rafmacalaba/datause-encoder-data huggingface dataset:
26 beliefs concerning the importance of term finance for firm performance. Longer maturity is positively associated with subsequent higher profitability and output growth. Moreover estimation of a Cobb Douglas production function, augmented with financial variables, suggests that the share of long term finance and DFI finance have a positive influence on firm level productivity. This could be because the availability of term finance may have favourable effects on the quality of fixed capital accumulation, in the sense of allowing access to better technologies. Another explanation is suggested by the positive impact of liquidity on performance. Firms facing a shortage of long terrn finance are likely to divert working capital to meet their terrm finance requirements which leads to poor performance. High leveraging, however, has a strong negative impact on productivity. This finding is important and has obvious implications for fiscal policies that encourage debt and also for the financing norms of financial institutions.

It is important to note that the data on which these results are based have been generated by a financial system which is marked by a lack of competition, and in which the financial institutions are state owned and not guided by the profit motive. In addition, in this system financial institutions have virtually no control over interest rates. In view of these facts, the counter-factual question as to whether short term finance would be more beneficial in a less regulated system, with greater flexibility for all the players, cannot be addressed. In any event, the observed benefits of term finance must be weighed against the costs. At the end of the decade of the eighties the capital base of a large number of the government owned financial institutions in India (both banks and DFIs) was severely eroded, their financial position was poor and they carried a large burden of non-performing assets. Further research on the issues addressed in this paper, using data that also cover the period after the introduction of financial sector reforms, would be very beneficial.
4 ar6 birth rates and mortality ratet affected, but also fertility and life expectancy indicators. By the same token, in the absence of sufficient health personnel, the causes of death may be poorly reported, and the toll of major diseases often has to be guessed, although 'ith advanced statistical methods.

The relative spreading over time of national censuses is a cause of scattered, sporadic data, too. Because of the lack of systematic censuses in many countries, it is often not possible to assess trends, as observations at two different points in time may not be available. This is especially true for Sub-Saharan Africa which is, in general, the region with the least and least reliable data.

Furthermore, when datA are collected, the quality of the collection procedure is not always guaranteed. Irternational organizations such as the World Health Organization (WHO) collect their data by means of standard annual questionnaires despatched, through foreign affairs ministries, to their individual member countries. How each country itself obtains its data is less well known and procedures vary from country to . -try. Data gathered from administrative sources may reflect some official, bureaucratic or procedural bias, such as urban or institutional coverage when reporting on hospitals, clinics, and the like.

Because of the lack of complete time series for many social indicators, experts in development organizations resort to statistical methods to generate the missing data. When faced with gaps between observations, they assess the trend for the available data and assign intermediate values -- i.e., they
<!-- Start of picture text --> GWh<br>70,000 5%<br>5%<br>60,000<br>4%<br>50,000 4%<br>40,000 3%<br>3%<br>30,000 2%<br>20,000 2%<br>1%<br>10,000 1%<br>- 0%<br>2010 2011 2012 2013 2014 2015<br>mmm Regulated Mam Manufacturing industry mam Mining<br>lm Other free customers —#-— Domestic demand growth<br><!-- End of picture text -->
from breaking relational contracts. This follows from the very nature of relation-based governance. Contractor’s freedom of action is not restricted by any legal requirement, but by concern for subsequent loss of business, reputation, or trust. For future benefits to be large enough to induce cooperative behavior, the discounted value of expected future profits must be larger than the gains that could be made from reneging on the deal. Two conditions in the SSA business environment negatively influence the value and predictability of future gains from such relationships and make it harder to establish and sustain cooperation.<sup>6</sup> First, all business relationships involve risks. Generally such risks are known and can be planned for or ways can be found to hedge them. But the shock-prone SSA environment adds an extra element of uncertainty to the equation. Uncertainty makes it more difficult to predict a business partner’s gains and undermines the effectiveness of repeated interaction incentives. When conditions are stable, contracts and business relationships have a predictable value. Firms offering credit, for example, know the loan value – it is predictable to the supplier and to the customer – and the amount of credit offered can be set to benefit the supplier when it is repaid. The value of continuing the relationship is thus predictable. Unforeseen shocks change all of this. The value of not making the required payment fluctuates because of the shocks. If the gains from breaking the promise to pay are large enough, then the customer will default. Relational contracts are thus much harder to sustain in shock-prone environments, because it is harder to predict the behavior of the business partner and to value the relationship. In addition, the costs of establishing relational contracts (i.e. costs of building trust) are much higher in shock-prone environments. Shocks induce unforeseen turnover and

