rafmacalaba/datause-encoder-data huggingface dataset:
primary factors driving intra-African trade expansion. Using a computable general equilibrium model, Abe and Wilson (2008) explore institutional trade facilitation indicators and find that reducing corruption and improving transparency in APEC countries to the average level of the region would increase trade in the region by 11 percent and global welfare would expand by $406 billion. Using detailed data on transit, documentation, and ports and customs delays on Africa’s exports collected by Doing Business at the World Bank, Freund and Rocha (2010) find that that transit delays have the most economically and statically significant effect on African exports. They find that a one-day reduction in inland travel times leads to a 7 percent increase in exports.

Iwanow and Kirkpatrick (2008) construct aggregated indicators of trade facilitation (in the on-the-border sense), and infrastructure for 2003 and 2004, by applying simple average to primary indicators mainly collected from Doing Business and the World Development Indicators. They estimate a standard gravity model augmented with these indicators and find a positive impact of the three indicators on exports. As their paper focuses on Africa, they interact their indicators with an African dummy, and find that policies that improve their indicators yield a higher effect in African countries compared with the rest of the world.

Francois and Manchin (2007) use principal components to construct two indicators on infrastructure and two indicators on institutional quality from various primary measures, mainly collected by the World Development Indicators and the Fraser Institute. Although their indicators are robust determinants of export performance, they are not easily interpretable.

Using indices of trade restrictiveness and trade facilitation developed at the World Bank, such as the Logistic Performance Index and Doing Business in a gravity model, Hoekman and Nicita (2008) suggest that tariffs and non-tariff measures continue to be a significant source of trade restrictiveness for low-income countries despite preferential access programs. This is because the value of trade preferences is quite limited: a new measure of the relative preference margin developed in the paper reveals that this is very low for most country-pairs. Most countries with very good (duty-free) access to a market generally have competitors that have the same degree of access.

Regarding more-specific dimensions of trade facilitation, such as the time to trade, Djankov, Freund, and Pham (2006) find that, on average, each additional day a product is 8
# **Appendix A: Additional Tables and Figures**

# **Table A.1. Overview of the key studies**

||**Studies**|**Country**|**FHH**<br>**definition**|**Reference**<br>**group**|**Conclusions**|
|---|---|---|---|---|---|
||||Self-<br>reported<br>FHH|Self-<br>reported<br>MHH<br>|FHHs have lower income, land ownership, and average level of education than<br>MHHs and are less likely to be employed (with fewer hours if employed). Children<br>of FHHs are significantly less likely to be enrolled in school than children of MHHs.|
|1|DeGraff and<br>Bilsborrow (1993)|Ecuador|Self-<br>reported<br>widowed<br>FHH|Self-<br>reported<br>MHH, other<br>self-reported<br>FHH|Widowed or divorced FHHs have higher income and amount of land owned than<br>MHHs but lower children's school enrollment and are less likely to be in school<br>than are children of MHHs and children of other FHHs.|
||||Self-<br>reported<br>married<br>FHH|Self-<br>reported<br>MHH, other<br>self-reported<br>FHH|Married FHHs have lower income and amount of land owned than MHHs and<br>enrollment rate similar to MHH, but children of married FHHs are significantly less<br>likely to be enrolled in school than are children of MHHs but more likely than<br>widowed FHHs.|
|2|Buvinic and Gupta<br>(1997)|65 studies<br>on<br>developing<br>countries|Self-<br>reported<br>FHH, de<br>facto, de<br>jure FHH|Self-<br>reported<br>MHH|Thirty-eight studies - FHHs are poorer than MHHs when poverty is measured by<br>(total/per capita/per equivalent) household income and consumption expenditures,<br>access to services, and ownership of land and assets. Fifteen studies - certain types<br>of FHHs are more vulnerable to poverty than others. Eight studies – poverty in<br>FHHs is not higher than in MHH.|
||||Self-<br>reported<br>FHH|Self-<br>reported<br>MHH|FHHs are similar to MHHs when poverty is measured by per capita expenditure.<br>The difference between FHHs versus non-FHHs does not change if using different<br>poverty indicators.|
||||Self-<br>reported de<br>jure, de<br>facto FHH|Non-FHH|Widows/divorced/separated FHHs have significantly higher headcount poverty in<br>indigenous areas when poverty is measured by per capita expenditure. FHHs have<br>lower education than non-FHHs|
|3|Fuwa (2000)|Panama|Self-<br>reported<br>married<br>FHH, FHH<br>with<br>unmarried<br>partners|Non-FHH|FHHs with unmarried partners have higher headcount poverty ratios in urban and<br>indigenous areas when poverty is measured by per capita expenditure. The result is<br>robust to applying an equivalence scale using alternative poverty measures and<br>poverty lines.|
||||<br>Potential<br>FHH|Non-FHH|FHHs are not poorer than non-FHHs when poverty is measured by per capita<br>expenditure.|
||||“Working”<br>FHHs|Non-FHH|FHHs are similar to non-FHHs when poverty is measured by per capita expenditure.<br>FHHs have higher education endowments than non-FHHs, except in indigenous<br>areas.|

