rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Can foreign investors trigger an efficiency-enhancing structural change in the recipient countries? While financial openness seems to boost overall investment and economic activity (Henry 2000a; Bekaert, Harvey, and Lundblad 2005; Gupta and Yuan 2009; Levchenko, Ranci`ere, and Thoenig 2009), things get more complicated when looking at the structural impact of foreign investors. Not only do foreign-owned firms affect the productivity and corporate governance of their immediate competitors and suppliers (Javorcik 2004; Albuquerque et al. 2019). The entry of foreign investors also brings economy-wide structural changes with ambiguous impact on economic efficiency. On the one hand, financial liberalization can trigger positive changes in corporate regulations and governance (Stulz 2005; Doidge, Karolyi, and Stulz 2007; Kaminsky and Schmukler 2008). On the other hand, it can decrease incentives for the government to pursue necessary economic reforms (Fern ́andez-Villaverde, Garicano, and Santos 2013; Challe, Lopez and Mengus 2019) and discourage domestic banks from proper monitoring of their corporate borrowers (Alessandria and Qian 2005; Tressel and Verdier 2011). An inflow of foreign capital can improve market efficiency by relaxing financing constraints (Gupta and Yuan 2009), but it can also worsen capital misallocation by benefiting mostly unproductive firms (Reis 2013; Gopinath et al. 2017). Identifying the full structural impact of financial liberalization thus requires data that are both disaggregated and cover the whole economy.

This paper examines whether the entry of foreign investors aligns the export portfolio of the recipient country with its comparative advantage. The use of disaggregated data covering countries’ entire export portfolios allows us to capture the full structural impact of foreign investors, including potential firm-to-firm spillovers and economy-wide externalities. Moreover, the product-level data on international trade are available and comparable across a large set of diverse countries. This makes them a valuable alternative to firm-level census data, which allow for an analysis of structural changes within one particular country (e.g., Varela 2018). Finally, the concept of comparative advantage in international trade provides a natural framework to examine allocative efficiency. After all, the idea that countries should specialize in exporting products where they possess 1
therefore promote exports of firms in non-member countries. This evidence shows that deep RTAs have positive spillover effects through nondiscriminatory provisions that can more than offset traditional negative effects on non-member firms of discriminatory measures.

Table 4: Third-country effects of deep RTAs with certain provisions

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Export pa|rticipation|||Export|entry||
|Exp Other...(t-1)|||||||||
|... RTA|0.025***|0.022***|0.010***|0.009***|0.011***|0.010***|0.006***|0.005***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|
|... Depth MFN|0.055***||0.036***||0.021***||0.015***||
||(0.003)||(0.002)||(0.001)||(0.001)||
|... Depth Pref|-0.036***||-0.021***||-0.014***||-0.009***||
||(0.002)||(0.002)||(0.001)||(0.001)||
|... SPSorTBT||0.013***||0.011***||0.005***||0.004***|
|||(0.001)||(0.001)||(0.000)||(0.000)|
|... Border|||0.058***|0.059***|||0.021***|0.021***|
||||(0.002)|(0.002)|||(0.001)|(0.001)|
|... Language|||0.007***|0.006***|||0.004***|0.004***|
||||(0.000)|(0.000)|||(0.000)|(0.000)|
|... Colony|||-0.016***|-0.017***|||-0.005***|-0.006***|
||||(0.001)|(0.001)|||(0.001)|(0.001)|
|Observations|20,797,980|20,797,980|20,797,980|20,797,980|20,694,604|20,694,604|20,694,604|20,694,604|
|R-squared|0.463|0.463|0.466|0.466|0.178|0.178|0.179|0.179|
|Firm-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Dest-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-dest FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm level, are in parentheses. The dependent variable _yij,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) to country _j_ in year _t_ . *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 We also find that agreements that include SPS or TBT provisions, which address sanitary and technical standards, have larger positive impact on entry decisions of Costa Rican firms. While an RTA without these provisions increases the probability of export entry for firms with previous export experience to one of the member countries by 0.5 percentage points, the effect for agreements that include SPS or TBT provisions is 0.4 percentage points higher than and statistically different from those do not include them (column 7). These findings support our hypothesis that deep integration entailing some form of regulatory convergence matters as it makes destination markets more “similar”. Specifically, it facilitates exports from third-country firms that already adapted to the standards of one of the RTA member countries. We explore this channel further in the next section by testing whether these effects are stronger for regulation-intensive goods.

