rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Impact of Anti-Pest Treatments (APT)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Anti-pest treatment (output)|-0.126|-0.353|-0.479|
||(0.219)|(0.298)|(0.297)|
|Anti-pest treatment (input)|3.756***|5.478**|9.235***|
||(0.885)|(2.167)|(2.559)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.695|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of anti-pest treatments (APT), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output APT_ is the share of HS-10 digits products subject to at least one APT within the corresponding five digit industry. For each five digit industry, _Inputs APT_ is the weighted sum of output APT in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

21
In the top panel of Table 1a, the base case is the 148 large cities in developed countries. Relative to these, Sub-Saharan African cities have higher measures across the board, including in particular the coefficient of variation and covariance terms, where they are respectively 44 and 27 per cent higher. Moreover, terms for Africa are higher than the rest of the developing world terms, including those for the coefficient of variation and covariance terms. With no separation into nocturnal and diurnal populations, we do not know if this involves greater clustering of residences or workplaces, or both; it is the ambient population as the measure of economic density. The bottom panel adds a control for ln GDPpc. This reduces the Africa terms making them smaller absolutely and relative to the rest of the developing world. Now the differentials on the coefficient of variation and covariance terms are insignificant. In summary, in the raw data Sub-Saharan African cities have higher coefficients on the coefficient of variation and covariance terms, which contradicts the presumption of the literature. Moreover, greater clustering seems to be negatively related to GDPpc, with developed countries having the lowest degree of clustering, perhaps where automobile cities like Atlanta and Houston form the stereotype.

In terms of just developing countries, Table 2, shows that relative to Asia, the outlier with lower clustering is Latin America even controlling for income. Sub-Saharan Africa, as well as North Africa and the Middle East, have similar measures of density and clustering as Asia. Overall compared to the rest of the developing world, Sub-Saharan African cities have (not controlling for GDP per capita) higher average densities of people, but no different degree of economic density as measured by PPD or RPA and no different degree of clustering. Controlling for income, Sub-Saharan African cities are similar to others in the developing world in all measures.

# **4 How are differences in economic density across the spatial hierarchy related to income differences?**

This section first describes the data on income and wages and then the characteristics of the sample of cities and LD settlements in the covered countries. After giving the base specification, we turn to a set of results on the relationship between agglomeration measures and 14
around the world are notorious for their inferior performance as asset managers compared with most mutual funds.<sup>23</sup> The involvement of insurance companies, which is necessary when longevity insurance is decentralized, may depress investment returns. Admittedly the creation of specialized entities with a high level of transparency may propel insurance groups to become more efficient asset managers but this remains to be seen.

A second concern is connected with the level of operating costs. High administrative costs are often incurred and large commissions are paid to agents and brokers as part of aggressive marketing campaigns to increase market share. Annuity providers amortize these costs over the longer run and charge high commission fees to annuitants. Annuity providers may also engage in transfer pricing whereby internal services offered by another unit of a large group are billed at artificially high prices. High operating costs depress the level of distributable profits.

A third concern is linked to the policy of profit distribution. Together with the level of operating costs, the rate of profit sharing should be one of the most important criteria for selecting providers of variable annuities. Past returns, which are often emphasized by sales agents, are not a good predictor of future performance, while regulating profitsharing rates introduces rigidity and may cause competitive distortions. A high level of transparency is a minimal and essential requirement.

# **Central register of pricing policies and performance**

To strengthen competition in the market for variable annuities, a central register needs to be created to compile comparative data on a consistent and informative basis on the performance and bonus policies of different providers. This will enhance the transparency of the system. The register should focus on key aspects of pricing policies and performance such as profit-sharing policies, levels of operating costs, levels of commission fees and consistency and soundness of investment policies rather than on a mere reporting of past performance data. At the same time, annuitants should be encouraged to select providers on the basis of factors that affect long-term performance.

However, retail financial markets are notorious for their limited responsiveness to improved sources of information. A certain degree of price dispersion, poor investment performance, and high operating fees are likely to persist under even a tight regulatory framework. Policymakers need to weigh the relative merits and drawbacks of decentralized competitive markets against the corresponding advantages and disadvantages of centralized provision.

# **Pricing policies of centralized providers**

The regulation of pricing policies of centralized providers faces fewer challenges. There is little concern about price dispersion and deceptive policies. The main requirement is for a high level of transparency and public accountability. Pricing policies need to reflect

> 23 An interesting study that documents in great detail the underperformance of insurance mutual funds is contained in Chen et al, 2006.