41
# DATA APPENDIX

# _The Surveys_

The village surveys in summer and fall 2005, fall 2007, and fall 2009 ask questions about education, experience, and survival of village and religious leaders; population composition by sex and age both before and after the tsunami; migration; occupational structure; destruction of village lands, seawalls, aquaculture areas, docking areas and mangroves; pre- and post-tsunami data on political, legal, and social institutions; pre and post tsunami information on physical capital (houses, boats, public buildings); detailed information on initial and ongoing operations of NGO’s, local governments, and relief agencies providing housing, boats, public buildings, and restoration of the coast line; and detailed information on the village fishing industry pre- and post-tsunami, including questions on marketing, fishing fleet composition, catch composition and boat replacement. The 2005 survey of 111 villages focused on benchmarking destruction and village conditions. The 2007 and 2009 surveys of 199 villages (including the original 111) focused on aspects of the aid effort and institutional transformation of villages, such as the democratic evolution and quality of aid as related to different types of aid agencies.

# _Geographic Coverage_

Overall, in terms of village coverage, we cover all villages in three contiguous districts (Banda Aceh, Aceh Jaya, and Aceh Besar) going south and north-east of the capital Banda Aceh, as well as off-shore islands of these districts. We also covered fishing villages in two other districts, up to a defined geographic limit moving east from Banda 31
the sample contain a large amount of random measurement error, even models with low R<sup>2</sup> can contribute significant amounts of additional information and improve the estimates.

<u>Table 3 - State-level Results</u>

||(1)<br>Male LFP|(2)<br>Female<br>LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
|**Average estimated mean**<br>**squared error (x1,000)**|||||
|Direct|0.3787|0.1983|0.0067|0.0031|
|SAE|0.0899|0.1263|0.0055|0.0021|
|**Average estimated mean**|||||
|**squared error (x1,000)**<br>**with equalized coverage rates**|||||
|Direct|0.4048|1.2653|0.0555|0.0105|
|SAE|0.0898|0.1264|0.0055|0.0021|
|**Coverage rate**|||||
|Direct|0.8750|0.7188|0.5625|0.5000|
|SAE|0.9375|1.0000|0.9062|0.6875|
|**Estimated mean absolute error**|||||
|Direct|0.0179|0.0193|0.0048|0.0032|
|SAE|0.0088|0.0073|0.0023|0.0024|
|**Median estimated relative bias**|||||
|Direct|2.1164|-3.0573|-10.7269|-12.3980|
|SAE|-0.0757|-0.4322|1.5223|15.1544|
|**Estimated rank correlation**|||||
|Direct|0.5652|0.7933|0.7911|0.6635|
|SAE|0.7185|0.9498|0.8867|0.7529|
|**Estimated Pearson correlation**|||||
|Direct|0.6048|0.8141|0.7528|0.6506|
|SAE|0.7614|0.9583|0.8698|0.5467|
|States|32|32|32|32|

