rafmacalaba/datause-encoder-data huggingface dataset:
|UGANDA North Eastern Road-corridor Asset Management Project|(PratapTvgssshrk|
|---|---|
|UGANDA North Eastern Road-corridor Asset Management Project|(PratapTvgssshrk|
|REPARIS Albania MDTF No. TF098923 Corporate Financial Reportin|gAndrei Busuioc|
|REPARIS Albania MDTF No. TF098923 Corporate Financial Reportin|gAndrei Busuioc|
|REPARIS Albania MDTF No. TF098923 Corporate Financial Reportin|gAndrei Busuioc|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Ghana - PPP Project|CraigDouglas Sugden|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Kabul Municipal Development Program|HyoungGun Wang|
|Third National Program for CommunityEmpowerment in Rural Are|aSonya Woo|
|Third National Program for CommunityEmpowerment in Rural Are|aSonya Woo|
|Third National Program for CommunityEmpowerment in Rural Are|aSonya Woo|
|Third National Program for CommunityEmpowerment in Rural Are|aSonya Woo|
|Third National Program for CommunityEmpowerment in Rural Are|aSonya Woo|
|Third National Program for CommunityEmpowerment in Urban Ar|eGeorge Soraya|
|Third National Program for CommunityEmpowerment in Urban Ar|eGeorge Soraya|
|Third National Program for CommunityEmpowerment in Urban Ar|eGeorge Soraya|
|Third National Program for CommunityEmpowerment in Urban Ar|eGeorge Soraya|
|JSDF: Growingwith our GUAGUAS(Children)|Nelson Gutierrez|
|JSDF: Growingwith our GUAGUAS(Children)|Nelson Gutierrez|
|JSDF: Growingwith our GUAGUAS(Children)|Nelson Gutierrez|
|JSDF: Growingwith our GUAGUAS(Children)|Nelson Gutierrez|
|JSDF: Growingwith our GUAGUAS(Children)|Nelson Gutierrez|
|COMMUNITY-BASED SETTLEMENT REHABILITATION AND RECONST|RGeorge Soraya|
|COMMUNITY-BASED SETTLEMENT REHABILITATION AND RECONST|RGeorge Soraya|
connections of few firms lead to political privileges, such as subsidies or regulatory protection, shielding these firms from competition and potentially reducing long-term growth.<sup>10</sup> We contribute to this literature by focusing on the impact of enforcing the elimination of regulatory barriers to market entry and competition that are inconsistent with national legislation on establishment productivity and markups within the controlled institutional environment of a single country. For this, we construct a unique data set of eliminated barriers to entry across 1,800 municipalities in Peru merged with establishment census panel data from 2008-15. The unique reform of Peru’s competition agency’s legal mandate further warrants that the roll-out of eliminated barriers can be considered as exogenous since the case against a specific municipality could only be made for a specific regulation that had already been confirmed as illegal based on the complaint of an individual firm elsewhere.

Importantly, a growing literature discussing shortcomings in the measurement of firm productivity is relevant for this study. Foster, Haltiwanger, and Syverson (2008) use U.S. data with information on producer-level quantities and prices to distinguish physical productivity from revenue productivity which is derived by deflating firms’ revenues with industry-level prices. They show that revenue productivity is positively correlated with firms’ markups which undermines the identification of the impact of regulatory reforms on firms’ technical efficiency. In fact, both measures are only proportional under perfect competition. But market power due to product differentiation or rents results in markups, driving a wedge between physical and revenue productivity. And it has been well documented that market power due to product differentiation is ubiquitous (Katayama, Lu, and Tybout, 2009; De Loecker and Goldberg, 2014). The elimination of subnational regulatory barriers to market entry may thus result in higher (lower) markups as firms face more demand and may be better able to differentiate their products (face more price competition reducing local rents).

Against this background, De Loecker and Warzynski (2012) apply the control function approach of Ackerberg (2015) to develop an integrated framework to estimate firm (revenue) TFP and markups. We follow this integrated approach to derive establishments’ (revenue) productivity and markups and estimate how they are impacted by the subnational regulatory changes, making it possible to infer the impact on physical productivity.

