rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> (MtCO,e)<br>600<br>518<br>500<br>432 -40% -55%<br>400<br>333<br>300 261<br>200<br>100<br>0 Historical Historical Baseline “Fitforss:<br>~100 1990 2017 2030 2030<br>Hnon-ETS METS wm Lulucf<br><!-- End of picture text -->
Regulation and private sector investment in infrastructure: Evidence from Latin America by Sheoli Pargal*

* The World Bank. Funding from the Regional Studies Program of the LAC Region of the World Bank is gratefully acknowledged, as is the invaluable guidance of Luis Serven. Thanks to Cesar Calderon and Luis Serven for disaggregated investment data and Luis Guasch for information on the characteristics of regulatory regimes. Thanks also to Phil Keefer, Michel Kerf, Arijit Sen, and Nils Tcheyan for helpful comments.
approaches may be used, but these also place specific restrictions on the covariance of the unobserved factors.

In Section 3.3 below we implement such upper-bound and mixed-effects approaches. However, for now, our interest is in a procedure consistent with an unrestricted linear model. This requires we treat both latent factors as fixed effects with a (possible) non-zero covariance – i.e., we are in a two-way fixed-effects setting. Simultaneous estimation of crossed factors raises distinct technical challenges. In most applications, including here, the dimensions of the effects are extremely high, and their design is unbalanced. Consequently, standard approaches, such as inclusion of a full set of dummy variables, are not computationally feasible. Also, some kind of normalization restrictions are required in order for the model to be identifiable due to model over-parameterization (Mittag, 2012). Following the contributions of Abowd et al. (2002), among others, various solutions to these challenges have been proposed. Guimarães and Portugal (2010) show how a partitioned iterative algorithm can be optimized to solve the normal equations of a least-squares problem including multiple high-dimensional fixed-effects. The algorithm avoids the problem of inverting a large sparse matrix and can provide direct estimates of the fixed-effects.<sup>11</sup> Two more specific challenges arise if the properties of the fixed effects are of standalone interest, as here. First, as the fixed effects are estimated with error, estimates of their variances will be biased upwards. Thus, variance shares calculated directly from the fixedeffects estimates will tend to over-state their importance relative to the residual component (Koedel et al., 2015). Second, Andrews et al. (2008) demonstrate that the covariance of the estimated fixed-effects vectors tends to be biased downwards. This is driven by a quasimechanical relation, whereby if one factor (e.g., household effects) is over-estimated then on average the other factor (e.g., schools) will be under-estimated (also Andrews et al., 2012). Intuitively, this reflects the general problem of model over-parameterization; and the magnitude of bias tends to be larger where fewer observations are available to estimate each effect.

Addressing these challenges remains an active area of research. Nonetheless, in Appendix A we set out the details of our proposed solutions. To correct for measurement error, we apply a conventional procedure that shrinks the variance contribution of estimated factors in accordance with the number of observations available to estimate each effect (see also Stanek et al., 1999; Koedel et al., 2015).

To deal with downward bias in the between-factor covariance, we propose a novel approach. Looking ‘under the hood’ of the iterative algorithm reveals that a part of the bias is driven by how starting values for the fixed-effects are calculated. In previous applications, extreme

> 11 Their procedure is implemented in Stata under the user-written _reghdfe_ command (Correia, 2017).

9
source (i.e. the respective website). After all the preparatory steps, the data collection starts, using robust web scraping and parsing algorithms which are replicable and scalable allowing for continuous data collection at a comparatively low cost (assuming source data format and structure remain the same). The last stage is the data evaluation. Even the best source annotation and data collection effort can result in data scraping or parsing errors. Evaluating the collected data is therefore critical for assuring quality data which is well structured and accurately mirrors the official government data. Once the first version of the collected data is ready, our research team verifies that the data structure corresponds to the GCO legislative data template (variable names and contents are permissible). Moreover, data content is checked on a sample to verify if information is correctly parsed from the web into our database and that there are no obvious errors such as variables are completely missing or stored in the wrong table. We check if the missing rates or rates of non-sensical values (e.g. zeros) for each variable are low. These checks typically reveal data quality and scope errors which then are fed back into improving the data collection and parsing algorithms. We cycle through data collection, checking and updates until data quality is sufficiently high.

