rafmacalaba/datause-encoder-data huggingface dataset:
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|---|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|Coral Reef Rehabilitation and Management Program- Coral Triangle Andre Rodrigues de Aquino|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|China GuangdongAgricultural Pollution Control<br>William R. Sutton|
|Support for upgradingthe Chamanculo C Neighbourhood in accordaKevin A. Milroy|
|Support for upgradingthe Chamanculo C Neighbourhood in accordaKevin A. Milroy|
|Second DPL in support of the Plan Maroc Vert<br>Xavier Chauvot De Beauchene|
|Second DPL in support of the Plan Maroc Vert<br>Xavier Chauvot De Beauchene|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|Access and Renewable EnergyProject<br>Lucia Spinelli|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|JO-Badia Ecosystem and Livelihoods<br>Banu Setlur|
|Shire River Basin Management Program(GEF)<br>Josses Mugabi|
|Shire River Basin Management Program(GEF)<br>Josses Mugabi|
|Shire River Basin Management Program(GEF)<br>Josses Mugabi|
|Shire River Basin Management Program(GEF)<br>Josses Mugabi|
_Social Capital, Household Welfare and Poverty in Indonesia_ _44_

**Table 11: Social Capital and Access to Credit**

||**Access to**<br>**(prob**|**Credit**<br>**it)**<sup>**1**</sup>**'**|**Ln (Am**<br>**Credit R**<br>**(tobi**|**ount of**<br>**eceived)**<br>**t)'**|
|---|---|---|---|---|
|Intercept|_||-1.7076|(0.58)|
|**_Social Capital Dimensions_**|||||
|Number of Memberships|0.0107|(1.85)|0.2089|(2.24)|
|Membership in Financial Associations|0.1314|(3.04)|2.2912|(3.23)|
|Heterogeneity Index|0.0016|(1.73)|0.0311|(2.05)|
|Meeting Attendance|0.0006|(0.24)|0.0235|(0.60)|
|Index of Participation in Decision Making|0.0011|(2.09)|0.0188|(2.28)|
|Cash Contribution Score|-0.0087|(1.16)|-0.1167|(0.84)|
|Work Contribution Score|-0.0046|(1.53)|-0.0801|(1.62)|
|Community Orientation|-0.0007|(1.09)|-0.0191|(1.77)|
|Household Size|0.0117|(1.53)|0.2344|(1.86)|
|Years of Education|-0.0081|(1.11)|-0.0611|(0.51)|
|Asset Score|-0.0194|_(0.59)_|0.2470|(0.46)|
|Female Head of Household|-0.0393|(0.69)|-0.5672|(0.60)|
|Age of Head of Household|0.0140|(1.91)|0.2650|(2.14)|
|Age of Head of Household Squared|-0.0002|(2.22)|-0.0031|(2.45)|
|Farmer Household|-0.0901|(2.21)|-1.7394|(2.63)|
|Jawa Tengah|0.0470|(0.87)|-0.0080|(0.01)|
|Nusa Tenggara Timur|-0.2326|(4.21)|-4.1669|(4.52)|
|Number of Observations|1137||1137||
|Log Likelihood|-686.7||-2784.3||
|<br>Chi-squared|138.0||163.7||
|Probability > Chi-squared|0.00||0.00||

Notes: 1. Probability derivatives at the mean of each explanatory variable (or for 0 to I change in the case of dummy variables) and z-scores based on robust standard errors. 2. Tobit coefficients and t-statistics.

# **6.3 Collective Action**

In addition to contributing to asset accumulation and access to credit, social capital has also been documented to aid in collective action and collective decision making. This is especially relevant in rural settings where common property resources, such as water, forestry or grazing land, need to be managed by a community (Narayan, 1995; Uphoff, 1992). In Indonesia, there is a strong tradition of mutual help and quite a few of the local associations inventoried by the LLI study were set up for that specific
# **Table A7: Bertelsmann Transformation Index (BTI)**

## Bertelsmann Foundation

**Data Provider Description**

**Description** Nongovernmental organization headquartered in Berlin, Germany, with goal to study social challenges and problems and propose solutions. **Website** www.bertelsmann-stiftung.de **Data Source** Bertelsmann Transformation Index **Type** Expert Assessment **Respondents** Staff of Bertelsmann Foundation **Frequency** Every three years since 2003 **Coverage** Global sample of countries **Public Access** Yes **Description** We use data on the subcomponents of the Status Index (SI -- rating countries along