31
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Water supply and sanitation

AMCOW (African Ministerial Council on Water) 2010. _Country Status Overviews on Water Supply and Sanitation 2010_ . Regional Synthesis Report, August.

- Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008.―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2009. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- IBNET (The International Benchmarking Network for Water and Sanitation Utilities). 2009. www.ibnet.org/.

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, World Bank, Washington, DC.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

- WHO (World Health Organization) Joint Monitoring Program (JMP). 2010. ―Zimbabwe Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

- ———. 2010. ―Zimbabwe Estimates for the Use of Improved Sanitation Facilities.‖ www.wssinfo.org/resources/documents.html.

World Bank. 2009a. _Zimbabwe WSS Sector Budget Review_ . Draft October 30, 2009.

# Other

IMF (International Monetary Fund). 2009. Zimbabwe: 2009 Article IV Consultation—Staff Report; Public Information Notice on the Executive Board Discussion; and Statement by the Executive Director for Zimbabwe. Report No. 09/139, May 2009.

- International Energy Association. 2010. ―World Energy Outlook: The Electricity Access Database.‖ www.worldenergyoutlook.org/database_electricity10/electricity_database_web_2010.htm.

- ———. 2011. ―Electricity/Heat Data for Zimbabwe.‖ www.iea.org/stats/electricitydata.asp?COUNTRY_CODE=ZW.

54
## **[Figure 12 about here]**

In sum, we find supporting evidence that GPNs have exacerbated the great trade collapse during the Great Recession through three channels: a compositional effect, a trade cost effect and a bullwhip effect. First, in line with the _compositional effect_ , we find that the sectors that contributed most to the Chinese exports collapse are those where processing trade is more prevalent. Second, we show that, within industries, processing exports consistently dropped more than non-processing exports during the Great Recession. This is in line with the _trade cost effect_ since intra-GPN trade should be more sensitive to trade costs than regular trade. Third, in line with the _bullwhip effect_ , we show that in virtually all industries, the drop in demand for China’s processing exports led to a magnified drop in processing imports.

# **7. Conclusion**

What role do trade costs have on intra-GPN trade? Recent theoretical work has demonstrated the importance of this question, yet it has proven to be hard to empirically evaluate. We have tackled this question by using a unique data set on China’s processing trade regime. Under this customs regime, firms are granted duty exemptions on imported raw materials and other inputs as long as they are used solely for export purposes. As a result, the data set provides information on trade between three sequential nodes of a global supply chain: the location of input production, the location of processing (in China) and the location of further consumption. This makes it possible to examine the role of both trade costs related to the import of inputs ( _upstream trade costs_ ) and trade costs related to the export of final goods ( _downstream trade costs_ ) on intra-GPN trade.

In a first step to evaluate the role of trade costs on China’s processing trade, we have developed a three-country industry-equilibrium model in which heterogeneous firms from two advanced countries, _East_ and _West_ , sell their products in each other’s markets. Each firm can use two modes to serve the foreign market. It can directly export its products from its home country. Alternatively, it can indirectly export to the foreign market by assembling its product in a third low-cost country, _China_ , which is assumed to be located closer to _East_ than to _West_ . Our model illustrates that China’s processing 33
## 1. **INTRODUCUlON**

Community driven development<sup>isnota project. It is anapproachthat aims to empower communitiesand</sup> local governments with<sup>resourcesand theauthorityto usethese flexibly, thus taking controlof their</sup> development.' Empowerment<sup>means the expansion of assets and capabilities of poor people to participate</sup> in, negotiate with and<sup>holdaccountableinstitutions that affect theirlives.2It means giving peopleaccess to</sup> voice and information.<sup>greatersocialinclusionand participation,greater accountability,and organizational</sup> strength. CDD aims<sup>toharness socialcapital throughempowerment, and increase socialcapital through</sup> scaling up.

When we talk of scaling<sup>up CDD, we primarily meanscalingup the entire approach to empowerment.This</sup> is ideally multisectoral.<sup>However, sectoral scaling upis part of CDD whenit incorporatesgenuine</sup> beneficiary participation.<sup>Communitiescan be eithergeographicalentities(urbanneighborhoods,villages,</sup> sub-districts) or groups<sup>with common interests(water usersassociations,herders,members of micro-credit</sup> groups). These sectoral<sup>communitieswill benefit from empowermentand scaling up no less than</sup> geographical entities.<sup>So thispaper deals withthescaling up of both sectoraland multi-sectoralCDD</sup> programs.