# **4 Regulation-intensive products**

To further pin down the mechanism at work, we exploit the information on products. Specifically, we test whether the third-country effect of deep trade agreements is stronger 13
||All|Top25%<br>Crossovers|Remaining75%<br>Crossovers|Difference|
|---|---|---|---|---|
||(4)|(2)|(3)|(4)|
|PanelA: Cognitive Skills|||||
|Digitspan Score (Score 0-8)|3.38|3.93|3.43|o49**|
||[1.34]|[1.31]|[1.28]|(0.16)|
|Raven Test Score (Score 0-12)|5.61|6.41|550|091|
||[2.89]|[2.66]|[3.11]|(0.61)|
|Education: Completed more than secondary (1=Yes; 0=No)|0.43|0.44|0.44|-0.01|
||[0.49]|[0.50)|[0.50]|(0.08)|
|PanelB: Commitment to Business<br>|||||
|Preference (1=Yes; 0=No)|0.34<br>|0.59<br>|0.36<br>|0.22*<br>|
||[0.47]|[0.50)|[0.48]|(0.12)|
|Saw market opportunitywhen starting (1=Yes; 0=No)|0.10|0.29|0.17|0.12|
||[0.30]|[0.46]|[0.38]|(0.07)|
|Business Knowledge Index (Score 0-7)|463|5.17|464|0.52***|
||[1.04]|[0.80]|[0.93]|(0.11)|
|Panel C: Connections|||||
|Married (1=Yes; 0=No)|0.65|0.76|0.70|0.06|
||[0.48]|[0.43]|[0.46]|(0.07)|
|Spouse works in a business (1=Yes; 0=No)|0.33<br>|0.66<br>|0.46<br>|0.19**<br>|
||[0.47]|[0.48]|[0.50]|(0.09)|
|Had a Male role model (1=Yes; 0=No)|0.26|0.44|0.25|0.18**|
||[0.44]|(0.50)|[0.44]|(0.08)|
|PanelD: NonCognitive Skills|||||
|Self Efficacy (Score 0-1)|0.84|0.87|0.85|0.02*|
||[0.09}|[0.06]|[0.08]|(0.01)|
|impulsiveness (Score 0-1)|0.63|0.60|0.65|-0.05**|
||[0.11]|[0.08]|[0.12]|(0.02)|
|Organized Person (Score 0-1)|077|0.80|0.78|0.02|
||[0.19]<br>|[0.16]<br>|[0.18]<br>|(0.03)|
|Observations|790|41|119||

Notes: (1) Columns (1), (2) and (3) report means with standard deviations in parentheses.

(2) Column (4) reports the test of differences of means across columns (2) and (3) for Crossovers vs NonCrossover Businesses with standard errors in parentheses.

(3) In all regressions, standard errors are clustered by subcity. (4) | NonCognitive Skills: Self-efficacy statements to determine index: | am strong enough to overcome life’s struggles; At root, | am a weak person; | am particularly good at realizing ideas; | can handle the situations that life brings; | often think that I'm a failure; | usually feel | can handle the typical problems that come up in life; | feel competent to deal effectively with the real world; I'm usually an unsuccessful person; | often feel that there is nothing | can do well. Impulsiveness statements to determine index: | save regularly and | make up my mind quickly. Organized person question: My family and friends would say | am a very organized person.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