We use data on the subcomponents of the Status Index (SI -- rating countries along dimensions of democracy and market economy status) and the Management Index (MI -- rating countries according to progress in achieving democracy and market economy status)

|**Voice and Accountability**<br>|2006<br>|2005<br>|2004<br>|2003<br>|2002<br>|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|Stateness (SI)<br>|X|X|X|X|X|..|..|..|
|Political Participation (SI)|X|X|X|X|X|..|..|..|
|Stability of Democratic Institutions (SI)|X|X|X|X|X|..|..|..|
|Political and Social Integration (SI)|X|X|X|X|X|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|Consensus Building (MI)|X|X|X|X|X|..|..|..|
|Governance Capability (MI)|X|X|X|X|X|..|..|..|
|Effective Use of Resources (MI)|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||
|Competition (SI)|X|X|X|X|X|..|..|..|
|**Rule of Law**|||||||||
|Rule of Law (SI)|X|X|X|X|X|..|..|..|
|Private Property (SI)|X|X|X|X|X|..|..|..|
|**Control of Corruption**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Country Coverage**|**119**|**118**|**116**|**116**|**116**|**..**|**..**|**..**|
|**Year of Publication**|**2005**|**2005**|**2002**|**2002**|**2002**|**..**|**..**|**..**|

45
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE about 4 percent each. Operating expenditure is fully funded by the public sector through either budgetary resources or user payments. The transport and energy sector together account for approximately 80 percent of total annual flows to infrastructure sectors. The public sector accounted for 71 percent of capital spending. Non-OECD financiers—predominantly China—are becoming an increasingly important source of infrastructure financing and contribute 16 percent of total capital expenditure. As is to be expected given the Republic of Congo’s resource-rich condition, ODA is a relatively small component of capital expenditure—only 7 percent. Finally, the private sector accounts for 6 percent of total capital expenditure, predominantly in ICT. Overall, capital expenditure absorbs 60 percent of total infrastructure spending (table 9).

**Figure 10. Spending needs in Congo are high, but similar to other African countries**

<!-- Start of picture text --> Sub-Saharan Africa<br>fragile low-income countries<br>nonfragile low-income countries<br>resource-rich countries<br>middle-income countries<br>Congo, Dem. Rep. of<br>Congo, Rep.<br>Benin<br>Nigeria<br>Cameroon<br>0 10 20 30 40 50 60 70 80<br>% of GDP<br>capital expenditure operations and maintenance<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and others 2008.

The country’s infrastructure spending is already quite substantial relative to the size of economy—8 percent of GDP, which is higher than the average for resource-rich countries and African peers (figure 11). That level of spending falls short of the 15 percent of GDP that China has invested in infrastructure over the last two decades, but it nevertheless represents a significant effort and commitment to developing infrastructure in a country that is emerging from the devastation of conflict.

