rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper **Serles**

||TItle|Author|**Date**|Contact<br>for paper|
|---|---|---|---|---|
|WPS137|1 The Evolution of Trade Treaties and<br>TradeCreation:Lessons for Latin Am|Sarath Rajapatirana<br>erica|October 1994|J. Troncoso<br>37826|
|WPS137|2 Administrative<br>Charges In Pensions<br>in Chile, Malaysia, Zambia, and the<br>Unitrd States|Salvador Vald6s-Prieto|October 1994|E. hline<br>37471|
|WPS137|3 Finr 3ehavior and the Labor Market<br>in t.H<br>Hungarian<br>Tmnsition|Simon Commander<br>Janos Kollo<br>Cecilia Ugaz|October 1994|B. Keller<br>35195|
|WPS137|4 Infi-IructureFinance: Issues,<br>Ins-:.utions, and Polides|Anand Chandavarkar|November<br>1994|M. Geller<br>31393|
|WPS137|5 Policy Lessons from a Simple<br>Open-Economy<br>Model|Shantayanan<br>Devarajan<br>Delfin S. Go<br>Jeffrey D. Lewis<br>Sherman Robinson<br>Pekka Sinko|November1994|<br>C. Jones<br>37699|
|WPS137|6 How Trade, Aid, and Remittances<br>Affect Internaional Migration|Maurice Schiff|November1994|<br>J. Ngaine<br>37947|
|WPS137|7 Macroeconomic<br>Adjustment to<br>Capital Inflows: Lafin American Style<br>versus East Asian Style|Vittoro Corbo<br>Leonardo HemAndez|Novemnber199|4<br>R.Vo<br>31047|
|WPS137|8 Mexico after the Debt Cris:<br>Is Growth Sustainable?|Daniel Oks<br>Sweder van sijnbergen|November1994<br>|<br>S. Harbi<br>37143|
|WPS137|9 Financing Infrastructure<br>in Developin<br>Countries: Lessons_from<br>the Railway<br>Age|g Barry Eichengreen<br>|November'1994|<br>WDR<br>31393|
|WPS138|0 Transfers and the Transiion from<br>Socialism: Key Tradeoffs|Kathie Krumm<br>Branko Milanovic<br>Michael Walton|November1994|<br>K Krumm<br>34263|
|WPS1 381|Welfare Economics,<br>Political<br>Economy and Policy Reform in Ghan|Ravi Kanbur<br>a|November1994|<br>M. Youssef<br>34614|
Table C. City Size Equations<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(1)|(2)|
|---|---|---|
||Spatial GMM|Spatial OLS|
|Ln(ruralpop. supply)|1.706***|1.216***|
||(0.635)|(0.386)|
|Ln(rural income opportunities)|-3.317***|-1.999***|
||(0.864)|(0.462)|
|Ln(marketpotential)|2.322***|1.426***|
||(0.660)|(0.468)|
|Average Schooling|0.181*|0.231**|
||(0.099)|(0.112)|
|Ln(inter-citytransport costs)|-1.346***|0.081|
||(0.280)|(0.083)|
|State capital dummy|-0.211|1.091***|
||(0.330)|(0.187)|
|time dummies|Yes|Yes|
|Observations|369|369|
|Hansen J statistic<br>(overidentificationtest)|1.659||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, port dummy, illiteracy rate (1970), ln(industry capital per worker, 1970), ln(distance to state capital)*ln(market pot. agric. land availability, 1970), ln(humidity), ln(avg. temperature), ln(rural pop. supply, 1970), ln(rural income opportunities, 1970), ln(market potential, 1970), and state capital and time dummies.

- b. Coordinate variables are latitude and longitude. Cutoffs are 1.5 standard deviations of latitude and longitude (10.23, and 8.20), which correspond to about 900 miles.

46
<!-- Start of picture text --> 4S Standard deviation in 2030<br>© 1.460<br>44 () 4.000<br>( 6.000<br>(7) 8.110<br>43 Data: Sample covered<br>O 1980_2014<br>6c 0 1990_2014<br>42 + 2000_2014<br>X 2005_2014<br>41 x Country typology<br>HO: LOGC BAI<br>fo) f ROC<br>9 40 WM NROC<br>N HOGC @ HoGc<br>c LOGC<br>5G 39 HOGC @ mMocc<br>=<br>38 All Loc<br>NROC<br>37 All--<br>mocc Q Xx ROCx HOGC<br>+ NROC<br>36 MOGC<br>x Locc<br>MOGC<br>35 |<br>34<br>34 35 36 37 33 39 40 Al 42 43 44 45<br>Median in 2015<br><!-- End of picture text -->
# Introduction

Organized labor has long been an important phenomenon in the political, social and economic development of nations, as the relationships between labor, industry and the state have proved critical elements in the creation of modem economies and democracies. As a result of the efforts to better understand these relationships, the study of organized labor has become extensive. One particular area of interest in this study has been the influence of unions on wages. However, previous research was concentrated in developed countries, where the experience of organized labor has been longer and information more readily available.