All figures represent unweighted averages or correlation across states. Equalized coverage rates refer to multiplying the estimated MSE of the direct estimates for each state by a constant to achieve the same coverage rate as the small area estimates. Rank or Pearson correlation is the unweighted rank or Pearson correlation between the estimated value and the actual state value. The absolute deviation is the absolute value of the difference between the estimate and the actual state value. Relative bias is 100 times the estimated minus the actual state value, divided by the actual state value.

Table A3 in the appendix presents a robustness check which calculates state-level results by aggregating municipality-level estimates up. The key difference between those results and the ones presented in this section are that the ones in Table 3 are estimated at the state level, without a random effect. The estimates in Table A3 are generally slightly worse than or similar to those in Table 3, with the possible exception of one accuracy indicator for male unemployment. The results in Table A3 give no empirical basis to prefer a method that aggregates the municipal estimates 16
(Board of Directors). For the GmbH and the Personengesellschaften with less than 2000 emp'oyees, the formation of an institution to represent shareholder interests with respect to management is optional, and is called "Beirat". The Beirat is an informal institution, whose charter is defined in the company's statutes. Firms with more than 2000 emplyees have to have a Board for labour representation.

It is therefore important to differentiate between shareholder representation in the AG and other corporate forms, mostly GmbH's. Gaugler and Heimburger [141 conducted a survey which showed that bankers are not often present in the Beirite of the firms in the Mittelstand so that our attention here is focussing on the Boards of the AG's and other large firms that are required to have a Board.

Moreover, it is very important to distingLish between the German Aufsichtsrat and the American Board of Directors. Although the two institution serve the same purpose, they nevertheless have significantly different strur.*ures. A comparison of the two structures has been provided by a detailed investigation by Bleicher, Leberl and Paul t5].10 Probably the most important difference they pointed at is that German corporations -*--overned by two legally separate bodies, the Vorstand, which is responsible for the day-to-day management, and the Aufsichtsrat, which is responsible for the supeivision of the Vorstand. In the US, there exists a distinction of inside and outside Board members, which are nevertheless part of the same body.

The important difference is that the authority of the Aufsichtsrat is limited, while the outside Board members have the same authority as the inside Board members. This leads to the effec-t that, however influential a German Board member may have been in pushing a decision, he is usually not legally liable for a managerial decision. An American outside Board member, however, is liable for management decisions to the extent that the "Duty of Care" principle can be applied against him.

That the German Aufsichtsrat is absolved from the consequences of actual management is an impoitant consideration when examining the role of German bankers in their client firms' Boards. Prowse [37], in an empirical study on the impact of close relations between Japanese banks and their clients argued that the threat of "equitable subordination" was a major deterrent for banks in the US trying to assume a more active role in the managements of ailing clients. He was arguing that the absence of such a legal threat in Japan fostered Japanese banks' close relationships with their clients. We are making the same case here for Germany.

The separation of management and Board is most certainly due to a belief that a management should not be in a position to control itself, which has always been a concern in the American system, resulting in several changes" for more autl-ority of outside members "After this quoted as BLP.

"BLP [S,p. 128-139] 7
# **INTRODUCTION**

In recent years, a central topic in the economic development literature has been the measurement of the distributive impact of growth (see Ferreira 2010). This literature has provided analytical tools to identify and quantify the effect of growth on distributional phenomena such as income poverty and income inequality. Indices for measuring the pro-poorness of growth have been proposed,<sup>1</sup> and the Growth Incidence Curve (GIC), measuring the quantile-specific rate of economic growth in a given period of time (Ravallion and Chen 2003; Son 2004), has become a standard tool in evaluating growth from a distributional viewpoint. The interplay among growth, inequality, and poverty reduction has also been investigated (Bourguignon 2004). All of these tools are now used extensively in the field of development economics to evaluate and compare different growth processes in terms of social desirability and social welfare (see Atkinson and Brandolini 2010; Datt and Ravallion 2011).

