rafmacalaba/datause-encoder-data huggingface dataset:
- Rajan, R. G., Zingales, L., 1998. Financial dependence and growth. American Economic Review 88 (3), 559–86.

- Santos Silva, J. M. C., Tenreyro, S., 2006. The log of gravity. Review of Economics and Statistics 88 (4), 641–658.

- Santos Silva, J. M. C., Tenreyro, S., 2011. Further simulation evidence on the performance of the poisson pseudo-maximum likelihood estimator. Economics Letters 112 (2), 220–222.

- Staiger, D., Stock, J. H., 1997. Instrumental variables regression with weak instruments. Econometrica 65 (3), 557–586.

- UNCTAD, 2010. World Investment Report 2010: Investing in a Low-Carbon Economy. New York and Geneva: United Nations.

- Winkelmann, R., 2008. Econometric Analysis of Count Data. Berlin: Springer-Verlag Berlin Heidelberg.

- Wooldridge, J. M., 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: The MIT Press, second edition.

- World Economic Forum, 2012. The Financial Development Report 2012. Geneva: World Economic Forum.

22
# Table 7: Analysis of classroom quality, teacher, and structural characteristics on children’s language <u>and cognitive development</u>

||no|DV: Languag|e & Cogniti|ve (Raw score<br>w/ teacher|)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.129|0.107|0.111|0.102|0.132|
||(0.079)|(0.080)|(0.080)|(0.081)|(0.096)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||-0.135|-0.109|-0.127|
||||(0.558)|(0.550)|(0.544)|
|% of teachers w/ post secondary edu.|||-0.167|-0.170|-0.180|
||||(0.580)|(0.573)|(0.568)|
|Mean years of teaching|||-0.001|-0.003|-0.004|
||||(0.012)|(0.012)|(0.012)|
|% of teachers w/ no ECED experience|||0.274<br>(0.172)|0.255<br>(0.170)|0.241<br>(0.169)|
|% of teachers w/ no training|||0.012<br>(0.220)|0.042<br>(0.217)|0.040<br>(0.217)|
|**Structural characteristics**||||||
|Student-to-staff ratio||||0.016|0.017|
|||||(0.011)|(0.011)|
|Hours of operation per week||||0.039*<br>(0.020)|0.039*<br>(0.020)|
|Constant|6.323***|-1.128**|-1.138|-1.437*|-1.496*|
||(0.248)|(0.505)|(0.765)|(0.786)|(0.785)|
|Observations|7,958|7,958|7,958|7,958|7,958|
|R-squared|0.002|0.266|0.268|0.270|0.269|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stage F-statistic of excluded<br>instrument|||||663.973|
|**Control included:**||||||
|Child characteristics|N|Y|Y|Y|Y|
|Kindergarten dummy|N|Y|Y|Y|Y|
|District dummy|N|Y|Y|Y|Y|

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, agesquared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Model 5 is a two-stage least squares model to correct for measurement error in the ECERS-R. The F-statistic of the first stage is reported.

* p<0.1, ** p<0.05, *** p<0.01

24
be needed in order to find 1,000 LGBT respondents in each member state. Second, FRA noted that LGBT people are likely to disguise their sexual orientation or gender identity in a screening interview with an unknown enumerator. Hence the self-identifying online survey was chosen, even though the sample would not allow a comparison with the larger population.<sup>57</sup> Anonymity was an important aspect of the study design, respondents were not asked for their names or any other personal information. No information about the computer itself was collected (for example through cookies). While the anonymity provided the respondents with security it had two major implications. Respondents had to take the whole survey in one go because there was no way of saving progress and continuing at a later stage. Furthermore, it is possible that respondents took the survey more than once since no data about them or their computer was stored to prevent multiple entries. These constraints are inherent of online surveys and should not diminish the overall potential these surveys have especially in areas where the internet is widely accessible. The involvement of local experts helped in reaching out to large numbers of LGBT people and facilitated the distribution of the survey. The implementing consortium of Gallup Europe and ILGA Europe prepared background notes on the local LGBT communities to target advertisement accordingly.<sup>58</sup>