> 6 One of the consequences of the difficult environment for sustaining cooperation in the SSA region is a very low level of subcontracting.  A comparison of data from firm-level surveys conducted under the World Bank’s Investment Climate Assessment program in China versus countries in the SSA region shows that, in China, 22% of manufacturing output is produced for other firms. In contrast, only 3% of manufacturing output is subcontracted in Africa.  When we look at the percentage of firms that work as subcontractors, with 10% or more of their output being subcontracting work, 35% of firms in China are subcontractors, compared with only about 6% in the SSA region.  China certainly cannot be held up as the best contracting environment in the world, but its relative economic and political stability does provide a superior platform for relation-based governance of cooperative business transactions.

9
UNHCR. (2021b). _Refugees in Jordan face escalating challenges as Syria conflict marks 10-year anniversary_ . https://www.unhcr.org/jo/14617-refugees-in-jordan-face-escalatingchallenges-as-syria-conflict-marks-10-year-anniversary.html UNHCR. (2022). _Syria Regional Refugee Response_ . https://data2.unhcr.org/en/situations/syria Verme, P. (2017). _The Economics of Forced Displacement: An Introduction_ . World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-8038 Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities: A review of the empirical literature in economics. _Journal of Development Economics_ , _150_ . https://doi.org/10.1016/j.jdeveco.2020.102606 Vintar, M., Beltramo, T., Delius, A., Egger, D., & Pape, U. J. (2022). _Impact of COVID-19 on Labor Market Outcomes of Refugees and Nationals in Kenya_ (9960; Policy Research Working Paper).

World Bank. (2020). _Jordan Economic Monitor: Weathering the storm_ .

https://doi.org/10.1126/science.370.6514.278 Zetter, R., & Ruaudel, H. (2018). Refugees’ right to work and access to labour markets: Constraints, challenges and ways forward. _Forced Migration Review_ , _58_ (June), 4–7.

33
investment. The solution in (7) provides us with the optimal marginal propensity to consume out of income, which we denote _αt._ Consider the first two periods that households receive transfers from OPORTUNIDADES. In this case, the optimal consumption level in the second period after transfers begin is: _Ct_ + 1 = αγ( _L_ )δ[δ+γ( _L_ )( 1 − α)] _At_ − 1 + α _PT t_ + 1 +[αγ( _L_ )( 1 − α)] _PTt_ + αε _t_ + 1 + [αγ( _L_ )( 1 −α)]ε _t_ (8) where _α_ is the marginal propensity to consume and (1 – _α_ ) is the marginal propensity to invest<sup>_15_</sup> . Consumption is a function of initial assets and family labor, current transfers, lagged transfers and productivity shocks. The coefficient on current transfers is just the marginal propensity to consume. The coefficient on lagged transfer consists of three structural parameters. Working backwards, the first is the marginal propensity to invest _(1-_ α ), which when multiplied times transfers yields the amount of the transfer invested. The second parameter is the marginal productivity of investment, _γ(L)_ , which when multiplied times investment yields income earned from the investment. The third parameter is the marginal propensity to consume, α , which when multiplied times income yields the amount of income earned from the investment that is consumed.