In recent years the examination of union-nommion wage differentials has increased dramatically, primarily due to the advent of increased computational power, the increased availability of micro-data and new econometric techniques (Hirsch and Addison 1985). Despite unriecedented analytical capability, with regards to the particular issue of unionnonunion wage differentials the literature largely ignores developing nations. Notable exceptions include Moll's study of South African unions (1993), Park's (1991) study of union-nonunion wage differentials in Korea and Standing's (1992) study of unions in Malaysia. The primary cause of this shortfall stems from the lack of adequate data (Nelson 1991). Although many economic surveys are completed each year in developing nations, few contain questions regarding unionization. The 1989 Mexican household survey upon which this paper is based fulfills this requirment. This data, coupled with growing interest in the Mexican labor market fueled by concerns over the North American Free Trade
The second dataset, SIFA, is used by the national agency that runs FA for administration and monitoring purposes. The data set is a longitudinal census of program beneficiaries from 2001 to the present. There is information on nearly 2.5 million families who have participated or are currently participating in the program. We limit this dataset to all adults from beneficiary households with a valid identity card (needed to vote) and match it with the electoral census (using each person’s national identification number). Overall, most people in SIFA (96.5%, corresponding to 3,608,733 program participants) are registered to vote.

Finally, we use the _SISBEN_ to identify non-participants that are comparable to FA beneficiaries, namely that they live in households with similar socio-economic and demographic characteristics as determined by their corresponding poverty index score. These data are also matched with the electoral census. Then we append Sisben-electoral census with SIFA-electoral census to produce a sample that is comprised of individuals below and above the cutoff score that establishes eligibility to FA. The matched sample shows that 86% of the FA beneficiaries are registered in the _SISBEN_ . The rest is mostly comprised of displaced individuals and indigenous people, who by law are not required to have a score below the eligibility threshold to participate in FA, and thus are not included in our analysis.

The sample is restricted in two ways. First, considering that the program is offered only to families with children under 18 years, the data are restricted to only include adults 25-46 years old at the moment of the presidential election. This age range is determined by the age distribution of mothers in urban Colombia as reported in vital statistics records in 2010. This is done to ensure that we focus on the group of parents whose voting behavior is more likely to be influenced by FA given the timing in which the program was rolled out throughout the country. The second restriction limits the data to only urban households. The reason for doing this is that by design, the algorithm that determines the poverty score in rural areas was defined to produce substantial discontinuities around the threshold of program eligibility for variables 9
# **What Is State Capacity?**

By Stuti Khemani Development Research Group, The World Bank<sup>1</sup>

# **Keywords: State Capacity, Public Policy, Politicians, Bureaucracy, Service Delivery JEL Codes: H1, H3, H4, H83**

1 I thank Luis Serven, James Habyarimana and Irfan Nooruddin for valuable comments. The ideas in this paper build upon World Bank (2016), which is the Development Research Group’s Policy Research Report on governance, Making Politics Work for Development. The views in this paper are those of the author and not necessarily of the World Bank or its members.
**Table 3.** Simulated Data, Model 3—results for model size and inclusion probability of the true model.