# **Australia**

In 2015, Australia conducted a survey of 272 intersex people, one of the largest surveys of its kind.<sup>59</sup> The survey was structured to be on, with and for the intersex community, as one means of redressing the historical role of intersex people as subjects of (often medical) inquiry. An online data collection tool was used with both forced-choice (quantitative) and open-ended (qualitative) questions. The survey was circulated through online networks, social media, and general advertising. The survey asked participants what term participants preferred to use to describe themselves to themselves, to family and friends, and when seeking medical assistance. The survey revealed high levels of mental health issues among respondents (60% had thought about suicide and 19% tried it; compared to less than 3% - combined – for the Australian population at large). The intersex respondents also reported having a much higher level of none-completion of secondary school (18%) compared to the general Australian population (2%). Further, a 41% of respondents earned less than $20,000 per year (minimum wage is $34,000).<sup>60</sup>

> 57 Ibid.

> 58 Ibid.

> 59 Jones, Tiffany; Hart, Bonnie; Carpenter, Morgan; Ansara, Gavi; Leonard, William; Lucke, Jayne. 2016. <u>Intersex: Stories and Statistics from Australia.</u>

> 60 Ebd.

17
Figure 10: Arithmetics and Politics and Fiscal Effort <!-- Start of picture text --> nw nw<br>ne ne<br>se se<br>sw sw<br>0 .05 .1 .15 .2 .25 .3 0 .05 .1 .15 .2 .25 .3<br>Tax Revenue, %GDP Tax Revenue, %GDP<br><!-- End of picture text --> Note: nw, ne, se, sw refer to the quadrants defined above in Figure 9. Namely, nw and ne refers to those countries where political influence concentration ( _I_ (Π)) is above average, and the poverty eradication capacity ( _I_ ( _P_<sup>_∼p_</sup> ) in the left panel, _I_ ( _P_<sup>_r_</sup> ) in the right panel) is, respectively, below and above average. Categories sw and se refers instead to those countries where political influence concentration ( _I_ (Π)) is below average, and the poverty eradication capacity ( _I_ ( _P_<sup>_∼p_</sup> ) in the left panel, _I_ ( _P_<sup>_r_</sup> ) in the right panel) is, respectively, below and above average. The figure shows a box and whiskers plot. The box ranges from the 25th percentile to the 75th percentile. The line in the middle of the box shows the median. The two lines on either side of the box extend to minimum and maximum values, excluding outliers. The dots indicate outliers.

# **6 Conclusion**

While the importance of domestic resource mobilization in achieving sustainable development is well-established, attention to the subject has been growing in recent years. Indeed, the focus in international policy-making circles has been shifting towards strengthening the capacity of governments to mobilize their own resources, and increasingly moving further away from reliance on foreign aid.

A country’s ability to mobilize its own resources–expanding the flow of taxes and other income into the government’s treasuries–has advantages in terms of ownership and accountability. Being able to raise resources to invest in public spending without external financing can increase ex-ante accountability. For example, it encourages countries’ development path to reflect domestic needs and long-term goals rather than donors’ (often changing) preferences and priorities. It can also strengthen ex-post accountability and legitimacy. As citizens contribute with taxes, they demand better governments; endowing governments with more incentives to be responsive to their constituencies, which can contribute to more trusted and effective institutions in a virtuous cycle.

21
# **_Korea_**

36. Korea provides an interesting case study of how industrial upgrading took place and how it can be supported through industrial policy.<sup>14</sup> 37. Since 1953, Korea‘s industrial structure changed substantially with the share of manufactures in GDP increasing from 9 percent to 30 percent in 1988, while the share of the agriculture and mining sectors shrank at the same time (Figure 12). In parallel, the dominant share of exports switched from wood manufacture and clothing to machinery and transport equipment (Figure 13).

