rafmacalaba/datause-encoder-data huggingface dataset:
Figure 8: Mean Adjusted R<sup>2</sup> , by Extraction and Model <!-- Start of picture text --> .024 .097<br>.022 .094<br>.02 .091<br>.018<br>.016 .088<br>.014 .085<br>* Extraction * .012 * Extraction * .082<br>Admin Zonal Mean Admin Zonal Mean<br>EA Zonal Mean EA Zonal Mean<br>Admin Simple Admin Simple<br>Admin Bilinear Admin Bilinear<br>Modified EA Simple Modified EA Simple<br>Modified EA Bilinear Modified EA Bilinear<br>EA Simple EA Simple<br>EA Bilinear EA Bilinear<br>HH Simple HH Simple<br>HH Bilinear HH Bilinear<br>Weather + WeatherWeather + WeatherWeather + Weather2 + FE + Inputs* Model 2 + FE*2 Weather + WeatherWeather + WeatherWeather + Weather2 + FE + Inputs* Model 2 + FE*2<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 10 20 0 10 20<br>Specification # - sorted by model & effect size Specification # - sorted by model & effect size<br>.238<br>.235<br>.232<br>.229<br>.226<br>.223<br>* Extraction *<br>Admin Zonal Mean<br>EA Zonal Mean<br>Admin Simple<br>Admin Bilinear<br>Modified EA Simple<br>Modified EA Bilinear<br>EA Simple<br>EA Bilinear<br>HH Simple<br>HH Bilinear<br>Weather + WeatherWeather + WeatherWeather + Weather2 + FE + Inputs* Model 2 + FE*2<br>Weather + FE + Inputs<br>Weather + FE<br>Weather 0 10 20 mean adjusted R2<br>Specification # - sorted by model & effect size 95% C.I.<br><!-- End of picture text --> _Note_ : The figure presents the mean adjusted R<sup>2</sup> , by extraction method and model specification, aggregated over country, weather metric, remote sensing source, and outcome variable. Across panels, the figure includes 77,760 regressions, with each panel including 25,920 regressions. Each column includes 1,296 regressions, which are for each specification model and each extraction method. The northwest panel presents results from the linear and quadratic model specifications. The northeast panel presents results from the linear and quadratic model specifications with fixed effects. The southwest panel presents results from the linear and quadratic model specifications with fixed effects and input controls. Orange diamonds identify bilinear extract methods that tend to perform particularly well (household, EA, and modified EA).

69
and individual use of a broad range of financial services.<sup>3</sup> Yet, even for policymakers and researchers interested in entrepreneurship, consumer finance should not be overlooked: many entrepreneurs depend on personal credit or collateral to finance the establishment and operation of new firms. Cross-country studies in this strand of the literature have shown that a formal institution is less likely to provide financing to female entrepreneurs or more likely to charge them a higher interest rate relative to male entrepreneurs (Muravyev, Schaefer, and Talavera, 2009; Demirguc-Kunt, Beck and Honohan, 2008).

However, evidence from Latin America suggests that there is no systematic difference in access to external finance between female-owned and male-owned firms (Bruhn, 2009). Similarly, evidence from high income countries including the United Kingdom, the United States, Canada, and New Zealand as well as certain developing countries such as Ecuador and Peru show that women applying for funding generally do not face arbitrarily higher denial rates than men. This result suggests that gender differences in the use of credit might be explained by differences in the demand for external financing (Buvinic and Berger, 1990; Aguilera-Alfred, Baydas and Meyer, 1994; Baydas, Meyer, and Aguilera-Alfred, 1994; Coleman, 2000 and 2002; Carter and Shaw, 2006).

In many developing countries (including Bangladesh, Malawi, India, Pakistan, Ethiopia, Kenya, Tanzania, Uganda, and Zambia), women entrepreneurs report facing greater and more systemic barriers to accessing formal financial services (Rose, 1992; Diagne, Zeller and Sharma, 2000; Goheer, 2003; Faisel, 2004; ILO/AfDB, 2004; Richardson, Howarth and Finnegan, 2004; GEM/IFC, 2005; Bardasi, Blackden and Guzman, 2007; Ellis et al., 2007a and 2007b; Demirguc-Kunt, Beck and Honohan, 2008; Narain, 2009). Though these studies do not find evidence of explicit legal discrimination against female borrowers, there is evidence that banks 3 See Klapper and Parker (2011) for a survey of the literature.