Many other agencies<sup>are addressingempowermentand scaling up. The PRSP processin many high-</sup> indebted poor countries<sup>aims to strengthencommunities and reduce povertywithin a frameworkof good</sup> macroeconomic and<sup>sectoral policies.</sup><sup>_Empowerment and Poverty Reduction: a Sourcebook_(World Bank</sup> 2002) disseminates<sup>successful approachesand tools/practicesthat can be usefulstarting points for scaling</sup> _up. "Scaling-up Issues_<sup>_and Options: SupportingGoodPractice and Innovation"_is anotherimportant</sup> document under preparation<sup>in the Agriculture and RuralDevelopment Department of the WorldBank.</sup> This paper draws on<sup>theseand otherefforts.</sup>

# _Why is scaling up CDD_<sup>**_so dficult?_**</sup>

We all are famniliar with<sup>islands of successin community-drivendevelopment. Theseempower a few</sup> villages, urban neighborhoods,<sup>or producer organizations.How wonderfulif they could be scaled up to</sup> cover all communities<sup>ina provinceor nation!But thereare preciouslyfewscaling-upsuccesses.Five key</sup> problems explain why: ' World Bank. CommunitY<sup>DnvenDevelopment:a Vision of PovertyReductionthroumh Empowennent. Washington,</sup> D.C., Dec. 2000.

> 2 Narayan, Deepa (Ed.). Empowerment and Poverty Reduction: A Sourcebook, The World Bank, Washington D.C. 2002.

_5_
# The New Traae Protection: Price Effects of Antidumping and Countervailing Measuires in the United States

by Ann Harrison

||Table of Contents||
|---|---|---|
|I.|Introduction|1|
|I.|Theoretical and Institutional Framework|3|
|III.|Empirical Specification and Data|9|
|IV.|Results|16|
|Concl|usion|32|
|Biblio|graphy|33|

> * I would like to thank David Card, Avinash Dixit, Mike Finger, Gene Grossman, and Corinne Krupp for very helpful comments and suggestions. I would also like to thank Paul Meo at the World Bank for providing access to the UNCTAD data and the International Trade Commission for providing a concordance between the U.S. tariff system and the SITC classification. Paula Holmes provided computational support in transferring the data.
OPTIMAL SIMPLE FISCAL RULES 10 3.1.5. _Welfare Approximation._ Assume the government assigns weight _ωU_ to the HtM and 1 _−ωU_ to the Ricardian HH so that different paths of consumption are ranked by the government according to the following social welfare function: Up to a second order, the problem of maximizing Equation (3.9) is equivalent to minimizing Equation (3.10), where _c_ ˆ<sup>_′_</sup> _t_<sup>and</sup><sup>_c_ˆ</sup><sup>_′′_</sup> _t_<sup>denotetheproportionaldeviationofRicardianandHtMcon-</sup> sumption from their steady state values: where One can interpret Equation 3.10 as the share of steady-state consumption that the household is willing to give up _each period_ to eliminate the variance of consumption over the business cycle. In a world with complete markets, the household could sign a state-contingent contract with foreign investors so that equilibrium consumption is constant and welfare loss is zero. However, due to the less sophisticated financial structure assumed in this model, income shocks will lead to consumption volatility and welfare losses. The higher the variance of consumption, the greater the welfare loss.<sup>13</sup> 3.1.6. _Fiscal Rules._ The simple fiscal rule dictates how transfers to each type of household change in response to _observable_ economic variables. In particular, we allow transfers to respond to deviations of public assets _At_ , non-resource income _Yt_ and the international commodity price _Pt_ from their respective long-term (steady state) levels. Note that transfers are written in per capita terms, so the total proportion of assets transferred (for example) is (1 _− ω_ ) _θa_<sup>_′_+</sup><sup>_ωθ_</sup> _a_<sup>_′′_</sup>