The Republic of Congo’s investment patterns differ significantly from its resource-rich peers, which is likely a result of the underlying sources of infrastructure financing. The country invests considerably more in transport infrastructure than African peers, about the same in power, and substantially less in ICT, water, and sanitation. The absence of PPI financing in power and transport is notable when compared with African peers. Similarly, ODA support to the water and sanitation sector is practically nonexistent, which is not typical of other countries of the region. The country relies heavily on public 27
TABLE 6. Estimation with 2 year lags using OLS productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Var|iable: ln(TFP)|I|II|III|IV|V|VI|VII|VIII|IX|X|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|||0.275|0.571|0.557|0.801|0.376|0.461|0.009|0.107|
|linkages||||(0.854)|(0.856)|(0.885)|(0.892)|(0.860)|(0.873)|(0.973)|(1.002)|
||FDI (share)|2.796*||2.583||3.019**||3.964*||3.536**||
|||(1.618)||(1.640)||(1.498)||(2.315)||(1.539)||
||FDI (threshold)||2.044***||1.974**||1.913**||3.056**||2.410***|
||||(0.774)||(0.768)||(0.754)||(1.422)||(0.836)|
||Concentration|||||5.027|4.778|5.533|4.738|5.047|3.803|
|||||||(4.448)|(5.095)|(4.634)|(5.123)|(4.625)|(5.213)|
||Privatization (share)|||||||-3.847|-8.109|||
|||||||||(6.894)|(8.291)|||
||Privatization (threshold)|||||||||-42.403|-47.615|
|||||||||||(37.922)|(34.873)|
|Manufacturing|FDI (share)|0.659||0.656||0.368||0.299||0.15||
|input linkages||(0.402)||(0.405)||(0.482)||(0.498)||(0.543)||
||FDI (threshold)||-0.172||-0.139||-0.354||-0.266||-0.363|
||||(0.424)||(0.427)||(0.475)||(0.507)||(0.481)|
||MFN Tariff|0.084|-0.014|0.085|-0.025|0.054|-0.009|0.046|-0.032|0.066|0.001|
|||(0.109)|(0.112)|(0.108)|(0.112)|(0.107)|(0.112)|(0.109)|(0.120)|(0.111)|(0.115)|
|Within sector|FDI (share)|-0.287*||-0.287*||-0.16||-0.142||-0.091||
|effects||(0.162)||(0.162)||(0.206)||(0.210)||(0.222)||
||FDI (threshold)||-0.134||-0.153||-0.026||-0.06||-0.029|
||||(0.127)||(0.127)||(0.185)||(0.200)||(0.191)|
||MFN Tariff|-0.039|0.01|-0.04|0.015|-0.018|0.015|-0.01|0.029|-0.024|0.005|
|||(0.052)|(0.053)|(0.051)|(0.053)|(0.052)|(0.052)|(0.055)|(0.058)|(0.055)|(0.054)|
||No. of obs.|5662|5662|5662|5662|5662|5662|5662|5662|5662|5662|
||R<sup>2</sup>|0.043|0.043|0.042|0.043|0.043|0.043|0.042|0.043|0.043|0.044|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are lagged two years. All models include firm and year fixed effects. Robust standard errors clustered on industry-year combinations are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.
data on the reform, we compared total fertility rates for districts implementing with the rates for those not implementing the reform, both before and after the reform.

We have estimated a substantial effect of the land reform on fertility outcomes. For the full sample, we estimated a 0.99 reduction in the total fertility rate, i.e., a reduction of lifetime fertility by one child per woman, a result clearly driven by the rural rather than the urban sample: for the rural sample, the estimated reduction in lifetime fertility rate is about 1.2 children. Looking at age-specific fertility rates, which were used to construct the total fertility rate, we have found a clear reduction in fertility across virtually all age groups in the rural sample. The effect is particularly pronounced for women in the most fertile age groups, 25—29 and 30—34. Doing robustness checks, we concluded that our impact estimates are neither biased by spillovers nor by policy endogeneity.

# References

- [1] Abate, T. (1997a) “Land Redistribution and the Micro-Dynamics of Land Access and Use in Amhara: The Case of Two Communities in South Wollo.” In: K. Fukui, E. Kurimoto, and M. Shegeta (Eds.) Ethiopia in Broader Perspective: Papers of the 13th International Conference of Ethiopian Studies, Kyoto: Shokado Booksellers.

- [2] Abate, T. (1997b) “Struggle over Policy Loose-Ends: Idioms of Livelihood and the Many Ways of Obtaining and Losing Land in South Wollo, Amhara.”Mimeo.

- [3] Adenew, B., and F. Abdi (2005) “Research Report 3: Land Registration in Amhara Region, Ethiopia.”International Institute for Environment and Development: London.

- [4] Altonji, J. G., T. E. Elder, and C. Taber (2005) “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools,” Journal of Political Economy 113(1): 151—84.

- [5] Amare, Y. (2002) “Socio-economic dimensions of rural poverty in Ethiopia, a qualitative study of two highland communities in north Shewa,” Journal of Ethiopian Studies 35(2), 112—40.

- [6] Arellano, M. (1987) “Computing Robust Standard Errors for Within-Group Estimators,” Oxford Bulletin of Economics and Statistics 49, 431—34.

- [7] Barro, R. J., (1974) “Are government bonds net wealth?” Journal of Political Economy 82(6), 1095—1117.