5
|**Appendix Table 6.2: Do the characteristic**|**s of firms which hire**<br> <br>Wage Sub|**and keep wor**<br> <br>sidy Treatmen|**kers differ f**<br>t Group|**rom those wh**<br> <br>C|**ich hire and le**<br> <br>ontrol Group|**tgo?**|
|---|---|---|---|---|---|---|
||Hire and<br>Let Go|Hire and<br>Keep|p‐value|Hire and<br>Let Go|Hire and<br>Keep|p‐value|
|Number of Paid Workers|0.23|0.43|0.309|0.36|0.38|0.944|
|Education (Years)|10.38|9.87|0.434|10.00|10.38|0.683|
|Raven Test Score|3.11|2.91|0.694|2.91|3.88|0.202|
|Digitspan Recall Score|6.50|6.17|0.339|6.16|6.88|0.164|
|Total Assets|287603|244850|0.651|240620|571325|0.132|
|Total Assets<1500LKR|0.02|0.09|0.110|0.00|0.00|.|
|Total Assets>935000LKR|0.06|0.04|0.740|0.07|0.13|0.574|
|Monthly Profits|15167|15036|0.969|17197|19921|0.618|
|Profit Data Missing|0.02|0.04|0.450|0.00|0.00|.|
|Monthly Profits<2000LKR|0.02|0.00|0.552|0.04|0.00|0.552|
|Monthly Profits>30000LKR|0.06|0.04|0.740|0.04|0.00|0.552|
|Business Practices Score|9.81|8.17|0.315|8.22|10.88|0.227|
|From booster sample|0.59|0.65|0.627|0.53|0.50|0.865|
|Retail Sector|0.36|0.17|0.101|0.27|0.63|0.046|
|Colombo|0.31|0.57|0.032|0.49|0.63|0.487|
|Kandy|0.58|0.30|0.024|0.44|0.38|0.721|
|Any paid worker at baseline|0.16|0.13|0.769|0.22|0.25|0.866|
|Monthly Sales|56787|57263|0.981|49618|61307|0.552|
|Owner's Age|35.41|34.30|0.509|35.60|38.38|0.250|
|Business is Registered for Taxes|0.31|0.22|0.393|0.29|0.25|0.826|
|Weekly hours worked|59.94|58.74|0.770|57.51|62.50|0.482|
|Sample Size<br>Notes: Hire and Let go indicates the firm<br>the number of employees compared to t<br>worker and did not then reduce their nu|64<br>hired a worker durin<br>he previous round in<br>mber of employees i|23<br>g the interven<br>one of round<br>n the first yea|tion period<br>4, 5, or 6. Hi<br>r after the in|45<br>(rounds 4 and<br>re and Keep i<br>tervention.|8<br>5), but then l<br>ndicates they|owered<br>hired a|

# **Appendix 7: Heterogeneity in Survival Impact**

We estimate equation (5), adding treatment interactions, the baseline value of the interacting variable, and the interactions between this interacting variable and survey round. We do this for the outcome of business survival, and report the results in Appendix Table 7. The first column shows no heterogeneity in the treatment effect on survival with respect to baseline business practices. The second column shows that firms with initially low capital have a higher treatment effect for survival. The interactions of treatment with above median baseline capital are significant at the 10 percent levels in years 2, and years 3 and 4. The magnitude of the coefficients is almost enough to entirely offset the treatment effect, suggesting the treatment effect only occurs for low capital firms. Appendix Figure 7 shows this graphically: the control group sample with low baseline assets dies at a faster rate over time than the control group sample with high baseline assets, whereas the death rates for the low asset treated are similar to those of the high asset treated 56
_Figure 6. Required Deficit Reduction as a percent of GDP_ <!-- Start of picture text --> RDR, alpha=0<br>4<br>2<br>0<br>-22005 2006 2007 2008 2009 2010 2011 95-97.5<br>5-95<br>-4<br>2.5-5<br>-6<br>-8<br>Baseline<br>-10<br>-12<br>-14<br><!-- End of picture text -->

# _3.6. Fiscal Policy Response to Shocks_

Bohn (1998) shows that governments concerned with fiscal sustainability should raise the primary surplus in response to increasing public debt ratios. If all other determinants of fiscal policy are stationary, a positive correlation between the primary surplus and the past level of the public debt ratio is sufficient to stabilize the public debt to GDP ratio, or, in other words, to guarantee fiscal sustainability.