38. The industrial upgrading of Korea since 1962 is often described as a good example of ̳Flying Geese catch up‖ in that Korea imitated countries that were more advanced (the ―lead ‖ ‖ geese such as Japan) using the ―advantage of backwardness .<sup>15</sup> For example, in the textile industry Korea moved from export of clothing to that of textile and to production of synthetic fibers (Lim 2011). In the electronics industry, production started with the assembly of radios from imported components (World Bank 1987), and then moved to electronic parts (i.e. transistor and semiconductor) and information communication & industrial electronics in the mid-1980s and -1990s respectively (Figure 12). Clothing industries had a sharp increase in revealed comparative advantage until the end of the 1960s, followed by footwear until the 1980s and the electronics industry in the 1990s. The competitiveness of the footwear industry benefited from joint ventures and technology cooperation with companies from Korea and Japan. The decline in the footwear industry since the mid-1990s reflects higher wages and led to the relocation of factories to China, Indonesia and Vietnam (Figures 14 and 15). Since the end of the 1980s, outward foreign investment of Korea‘s labor-intensive industries has increased and its main destination has been Asian countries (Figure 16).

39. Korea‘s process of industrial upgrading reflected the country‘s shifting factor endowment structure: The success of labor-intensive industries led to capital accumulation and an increase in the capital intensity of industries (Lin and Chang 2009). Like Japan, learning and capacitybuilding played a very significant role in Korea‘s growth process. Notably, when Korea decided to promote ship-building it imported large numbers of foreign workers who had relevant skills and were able to pass on those skills to the indigenous population, thus creating the type of vocational training necessary for the future growth of the industry.

> 14 Chandra, Lin, and Wang (2012).

> 15 Korea‘s industrial upgrading process between the 1960s and the 1980s can be roughly divided into 3 phases: i) the ―takeoff‖ phase (1962–1973) ii) the Heavy and Chemical Industry (HCI) drive phase (1973–1979) and iii) the liberalization phase (After 1980) (World Bank, 1987). For the details of Korea‘s industry policies, see World Bank (1987), Krueger (1997), Suh (2007) and Lim (2011).

17
Policy ReseaRch WoRking PaPeR 4762

# **Abstract**

Political economy explanations for fiscal profligacy are dominated by models of bargaining among organized interest groups over group-specific targeted benefits financed by generalized taxation. These models predict that governments consisting of a coalition of political parties spend more than single-party regimes. This paper presents an alternative model—that of populist pressure on political parties to spend more on the general public good, financed by costly income taxation—and obtains the opposite prediction. According to this model, public spending and taxes are lower under coalition governments that can win elections more cheaply. Indeed, in order to win elections, coalition partners need to satisfy a smaller share of swing voters than does a singleparty government that enjoys narrower support from its core constituency. A coalition government therefore spends less on the public good to capture the share of the swing vote necessary for re-election. Using data from more than 70 countries during the period 1970–2006, the paper provides robust supporting evidence for this alternative model.

This paper—a product of the Growth and the Macroeconomics and Human Development and Public Services Teams, Development Research Group—is part of a larger effort in the department to understand the political economy of public policy choices. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at skhemani@worldbank.org and wwane@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> aTM»<br>o<br>2 — We eweenaea ae ee me<br>a)<br>ot—_ Te,<br>o<br>2 —<br>4 oe _<br>fe —_ _—<br>2 oN aa<br>2011 2012 2013) (2014) 2015) 2016) )=6 2017) 2018) §=962019 = =2020<br>—— Job creation ------- Job destruction<br>——— Netemp. change ------- Job reallocation<br><!-- End of picture text -->
|_Azithromycin tabs/caps/oral available_|0.35|0.26|0.09|0.16|0.27|0.57|
|---|---|---|---|---|---|---|
|_Amoxicillin tabs/caps/oral available_|0.91|0.91|0.82|0.77|0.75|0.93|
|_Ampicillin powder available_|0.07|0.11|0.05|0.15|0.28|0.59|
|_Any antibiotic available_|0.93|0.91|0.83|0.77|0.80|0.95|
|_All antibiotics available_|0.05|0.10|0.02|0.09|0.12|0.43|
|**Rehydration**|||||||
|_ORS packet available_|0.81|0.77|0.76|0.81|0.85|0.92|
|_Sodium chloride inj. available_|0.52|0.31|0.72|0.59|0.67|0.76|
|_Has ORS and sodium chloride available_|0.45|0.29|0.62|0.49|0.58|0.70|
|_Neither ORS nor NaCl available_|0.12|0.21|0.14|0.09|0.06|0.01|

±Breakdowns by cadre and education were unavailable for facilities with over 50 medical staff in Tanzania so the counts of doctors, clinical officers, nurses, and other (and counts of staff with different levels of education) are only available for facilities with 50 or fewer medical staff. However, the counts of total medical staff include these larger facilities (Tanzania has 35 facilities with more than 50 medical staff, which increase the averages of total medical staff from 17.12 for urban facilities and 5.69 for rural facilities to the 25.47 and 6.29 reported in the table above).

