and trade is likely to occur in goods where comparative advantage is actually missing. Finally, Calderón, Loayza, and Schmidt6
# **Table 3 Summary statistics of target variables by division**

|**Division**|**Nominal per**<br>**capita**<br>**expenditure**<br>**(in 2010 taka)**|**Nominal total**<br>**household**<br>**expenditure**<br>**(in 2010 taka)**|**Poverty**<br>**headcount ratio**<br>**(%)**|
|---|---|---|---|
|Barisal|2,154<br>(78)|10,708<br>(425)|39.35<br>(3.33)|
|Chittagong|2,887<br>(144)|15,765<br>(797)|26.20<br>(1.99)|
|Dhaka|2,870<br>(129)|14,008<br>(703)|26.08<br>(1.64)|
|Mymensingh|1,827<br>(60)|9,310<br>(334)|47.36<br>(3.43)|
|Khulna|2,182<br>(65)|10,335<br>(323)|32.07<br>(2.28)|
|Rajshahi|2,231<br>(59)|10,588<br>(345)|29.67<br>(2.11)|
|Rangpur|1,937<br>(63)|9,434<br>(343)|42.32<br>(3.22)|
|Sylhet|2,184<br>(93)|13,936<br>(727)|28.10<br>(3.02)|

Note: Data for the estimation of these statistics comes from the HIES 2010. All estimates reported in this table have been produced using population level weights to match official statistics. For the proposed sampling design, however, we use household level weights. Standard errors adjusted for survey design are reported in parenthesis.

A second important decision when designing a sample is selecting accuracy statistics. These are used to evaluate the performance of the proposed sample design to achieve a certain level of precision in the estimation of these statistics for the target variables of interest. The most common accuracy statistics are “standard errors” (SE) of the mean and “relative standard errors” (RSE) of the mean; most household surveys measuring living conditions try to achieve SEs of the mean of the target variable(s) that are no more than 5 percent, and RSEs of the mean that are no more than 10 percent.<sup>12</sup> For HIES 2016/17, we use three accuracy statistics to assess

> 12 <mark>The RSE of a sample mean is the SE of the mean divided by the mean and then expressed as a percentage.</mark> 12
41

|**Variable/Statistic**|**Coefficient**<br>**[std. error]**|
|---|---|
||67.751***|
|_1982_83|[2.460]<br>|
||52.307***<br>|
|_1983_84|[2.583]|
||43.122***<br>|
|_1984_85|[2.768]|
||-15.244***|
|_1985_86|[3.987]|
|_1986_87|38.746***<br>[3.389]|
||23.130***|
|_1987_88|[3.306]|
||77.938***|
|_1988_89|[3.569]|
||17.989***|
|_1989_90|[3.741]|
||25.822***|
|_1990_91|[3.802]|
||17.885***|
|_1991_92|[4.102]|
||10.525**|
|_1992_93|[4.181]|
||17.658***|
|_1993_94|[4.765]|
||70.943***|
|_1994_95|[4.986]|
||-38.526***|
|_1995_96|[5.218]|
||-4.783|
|_1996_97|[5.851]|
|_1997_98|-61.079***<br>[5.504]|
||-50.104***|
|_1998_99|[5.668]|
||24.010***|
|_1999_00|[5.648]|
|Observations|2375|
|(within)|0.775|
|(between)<br>R<sup>2</sup>|0.358|
|(overall)|0.684|
|std. error regress.|[19.94]<br>|
|White’s Test for|χ<sup>2</sup>(42) = 349.60<br>|
|Heteroskedasticity|Prob>χ<sup>2</sup>=0.00|
|F-tests|F(32,2188)= 235.87|
||(3,2221) = 3.49|
|RamseyResetTest|<br>Prob > F = 0.02|

Robust standard errors in brackets<sup>43</sup> *significant at 10% ** significant at 5%; *** significant at 1% 43 The F-test performed in a regression with White corrected variance-covariance matrix is actually a Wald test. The econometric package used (STATA), however, converts the Wald value to an F-test value, which it then reports, as in Table 6 above.
**Table** **<u>3: Highest ranked cities (by agglomeration)</u>**

|**City**|**Localization**<br>**Index**|**City**|**Herfindahl**<br>**Index**|
|---|---|---|---|
|**_Addis Ababa_**|0.53|**_Addis Ababa_**|0.06|
|**_Bedele_**|0.22|**_Asela_**|0.07|
|**_Gimbi_**|0.19|**_Jijiga_**|0.08|
|**_Sebeta_**|0.15|**_Shashemene_**|0.09|
|**_Dila_**|0.13|**_Adigrat_**|0.10|