**Table B1. Overview of GCO legislative datasets**

|**Country**|**Coverage**<br>**(Years)**|**n (laws)**|**n (total)**|
|---|---|---|---|
|Bulgaria|2006-2022|1,659|3,061|
|Chile|1900-2022|(2,504)|13,971|
|Colombia|1998-2022|1,598|10,657|
|France|2012-2022|473|1,700|
|Hungary|1998-2022|4,365|5,743|
|Jordan*|1900-2022|2,065|2,065|
|India|1952-2022|3,433|9,022|
|Portugal|1976-2022|4,810|12,974|
|Russian Federation|1995-2022|9,616|31,438|
|United Kingdom|2006-2022|534|3,116|
|United States|1995-2022|4,425|135,052|
|**Total**||**48,087**|**315,256**|

_Notes: *The total number of observations for Jordan includes laws and regulations. For all other countries the total includes non-enacted (or ongoing) bills and enacted laws._ 28
|1_st_ stage<br>(7)<br>-0.006_a_<br>(0.002)<br>0.012_a_<br>(0.011)<br>-0.004<br>(0.071)<br>0.020_a_<br>(0.002)<br>-0.004<br>0.003<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>no<br>no<br>no<br>2.60<br>42.42_a_<br>42<br>7706<br>5.88_b_<br>2.02<br>(0.16)<br>olumn 6, we<br>product-year<br>).|
|---|
|d efects<br>IV<br>(6)<br>0.083_a_<br>(0.038)<br>-0.001<br>(0.001)<br>-0.0007_c_<br>(0.00004)<br>-0.021<br>(0.031)<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>no<br>no<br>no<br>2.01<br>1.83<br>(0.18)<br>evels. In c<br>he country-<br>Wang (2010|
|s: Controlling for Endogeneity<br>port link in t+1<br>Linear probability - fxe<br>(2)<br>(3)<br>(4)<br>(5)<br>0.007_a_<br>0.007_a_<br>0.006_a_<br>0.006_a_<br>(0.001)<br>(0.001)<br>(0.001)<br>(0.001)<br>-0.001<br>-0.001<br>-0.0012_b_<br>0.003_b_<br>(0.001)<br>(0.001)<br>(0.0005)<br>(0.002)<br>0.001<br>0.004_a_<br>-0.001<br>-0.0005_b_<br>(0.001)<br>(0.001)<br>(0.001)<br>(0.0002)<br>-0.028<br>0.034<br>-0.037_c_<br>0.153_c_<br>(0.034)<br>(0.025)<br>(0.021)<br>(0.089)<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>4,374,850<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>no<br>yes<br>no<br>no<br>no<br>no<br>yes<br>no<br>no<br>no<br>yes<br>no<br>8.9<br>12.7<br>12.1<br>6.6<br>nce at the 1%, 5% and 10% confdence l<br>y foreign export) by the interactions of t<br> in the province-year taken from Wei and|
|xport Spillover<br>Domestic new ex<br>conditional logit<br>(1)<br>0.023_a_<br>(0.001)<br>0.001<br>(0.001)<br>0.004_b_<br>(0.001)<br>-0.255<br>(0.215)<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>no<br>no<br>no<br>12.7<br> indicate signifca<br>me product/countr<br> High-Techn zones|
|Table 5: Impact of Foreign E<br>Explained variable<br>Estimator<br>Foreign Spillovers<br>Same product/country foreign export<br>Same country-other products foreign export<br>Other countries-same product foreign export<br>Other countries-products foreign export<br>Country-product-year world imports _×_ # EPZ<br>Country-product-year world imports _×_ # High-Tech zones<br>Control for Macro export<br>Control for Macro export lags<br>Control for domestic presence<br>Control for GDPs<br>Observations<br>Province-product(HS4)-country fxed efects<br>Year fxed efects<br>Province-product-year fxed efects<br>Country-product-year fxed efects<br>Province-country-year fxed efects<br>R-squared (%)<br>F-test of excluded instruments<br>Kleibergen-Paap F-stat<br>Weak Cragg Donald F-test<br>Underid test Kleibergen-Paap<br>Hansen overid test<br>p-value<br>Endogeneity<br>p-value<br>Standard errors are clustered at the province level.<br>_a_, _b_ and _c_<br>instrument the product-country specifc spillovers indicator (Sa<br>total import value with the number of EPZs and the number of|

