rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 30.5<br>2.4 30 15.00<br>30.0<br>2.2 14.75 35<br>3 14.50 29.5<br>2.0 20 14.25 29.0<br>30<br>2 10 25<br>20<br>2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015<br>Mean Max<br>Uncertainty<br><!-- End of picture text --> Figure 2: Time series of temperature variables. Each plot illustrates the annual trajectory of temperature variables across all grids, with separate plots for the temperature uncertainty (left) , mean (middle), and max (right). The black line in each plot denotes the annual mean value of the respective temperature variable, accompanied by a grey shaded area that represents the 95% interval. Additionally, an inset plot highlights the mean values without the 95% interval, emphasizing the overall trend in these mean values over the years.

It is worthwhile to note that the trends and patterns identified in Figure 2 do not necessarily represent regional or global averages of the weather variables. The analysis is specifically based on the grids where firms from the Orbis dataset are located. Therefore, the findings are reflective of the weather variable characteristics in these particular economic areas, rather than providing a broader, generalized view of regional or global weather trends.

Scatterplots of temperature uncertainty and asset growth. To demonstrate the overall relationship between temperature uncertainty and fixed asset growth, we present a scatterplot of these variables in Figure 3. The plot includes all country-grid-year observations; each dot represents the mean value of fixed asset growth and the temperature uncertainty for a specific country, grid, and year. The result shows that there is a strong negative relationship with a slope of -4.77, as shown by the grey linear line.

Figure 8 in Appendix B displays the cross-sectional (grid-level) relationship for each year, confirming a negative relationship.

# 4 Empirical method and results

In this section, we explore the potential negative correlation between temperature uncertainty and firms’ annual fixed asset growth, structured into three parts. First, we assess the overall relationship at the grid level. Next, we examine industry-level differences by analyzing how the impact of temperature uncertainty varies with sectoral investment irreversibility, measured by the ratio of tangible fixed assets to total assets. Finally, we investigate how income levels might influence this relationship.

Table 2 presents the correlation matrix for all variables in our regression models. Temperature uncertainty is negatively correlated with firm size growth, with a coefficient of -0.09, indicating that higher uncertainty is associated with lower fixed asset growth. Temperature uncertainty also shows a negative correlation with both mean temperature (-0.59) and maximum temperature (-0.28), reflecting regional patterns where warmer areas tend to exhibit less temperature uncertainty. As expected, the 12
programs with similar designs, there is a need for greater understanding of the mechanisms through which cash transfers impact health.

We examine the impacts of a 2010 pilot CCT program in rural Tanzania on a range of health investments and outcomes. Among 80 study villages, 40 were randomly assigned to receive the CCT program, allowing us to estimate its causal impacts. Beneficiaries included both children aged 0–15 and elderly individuals aged 60 and older. Conditions of the program included visits to health clinics by young children aged 0–5 and by the elderly. Households were surveyed at baseline in 2009, again in 2011 after 18-21 months (about 1.5 years) of transfers, and finally in 2012 after 31-34 months (about 2.5 years) of transfers.

We find nuanced impacts of the CCT program. An initial surge in clinic visits after 1.5 years—due to more visits by both those already complying with program health conditions and non-compliers—disappeared after 2.5 years, largely due to compliers reducing aboveminimal visits. We also find significant increases in take-up of health insurance. After 2.5 years, the program made households in treatment villages 36 percentage points more likely to participate in the government-run health insurance program (the Community Health Fund, or CHF) and raised the likelihood of financing treatment with health insurance by 16 percentage points. These impacts on health insurance are particularly interesting. Little previous work has examined the impact of cash transfers on participation in health insurance programs. Evidence from Mexico suggests that participation in a CCT program increased participants’ awareness that they were enrolled in a health insurance program, but in that case, actual enrollment was automatic upon enrollment in the cash transfer program (Biosca and Brown, 2014). The CCT additionally increased the likelihood of seeking treatment when ill. This latter result is important given recent research showing that timely clinic attendance when ill improves child health outcomes in Tanzania (Adhvaryu and Nyshadham, 2015). We also find that the program led to significantly higher investments in preventative health measures, including an 18 percentage points increase in shoe ownership, which the public health community associates with lower exposure to helminths (Mascarini-Serra et al., 4
28 <!-- Start of picture text -->
1.;)30  _  .<br><!-- End of picture text -->

- 34;5Figure 5A: Base case output growth with and without accelerated depreciation.

<!-- Start of picture text --> 3.14 .- --- -- ---<br>0.13 S<br>i9sB 1959 199D 1991 1993 1993 1994<br><!-- End of picture text --> Figure 5B: Base case private investment with and without accelerated depreciation.
to the demand curve. This is most evident in the returns to higher education (see Figure 5). While enrollment in higher education has gone up three-fold since 1970, the returns have not changed overall. The increased share of the labor force with higher education should have reduced the rate of return on the investment. Yet, the rates of return over time do not fluctuate much because as the supply of educated labor increases, so does the demand for higher skills, hence not depressing the returns to education. As discussed by Goldin and Katz (2010), the race reflects, on the one hand, the skill-biasedness of technological progress with its consequences for income inequality and, on the other hand, to the pivotal role of education in mediating this relation. While higher education increased substantially, the premium on high skills continued to increase. This suggests that educational advancements were insufficient to countervail demand due to technological progress. The rising inequality implies that technology is winning this race.

**Figure 5: The Race between Higher Education and Technology**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1970 1980 1990 2000 2010<br>Returns Enrollment<br><!-- End of picture text --> _Income level and regional differences_ Private returns to schooling are higher in low-income countries by about one percentage point relative to high-income countries (see Table 2). This is the case even though the mean years of schooling differs by a factor of almost two between country groups. In fact, years of schooling will peak, to about 10 years by 2050.