A common feature of this literature is the focus on individual achievements, such as (equivalent) income or consumption, as the proper “space” of distributional assessments.

In contrast, recent literature in the field of normative economics has argued that equity judgments should be based on opportunities rather than on observed outcomes (see Dworkin 1981a,b; Cohen 1989; Arneson 1989; Roemer 1998; Fleurbaey 2008). The equal-opportunity framework stresses the link between the opportunities available to an agent and the initial conditions that are inherited or beyond the control of this agent. Proponents of equality of opportunity (EOp) accept the inequality of outcomes that arises from individual choices and effort, but they do not accept the inequality of outcomes caused by circumstances beyond individual control. This literature has motivated a rapidly growing number of empirical applications interested in measuring the degree of inequality of opportunity (IOp) in a distribution and evaluating public policies in terms of equality of opportunity (see, among others, Aaberge et al. 2011; Bourguignon et al. 2007; Checchi and Peragine 2010; LeFranc et al. 2009; Roemer et al. 2003). Book-length collections of empirical analyses of EOp in developing countries can be found in World Bank (2006) and de Barros et al.

> 1See Essama-Nssah and Lambert (2009) for a comprehensive survey.

2
foreign intermediate inputs. The greater the reliance on foreign inputs (lower the domestic value added), the more an exchange rate depreciation will increase input costs and hence dampen the competitive advantage from a depreciation. In other words, a greater reliance on foreign inputs is analytically analogous to a lower passthrough which theory predicts will dampen the spillover effect. We test this proposition in the data. We use the classification in Koopman, Wang and Wei (forthcoming) to divide our data into two samples: those characterized by a high degree of foreign inputs and those with a low degree.<sup>20</sup> In Table 8 we estimate our core specification for each of these samples. We find that, consistent with theory, our spillover effect is in fact dampened for products with a high degree of foreign inputs (compare columns [2] versus [1] and [5] versus [4]). Columns [3] and [6] confirm that the differences between the two samples are statistically different: the coefficient on the core interaction term is about 13 percent greater (in absolute value) for the sample with the lower degree of foreign intermediate inputs in the case of the value based index and 10 percent greater for the count-based index.<sup>21</sup>

# **Discussion of Magnitudes**

Recall that the spillover effect we estimate in equation (13) is given by: Our estimations identify which we can multiply by the relevant value of the index of competition to obtain the average spillover effect. The range of estimates for different combinations of the two indices of competition and estimates of are shown in Table 9. For the baseline estimate of our elasticity (columns [4] and [8] in Table 1) and for the median index of competition, we get a total spillover effect of -0.14 and -0.20 for the value and count-based

> 20 The classification of sectors by domestic value added is restricted to manufacturing, and is based on ISIC data which we concorded with HS 4-digit data.

> 21 The same result holds for an alternative classification by share of processing exports (with high domestic value added products being those with low share of processing) due to Koopman et. al. (forthcoming).

21
- Meggison, L. William, 2011, Privatization and Finance, _Annals Review of Financial Economics_ 2, 145174.

- Mian, Atif, Amir Sufi, and Francesco Trebbia, 2010, The Political Economy of the US Mortgage Default Crisis, _The American Economic Review_ 100, 1967-1998.

- Mian, Atif, Amir Sufi, and Francesco Trebbia, 2013, The Political Economy of the Subprime Mortgage Credit Expansion, _Quarterly Journal of Political Science_ 8, 373-408.

- Moe, Roland, 1996, Managing Privatization: A New Challenge to Public Administration, In _Agenda for Excellence 2: Administering the State,_ edited by B.G. Peters and B.A. Rockman, 135-148. Chatham, NJ: Chatham House.