> 10 See Fisman (2001), Faccio (2007), Diwan et al. (2018), and Schiffbauer et al. (2015).

7
Table B1: Robustness Tests for Weeks Pregnant at 1<sup>st</sup> Prenatal Visit

||(1)|(2)|(3)|
|---|---|---|---|
||Intervention Period|Post‐Intervention<br>Period I<br>(Jan 2011 – March 2012)|<br>Post‐Intervention<br>Period II<br>(April – Dec 2012)|
|**A. Results from Table 4**||||
|Treatment|‐1.47**|‐1.63**|‐2.47**|
||(0.71)|(0.75)|(1.02)|
|Large Sample_p_‐value|0.04|0.03|0.02|
|Wild Bootstrapped_p_‐value|0.08|0.03|0.03|
|Control Group Mean|17.80|17.90|20.10|
|Sample Size|769|1,296|710|
|**B. Estimates Using Restricted**|**Sample**|||
|Treatment|‐1.47*|‐2.01***|‐2.01*|
||(0.77)|(0.70)|(1.11)|
|Large Sample_p_‐value|0.06|0.00|0.07|
|Wild Bootstrapped_p_‐value|0.09|0.02|0.12|
|Control Group Mean|17.96|18.32|17.01|
|Sample Size|760|1,326|425|
|**C. Difference‐in‐Differences E**|**stimates**|||
|Treatment|‐1.35**|‐1.74***|‐2.35*|
||(0.64)|(0.63)|(1.31)|
|Large Sample_p_‐value|0.036|0.005|0.072|
|Wild Bootstrapped_p_‐value|0.060|0.014|0.144|
|Control Group Mean|17.80|17.90|20.10|
|Sample Size|4,015|4,015|4,015|

Notes: This table reports LATE estimates of the treatment effect of the modified fee schedule on weeks pregnant at 1<sup>st</sup> prenatal visit. The _p_ ‐values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 15‐month period following the end of the intervention (January 2011 – March 2012). Column (3) reports the results for the 9‐ month period after the change in the coding of the first prenatal visit (April 2012 – December 2012). Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

48
INTERSECTORAL FINANCIAL FLOWS IN DEVELOPING COUNTRIES

# 1 INTRODUCTION

This paper is about financial flows in developing countries. It reviews the evidence on who are the borrowers, who the lenders. It asks questions such as: to what extent is the financing of business investment dependent on the availability of foreign funds? Does the degree to which households accumulate financial assets vary according to the state of development of the economy or according to availability of foreign sources of finance? What kinds of assetr characterize financial intermediation?

Quantitative information exists on these matter&, for many developing countries, though the coverage is uneven, and much remains unknown. The present exercise is an attempt to draw on information from some of the best documented countries.

Apart from collecting and summarising available data from some seventeen countries the paper presents the results of an econometric analysis of the interactions between the net lending behavior of the different sectors. The main conclusions of this analysis are as follows:

- The business sector, though it finances a substantial proportion of its own investment needs from savings, also relies heavily on external finarncing. This reliance varies widely, but a typical figure would be 50 per cent of investment. The household sector is a net supplier of funds.

- Intersectoral financial flows in developing countries are achieved to a notable extent through the banking system. The remainder of the financial system is typically relatively underdeveloped. To the extent that the domestic financial system is not able to intermediate funds with

1
# **Annex: The Herfindahl Index**

In the data set, the Herfindahl Index has been calculated based on premium based market shares. That is:

> Let _p j_ , _t_ be the premium for company _j_ at time _t_ .

and The same approach could be taken to a number of other measures of market share such as assets or new business premium or to product or geographic regional subsets.

# **Annex: References**

AXCO Database – for more information refer to <u>www.axco.co.uk</u>

> Swiss Re _Sigma_ (Various Years) available at <u>www.swissre.com</u>

23
financial and business services, usually account for larger shares in total value added in countries with more developed services sectors. Keeping in mind that this may not always be the case for other industries such as agricultural and manufacturing industries as suggested by the literature on structural change (see, e.g., Kongsamut, Rebelo and Xie (2001), among others).