21
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greater incentives to production. We also assume that individuals are forward-looking, with expectations that are consistent with the properties of the underlying model. But we allow individuals who do not believe that a quick transition to the rule of law will occur to adapt their economic activities accordingly. _Costs of exiting the lawless state arise endogenously from these adaptive behaviors and engender resistance to reform._ We show this in a simple, dynamic model that builds on our earlier static model of coordination.<sup>5</sup> In the earlier model, agents with control rights over enterprises face two choices: one economic, whether to build the value of their assets or strip them; and one political, whether to adopt the rule of law or not. Given the static nature of that model, only those who choose to build the value of their assets benefit from the rule of law. Thus, the probability distribution of the political outcome depends on the fraction of the population that chooses to build value, which itself depends on the probability distribution of the political outcome. We showed that self-fulfilling Pareto-inferior equilbria may exist in which few agents build value and thus few demand the rule of law.<sup>6</sup> In the dynamic setting that we explore here, all individuals obtain a future benefit from the establishment of the rule of law. Therefore, individuals who strip assets in the current period may vote _for_ creating the rule of law. Their choice depends on the trade-off between the loss from the expected recapture of part of their stripping income in the transition to the rule of law, and the gain with respect to future economic activities.

Asset stripping in our model is like getting “blood on one’s hands,” in that it makes an individual vulnerable to a loss in the transition to the rule of law. We do not assume that the blood is never washed away. On the contrary, we make the minimalist assumption that only the current period’s return from asset stripping is vulnerable to recapture. However, as long as the non-rule of-law state persists, some agents may choose to strip assets, period after period. Thus the blood on their hands would be fresh when the rule of law state was created, and so they would gain from the establishment of

> 5 See Hoff and Stiglitz (2004a), which also provides a brief review of the transition from communism in Russia.  For a diagrammatic exposition, see Hoff and Stiglitz (2004b).

> 6For another example of political-economic links with self-fulfilling equilibria, see Chang (2006).

4
Our theory is that being part of an association signals a participant’s trustworthiness and reliability. A key assumption is that trustworthiness is rewarded by firms with better transactions and more access to the services they provide. Specialized associations that are somehow selective about their members and that involve direct or indirect membership costs may contribute to reduce asymmetry of information and thereby may be beneficial to enhance credit markets. Furthermore, productive associations may help the public sector identify good candidates to benefit from programs that require conditionality and targeting.

Estimates obtained in this article indicate that households that are involved in productive associations in Nicaragua are associated with 6% to 10% higher access to credit and to social assistance agricultural programs that promote investments, mainly among the rural poor.

Results presented here focus on the signaling impact of productive associations (i.e. those that are designed to bring tangible benefits to their members) as opposed to that of any other type of association. The logics behind this choice is that productive associations usually entail direct or indirect costs to their members, which is the driving force that contributes to obtain a signal about their trustworthiness. Nevertheless, other non-productive associations, such as some religious groups, may promote values that may enhance the trustworthiness of their members. Testing this hypothesis is an empirical question that may be worth analyzing in future research in this area.

# **References**

- Abadie, A. and Imbens, G. (2006) On the Failure of the Bootstrap for Matching Estimators. NBER Working Paper.

- Almond, G. and Verba, S. (1965) The Civic Culture: Political Attitudes and Democracy in Five Nations. Boston: Little and Brown.

- Ayala, L. (2000) Trained for Democracy: The Differing Effects of Voluntary and Involuntary Organizations on Political Participation. Political Research Quarterly. 53 (1) March.

- Cho, I. and Kreps, D. (1987) Signaling Games and Stable Equilibria, Quarterly Journal of Economics. Vol.  102(2), pp. 179-221.

- Dehajia, R. and Wahba, S. (2002). Propensity Score Matching for Non Experimental Causal Studies. _Review of Economic and Statistics_ , Vol. 84: 151-61

- Grossman, J. (1994). Evaluating Social Policies: Principles and U.S. Experience. _World Bank Research Observer_ , Vol. 9 (2): 159-80

- Gugerty, M. and Kremer, M. (2006) Outside Funding and the Dynamics of Participation in Community Associations. Working Paper.
19