Source: UBEUS (2012), CSA

# **4 What determines city success in job creation?**

The previous section lays out the different factors that could possibly determine the differences in employment across industries and cities. Firms are the agents that create employment, and firms ability to survive, grow and create jobs is a function of the business environment within which they operate. It is important to test whether individual firm’s abilities to create employment are based on agglomeration economies, or on other factors, such as good business environment, access to infrastructure or labor markets. In theory, if the Government of Ethiopia is interested in encouraging industrial growth in particular cities, it should have a clear understanding of what factors drive employment.

The analysis differentiates between drivers of competitiveness at the level of the firm and at the level of the city. Individual firm-level performance is driven by factors internal to the enterprise, and those of its immediate business environment. The performance of clusters of firms within a city is affected by the business environment, but additionally by the interactions between individual firms and industries. If the focus were entirely at the level of firms, this would miss the effect of dynamic relationships between groups of industries.

# **_III.A: Firm-Level Model_**

Thus, in this section, an econometric model is used to test the importance of the different sets of factors. In particular, the growth of employment at the firm-level is modeled as a function of economic geography (i.e. agglomeration), business environment and infrastructure – these variables include factors that are common to the city and those that are common to particular industries within the city. The main estimating equation is as follows: ������_���������� ������������������, �������� ��������������, ��������������������� ��� ��� ��� ����� Where, the dependent variable is the log employment growth<sup>11</sup> for firm _i_ in industry _j_ and city _k_ (between year � and ��1). Agglomeration includes localization and

> 11 Log employment growth is used as dependent variable (instead of, for example, the DavisHaltiwanger-Schuh employment measure) to allow for similar dependent variables at the firm-level and the city-level models.

21
# **6. Concluding Remarks**

Following the end of the Cold War, multidimensional peacekeeping operations have been implemented to facilitate the transition from war to peace in Africa. Disarmament, demobilization, and reintegration programs have been an essential component of this process. These programs aim at “disarming combatants, removing them from military structures, and socially and economically integrating them into society” (World Bank 2009).

Burundi is recovering from a civil war that lasted more than a decade. The armed conflict ended in 2009 with the voluntary demobilization of the last Hutu rebel group, the PalipehutuFNL. In exchange for laying down their arms, ex-combatants received reinsertion allowances equivalent to an 18-month salary in the army. Four years before, another Hutu rebel group, the CNDD-FDD, benefited from the same allocations, and additionally received in-kind payments.

In this paper, we assessed the impact of this disarmament, demobilization, and reintegration program by analyzing a panel dataset collected in 2006 and 2010 in three rural provinces heavily affected by the conflict. Our objectives were to assess the short- and long-run impacts of the demobilization program on ex-combatants economic outcomes but also to capture the spillovers to civilian households.

Our empirical analysis highlighted the positive and significant impact of the demobilization program on ex-combatant households in the short run. The large amounts of money that were introduced in the local economy through the demobilization program generated an economic boom in the short run. As a result, households that did not participate actively in the conflict also indirectly gained from the demobilization program. In the long run, however, the positive impact of the DDR on beneficiaries and the positive externalities on nonbeneficiaries seem to have vanished as the money received by demobilized ex-combatants ran low.

The conclusions of our empirical analysis should be considered by policymakers while implementing DDR processes in other regions. In the short run, the positive direct effect of the DDR program shows that the program indeed provides a safety net to ex-combatants. The positive spillovers further show that the program does not only benefit beneficiaries but also communities where ex-combatants returned, increasing the range of impact. This second effect is likely to ease the return of ex-combatants to their villages. Our study therefore supports the reinsertion program implemented in postconflict countries as a short-run strategy to reduce the risk of relapse into conflict by alleviating poverty of both recipient and nonrecipient households. However, our results suggest that the reinsertion phase is not sufficient for creating a virtuous circle towards economic development, as the positive impact of cash transfers is short-lived. The return to a precarious economic situation may encourage ex-rebels to take up arms again and re-engage in violence (Colombo et al, 2014).