We control for the levels of countries’ overall development using the GDP per capita data from the Penn World Tables. Other determinants of manufacturing RCA considered in this paper include the following: productivity measured by total factor productivity (TFP), scale economy measured by a manufacturing sector’s employment in logarithms, factor endowment variables including capital-labor ratio (K/L) and skill ratio (SKratio, defined as the share of the wage payment to high skill workers in total wage payment), relative wages in manufacturing sectors defined as a country’s average wage per worker over world average wage per worker. These variables vary across countries and sectors. The data for these variables are obtained from or estimated based on the WIOD Social-Economic Account database (SEA). The total factor productivity (TFP) growth rate for each WIOD manufacturing sector is estimated using the dual approach as in Hsieh (2002). It is calculated as a weighted average of the growth rates of labor prices (w) and capital prices (r), weighted by the share of payment to labor (L) and capital (K). For this method to be valid, no assumptions are needed for the relations of factor prices to social marginal products or about the production function form as long as the total factor payments add up to total output (i.e., Y = r*K + w*L).

Finally, we also include a measure for GVC participation. Wang et al. (2017) propose a framework to decompose total production activities to different types, depending on whether they are for pure domestic demand, traditional international trade, simple GVC activities, and complex GVC activities. Then they construct indices of GVC participation to measure the degree of a sectors’ GVC participation – a concept similar to the vertical specialization (VS1) as in Hummels, Ishii, and Yu (2001) but with a few important improvements. We include a measure of forward industrial linkage-based GVC participation to estimate how a country/sector’s engagement in GVC activities strengthens its overall export performance.

Table 1 provides the descriptive statistics of these variables and their definitions.

_III.6 Empirical results_ 19
Given the baseline assumptions (see section 3.3.) and a discount rate of 5 percent, the gross implicit New Zealand Superannuation (NZS) liability faced by the government of New Zealand for the period 1995-2050 comes to about 236 percent of 1995/96 GDP. Although this value is relatively high, it compares favorably with most OECD countries. (For international comparisons, see IMF 1996a.)

# **Table 1. Baseline estimates of gross liabilities of public pension systems in selected industrial countries**

(percent of 1995 GDP)

|**Country**|
|---|
|United States<br>206|
|Japan<br>261|
|Germany<br>456|
|France<br>523|
|Italy<br>559|
|United Kingdom<br>148|
|Canada<br>214|
|Sweden<br>291|
|New Zealand<br>236|
|**Average**<br>322|
|Source:<br>Estimates by IMF staff and the NZ Treasury fiscal model.|

The sensitivity of gross public pension liabilities to the underlying economic assumptions varies by type of pension scheme (e.g. an earnings-related versus a flatpension). For the NZS, the sensitivity of the gross liability is weak. With respect to productivity, for example, a forecasted 2 percent productivity growth a year brings the gross liability to about 266 percent of 1995/96 GDP, whereas a forecasted 1 percent productivity growth a year implies the gross liability of about 211 percent of 1995/96 GDP. This difference is caused mainly by the wage indexation of NZS benefits, which causes higher productivity growth to lead to greater NZS expense in nominal terms (even though the expense remains constant as a percentage of then-current GDP).

# **2.2. Saving and Public Pension Adequacy**

Most international time-series studies confirm that possible adverse behavioral effects of public pensions are correlated with the expected generosity of the plan (see the literature surveys in World Bank 1994 and IMF 1996a, b). Arguments against any 5
correlation with IDP presence.<sup>13</sup> The results shown in the online appendix of strong first stages leads us to conclude that 2006 ethnic diversity can be used as an instrument to the IDP presence indicator for the commune- and household-level estimation under the assumption that lagged ethnic diversity does not have a direct effect on wealth outcomes post-2014.

Figure 9a presents the results from the IV estimation of the household-level regressions of IDP presence on non-agricultural household income, and total household consumption. The results show a statistically significant positive relationship on household consumption. More specifically, they suggest that when a commune hosts IDP households, total household expenditures on average increases by 25.4%. Such a level of consumption change would be consistent with the scale of effects found for refugees by Taylor et al. (2016). There is, however, no statistically significant evidence in the IV regressions that IDP presence has an effect on non-agricultural household income.