- [8] Barro, R. J., and G. S. Becker (1989) “Fertility choice in a model of economic growth,” Econometrica 57(2), 481—501.

19
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv. . https://doi.org/10.48550/ARXIV.1810.04805 Hansen, Stephen, Michael McMahon, and Andrea Prat. 2018. “Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach.” _The Quarterly Journal of Economics_ 133 (2): 801–70.

Hjort, Jonas, Diana Moreira, Gautam Rao, and Juan Francisco Santini. 2021. “How Research Affects Policy: Experimental Evidence from 2,150 Brazilian Municipalities.” _American Economic Review_ 111 (5): 1442-80.

<mark>Jolliffe, Dean, Daniel Gerszon Mahler, Malarvizhi Veerappan, Talip Kilic, Philip Wollburg. 2023. “What Makes Public Sector Data Relevant for Development?”</mark> _<mark>World Bank Research Observer</mark>_ <mark>38 (2): 325-346.</mark> Kleinberg, Bennett, Maximilian Mozes, Arnoud Arntz, and Bruno Verschuere. 2018. “Using Named Entities for Computer-Automated Verbal Deception Detection.” _Journal of Forensic Sciences_ 63 (3): 714–23.

Lo, Kyle, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Daniel Weld. 2020. “S2ORC: The Semantic Scholar Open Research Corpus.” In _Proceedings of the 58_<sup>_th_</sup> _Annual Meeting of the Association for Computational Linguistics_ , 4969–83. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.447.

National Science Board, National Science Foundation. 2019. “Publications Output: US Trends and International Comparisons. Science & Engineering Indicators 2018.” _National Science Foundation_ .

Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative Style, High-Performance Deep Learning . Library.” arXiv. https://doi.org/10.48550/ARXIV.1912.01703 Phillips, Brian J., and Kevin T. Greene. 2022. “Where is Conflict Research? Western Bias in the Literature on Armed Violence.” _International Studies Review_ 24 (3): viac038.

Porteous, Obie. 2020. “Research Deserts and Oases: Evidence from 27 Thousand Economics Journal Articles on Africa.” _Oxford Bulletin of Economics and Statistics_ .

Robinson, Michael D, James E Hartley, and Patricia Higino Schneider. 2006. “Which Countries Are Studied Most by Economists? An Examination of the Regional Distribution of Economic Research.” _Kyklos_ 59 (4): 611–26.

Sabet, Shayda Mae, and Annette N Brown. 2018. “Is Impact Evaluation Still on the Rise? The New Trends in 2010–2015.” _Journal of Development Effectiveness_ 10 (3): 291–304.

Sanh, Victor, Lysandre Debut, Julien Chaumond, and Thomas Wolf. “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.” _arXiv preprint arXiv:1910.01108_ (2019).

25
|**Table 21. Estimation Results of Model of the De**|**terminants of Tot**<br>OLS,all variables|**al Lending**<br>OLS,stepwise|
|---|---|---|
|Supply: Fund Capital|||
|Initial VF capital|0.569***|0.574***|
||_(0.170)_|_(0.165)_|
|VF allows for saving|-22.907<br>||
||_(69.819)_||
|VF requires members to save|23.191<br>||
||_(56.698)_||
|Supply: Characteristics of VF Committee|||
|Proportion who have some higher education|-65.063|-84.864*|
||_(58.501)_|_(48.448)_|
|Proportion who have accounting experience|57.488<br>||
||_(290.972)_||
|Need training in loan appraisal (yes=1)|<br>40.378<br>_(52.513)_||
|Accounts are computerized (yes=1)|119.385|105.390|
||_(75.903)_|_(76.260)_|
|VF officers are chosen, not elected (yes=1)|-65.155<br>_(57.697)_||
|VF would cut loans equally if funds were short (yes=1)|-29.936<br>_(39.927)_||
|Demand: Village Size and Organization|||
|Number of households in village|0.165<br>_(0.170)_||
|Village has a market (yes=1)|<br>-38.254<br>_(92.823)_||
|Rural (yes=1)|-57.792||
||_(67.160)_||
|Demand: Household Characteristics|||
|Proportion of households who are self-employed farmers|177.577<br>_(143.539)_||
|Proportion of income generated by wages|57.048<br>_(174.001)_||
|Proportion of population with some secondary education|<br>-368.638<br>_(226.790)_|-351.317***<br>_(80.353)_|
|Household assets (excluding land)|4.421<br>||
||_(31.845)_||
|Proportion who have a loan from a formal bank|127.967<br>_(199.043)_||
|Proportion of borrowers who are women|-272.871**|-177.872**|
||_(128.969)_|_(89.424)_|
|Expenditure per capita|2.761<br>_(17.623)_||
|Loan terms and conditions|||
|VF borrows to on-lend to members|611.210***|584.225***|
||_(149.176)_|_(114.571)_|
|Memo items|||
|Number of observations|2,102|2,343|
|R2|0.164|0.160|