We emphasize three limitations of our study, which imply the need for further research.

27
11 conducted in 2003.<sup>10</sup> A question on the survey asked FARA agents to indicate the proportionate breakdown of their total time and resources among various activities. These are summarized in Table A1. The first column describes seven possible types of activities, the second column shows the mean proportion of time and resources devoted to each type of lobbying activity across 13 FARA agents who had indicated serving at least one foreign client from the tourism industry, the third column shows similar means across 49 FARA agents who indicated not serving any foreign clients in tourism, and the last column shows the means across all 62 respondents. FARA agents are known by the sectors in which they specialize. A similar breakdown would be evident if instead of tourism we separated agents into, say, energy and nonenergy sector experience. Thus, foreign clients in tourism would be drawn to FARA agents with lobbying experience in tourism.

Agents that did represent one or more clients in tourism reported spending a far greater fraction of their time informationally lobbying individual policymakers (15.3%) and the media (26.4%) than did FARA agents operating in sectors other than tourism. They also spent far less time learning about policymakers’ positions (only 6.9%) and the likely consequences of policy decisions (8.2%) than did FARA agents not operating in tourism (16.4% and 20.5%, respectively). These differences are both statistically and economically significant. Foreign principals operating in tourism are clearly more interested in disseminating information to US policymakers. They are less interested in prior beliefs and ex ante positions of policymakers, and are likely to presume such priors to be weak anyway. Thus, their focus is, as in the theoretical model, on getting policymakers to update their priors by informing them via their FARA agents. They also appear to be well informed of the consequences of policy decisions, and hence spend less time on that than on informational lobbying. Foreign principals in other sectors are more concerned about these because (i) they might want to decide who their “friends” might be in order to target their limited resources, (ii) their lobbying activities may be of the quid pro quo kind which requires substantial resources, and therefore principals want to know much more about policymakers and policies before attempting to “purchase” a policy.

The theory clarifies that lobbying spending may vary widely across lobbyists, due to the variation in the underlying parameters ( _c_ , _p_ , structure of payoffs), and that even though the

> 10 The survey, was conducted by the World Bank in 2003. The response rate was low, around 5%, not surprising given the political sensitivity of the subject.  62 FARA agents responded.
Annex 12: Togo - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**<br>|**50% increase**<br>|**25% increase**<br>|**50% increase**<br>|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Togo National**||||
|**Rice**|3.5|92.2|**61.6**|62.2|62.9|62.2|62.8|
|**Bread**|0.6|27.0||61.7|61.8|||
|**Milk**|0.7|31.1||61.7|61.8|||
|**Huiles**|1.1|81.3||61.8|62.0|||
|**Sugar**|0.7|72.3||61.8|61.9|||
|All|6.5|97.4||62.7|63.7|62.6|63.6|
|||||**Togo Lomé**||||
|**Rice**|2.5|93.3|**24.4**|24.8|25.2|24.8|25.2|
|**Bread**|0.9|50.5||24.5|24.5|||
|**Milk**|0.9|56.0||24.5|24.5|||
|**Huiles**|0.7|91.3||24.5|24.6|||
|**Sugar**|0.5|86.3||24.4|24.5|||
|All|5.6|97.3||24.9|25.8|24.9|25.8|
||||**Togo**|**Other Urban Area**|**s**|||
|**Rice**|3.7|95.2|**54.5**|55.1|56.1|55.1|56.1|
|**Bread**|0.4|24.3||54.6|54.6|||
|**Milk**|0.7|34.5||54.6|54.7|||
|**Huiles**|1.3|89.3||54.8|55.0|||
|**Sugar**|0.7|73.3||54.8|54.8|||
|All|6.9|99.0||55.6|57.4|55.6|57.3|
|||||**Togo Rural**||||
|**Rice**|4.3|91.1|**74.3**|75.0|75.6|74.9|75.5|
|**Bread**|0.3|19.3||74.3|74.5|||
|**Milk**|0.4|21.3||74.4|74.5|||
|**Huiles**|1.4|75.8||74.5|74.7|||
|**Sugar**|0.8|67.1||74.4|74.6|||
|All|7.1|97.1||75.4|76.4|75.4|76.3|

Source: Authors’ estimation using country household survey data 31