Figure 9b presents the results from the IV estimation of commune-level regressions of IDP presence on inequality and poverty. The results show no statistically significant relationships between IDP presence, and host community inequality, and poverty. Note that the IDP coefficients on all the inequality and poverty measures are pretty close to 0 or negative with substantially larger imprecision. These results suggest that the true effect of IDP presence on poverty and inequality may be close to 0, consistent with our finding from the DID analysis.

> 13Columns 1 and 2 of Table 7 in the online appendix show the results from the OLS estimation of regressions of IDP presence on ethnic diversity. The column 1 estimate at the commune level indicates that ethnic diversity is not meaningfully correlated with the number of IDP households. However, The column 2 shows that it has a strong statistical association with IDP presence with a F-statistic of 61. Both of the endogenous variables are strongly correlated with 2006 ethnic diversity at the household level as Columns 3 and 4 show. We have also considered the number of conflicts and fatalities within 50km outside of each commune’s boundary as an instrumental variable to IDP presence. As Table 8 in the online appendix shows no meaningful statistical relation between nearby conflicts and IDP presence. Thus, we only use the ethnic diversity index as an instrument in this section.

26
- Bhalotra, Sonia, Diogo Britto, Paolo Pinotti, and Breno Sampaio. 2020. “Job Displacement, Unemployment Benefits and Domestic Violence.”

- Bhanot, Syon P., Jiyoung Han, and Chaning Jang. 2018. “Workfare, wellbeing and consumption: Evidence from a field experiment with Kenya’s urban poor.” _Journal of Economic Behavior & Organization_ 149:372–388.

- Bhat, Bhargave, Jonathan deQuidt, Johannes Haushofer, Vikram Patel, Gautam Rao, Frank Schilbach, and Pierre-Luc Vautrey. 2021. “The Long-Run Effects of Psychotherapy on Depression, Beliefs, and Preferences.” _Working Paper_ .

- Bhatia, Abhishek, Ayesha Mahmud, Arlan Fuller, Rebecca Shin, Azad Rahman, Tanvir Shatil, Mahmuda Sultana, K. A. M Morshed, Jennifer Leaning, and Satchit Balsari. 2018. “The Rohingya in Cox’s Bazar.” _Health and Human Rights_ 20 (2):105–122. Tex.pmcid: PMC6293360.

- Blakemore, Erin. 2019. “Who are the Rohingya people?” _National Geographic_ URL `https: //www.nationalgeographic.com/` . Section: Culture.

- Burchardi, Konrad B., Jonathan de Quidt, Selim Gulesci, Benedetta Lerva, and Stefano Tripodi. 2021. “Testing willingness to pay elicitation mechanisms in the field: Evidence from Uganda.” _Journal of Development Economics_ 152:102701. URL `https://www. sciencedirect.com/science/article/pii/S0304387821000778` .

- Cantril, Hadley. 1965. _The pattern of human concerns_ . Rutgers University Press.

- Case, Anne and Angus Deaton. 2020. _Deaths of Despair and the Future of Capitalism_ . Princeton University Press.

- Cobb-Clark, Deborah A., Sarah C. Dahmann, and Nathan Kettlewell. 2019. “Depression, Risk Preferences and Risk-Taking Behavior.” SSRN Scholarly Paper ID 3390275, Social Science Research Network, Rochester, NY. URL `https://papers.ssrn.com/abstract= 3390275` .

- Council, National Research. 2014. _The Growth of Incarceration in the United States_ . 10.17226/18613. Washington, DC: The National Academies Press.

- Cuijpers, Pim, Annemieke van Straten, and Lisanne Warmerdam. 2007. “Behavioral activation treatments of depression: A meta-analysis.” _Clinical Psychology Review_ 27 (3):318–326. URL `https://www.sciencedirect.com/science/article/pii/ S027273580600136X` .