*Significant at the 10 percent level.
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Sierra Leone‘s existing spending amounts to almost 10 percent of 2009 GDP (figure 21). This represents a moderate level of effort, somewhere between average spending burdens for low income fragile and low income non-fragile countries. Relative to its peer group, Sierra Leone is much more reliant on ODA for capital funding in power, transport, and water sectors. Sierra Leone‘s investment effort on all sectors, except water and sanitation, is substantially higher than the respective average for other LIC fragile countries (figure 22). The largest share of total spending goes to transport (45 percent), followed by power (27 percent), and ICT (17 percent). Only 12 percent of total funding is channeled to WSS, the sector with the highest spending needs. In addition, investments into the water sector in Sierra Leone are almost exclusively financed by ODA.

**Table 13. Financial flows to Sierra Leone’s infrastructure**

<u>US$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|0|0|0|5|17|23|23|
|Power|13|7|12|4|0|23|36|
|Transport|21|9|25|6|0|39|60|
|WSS|7|1|8|0|0|9|16|
|Total|40|17|45|15|17|94|134|

_Source_ : Derived from Foster and Briceño-Garmendia 2009.

_Note_ : The public sector figures are averages of actual spending for 2007–09, in the case of government data. In the case of SOEs, the average spans 2004–08 and in some cases 2004–09. Funding from external financiers is averaged over the 2002–07 period. O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

# How much more can be done within the existing resource envelope?

About $66 million of additional resources could be recovered each year by improving efficiency (table 14). The most vexing problem is curbing distribution losses in power and underrecovery of costs in roads and water. Reducing distributional losses could save $3 million in water and S$16 million in power annually. Every year up to $19 million is lost due to under charging for services. A better-aligned roadfuel levy could bring in an additional $11 million for roads, and raising water tariffs would capture in additional $8 million. Collection of bills for water and power services needs to be improved. Sierra Leone is losing $0.9 million per year on water and $8.3 million on power due to undercollection of bills for the services. Overstaffing in the power parastatal is quite significant. Cutting the staff levels to economically viable benchmarks could save an estimated $2.4 million on power. Improving execution of budgeted governmental funds in roads could translate into $6 million of additional financing for the roads sector and $1.4 million for the power sector annually. Looking across sectors, the power sector can benefit the most from tackling identified inefficiencies, followed by the road and water sectors.

41
- 44 -

- (1) Increased attention must be given during project preparation to the demand for project services and to their fit with beneficiary needs especially where the poor are the primary beneficiaries. Well-focused sector work can aid these efforts. Bank staff must be encouraged to provide for beneficiary or community participation in projects and to facilitate the organization of the poor if appropriate in the project context. This may add to the time and resources required for project preparation but result in more sustainable projects.

- (2) Institutional diagnosis and design done jointly by the Bank and borrower should be the norm so that a greater sense of ownership and commitment on the part of the latter is created and a firm foundation for sustainability is laid.

- (3) Greater attention to project supervision is necessary as more complex ID issues are being dealt with. Discussions irith Bank staff reveal a growing concern that a major casualty of the tightening of supervision will be ID performance. OED data on SID and NID cases have clearly der,onstrated the key role of supervision and staff continuity.

While these are important actions, the fact remains that projects which are judged by their implementation record as opposed to their sustainability are likely to be unduly swayed by a short-term focus. Furthermore, as noted above, the traditional investment project is too weak an instrument to deal with issues such as long-term incentives and sector ID strategy. Fortunately, the Bank has in recent years found new lending instruments such as adjustment operations to compensate for this lack at least partially. Similarly, public investment and expenditure reviews can be used to aid sustainability now. While these are positive developments, the macrolevel focus in adjustment lending, its quick disbursing nature, and its tendency to support mostly top down and standardized reforms have left an important gap<sup>34</sup> .