Celasun, Debrun and Ostry (2005) have estimated a fiscal policy reaction function for a panel of emerging market countries, whereby primary balance is modeled as a response function of previous period debt stock, business cycles, and random shocks. Data permitting, such a fiscal policy reaction function could be estimated for the concrete country – in this case Turkey.<sup>19</sup> The fiscal sustainability tool provides fiscal policy reaction function as an additional option available for all the stress tests and stochastic simulations. Specifically, the user can supply a value for alpha, which determines the debt feedback rule. Note that any positive value of alpha would imply that fiscal surplus is reacting to past level of debt. One plausible assumption is that this coefficient is set between zero – no fiscal reaction and the real rate of interest on public debt. In this way, the user can check the sensitivity of projected debt dynamics to various coefficients of correlation between the primary balance and past debt-to-GDP ratio. In principle, the panel data

> 19 See also Appendix 2 two for Turkey specific estimations.

31
## _Shock Statistics_

The LSMS dataset also matches household level GPS coordinates with geospatial characteristics, most notably the level of rainfall.<sup>11</sup> By comparing it to long term trends we can construct the standardized deviation (Z score) _Zi,t_ of total rainfall in the wettest quarter, which farmers rely on most for their crops. These deviations allow us to objectively quantify weather shocks a household has experienced in a given year, and infer whether land fragmentation mitigates or exacerbates the effect of these shocks on food security.

# **Results**

We model the relationship between our measures of food security ( _Yi,t_ ) and land fragmentation fragmentation ( _Fi_ ) in the following manner: where _εi,t_ is a time variant error term.<sup>12</sup> _δt_ controls for time fixed effects. Our measure of land fragmentation is based on the data provided in the first round of the LSMS-ISA.<sup>13</sup> For this reason, we control locality (kebele) fixed effects ( _ki_ ), kebeles being the smallest administrative unit in which our households reside, the total amount of farm land (ha) operated by the household as ( _Ai,t_ ) as well as saturating the model with household level controls _Xi,t_ . These include whether

> 11. In addition to plot level geovariable characteristics mentioned earlier, the dataset includes measures of distance, climatology, soil and terrain, and other environmental factors matched using household geo-referenced coordinates. Rainfall data are drawn from NOAA CPC Rainfall Estimates.

> 12. We use population level weights in all our estimation, and cluster errors at the household level.

> 13. Fixed effects would absorb the exogenous variation due to our natural experiment, while inter-temporal variations are largely driven by decisions to rent-in or rent-out land. We therefore fix fragmentation to the first round and run a pooled regression.

15
control for any potential nutrition, health, and labor market linkages, time variant and invariant household characteristics ( **X** _hjvst_ ), village food prices ( **P** _vst_ ), dummy variables for survey round ( **R** _t_ ) and sites ( **S** _s_ ), and characteristics of household head ( **X** _mjvs_ ) and his wife ( **X** _wjvs_ ).

Controlling for individual and household characteristics, gender difference in nutrient adequacy ratios will be reflected in the coefficient vector _α_ **1**<sup>**k**.Controllingforhouseholdcomposition,</sup> any potential household scale (dis)economies (Deaton and Paxson, 1998) that might make individuals of a larger household (worse)better-off in nutrient consumption (at the same level of per capita expenditure) will be captured by the household size. Controlling for aggregate household resources, spouses’ characteristics are of interest from intrahousehold bargaining perspective.

# **4.1 Health Endowment and Occupation**

A challenge is to obtain consistent estimate of an individual’s unobserved health endowment to control for nutrition-health-labor market linkage. I follow PRH approach of estimating endowment through a residual approach first used by Rosenzweig and Schultz (1983) in which the health technology (equation 2) is estimated directly. Then based on the technology parameter estimates and actual resources consumed or spent by individuals, individual-specific endowments are computed. However, in estimating the technology, I differ from PRH in terms of econometric method. A problem with the residual approach, as PRH argued, is that the consistent estimates of the technology parameters could not be obtained using OLS as estimated technology parameters could be biased if **C** _i_ , and _ei_ are correlated with unobserved individual endowment, _μi_ in equation 2. So PRH followed a 2SLS approach to estimate a health production function similar to the following form: where, The notations for _i_ , _j_ , _s_ , and _t_ are the same as above, _h_ is an individual’s weight for height, c is calorie intake, **D** is a vector of dummy variables indicating whether or not an individual’s occupation is highly energy intensive and whether the individual is pregnant or lactating, **X** is a vector of household characteristics, and _θijst_ is a random error term. In terms of household characteristics, PRH used drinking water source. While the error term, _ηijst_ could contain 9
Figure 25: GDP in the case of highly ambitious climate policy 8% per year

## **GDP, percent deviation from baseline**

<!-- Start of picture text --> 5<br>Scenario 1: Carbon tax and debt repayment<br>4<br>Scenario 2: Carbon tax and reduction in direct taxes<br>Scenario 3: Carbon tax and an increase in hh. transfers<br>3<br>2<br>1<br>0<br>-1<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->

# 4.2 Climate change and economic performance

As discussed above, climate change will have deleterious effects on the economy. This section quantifies the impacts on labor productivity, agricultural output from higher temperatures and increased rainfall variability, including the damage caused by flooding.