28
**Figure 2: Evolution of the number of drug trafficking organizations, 2006-2011**

<!-- Start of picture text --> Year 2006 2007-2009 2010 2011<br>Cártel de Sinaloa Cártel de Sinaloa Cártel de Sinaloa<br>Pacífico Sur<br>Pacífico Sur La Mano con Ojos<br>La Nueva Administración<br>Cártel de Sinaloa<br>Beltrán Leyva<br>Cártel del Charro Cártel del Charrro<br>Cártel Independiente de<br>Cártel Independiente de Acapulco<br>Acapulco<br>Cártel de Juárez Cártel de Juárez Cártel de Juárez Cártel de Juárez<br>Cártel de Tijuana<br>Drug-Trafficking Cártel de Tijuana Cártel de Tijuana Cártel de Tijuana<br>Organization Francción del El Teo<br>Cártel del Golfo Cártel del Golfo<br>Cártel del Golfo Golfo-Los Zetas<br>Los Zetas Los Zetas<br>Los Caballeros Templarios<br>La Familia Michoacana La Familia Michoacana La Familia Michoacana Los Incorregibles<br>La Empresa<br>La Resistencia La Resistencia<br>Milenio Milenio Cártel de Jalisco Nueva Cártel de Jalisco Nueva<br>Generación Generación<br>La Nueva Federación<br>Total 6 8 11 16<br><!-- End of picture text --> Source: using data from Guerrero (2011)

# **_2.2 THE RISE AND FALL OF THE NORTHERN STATES_**

The period 1995-2007 in Mexico saw a rapid expansion of international trade fueled mainly by the country’s comparative advantage in the production of manufactured goods. The North American Free Trade Agreement (NAFTA) increased demand for Mexican exports to the United States, particularly manufactured products. Exploiting their proximity to the United States and favorable tax conditions, _maquiladoras_<sup>_6_</sup> flourished, creating many jobs in the northern Mexican states. The maquila boom brought about improvements in employment opportunities and increased real wages of relatively unskilled workers, particularly in municipalities bordering the United States (Hanson 2004 and Robertson 2006). Improvements in labor market outcomes for unskilled youth in border cities attracted them

> 6 _Maquila_ is a labor-intensive manufacturing operation where factories import raw material on a duty-free basis, perform an assembling process, and then export the final product, usually back to the raw materials' country of origin.

9
Figure 1: Experimental design, take-up and data sources

# **Stage of experiment**

# Initial

# Sample

# First-Stage Randomization

10/2015 – 02/2016 Pr(Flex)=33% 03/2016 – 03/2017 Pr(Flex)=20%

# Loan Approvals and Disbursements

# Second-Stage Randomization

Pr(Flex)=50% among standard loans

# Pass Use

(At least one pass is used) Repayment Period 10/2015 – 10/2019

# **Data Sources**

|Mark<br>|eting and Recruitment<br>N=8,610<br> <br><br>Data about offer<br>process;|
|---|---|
|Stan<br>Loan|dard<br>Offer<br>Flexible<br>Loan Offer<br>N=8,610 potential<br>clients|
|N=8<br>+<br>N=5,|16<br><br>869<br>N=459<br>+<br>N=1,466|
|Stan|dard<br>Flexible|
|Lo<br>N=1,|Administrative data<br>on clients and loans<br> <br>an<br>893<br>Loan<br>N=582|
|Take|N=2,475 loans<br>-up<br>Take-up|
|28|%<br>30%|
|Standard|Flexible<br>Flexible|
|Contract|Contract<br>Contract<br>|
||(Surprise)<br>(No-Surprise)<br>Admin. repayment<br>data;|
|N=922|N=971<br>N=582<br>N= 2,475 loans;<br>cover at least 30|
|Standard<br>|months post<br>disbursal<br>Flexible<br><br>Flexible<br>|
|Contract|Contract<br>Contract|
||(Surprise)<br>(No-Surprise)|
|Loa<br>me<br>Durati<br>A<br>Principal:<br>2%|Phone survey<br>(by lender);<br>N=575 surveys, for<br>457 clients<br>10-month follow-up<br>survey<br>(by IPA);<br>N=1,706 clients<br>n characteristics;<br>dian and (range):<br>on: 12 months (6-24)<br>PR: 36% (36-42)<br>1.1 million COP (0.5-5.5)<br>33%<br>38%|