There is at present no systematic way in which operational staffs' knowledge and cumulative project experience on institutional constraints are tapped and used to evolve selective country or sectoral ID strategies on which the Bank and borrowers can work together. Indeed, problems in this category are sometimes addressed through adjustment operations, TA loans, etc. The issue is one of developing an approach to the problem of sustainability by building on project level experience.

Take, for example, the promŽlem of TA. When design capabilities and specialized skills are absent in a country, a project typically provides for expatriate consultants (in transport, agriculture, industry, etc.).

34 A review of 33 SALs approved in the last three years shows that nearly two-thirds had measures to reduce public employment and wage bill while only a third had plans to strengthen economic management/personnel reforms.
Policy Research Working Paper 8377

# **Abstract**

Based on a randomized evaluation, the paper shows that a household-targeted Philippine cash transfer program significantly raised the local price of key foods relevant for child nutritional status. This shift in prices increased stunting among young nonbeneficiary children by 34 percent (11 percentage points). Price and stunting effects increase in program saturation; at median saturation, the village income shock is 15 percent. These effects persist 2.5 years after program introduction. The authors confirm the price patterns in their experimental sample against price information from nationally-representative household expenditure surveys across the 6-year rollout of the program. Failing to consider such general equilibrium effects may overstate the net benefits of targeted cash transfers. In areas where targeting of social programs covers a large proportion of the population, offering the program on a universal basis may avoid such long-lasting negative impacts at moderate additional cost.

This paper is a product of the Development Research Group and the Social Protection and Labor Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at ekandpal@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
6

# III. **METHODS, DATA AND DESCRIPTIVE STATISTICS**

We model the effect of insurance on the probability of an individual incurring large annual out-of-pocket payments, with large being defined relative either to the sample average income or the per capita income of the individual’s own household.

Use of a basic probit or logit model on cross-section data could reasonably be criticized on the grounds that selection into health insurance is non-random and occurs in such a way as to generate a correlation between the insurance dummy and the error term. This could in principle apply to the CMS, GIS/LIS schemes and private/other schemes. In the case of the CMS scheme, it seems more likely that any selection is adverse to the scheme: people with unobservables that predispose them to a higher-than-expected risk of ‘high’ expenses are more likely to enroll, biasing the coefficient on insurance upwards. In the case of the GIS/LIS scheme, and possibly the private/other category as well, the bias seems likely to be in the opposite direction: the scheme’s members are likely to have a very low risk compared to the population at large, and we may only partially capture this by the observables that we enter in our probit model. We try to get round the possible endogeneity of insurance in two different ways, depending on the dataset. Two of the three household surveys we use are panels. These permit us to allow for an unobserved time-invariant fixed effect capturing unobserved risk, which could be correlated with insurance coverage; the model we use in these surveys is a fixed-effect logit model (cf. e.g. Wooldridge 2002). In the cross-section survey, we use instrumental variables (IV). We capture insurance coverage using two variables, the first indicating whether the individual is a member of a CMS, the second capturing membership of the GIS or LIS.<sup>5</sup> As instruments for our insurance variables, we use a variable capturing whether or not a CMS exists in the village where

> 5  In the survey in question, very few respondents said they had private or ‘other’ insurance. This reflects the date of the survey (1998) and the fact that it covers China’s poorer western and central provinces.
What explains aid project success in post-conflict situations?

Lisa Chauvet<sup>_∗_</sup> , Paul Collier<sup>_†_</sup> and Marguerite Duponchel<sup>_‡§_</sup>

> _∗_ IRD, DIAL

> _†_ University of Oxford, CSAE

> _‡_ The World Bank

> _§_ We are grateful to Jean-Claude Berth ́el ́emy, Fabrizio Coricelli and Eliana La Ferrara for useful comments. We also thank participants to the CSAE conference (2009) and to the UNU-WIDER conference _Africa: myths and realities_ in Accra (2009). This paper constituted a chapter of Marguerite’s PhD thesis (University Paris 1 Panth ́eon-Sorbonne, CES). The usual disclaimers apply.