For each round of data from 1998 through 2003 where there is consumption data available, we estimate (8) as the following reduced form: where _Cijt_ is monthly per capita adult equivalent consumption<sup>16</sup> of household _i_ living in community _t j_ at time _t_ ; _PTijt_ is the current monthly OPORTUNIDADES transfer per capita; and ∑ _PTijt_ − _s_ are 6- _s_ = 1 months lagged total per capita cumulative transfers, i.e. the total transfer amount the household had accumulated until six months prior to the current round of the ENCEL survey ( _t_ ). Since

> 15 For simplicity, we assumed that the marginal propensity to consume is constant over time. However, if OPORTUNIDADES significantly relaxed the liquidity constraint, the marginal propensity to consume might fall over time. We tested and rejected this assumption against the alternative of a constant marginal propensity to consume.

> 16 We define adult equivalent household size as the number of children age 12 or younger times 0.5 plus the number family members older than 12.

18
The database includes a comprehensive set of preferential trade agreements among the Latin American countries in the 1990s. In particular, the following agreements are included: Partial Preferential Agreements (in Spanish “Acuerdos de renegociación del patrimonio histórico”): Colombia--Paraguay; Paraguay--Peru; Paraguay--Venezuela; Colombia--Uruguay; Uruguay--Venezuela; Ecuador--Mexico; Peru--Uruguay; Mexico--Paraguay); Free Trade Agreements (in Spanish “Acuerdos de Complementación Económica or ACE”): Brazil--Uruguay (ACE 2--1990); Argentina--Mexico (ACE 6--1993); Mexico--Peru (ACE 8--1990--1995); Argentina--Paraguay (ACE 13 /1990); Argentina--Brazil (ACE 14 /1990); Argentina--Brazil-Paraguay--Uruguay (MERCOSUR--1991); Chile--Venezuela (ACE 23--1993); Chile--Colombia (ACE 24--1994); Ecuador--Uruguay (ACE 28--1994); Ecuador--Paraguay (ACE 30--1994); Chile-Ecuador (ACE 32--1994); Mexico--Colombia--Venezuela (G3--1994); MERCOSUR--Chile (ACE 35--1996); Chile--Peru (ACE 38--1998); Brazil--Colombia--Ecuador--Peru--Venezuela (ACE 39-1998); Chile--Mexico (ACE 41--1998); Argentina--Colombia--Ecuador--Peru--Venezuela (ACE 48--2000); Brazil--Mexico (ACE ACE 53--2000); Andean Community (Colombia--Ecuador--Peru-Venezuela; Acuerdo de Cartagena 1969); Mexico--United States (NAFTA--1994) for Mexican tariffs on US products.

Compilation of MFN applied rates was straightforward using official national sources compiled by different regional institutions and other specialized information systems. In particular, the following sources were used: UNCTAD-TRAINS (Trade Analysis and Information System); WTO-IDB Integrated Data Base; Inter-American Development Bank (IADB) Hemispheric Tariff Database as well as selected national customs sources for specific countries and years provided by ALADI Secretariat and the Andean Community Secretariat. MFN tariff data were collected at the tariff line level based on national nomenclatures, which 39
4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity However some authors argue that achievements such as improved highways, public transit services (Glaeser, 2014; Chatman and Noland, 2014) and advances in communication technologies (Partridge et al, 2009) have helped to reduce the productivity costs of sprawling.