||||||||Mo|del 3||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||_k_ =|20|||_k_ =|40|||_k_ =|80||
||True model size||7||7||7||7||7||7|
|P|rior mean model size||10||10||20||20||40||40|
||_n_||50||100||50||100||50||100|
|||||me<br>media|dian <br>n po|avera<br>sterio|ge pos<br>r prob|terio<br>. tru|r mod<br>e mod|el siz<br>el (in|e: _η_<br> %): _γ_|||
||Prior on _g_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|
|1|Benchmark _c_= 1|6.7|1.1|8.1|23|5.3|0.1|7.6|29|4.1|0.00|7.1|30|
|2|Benchmark _c_= 0_._1|7.0|1.0|8.2|21|6.3|0.1|8.1|20|5.3|0.01|7.9|19|
|3|Benchmark _c_= 0_._01|7.1|1.0|8.2|20|6.6|0.1|8.2|18|6.1|0.01|8.3|15|
|4|Maruyama-George|7.1|1.0|8.2|20|6.7|0.1|8.3|18|6.2|0.01|8.4|14|
|5|Bottolo-Richardson|7.9|0.5|9.1|10|7.3|0.1|9.0|9|6.7|0.00|9.2|6|
|6|Hyper-_g_, _a_= 3|7.5|0.8|8.5|16|7.0|0.1|8.5|15|6.5|0.00|8.6|11|
|7|Hyper-_g_, _a_= 4|7.6|0.8|8.6|15|7.1|0.1|8.6|14|6.6|0.01|8.7|10|
|8|F-Z, _a_= 2 + 2_/k_<sup>2</sup>|7.1|1.0|8.2|20|6.7|0.1|8.3|18|6.2|0.01|8.4|13|
|9|F-Z, _a_= 2 + 2_/n_|7.1|1.0|8.2|20|6.7|0.1|8.3|18|6.2|0.01|8.3|14|
|10|Horseshoe|7.3|1.0|8.3|18|6.8|0.1|8.3|17|6.4|0.01|8.5|12|
|11|Forte _et al._|7.3|0.9|8.3|18|6.8|0.1|8.4|17|6.4|0.01|8.5|12|
|12|Hyper-_g/n_|7.1|1.1|8.2|20|6.7|0.1|8.3|18|6.1|0.01|8.4|13|
|13|Horseshoe/_n_|7.2|1.0|8.2|19|6.7|0.1|8.3|18|6.3|0.01|8.4|13|
|14|Zellner-Siow|7.2|1.0|8.3|19|6.8|0.1|8.3|18|6.3|0.01|8.4|13|
|15|_g_ =_n_|7.5|0.8|8.7|14|7.1|0.1|8.6|13|6.6|0.01|8.8|8|
|16|_g_ =_k_<sup>2</sup>|6.2|0.9|7.8|31|4.9|0.0|7.2|36|3.8|0.00|6.6|28|

on the log predictive score, abbreviated to LPS (Fern ́andez _et al._ , 2001a,b).<sup>14</sup> In addition, analyzing the 50 estimation subsamples allows us to get a certain amount of robustness with respect to possible unusual data points, partly addressing some of the concerns voiced in Ciccone and Jaroci ́nski (2010)<sup>15</sup> with respect to data sensitivity. Bayes factors between models with different priors will be computed on the basis of the full sample. We consider four different priors on the model space: fixed _θ_ and random _θ_ as in (5), both with prior mean model sizes of _m_ = 7 and _m_ = _k/_ 2.

# _8.1. Cross-country growth regressions_

The context of cross-country growth regressions is one of the first areas within economics where the use of BMA has become popular. This area of macroeconomics is characterized by a particularly large number of potential drivers for growth and a scarcity of observations,

> 14 Alternative proper scoring rules are discussed in Gneiting and Raftery (2007).

> 15 However, also see the comment in Feldkircher and Zeugner (2010).

22
Table 5: Explaining **the Difference** in **Growth-Rate Changes between East Asia and Latin America (period** 1991-93 **compared** to 1986-90)

||Average chang<br>the periods 19<br>1991-|e between<br>86-90 and<br>93|Difference<br>East<br>Asian<br>|Predicted differ<br>rate changes<br>Miracles - La|ence in growth-<br>: East Asian<br>tin America|
|---|---|---|---|---|---|
||East Asian<br>miracles|Latin<br>Amenrca|miiracles-<br>Latin<br>Amenca|Including<br>investment|Excluding<br>investment|
|Per capita GDP growth|-1.12|2.04|-3.16|-2.77|-3.42|
|Policy indicators (total)||||-1.15|-1.63|
|Volume of trade/GDP|14.58*|20.43*|-5.85|-0.15|-0.07|
|Government<br>consumption/GDP|-5.27*|-5.10*|-0.17|0.00|0.00|
|Inflation rate|0.55*|-20.61*|21.16|-0.71|-1.13|
|M2/GDP|12.66*|17.13*|-4.47|-0.08|-0.02|
|Black market premium|-0.88*|-22.14*|21.26|-0.24|-0.41|
|Investment/GDP|10.55*|10.03*|0.52|0.02||
|Other detenninants of<br>growth (total)||||-1.63|-1.79|
|Initial GDP per capita|28.65*|0.22*|28.43|-1.35|-1.59|
|Average number of<br>secondary-school years in<br>the labor force (initial)|0.20|0.14|0.06|0.01|0.02|
|Percent change in terms<br>of trade|0.68|1.75|-1.07|-0.08|-0.08|
|Population growth|-0.42|-0.62|0.20|-0.20|-0.13|

* Average percentage change (log difference) from 1986-90 to 1991-93. As in the estimation regression, the variables inflation and black market premium are presented as one plus the respective rate.