> _++_ Clinical Officers were not a medical staff cadre in Sierra Leone

δ Provider education was missing for 1.1% of the sample resulting in the sums of the categories of education equaling less than the total number of medical staff

*Acceptable waste disposal methods include: Open burning in pit or protected ground, placed in covered pit or pit latrine, placed in pit or protected ground, off-site removal (with protected storage) and one or two chamber incinerators.

> _+_ Other arrangements for emergency transport include other official vehicles, private vehicles or another mode of emergency transport.

30
control, or enforce laws may not necessarily match up with an ability to provide public goods and services like quality healthcare and education. Thus, broader measures of state capacity are weak substitutes for directly measuring a state’s ability to deliver public goods and services<sup>1</sup> .

Measures of state capacity to deliver public goods are also often contained within measures of governance. For example, the World Bank uses six indicators to measure governance: voice and accountability; political instability and violence; government effectiveness; regulatory quality; rule of law; and control of corruption (Kaufmann, Kraay, and Mastruzzi, 2005.) But while weak governance might be linked to a lower capacity to deliver public goods and services, it is not always the case. For example, some authoritarian states are ranked low on governance but have a high capacity to deliver public goods and services.

Furthermore, governance is generally measured at the national level by observing, among others, the regime type, political institutions and legal systems. Thus, these measures cannot explain stateby-state/within-country variations in the ability to deliver public goods. For example, the infant mortality rate in the Indian state of Kerala is comparable to countries within the Organization for Economic Co-operation and Development (OECD), while the infant mortality rate in the Indian state of Madhya Pradesh equals that of poor and less developed countries such as Haiti and Liberia (Bellinger 2016). These differences within India cannot be attributed to most existing measures of governance because they seldom demonstrate variations within the country<sup>2</sup> .

> 1 An alternate view focuses on incentives to deliver public goods as against capacity to deliver public goods. Capacity, under this framework, results from available incentives. For example, effective monitoring and incentives might result in lower doctor/nurse absenteeism. Lower absenteeism, in turn, might result in higher capacity to provide universal healthcare. For more examples refer; Das et al. 2007, Banerjee, Duflo, and Glennerster 2008, Muralidharan and Sundararaman 2011, Callen et al. 2016, Duflo, Hanna, and Ryan 2012, Duflo, Dupas, and Kremer 2015, Dhaliwal and Hanna 2017.

> 2 For more examples, see “Spotlight on Kerala and Uttar Pradesh: One Nation, Worlds Apart”, Page 44, World Bank (2004); Dreze and Sen (2002); and World Bank (2006).

3
Table A.12: Breakdown of per student costs by treatment arm

||**In-kind**|**Recognition**|
|---|---|---|
|Data Collection|$ 5.57|$ 5.57|
|Award Ceremonies|N/A|$ 2.72|
|In-kind Rewards|$ 6.18|N/A|
|**Total**|$ 11.75|$ 8.29|

_Notes._ Costs are in USD, 2014 prices. Data collection costs are from 2014 (endline), when a random sample of students in every school were assessed . Implementation costs data are from 2013 (midline) but adjusting for one year of inflation (the award ceremonies did not take place in 2014). We do not include the costs of the communication campaign conducted prior to the start of the program, since this is a fixed cost and future communication can take place in combination with the award ceremonies. The schools in our sample have on average 77 students enrolled in both grade 3 and grade 4.