When thinking of urban form, urban economists commonly use population density and city size as proxies. However, the main limitation of this kind of variables is that they assume a uniform distribution of people across space. Such an assumption ignores the fact that the urban form is the result of the interaction of individuals, firms, and government decisions on where to live, where to locate, and where to place infrastructure. Such interactions are often reflected in the layout of the city, e.g. the street layout, and the land use patterns across the urban space. In other words, urban form and land use patterns cannot be properly identified from density (Melo, et al., 2017). Nowadays, the evolution of areas such as Geographic Information Science and Computational Geometry, which has been reflected in friendlier and inexpensive analysis tools, together with the increase of availability of georeferenced data, has allowed economists to look beyond density and size when thinking of the effect of urban form on city productivity.<sup>1</sup> An example of this trend can be found in Harari (2016), who takes a slightly different approach and estimates the impact of urban form on city productivity. In her work, Harari uses DMSP/OLS Night-time Lights to delineate the urban extents of a set with over 450 Indian cities and uses those extent boundaries to calculate geometry-based shape metrics with an open source library written in Python 2.5 for ESRI’s ArcGIS, The Shape Metrics Tool, described in Angel et al. (2010). Contrary to other studies, she finds that more compact cities (in terms of the geometry of the urban extent as opposed to population density) are characterized by larger populations, lower wages, and higher housing rents. This suggests that a city’s residents value compactness as a consumption amenity. By contrast, firms do not appear to be directly affected by city shape in their location choices and there is no evidence of a significant impact on the productivity of firms.<sup>2</sup> Most recently, in their analysis of urban development patterns of Indian cities, Tewari, Alder and Roberts (2016) find that there is a robust and positive relationship between a city’s initial level of

> 1 A similar phenomenon has occurred with the use of remote sensing data by economists.

> 2 Lall, Shalizi and Deichmann (2003) found similar results from an analysis of agglomeration economies and productivity in Indian industries. The authors find that benefits for firms to locate in dense urban areas do not appear to offset the associated costs.
prone to face a larger informal competition.<sup>20</sup> Further evidence supporting our argument can be established when dividing our sample between the firms that never exit in our period of analysis (which are part of the balanced panel) and the firms who exit at some point. Our main estimates use the unbalanced panel of firms (all firms), as such, the results observed in Tables I and II correspond to the firms that are in the market each period of analysis (but may exit in the future). The size, in terms of production, of the firms who exit anytime between 1995 and 2010 is smaller relative to the firms that stay during the whole period of analysis. In particular, the mean production of the firms in the balanced panel is 20,300,000 (6,376 observations) and the mean production of the firms who exit sometime between 1995 and 2010 is 10,900,000 (115,885 observations). To clarify how the sample composition is affecting our main results we estimate the effects of IDP migration for the sample of firms in the balanced panel (i.e., the largest firms) in Appendix IV. We find no effects of IDP migration on any of the outcomes that we study. These results imply that the negative effects that we observe on Tables I and II are driven by the smaller firms, which ultimately exit the market at some point during our period of analysis. These results also support our argument as smaller firms should be disproportionately affected by the competition of an enlarged informal sector relative to larger firms.

# **VI.1 Do other sources of data support our hypothesis?**

We also use the Colombian household surveys to test how higher IDP inflows affect the size informal sector. Particularly, we examine whether larger IDP inflows are indeed affecting the size of the informal sector by classifying all workers age 15 and 62 into two groups according to whether their primary job is part of a sectors that tends to have high levels of informality (such as retail sales and construction) or low levels of informality (such as financial services, machinery production, production of chemical or pharmaceutical products, and highly technical jobs).<sup>21</sup> Sector classification codes are only available in the labor force surveys beginning in 2002. Our sample,

> 20The only exception is firm exit for which we find significant effects for both groups of firms. 21 We use the same classification employed when we divide the manufacturing survey into firms more and less exposed to informal competition for consistency.