- Montero, Mercedes García, and Francisco Sánchez López. Las comisiones legislativas en América Latina: una clasificación institucional y empírica. Vol. 212. Institut de Ciències Polítiques i Socials, 2002.

Morales, Alberto. “Congreso lidera en Comisiones: Integralia”. El Universal. August 31, 2015.

- Olken, Benjamin A, 2007, Monitoring Corruption: Evidence from a Filed Experimen in Indonesia, _Journal of Political Economy_ 115, 200-249.

- Olken, Benjamin A., and Patrick Barron, 2009, The Simple Economics of Extortion: Evidence from Trucking in Aceh, _Journal of Political Economy_ 117(3), 417-452.

- Olken, Benjamin A, and Rohini Pande, 2011, Corruption in Developing Countries, _Annal Review of Economics_ 4, 479-505.

- Petersen, Mitchell A, and Raghuram G. Rajan, 2002, Does Distance Still Matter? The Information Revolution in Business Lending, _Journal of Finance_ 57(4), 2533-2570.

- Reinikka, Ritva and Jakob, Svensson, 2004, Local Capture: Evidence from a Central Government Transfer Program in Uganda _Quaterly Journal of Economics_ 119(2), 678-705.

- Rendón Arias, Iliana. "Las prácticas parlamentarias en el congreso mexicano: los casos de las comisiones de hacienda y de gobernación durante el primer año de la LXII legislatura (2012-2013)." (2014).

Roland, Gerard, 2008, _Privatization:Success and Failtures_ New York: Columbia University Press.

- Sapienza, Paola, 2004, The Effects of Government Ownership on Bank Lending, _Journal of Financial Economics_ 72, 357-384.

- Sequeira, Sandra, and Simeon Djankov, 2014, Corruption and Firm Behavior: Evidence from African Ports, _Journal of International Economics_ 94(2), 277-329.

- Shleifer, Andrei and Robert, Vishny, 1994, Politicians and Firms, _Quarterly Journal of  Economics_ 109,9951025.

- Sun, Qian, and Wilson H.S Tong, 2003, China Share Issue Privatization, _Journal of Financial Economics_ 70(2), 183-222.

29
Table A15: Heterogeneous effects by stability of partnerr

|_Dep. Var.:_|_HIV knowledge_|_HIV attitudes_|_HIV testing_|_Attitudes_<br>_towards_<br>_risky sexual behav-_|_Risky sexual behav-_<br>_ior (for sexually ac-_|
|---|---|---|---|---|---|
|||||_ior_|_tive)_|
||(1)|(2)|(3)|(4)|(5)|
|Treated|0.690|0.702|0.999**|0.615|0.081|
||(0.708)|(0.434)|(0.395)|(0.414)|(0.561)|
||[0.339]|[0.208]|[0.044]|[0.276]|[0.884]|
|Treated * Stable Partnert-1|-0.045|-0.496|-0.927**|-0.623|0.027|
||(0.757)|(0.456)|(0.401)|(0.426)|(0.614)|
||[0.956]|[0.485]|[0.071]|[0.281]|[0.947]|
|Stable Partnert-1|-0.489|0.332|0.879***|0.565|-0.745|
||(0.610)|(0.395)|(0.243)|(0.341)|(0.549)|
|Treated*Y_t−_1|-0.043|-0.016|-0.011|0.064|-0.110*|
||(0.047)|(0.045)|(0.037)|(0.046)|(0.062)|
||[0.750]|[0.926]|[0.926]|[0.165]|[0.162]|
|Y_t−_1|0.371***|0.365***|0.436***|0.260***|0.339***|
||(0.039)|(0.040)|(0.031)|(0.036)|(0.050)|
|Observations|2,827|2,827|2,827|2,827|2,777|
|R-squared|0.195|0.179|0.250|0.109|0.160|
|Controls<sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|
|Mean Dep. Var. (Control)|0.040|0.0439|-0.139|0.00186|-0.0631|
|Std Dev Dep. Var. (Control)|6.106|3.474|4.145|3.452|3.625|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. FWER adjusted p-values in square brackets, based on 10,000 replications (a) Controls in each regression include: female, age, currently attending school, years of education, english spoken, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks yoruba as a native language.