> 13In our baseline calibration, we assume that _Css′′_<sup>=</sup><sup>_C_</sup> _ss_<sup>_′_(with</sup><sup>_ω_=</sup><sup>_ωU_=0</sup><sup>_._5),whichresultsinequally-weighted</sup> variances of the two households. Alternatively, if HtM HHs had half the steady state income as Ricardian households (with _σ_ = 2), then Φ = 2 and Ψ = 2 _/_ 3, which means the variance of HtM consumption would have twice the weight as variance of Ricardian consumption.
Those who are not admitted to any government school may choose to re-take the examination the following year, or may consider schooling in Uganda, vocational education, or private schools with different standards. Though an official letter of admission to a government secondary school is rare below this cutoff, it is still not guaranteed for those above it because the number of candidates passing the KCPE may exceed the number of spaces available in public schools (Aduda 2008, Akolo 2008). Among those who are admitted to secondary school, however, many are still unable to afford tuition and assorted fees: while primary school has been inexpensive for many years, and was made nominally “free” in 2003, even the lowest-tier district secondary schools cost hundreds of dollars per year during the period observed in this study.<sup>5</sup>

# **2.1 Data: KLPS2 surveys**

This admission rule suggests a fuzzy regression discontinuity design for estimating the impacts of secondary schooling. The primary dataset used in this study is the Kenyan Life Panel Survey (KLPS), an ongoing survey of respondents originally from Funyula and Budalangi Divisions of Busia District, Kenya (Baird, Hamory, and Miguel 2008). The respondents were sampled from the population attending grades 2 through 7 at rural primary schools in 1998. The first round of surveying (KLPS1) was carried out from 2003 to 2005, while the second (KLPS2) ran from 2007 to 2009, both times tracking respondents across provincial and even national boundaries. Because the

> 5Policy changes after 2008 made low-tier secondary schools considerably less expensive in Kenya.

7
The Impact of Mass Bed Net Distribution Programs on Politics: Evidence from Tanzania Kevin Croke<sup>_∗_</sup> World Bank and Harvard T. H. Chan School of Public Health JEL codes: D72, F35, H43, I15, C21 Keywords: health, malaria, insecticide-treated bed nets, political economy, regression discontinuity design, Tanzania

> _∗_ email: kcroke@worldbank.org. This article benefited from helpful conversations and input from Jessica Cohen, G ̈unther Fink, Kristen Himelein, Jon Kastelic, John Marshall, Horacio Larreguy, Dan Rogger, and seminar audiences at the Harvard School of Public Health, the Working Group on African Political Economy (WGAPE), APSA, the African Studies Association, and the World Bank.
A key issue in analyzing any intervention using survey data is the extent to which the treatment changes self-reporting of outcomes holding actual outcomes constant. For example, if respondents try and give socially-desirable outcomes, they may say they have saved after the training, even if they have not, because the training emphasized savings as a desirable activity. We use two main approaches to help mitigate this problem and ensure that any effects measured are likely to be genuine. First, since much of our analysis involves a comparison across treatments, this form of potential bias is likely to be lower since we can effectively difference out the common effect of treatment on self-reporting. Second, we trace out the causal chain from knowledge, to behaviors and then outcomes and look for consistency in impact throughout this chain.

# **3. Analysis**

To analyze the impact of financial literacy training on different outcomes of interest, we estimate the following regression equation: Outcomei,t = a+ b∗TAi + c∗TBi + d∗TCi + e∗Outcomei,0 + f∗Roundt + εi,s (1) Where _TAi_ , _TBi_ and _TCi_ are dummy variables indicating assignment to treatment A (Migrant Only), B (Family Only), and C (Migrant and Family), respectively. To increase power we pool together the three follow-up rounds (McKenzie, 2011).<sup>7</sup> Survey Round indicators are included in the regressions. Robust (White-corrected) standard errors, clustered at the individual level, are reported in parentheses under the coefficients in the tables. Since the regressions are based on original assignment to treatment, all coefficients are estimating _intention to treat_ (ITT) effects. The ITT estimate is the relevant measure to focus on for overall policy impacts.<sup>8</sup> The pooled sample takes into account all available data and we analyze an unbalanced panel with 394 clusters.<sup>9</sup> Since our surveys contain a number of questions related to financial knowledge, behaviors, and outcomes, we follow Kling et al. (2007) in creating aggregate indicators for different families of outcomes. For binary variables, this aggregate outcome is simply the average of the individual questions, while for continuous outcomes it is the average z- score (obtained by subtracting the mean of each variable and dividing it by its standard deviation). Appendix Tables A2-A4 present impacts on the individual questions which make up these aggregates.

> 7 Appendix Tables A5 and A6 show round-by-round results.

> 8 The take-up of treatment was fairly high, as analyzed in the previous section, and our results are robust when estimating _Treatment on Treated_ (TOT) effects.

> 9 We have re-run our entire analysis using a balanced panel across all waves and the results are robust.

11