Three simulations are presented. The first is a counterfactual baseline scenario where it is assumed that there will be no further climate change. This hypothetical baseline case is only presented to help quantify the economic consequences of the climate change that Pakistan is likely to undergo over the coming decades. Two additional simulations are based on two climate scenarios developed by climate scientists, see Table 7. These damage scenarios are based on the representative concentration pathways RCP 2.6 and RCP 8.5. RCP 2.6 is used to reflect an attainable mitigation scenario, being the pathway closest in line with the targets of the Paris Agreement. RCP 8.5 is used as a “Business as Usual” (BAU) scenario. While we recognize that there is an emerging consensus that without climate policy, emissions will not be as high as assumed in the RCP 8.5 scenario (Hausfather and Peters 2020), we nevertheless use RCP 8.5 because of the abundance of data and analysis that exists for this scenario, which should facilitate evaluation of PAKMod results.

PAKMod includes five kinds of climate damages, two damages associated with changed productivity due to climate change (declines in labor productivity due to higher temperatures; and declines in agricultural productivity due to higher temperatures and changes in rainfall patterns); two damages associated with reduced labor supply (days lost and lives lost due to pollution); and one associated with the increased incidence of extreme weather events (increased incidence of flooding).

In order to link the RCP scenarios to temperature changes, we use the temperature estimates for the RCPs provided in IPCC (2014b), see Table 7. The year 2020 is the first year of the forecast period. The data for 2050 and 2100 are taken directly from Table SPM.2 in IPCC (2014b). The value of 0.5 for the year 2020 is based on a simple linear interpolation of the IPCC (2014b) forecast that ”the global mean surface 72
Table 5 - Influence of Doing Business Rankings on FDI Inflows to Developing Countries Dependent Variable: Value of FDI Inflows (100s of million USD) GMM based on the Arellano-Bond methodology

||Column A|Column B|Column C|Column D|Column E|Column F|Column G|Column H|Column I|
|---|---|---|---|---|---|---|---|---|---|
|DBR|-1.47|-1.37|-1.07|-0.58|-0.6|-1.48|-1.81|-1.91|**-0.36***|
||_1.01_|_1.01_|_0.9_|_1.07_|_0.95_|_1.09_|_1.25_|_1.23_|_0.19_|
|GDPGR|11.68|10.56|11.99|9.49|8.41|12.01|8.56|12.74|11.03|
||_8.64_|_7.01_|_8.84_|_7.01_|_6.47_|_7.32_|_6.35_|_8.77_|_9.11_|
|GDPCAP|**0.00****|**0.00****|**0.00***|**0.01***|**0.01***|||||
||_0_|_0_|_0_|_0_|_0_|||||
|REER|2.99|3.02|3.16|3|3.26|3.47|-0.1|3.25|2.88|
||_2.91_|_2.65_|_2.97_|_3.19_|_3.41_|_2.71_|_0.26_|_2.94_|_2.83_|
|INF|-8.1|-4.83|-8|-3.1|-2.62|-5.56||-8.64|-5.62|
||_6.61_|_4.02_|_6.58_|_4.02_|_3.68_|_4.03_||_6.34_|_5.17_|
|OPEN|-4.16|-4.31*|-4.09|-4.42|-3.49|**-4.93***|-4.6|-4.92||
||_2.81_|_2.61_|_2.74_|_3.17_|_2.62_|_2.84_|_3.57_|_3.31_||
|VA||-3.46||-6.32|-6.19|-3.26||||
|||_2.69_||_4.71_|_4.8_|_2.67_||||
|PV||2.43||5.71|4.88|2.81||||
|||_2.01_||_4.32_|_3.86_|_2.16_||||
|CC||1.5||0.59|0.96|1.79||||
|||_1.78_||_2.6_|_2.09_|_1.88_||||
|GOV|||1.23|||||||
||||_1.23_|||||||
|TAXPROFITS||||-0.6||||||
|||||_1.51_||||||
|TAXINT|||||1.74<br>_3.1_|||||
|Hansen J test|0.26|0.17|0.28|0.93|0.75|0.19|0.26|0.15|0.01|
|No. of observations|218|218|218|125|129|218|226|218|232|
|No. of groups|57|57|57|36|38|57|58|57|59|
|No. of instruments|43|46|44|47|47|38|34|35|27|

Standard errors corrected for robustness in italics. *** Significance at the 1 percent level, ** Significance at the 5 percent level, * Significance at the 10 percent level. A negative coefficient for the DBR variable suggests on average, an increase in the Doing Business Ranking will increase FDI Inflows all else constant (i.e. higher rankings are a lower numerical value). For the remaining variables, the opposite is true; an increase in that variable suggests, on average, an increase in FDI Inflows all else constant.