The dummy ”Stable partner” is measured at baseline and takes value one if the respondent reports being married or living with someone or having a main partner.

xxii
with demand for inventory and once without. The calibrated demand and supply parameters are used to numerically calculate the effect of each of the different shocks on the observed price in a given year.

**Table 3. Range of elasticities contained in the literature cited in FAPRI database**

|||**Supp**|**ly (1) **|**Dema**|**nd(2) **|**Incom**|**e(3) **|
|---|---|---|---|---|---|---|---|
|**Commodity **|**Region**|**min**|**max**|**min**|**max**|**min**|**max**|
|**Corn**|**Argentina **|0.65|0.75|-0.4|-0.3|0.35|0.45|
||**Brazil**|0.37|0.47|-0.4|-0.1|0.35|0.45|
||**China**|0.08|0.18|-0.14|-0.6|0.75|1|
||**EU**|0.01|0.13|-0.44|-0.24|0.1|0.2|
||**India**|0.16|0.26|-0.28|-0.22|0.75|1|
||**U.S.**|0.45|0.55|-0.24|-0.1|0.05|0.1|
||**ROW**|0.45|0.55|-0.43|-0.21|0.4|0.6|
|**Soybeans **|**Argentina**|0.27|0.37|-0.3|-0.2|0.35|0.45|
||<br>**Brazil**|0.29|0.39|-0.21|-0.11|0.35|0.45|
||**China**|0.4|0.5|-0.25|-0.15|0.75|1|
||**EU**|0.14|0.24|-0.3|-0.2|0.1|0.2|
||**India**|0.31|0.41|-0.35|-0.25|0.75|1|
||**U.S.**|0.18|0.28|-0.48|-0.31|0.05|0.1|
||**ROW**|0.18|0.28|-0.48|-0.31|0.4|0.6|
|**Rapeseed **|**Argentina**|0.53|0.63|-0.35|-0.03|0.35|0.45|
||**Brazil**|0.53|0.63|-0.35|-0.03|0.35|0.45|
||**China**|0.21|0.31|-0.35|-0.25|0.75|1|
||**EU**|0.23|0.33|-0.13|-0.03|0.1|0.2|
||**India**|0.29|0.39|-0.3|-0.2|0.75|1|
||**U.S.**|0.53|0.63|-0.35|-0.03|0.05|0.1|
||**ROW**|0.53|0.63|-0.35|-0.03|0.4|0.6|
|**Rice**|**Argentina**|0.27|0.37|-0.43|-0.38|0.35|0.45|
||**Brazil**|0.27|0.37|-0.43|-0.38|0.35|0.45|
||**China**|0.27|0.37|-0.71|-0.54|0.75|1|
||**EU**|0.27|0.37|-0.43|-0.38|0.1|0.2|
||**India**|0.27|0.37|-0.43|-0.38|0.75|1|
||**U.S.**|0.27|0.37|-0.87|-0.77|0.05|0.1|
||**ROW**|0.27|0.37|-0.43|-0.38|0.4|0.6|
|**Wheat**|**Argentina**|0.36|0.46|-0.39|-0.28|0.35|0.45|
||<br>**Brazil**|0.38|0.48|-0.38|-0.27|0.35|0.45|
||**China**|0.04|0.14|-0.18|-0.07|0.75|1|
||**EU**|0.07|0.17|-0.33|-0.26|0.1|0.2|
||**India**|0.24|0.34|-0.37|-0.32|0.75|1|
||**U.S.**|0.43|0.53|-0.35|-0.25|0.05|0.1|
||**ROW**|0.43|0.53|-0.35|-0.25|0.4|0.6|

1. Own price elasticity of supply

2. Own price elasticity of demand 3. Income elasticity of supply 33