22
**Table 2. Bi-Variate Correlations**

||uspatpop|epopatpop|rdperpop|rdexpgdp|rdperfhegdp|rdperfprodgdp|rdperfpubgdp|rdfinprodgdp|rdfinabrogdp|ypc|
|---|---|---|---|---|---|---|---|---|---|---|
|uspatpop<br>epopatpop|1<br>5416<br>0.70 *|1|||||||||
||2492|2504|||||||||
|rdperpop|0.58 *|0.45 *|1||||||||
||1168|769|1218||||||||
|rdexpgdp|0.70 *|0.60 *|0.79 *|1|||||||
||1358|923|932|1390|||||||
|rdperfhegdp|0.49 *|0.56 *|0.55 *|0.76 *|1||||||
||811|617|667|818|818||||||
|rdperfprodgdp|0.76 *|0.61 *|0.80 *|0.97 *|0.64 *|1|||||
||741|598|629|748|747|748|||||
|rdperfpubgdp|0.26 *|0.15 *|0.42 *|0.58 *|0.31 *|0.44 *|1||||
||740|598|628|747|747|747|747||||
|rdfinprodgdp|0.71 *|0.60 *|0.79 *|0.89 *|0.60 *|0.93 *|0.37 *|1|||
||899|656|718|906|749|685|684|906|||
|rdfinabrogdp|0.04|0.20 *|0.12 *|0.25 *|0.30 *|0.17 *|0.34 *|0.05|1||
||773|637|632|776|679|653|652|764|776||
|ypc|0.58 *|0.50 *|0.64 *|0.67 *|0.60 *|0.66 *|0.26 *|0.64 *|0.18 *|1|
|Confidence level: *9|4049<br>9%|1979|961|1244|753|686|685|839|716|4375|

Number of observations are shown under correlation coefficients uspatpop Patents granted by the USPTO* epopatpop Patents granted by the EPO* rdperpop R&D personnel* rdexpgdp R&D expenditure** rdperfhegdp R&D performed by high education** rdperfprodgdp R&D performed by the productive sector** rdperfpubgdp R&D performed by the public sector** rdfinprodgdp R&D financed by the productive sector** rdfinabrogdp R&D financed from abroad** ypc Real GDP per capita for year 2000 (1985 U.S.$ PPP)

* Per 10,000 inhabitants

** As percentage of G.D.P.

_C. Inter-regional comparisons over time_ During the period of study, high-income countries show higher levels of R&D inputs as well as patent grants than the world as a whole and developing countries. Table 3 provides the median values for each innovation variable for the world as a whole, high-income countries, and developing countries (all remaining regions). High-income countries have much higher levels of patents granted by both patent agencies than the median for the world. This pattern is also observed in the rest of the R&D variables. In general, the median for the former group is between 30-100% higher than the world compared to levels of 50-70% lower than the world median in developing countries. For example R&D investment in high-income countries is 11
matched with estimates of community open defecation rates and census population density data for 1,800 sub-national regions. For the second analysis, we use Geographic Information System (GIS) codes to create a new dataset of children in Bangladesh that allows us to identify the effect of the interaction of population density with local sanitation on child height. These new data allow our measure of population density to be more precise than is possible in the international dataset, and they allow us to control for higher-resolution geographic fixed effects.

We motivate the international analysis by confirming the results of prior papers which find that urban children in developing countries are less likely to die in the first year of life than rural children. Using the dataset of 172 DHS surveys, we find that part of this difference is explained by the fact that rural children are exposed to more open defecation, on average, than urban children. However, a positive interaction of urban place with local open defecation suggests that the urban survival advantage is less pronounced where open defecation is high. Further controlling for the interaction of population density and local sanitation clarifies that higher average population density in urban areas is the mechanism through which urban residence likely moderates the effect of sanitation on infant mortality.

We then focus directly on the population density–sanitation interaction and show that it is robust to a variety of respecifications. We also perform falsification tests to show that other variables do not similarly interact with population density to predict infant mortality in these data. Finally, we plot the shape of the interaction between local open defecation and population density and find that it is steeper at higher population densities.

The second analysis seeks to further test the internal validity of the interaction between sanitation and population density in predicting child height. We use GIS codes to match children in the Bangladesh DHS to the population density for their area of residence using highly disaggregated census data. This allows us to construct an interaction of population density and local sanitation that provides a more precise measure of exposure to density of open defecation than we are able to use in the international dataset. We then regress 4