13
# **1. Introduction**

The perceived lack of long-term finance for firms in developing countries is a major concern for academics and policy makers (G20, 2013; Beck, 2016; World Bank, 2016). Long-term debt allows firms to pursue costly investments that take time to mature. Moreover, the relative short-term liability structure, or “short-termism,” in corporate balance sheets can lead to costly financial crises if shortterm debt becomes difficult to roll over.<sup>1</sup> However, little evidence exists on the actual maturity of firms’ liabilities. Most of the empirical evidence is based on the so-called “short- and long-term debt” (below and above one year) gathered from firm-level balance sheet information. Using this type of evidence, studies find that the ratio of long-term debt (maturity greater than one year) to total liabilities is typically lower in developing countries than in developed ones (Mayer, 1990; Caprio and DemirgüçKunt, 1998; Demirgüç-Kunt and Maksimovic, 1999; Giannetti, 2003; Fan et al., 2012; Demirgüç-Kunt et al., 2015). Similar evidence has been shown using bank-level balance sheet data, which focuses on total bank credit granted to corporations, aggregated in broad maturity buckets (World Bank, 2016).

In this paper, we aim to complement this literature by examining with more granularity the maturity at which firms from developing countries borrow in primary debt markets, and by systematically analyzing where there might be significant differences in the debt maturity at issuance between developing and developed countries. Hence, we are able to assess the extent to which firms borrow at the short end (maturities closer to one year) vs. the long end (maturities closer to ten years or longer) of the maturity spectrum. Moreover, we analyze the debt issued by developing and developed country firms when using different instruments and market locations, when borrowing for different purposes, and when firms vary by size. We also contrast the evidence on maturity from primary debt markets with that from firm balance sheets.

> 1 For a broader discussion on short-termism in developing countries including the sovereign sector, see Eichengreen and Hausmann (1999), Rodrik and Velasco (2000), Tirole (2003), Borensztein et al. (2005), Brunnermeier (2009), Jeanne (2009), Raddatz (2010), Broner et al. (2013), and Opazo et al. (2015), among others.

1
each 1 percentage point increase in inflation is associated with an increase in the likelihood of bidding by 0.2 percentage points. Surprisingly, inflation is associated with lower kickbacks (significant at .10 level). Recall that kickbacks is measured as a share of contract value, and it is possible that bribe payments tend to adjust more slowly than contract values to inflation.

Third, we controlled for legal heritage, using the classifications from LaPorta et al. (2008). Pimenta and Rezai (2015: 331-332) argue that “public procurement systems in Latin America are endowed with an emphasis on legalism, control and procedural formality...triggering excessive rigor in the application of norms that stifle competition and increase the costs of doing business with government.” They attribute these characteristics to the region’s legal heritage, however, and specifically to “principles of Napoleonic or Roman law.” We therefore controlled for legal heritage, but found no significant differences between groups (British, French and German). Moreover, results on the procurement indicators are unaffected.

Fourth, we controlled for the professionalization of government bureaucracy index from the Quality of Government project’s survey of experts (Dahlstrom et al., 2015). Higher scores reflect greater reliance on merit in recruitment and promotion. Ware et al. (2007: 317-318) and OECD (2009: 30) argue that corruption in procurement is likely to be lower where procurement staff are more independent from political pressure, and where remuneration, career prospects and personnel development are sufficient to attract and retain capable staff willing to abide by ethics codes. The professionalization index is a rough proxy for such arguments. Among the 88 countries in the sample, 37 are missing data on the professionalization index. Coefficients on COMPLAINTS-AB and INDEX remain significant in the KICKBACK regressions despite the reduction in sample size. However, results on METHODS-A and INDEX in the BID regressions are no longer significant with this smaller sample. Moreover, the professionalization index itself is not significant in the BID or KICKBACKS regressions.

Finally, we controlled for two more PEFA indicators potentially related to bidding or kickbacks. If payments to suppliers are often delayed, bidding may be deterred, particularly for smaller firms. The PEFA assessments include (but for only 65 of the 88 countries in our sample) an indicator (question 4 in the PEFA 2011 Framework) on “stock and monitoring of payment arrears” with higher scores assigned to countries with smaller stocks of arrears. This variable can be used as a rough proxy for payment delays to suppliers, although it covers arrears to employees and creditors, as well as to contractors and suppliers. When added to our regressions, however, it turns out to be 23