13
<!-- Start of picture text --> East Asia Financial Crisis<br>@ before<br>Hafter<br>Latin America Financial Crisis<br>0 0.02 0.04 0.06 008 O01<br><!-- End of picture text --> <!-- Start of picture text --> East Asia Financial Crisis<br>@ before<br>after<br>Latin America Financial Crisis<br>0 20 40 60 80<br><!-- End of picture text --> <!-- Start of picture text --> In GDP***<br>Maboveavg<br>4| @ below avg<br>In_pop***<br>0 0.05 0.1 0.15<br><!-- End of picture text -->

<!-- Start of picture text --> In_GDP*** ‘mee<br>Waboveavg<br>4 belowapal<br>In_pop*** -pe<br>-:-:<br>020 40 60 80 100 120<br><!-- End of picture text -->
Table 7c: Heterogeneous effects by the nature of mining activities

||_U_<br>|_SGS Consta_<br>|_nt prices_<br>||_World_<br>|_Bank (GE_<br>|_M) real pri_<br>|_ces_<br>|
|---|---|---|---|---|---|---|---|---|
|_Dependent variables:_|_Per capita_<br>_Growth_|_Pop._<br>_density_|_Spatial_<br>_Gini_|_Sen_<br>_Index_|_Per capita_<br>_Growth_|_Pop._<br>_density_|_Spatial_<br>_Gini_|_Sen_<br>_Index_|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|Post x Extract|0.018|66.288|-0.022|0.451|0.034|-33.500|-0.031|0.493|
||[0.068]|[85.842]|[0.016]|[0.310]|[0.094]|[24.096]|[0.020]|[0.451]|
|Post x Transform|0.178<sup>*</sup>|240.262|-0.008|1.523<sup>**</sup>|0.281|-18.436|-0.039|1.398<sup>***</sup>|
||[0.090]|[219.465]|[0.035]|[0.566]|[0.246]|[57.794]|[0.024]|[0.471]|
|Log(Prices)|0.089|-79.908|-0.009|0.212|0.146|23.437|-0.022|0.872<sup>**</sup>|
||[0.071]|[62.417]|[0.011]|[0.274]|[0.196]|[22.827]|[0.015]|[0.385]|
|Inactive mines x Year 2000|-0.365|16.149|-0.024|0.138|-0.559|-4.189|0.006|0.237<sup>**</sup>|
||[0.248]|[19.272]|[0.015]|[0.145]|[0.599]|[7.002]|[0.009]|[0.092]|
|Climate|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Conflict dummy|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|22764|22864|18378|18378|21451|21547|17201|17201|
|R-squared|0.076|0.969|0.792|0.950|0.076|0.972|0.796|0.953|
|Dep. Variable [mean]|0.029|283.912|0.247|3.626|0.030|273.537|0.240|3.642|

Notes: (1) The table shows the regression results across districts in Africa during 2002–2007. (2) The results show the local economic impacts of China’s WTO accession based on the nature of mining activities in Africa. (3) The standard errors, clustered at the country level, are in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. (4) Population density is measured as population per square kilometer. (5) Growth, Gini, and Sen Index are measured using lights data as described in the data section. (6) Fixed effects include time-fixed effects, district-fixed effects, and country-year-fixed effects. (7) Climate is measured using the standard deviation of rainfall distribution at the district level.

Source: Authors’ estimations Table 8: List of countries

|Algeria|Gabon|Mozambique|South Sudan|
|---|---|---|---|
|Angola|Gambia|Namibia|Sudan|
|Benin|Guinea-Bissau|Niger|Tanzania|
|Botswana|Kenya|Nigeria|Togo|
|Cameroon|Liberia|Republic of Congo|Tunisia|
|Chad|Madagascar|Rwanda|Uganda|
|Côte d'Ivoire|Malawi|Senegal|Zambia|
|Democratic Republic of the Congo|Mali|Sierra Leone|Zimbabwe|
|Djibouti|Mauritania|Somalia||
|Ethiopia|Morocco|South Africa||

Source: USGS data.

34
WPS5238 Policy Research Working Paper 5238 Assessing Ex Ante the Poverty and Distributional Impact of the Global Crisis in a Developing Country A Micro-simulation Approach with Application to Bangladesh

# _Bilal Habib_

_Ambar Narayan Sergio Olivieri Carolina Sanchez-Paramo_ The World Bank Poverty Reduction and Economic Management Network Poverty Reduction and Equity Group March 2010