24
trade events on the country under study: Madagascar (maize) and South Africa; and those dealing with the effect of a country’s own trade policy decisions: all others. Yet another way to categorize the cases would be according to the drivers of the trade policy event: multilateral (WTO) agreements: China and Vietnam; unilateral policy decisions: Chile, India and Madagascar (horticulture); and policy actions external to the country under study: Madagascar (maize) and South Africa.

The latter way of looking at things may yield some interesting insights into the political economy of trade policy. But to a trade economist, the first classification scheme mentioned above is the most economically meaningful, since it basically differentiates those cases whose effects are on a general equilibrium (economy-wide) level from those whose effects are mainly partial equilibrium. Of course, in making this distinction we have to recognize that, in the real world, the division between these two classes of cases is not black and white. For example, even though new opportunities to export cash crops were direct trade-related drivers of change in China’s Pingbian County, internal migration and the emergence of new domestic markets brought about by broader changes in the Chinese economy deeply affected livelihoods. And, as is suggested by the other taxonomies, each of the two categories includes examples of cases that differ in many ways from others in that category.

The trade events affecting mainly one product market are the most straightforward to conceptualize, since the linkages between the event and the specific market being studied are more direct. For example, the Madagascar maize study focuses on the effects of an external policy program in Europe that opened specific opportunities for maize exports while Madagascar was undergoing a more general process of trade policy reforms, which, in turn, amplified the impact of the market-specific policy. And the South Africa study modeled the potential impact on the sugar industry of a rise in sugar prices that would result from external trade liberalization. In these cases, the link between the event and the product market being studied is direct and linear: the event causes an increase or reduction in the demand and price of the product, without significantly affecting other markets.

In cases in which the trade event was general trade liberalization, the link with the market under study is more subtle. By “general trade liberalization” we mean a process of reducing barriers to trade more or less across the board, including import tariffs and non13
**Table 3.** Effect of the IST intervention at 12 and 24 months follow-up on health outcomes anemia and _Plasmodium falciparum_ <u>prevalence for study children.</u>

# **Outcome**

||**C**<br>**(50**|**ontrol**<br>**schools)**|**Inte**<br>**(51**|**rvention**<br>**schools)**|**Risk ratio**<sup>**a**</sup><br>**(95% CI)**|**p-value**|**Cluster-**<br>**size; range**<br>**(average)**|
|---|---|---|---|---|---|---|---|
|||**n (%)**<sup>**b**</sup>||**n (%)**<sup>**b**</sup>||||
|**12 month follow-up**|N=2478||N=2631|||||
|**Prevalence of anemia**<sup>**c**</sup>||||||||
|Unadjusted|2146|837 (39.0%)|2297|920 (40.1%)|1.03 (0.91,1.16)|0.646|15-55 (44.0)|
|Adjusted|2048|788 (38.5%)|2142|858 (40.1%)|1.03 (0.93,1.13)|0.621|15-55 (41.5)|
|**Prevalence of****_P.falciparum_**||||||||
|Unadjusted|2106|302 (14.3%)|2276|243 (10.7%)|0.76 (0.49,1.18)|0.221|11-55 (43.4)|
|Adjusted<sup>d</sup>|2106|302 (14.3%)|2276|243 (10.7%)|0.71 (0.46,1.11)|0.131|11-55 (43.4)|
|**24 months follow-up**|N=2468||N=2619|||||
|**Prevalence of anemia**<sup>**c**</sup>||||||||
|Unadjusted|2027|809 (39.9%)|2173|910 (41.9%)|1.05 (0.91,1.21)|0.514|15-55 (41.6)|
|Adjusted|1935|765 (39.5%)|2027|842 (41.5%)|1.00 (0.90,1.11)|0.953|14-55 (39.5)|
|**Prevalence of****_P.falciparum_**||||||||
|Unadjusted|2001|169 (8.5%)|2139|253 (11.8%)|1.42 (0.84,2.42)|0.192|15-55 (41.0)|
|Adjusted<sup>d</sup>|2001|169 (8.5%)|2139|253 (11.8%)|1.53 (0.89,2.62)|0.124|15-55 (41.0)|

Results presented (i) for all children with outcome data (unadjusted) and (ii) for those with baseline measurements of each outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis.