## <u>Table 4: Subnational surveys on the health equity side of HEFPI</u>

|HEFPI survey identifier|Country|Year|Subnational area sampled|# of<br>data<br>points|
|---|---|---|---|---|
|CAF_2010_STEPS_v01_FS|Central African Rep.|2010|Bangui|12|
|COD_2005_STEPS_v01_FS|Congo, Dem. Rep.|2005|Ville-Province de Kinshasa|12|
|COL_2010_STEPS_v01_FS|Colombia|2010|Department of Santander|15|
|GAB_2009_STEPS_v01_FS|Gabon|2009|Libreville and Owendo|12|
|GHA_2006_STEPS_v01_FS|Ghana|2006|Greater Accra Region|3|
|GIN_2009_STEPS_v01_FS|Guinea|2009|Conakry and Basse Guinée|13|
|IDN_2003_STEPS_v01_FS|Indonesia|2003|Abadijaya, Depok municipality|6|
|IDN_2006_STEPS_v01_FS|Indonesia|2006|Depok|5|
|KNA_2007_STEPS_v01_FS|St. Kitts and Nevis|2007|Saint Kitts|9|
|LAO_2008_STEPS_v01_FS|Lao PDR|2008|Vientiane|3|
|MDG_2005_STEPS_v01_FS|Madagascar|2005|Antananarivo and Toliara|12|
|MDV_2004_STEPS_v01_FS|Maldives|2004|Malé|8|
|MDV_2011_STEPS_v01_FS|Maldives|2011|Malé|12|
|MLI_2007_STEPS_v01_FS|Mali|2007|District of Bamako, Commune<br>of Kati Central, and Commune<br>of Ouéléssebougou|<br>3|
|MRT_2006_STEPS_v01_FS|Mauritania|2006|Nouakchott|4|
|OMN_2006_STEPS_v01_FS|Oman|2006|Sur City|7|
|SDN_2005_STEPS_v01_FS|Sudan|2005|Khartoum state|10|
|TCD_2008_STEPS_v01_FS|Chad|2008|N’Djaména|4|
|ZMB_2008_STEPS_v01_FS|Zambia|2008|Lusaka|5|

# **4. Conclusion**

The HEFPI database will continue to evolve. New datapoints will continue to be added, through a mix of adding new indicators and additional datapoints to already included indicators. The process of improving the reliability of estimates will also continue, meaning that some existing datapoints may be revised or dropped in future versions. Documentation (like this working paper) will be released to accompany new versions of the HEFPI database.
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be more effective in the developing world due to scarcity of detailed information on the prevailing business conditions, rules and regulations and due to high costs of gathering such information. Alternatively, one can argue that in developing countries lacking a “good product to market” (i.e. good business climate), investment promotion efforts may be a waste of resources, while in developing countries with an acceptable business environment low labor costs may be attracting FDI inflows even in the absence of investment promotion. Second, this study examines whether the status and the reporting structure of investment promotion agencies matter for their effectiveness. Third, our analysis goes beyond the existing literature by examining the effect of regional competition in FDI incentives.

# **_3. Empirical strategy and data_**

## **3.1 Empirical strategy**

Our empirical analysis will rely on two datasets: (i) country-sector panel data and (ii) aggregate country-level panel data. The basic empirical specification in the sector-level analysis is ln _FDIflowcit_ = α 1 + β 1 _Sector_targetedcit_ +γ _ci_ +γ _ct_ +γ _it_ + ε _cit_ The dependent variable is the log of inflow of foreign direct investment into sector i in country c at time t. Sector_targetedcit equals one if country c targets sector i at time t and zero otherwise. γci, γct and γit are country-industry, country-time and industry-year fixed effects, respectively.

The question of interest is whether targeted sectors receive higher FDI inflows in the post targeting period (relative to pre-targeting period and non-targeted sectors). Note that timeinvariant characteristics that differentiate sectors chosen for targeting from other sectors will be captured by country-sector fixed effects. Shocks common to all sectors in a particular country in a particular year will be captured by country-year fixed effects. Shocks affecting supply of FDI in a particular sector will be controlled for by sector-year fixed effects. The model will be estimated on a sample of countries that have or have not practiced sector targeting. Narrowing the sample to only countries engaged in targeting does not change the conclusions of the study.

In the aggregate analysis, we will estimate the following model: where the dependent variable is the log of aggregate inflow of foreign direct investment into country c at time t. IPAct equals one if country c had an investment promotion agency at time t 11
implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional.

The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.

- Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.

- Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day<sup>9</sup> the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5<sup>th</sup> house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point. If 18 interviews had to be conducted, the enumerator was assigned three different points of departure.

- Mauritania and Niger: In the refugee camp in Mauritania the same method was followed as in Gao, Timbuktu and Kidal. Listing data were available for the camp in Niger, thus a similar methodology to that in Bamako was adopted.

- The method used to select a target individual within the household (who would also receive follow up interviews by phone) was uniform across the survey. To attain a 50/50 balance, all males or females above the age of 18 in the household were assigned a random birthdate using a prefabricated table. The male or female whose artificial birthday was closest to the actual date was selected as the individual to participate in the survey.

> 9 The code of the day is the sum of the numbers of today’s date but it always has to equal less than 10. For example, Sept 13 = 1+3=4. But if it were Sept 29 = 2+9=11 – which would have to be converted to 1+1=2.

5