Sources: Village Fund Survey 2010, and SES 2009. Robust standard errors in parentheses. Stepwise regression uses p=0.2 cutoff for dropping variables. * p<0.05, ** p<0.01, *** p<0.001.

33
4 reviews the empirical literature of trade liberalization and growth. Section 3 describes the methodology used in this paper to assess the impact of trade liberalization on output growth and other relevant economic variables. Section 4 presents the data used in this empirical exercise, the sample selected, and a description of the main economic patterns of countries in and out of the sample. The statistical evaluation of the economic outcome of trade liberalization is undertaken in Section 5. We implement the methodology described in Section 3, and test the robustness of its results using different specifications and time periods. Section 6 presents conclusions supported by the statistical evidence of Section 5.

# **2. LITERATURE REVIEW**

Attempts to measure the impact of trade policy openness on growth go back several decades, but until the early 1990s it simply involved comparisons of GDP growth between opened and closed countries, or before and after trade liberalization (see Appendix 1). Classic examples of the pure before and after approach (so-called event studies) are two publications by the World Bank (Nash and Thomas, 1991; and Papageorgiou et al 1991) that identified a year of liberalization and after observing higher GDP growth after the reforms, concluded that trade liberalization indeed leads to higher growth. The major caveat under this approach is that it does not properly study or control for other factors that could have boosted growth after trade liberalization. For instance, it did not account for the fact that many countries liberalized trade and at the same time exited a communist regime, ended a political conflict, or rebounded from a short-term recession.

A representative study of the cross-country comparison approach is Balassa (1978), which concluded a positive relation between an outward oriented regime and economic growth by observing that countries that experience higher exports growth also had a significantly higher GDP growth, even after removing exports from GDP accounting. Since exports growth can be fostered by GDP growth itself or policies other than trade liberalization, then this observed relation does not demonstrate causality from trade policy to growth. An alternative path is taken in the 1987 WDR (World Bank,
inequality based on the 1998 benchmark data, Milanovic (2005, p. 104) used 63 consumption instruments and 59 income instruments. This represents a significant increase in the number of consumption instruments compared with ten years before (80 income-based and 22 consumption-based distributions), but for some of the most important counties (like China) one still depends on income data alone. If a guess had to be ventured, it could be said that there is likely to be a tendency toward greater use of household per capita consumption as the welfare indicator. This, while quite defensible from a strictly methodological perspective, will open up a number of issues of comparability since most historical income distribution statistics (e.g. in the United States, UK, France, Germany) are income-based. One thus needs to balance methodological and quality improvements in the snapshots of today’s income distribution against the break in the historically existing series.

After the problems of what is the proper welfare indicator, it would seem that other methodological issues should be easier to deal with. This is true. Whatever “income” is, that “income” should be expressed in per capita terms and should be equal across members of a household. This means that the two issues that are often debated in national inequality studies are “solved” here: the issue of equivalence scales, and intrahousehold inequality. These are “solved” because at the current level of statistical development, there is simply no way to account for economies of scale and size across different countries. The main reason for this is that economies of scale and size depend on relative prices of public and private goods (if housing is very cheap, economies of size will be small) and they systematically differ between poor and rich countries.<sup>20</sup> Until we have a better handle on the relative prices of public and private goods,<sup>21</sup> we cannot adjust internationally for equivalent units. A use of a given equivalence scale across all

> 20 See Lanjouw, Lanjouw, Milanovic and Paternostro (2004).

> 21 And also relative prices of children vs. adult goods if we are to adjust for household composition (and not only for household size).

12