1
# **Land fragmentation, cropland abandonment,**

# **and land market operation in Albania**

Klaus Deininger<sup>1</sup> , Sara Savastano<sup>2</sup> , Calogero Carletto<sup>1</sup>

- 1 World Bank, Washington DC, USA

- 2 University of Rome Tor Vergata, Italy

**Keywords** : Fragmentation, efficiency, land markets, agricultural productivity, investment JEL Codes: O 13; Q 15

**Sector Board:** ARD

We thank G. Adlington, H. Binswanger, B. Blarel, M. Childress, L. Christiaensen, W. Martin, H.K. Nagarajan, J. Swinnen, M. Torhonen, and four anonymous reviewers for insightful comments and discussions. Funding from the Norwegian Governance Trust Fund is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Executive Directors, or the countries they represent.

1
most effective (hurts the most) against formal firms. In Section 3, we investigate which business environments are more conducive to effective informal competition. Section 4 concludes.

# **1 The data**

The data we use come from three World Bank datasets; Enterprise Surveys, _Doing Business_ and the _Worldwide Governance Indicators_ .

The _Enterprise Surveys_ collect data from key manufacturing and service sectors in every region of the world. For our analytical purposes, we focus on manufacturing firms and we use the 2006 World Bank Enterprise Surveys for Latin America. In this newly collected roll-out, 6,466 formal manufacturing firms were surveyed in fourteen Central and South American countries; Argentina, Bolivia, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru and Uruguay. The Enterprise Survey roll-out in Latin America was done at the same time and with the same questionnaire and the same survey implementation and sampling strategy. Given the standardized methodology (discussed below) the data are optimized for the type of cross-country comparisons employed throughout this paper.

The surveys employ standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across economies. The sampling methodology of the Enterprise Surveys generates sample sizes appropriate to achieve two main objectives: first, to benchmark the investment climate of individual economies across the world and, second, to conduct firm performance analyses focusing on determining how business environment constraints affect productivity and job creation in selected sectors. The universe of industries is stratified by region, size and industry. The number of manufacturing industries to be defined as an individual stratum in each country is chosen based on the Gross National Income (GNI) level of each country.

The standardized Enterprise Survey questionnaire includes both objective and subjective questions referring to the business environment. Subjective variables are based on the perceptions of the surveyed firms regarding key factors that constrain their operations. The objective questions refer to specific quantifiable measures of firms’ activities (sales, number of workers, type of credit received, etc.) and objective description of constraints they face (number of power outages, number of days to get an electricity connection, losses due to theft, etc).

To measure the fixed and variable regulatory costs that a firm incurs to start and operate a business in Latin America we use several of the 2006 _Doing Business_ indicators. The _Doing Business_ database provides comparable, objective measures of business regulations for 175
22 Surprisingly, debt burden seems to have had no serious effect on the performance of the countries in attaining stabilization targets. On export growth, the countries with comparatively higher debt burden seem to have generally done better tan groups with less debt burden.

Annex II presents the comparison of projected values and actual outcomes by various groups of countries.

IV. IeoomMendatona Further country by country analysis of PFP projections and actual outcomes will be useful to ascertain how much of the observed divergence between projections and actual outcomes is due to external factors (such as weather and terms of trade effect) and how much of it is due to lack of progress in policy reforms. The latter can be due to factors that were unforeseen - as for example, political changes, or internal strife can derail progress. Less than targeted progress can bejust due to unrealistc targets that did not fully take into account politcal and institutional reality. A future country by country review can look into these questions in depth.

In the future, the quality and realism of PFP projertons is likly to improve if certain steps are taken while mnalang these projections. First, all PFPs after the first one should contain a review of the outcomes in relation to projections of previous PFPs. Second, whenever the chances of falling short of projected or targeted outcomes are high, they should be noted. Third, using references such as this review, the projections could