N=number of children eligible for follow up (not withdrawn or deceased)

> **a** Risk ratios (intervention/control) presented for binary outcomes (anemia & _P. falciparum_ prevalence) and are obtained from GEE analysis accounting for school-level clustering.

> **b** Number and percentage with outcome

> **c** Age-sex specific anemia was defined using age and sex corrected WHO thresholds of hemoglobin concentration: <110g/l in children under 5 years; <115g/l in children 5 to 11 years; <120g/l in females 12 years and over and males 12 to 14.99 years old; and <130g/l in males ≥ 15 years. All female adolescents are assumed to not be pregnant.

> d Not including baseline _P.falciparum_ infection

**Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics. **Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.

22
likely than people in low-trust regions to limit their mobility in response to public pandemic prevention measures (Bargain and Aminjonov 2020). Low levels of cohesion, by contrast, are linked with social discontent, political instability, and social tensions such as strikes, demonstrations, riots, and unrest (Alesina and Perotti 1996; Esteban and Ray 2011). While cohesion is generally positive, it can also lead to the exclusion of certain groups (such as ethnic minorities or those with a different sexual orientation) or be used to build support for violence toward a particular group (Chatterjee, Gassier, and Myint 2022).

Emerging rigorous evidence from randomized controlled trials, meta-analyses, and systematic reviews also shows the potential impacts of efforts to strengthen cohesion. For example, an impact evaluation of a community-driven development (CDD) program in the Kyrgyz Republic found that it increased the community’s sense of unity, cooperation, and perceptions of local governance and educational services, with weaker but still positive effects on addressing entrenched perceptions, changing attitudes, and building trust (Esenaliev et al. 2018). Likewise, a systematic review of peace-building interventions aimed at promoting cohesion in low- and middle-income FCV countries (e.g., intergroup dialogues and workshopbased peace education) found positive effects on trust, sense of belonging, willingness to participate and help, and acceptance of diversity – though the effects were small, suggesting the significance of broader challenges and structural or contextual barriers (Sonnenfeld et al. 2021).

# _Inclusion_

A broad body of evidence illustrates the costs of social exclusion – most commonly measured in terms of wages, lifetime earnings, education, and employment outcomes (Buehren, 34
<!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Cost of domain name registration (US$) Time required for domain name registration (days)<br>Cost (US$)<br>Number of days<br><!-- End of picture text --> _Source:_ Digital Business Indicators.

The increasing complexity of digital transactions as well as the increasing consumer demands on dataintensive contents such as streaming require improvements in connectivity technologies. The recent wave of the Internet of things (IoT), defined as “the use of sensors, actuators, and data communication technology built into physical objects,”<sup>66</sup> further expands the connectivity scope to physical objects. This movement offers businesses new opportunities to take the platform model to a higher level that connects everything from crop monitoring to solid waste management. The accompanying soaring data traffic calls for strong and reliable connectivity. The World Bank has announced an initiative to partner with the GSMA to build a global IoT infrastructure to intensify connectivity in developing countries, which is essential to unlocking new drivers of economic development.

Another area for further study is the efficiency of spectrum management in different countries. In the transition to 5G technology, the flexibility of market-based mechanisms in spectrum management will determine the pace at which countries can adopt next-generation technology to further promote the growth of digital businesses. 5G technology requires spectrum at higher frequencies. Identification of the available spectrum through regulations for the licensed, unlicensed, and shared spectrum will be crucial to countries’ efforts to manage the exponential increase in data traffic and provide ultra-fast and ultra-reliable internet connections. Such an effort also requires working at the local, regional, and international level to reduce interference.

# _Payment_

Digital payment infrastructure is recognized as the bedrock to ensure smooth transactions among all market players in the digital economy. According to Findex 2017 data, globally 52 percent of adults have made or received a digital payment in 2016/2017. Payment using a debit or credit card through a mobile money account or the internet are included in this finding. Developing countries’ use of digital payments is growing rapidly. In low-income countries, the share of the adult population using digital payments increased from 15 percent in 2014 to 26 percent in 2017. (figure 6).

# **Figure 6. Digital payments have been rising across all countries in recent years**

17