**<u>Table 2: Private Returns to Schooling by Income Group</u>**

|Country income level|Overall rate of return (%)|Mean years of schooling|
|---|---|---|
|Low|9.3|5.0|
|Middle|9.2|7.0|
|High|8.2|9.2|
|World average|8.8|8.0|
|Notes: Country per capita<br>2015 US$: low=$1045 or|income levels based on World<br>less; middle=$1046-$12,735|Bank (2016) classifications in<br>; high=$12,736 or more|

9
Tabim 1: Variable Namms. oniaMrtiam. mmm andi Standard Owatiamlow Thluiland. 1291-2

|||||S<br>|amie<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Oescriptiona|To<br>|talb<br>|Ru<br>|ralC<br>|Ur<br>|band<br> <br>|
|||Iban|S.D.|Mean|S.O.|Mean|<br>S.D.|
|YROT|Posttestmthscore|11.89<br>|<br>4.46<br> <br>|0.52<br>|4.32<br>|13.09<br>|<br>4.50<br> <br>|
|XROT<br>|Pretest mth scare<br>|8.58<br>|<br>3.77<br> <br>|7.38<br>|3.57<br>|9.71<br>|<br>3.85<br> <br>|
|XSEX<br>|Sex (lufemale; 2Ie)<br>|1.53<br>|<br>.24<br> <br>|1.54<br>|.25<br> <br>|1.52<br>|<br>.24<br> <br>|
|XAGE<br>|Age (In amnths)<br>|171.13<br>|<br>4.48<br> <br>|172.15<br>|<br>4.31<br>|170.20<br>|<br>4.53<br> <br>|
|YFOCCI<br>|Father's occupatlonal status (11low;**4hil0g)**<br>|2.39<br>|<br>**.45**<br> <br>|2.25<br>|.40<br>|2.52<br>|<br>.48<br> <br>|
|YMEDUC<br>|Mother'seducatlonal attalrunt (1.10w; 4h10Ih)<br>|1.95<br>|<br>.38<br>|1.80<br>|.33<br>|2.09<br>|<br>.41<br> <br>|
|HCALC<br>|Calculator at hore (lbyes)<br>|.23<br>|.22<br>|.21<br>|.20<br>|.38<br>|<br>.23<br>|
|SPCI81<br>|01st, Iet Per Capita lnOr (In bhats)<br>|13495.0<br>|8 2547.38<br>|9208.07<br>|1247.U<br>|17363.0<br>|1839.15<br> <br>|
|YHLANG<br>|Lse langme of Instructlon at hame (1 yes)<br>|.52<br>|.24<br> <br>|.47<br>|.25<br>|.56<br>|<br>.24<br> <br>|
|YMOREE<br>|D Educatloai elaectatlos (1-low)<br>|3.71<br>|<br>.46<br> <br>|3.59<br>|.50<br>|3.82<br>|<br>.42<br> <br>|
|YPAREN<br>|C Parental mncouragnt (1uhlgi)<br>|2.14<br>|<br>**.4**<br> <br>|2.11<br>|.47<br>|2.16<br>|<br>.46<br> <br>|
|YPERCE<br>|V Perceived math ability (1uhi0g)<br> <br>|4.06<br>|<br>.43<br> <br>|4.08<br>|.42<br>|4.04<br>|<br>.44<br> <br>|
|YFUTUR<br>|Percolved future importance<br>of math (1ulo)<br> <br>|2.08<br>|<br>.42<br> <br>|**2.08**<br>|.43<br>|2.05<br>|<br>.42<br> <br>|
|YDESIR|E Motivatlon to suceed Inmath<br>(1low)|5.60|<br>.35|5.53|.37|5.61|<br>.34|

Notes: (a) For a complete descriptl')n of variables, coult text. (b) MlninA sample size for total sample is 3747. (c) MIninA sample size for rual saiple Is 1893. (d) MinInu sample size for urban sample Is 2054.

- 24 -
developing countries. As many of the earlier observations in this source are also reported in the Deininger-Squire and UN-WIDER database, we obtain only an additional 118 recent observations from this source. Finally, we augment our dataset with 32 observations primarily from developed countries not appearing in the above three sources, that are reported in Lundberg and Squire (2000). This results in an overall sample of 953 observations covering 137 countries over the period 1950-1999. To our knowledge this is the largest dataset used to study the relationship between inequality, incomes, and growth. Details of the geographical composition of the dataset are shown in the first column of Table 1.

This dataset forms a highly unbalanced and irregularly spaced panel of observations. While for a few countries continuous time series of annual observations on income distribution are available for long periods, for most countries only one or a handful of observations are available, with a median number of observations per country of 4. Since our interest is in growth over the medium to long run, and since we do not want the sample to be dominated by those countries where income distribution data happen to be more abundant, we filter the data as follows. For each country we begin with the first available observation, and then move forward in time until we encounter the next observation subject to the constraint that at least five years separate observations, until we have exhausted the available data for that country.<sup>9</sup> This results in an unbalanced and irregularly spaced panel of 418 country-year observations on mean income of the poor separated by at least five years within countries, and spanning 137 countries. The median number of observations per country in this reduced sample is 3. In our econometric estimation (discussed in the following subsection) we restrict the sample further to the set of 285 observations covering 92 countries for which at least two spaced observations on mean income of the poor are available, so that we can consider within-country growth in mean incomes of the poor over periods of at least five years. The median length of these intervals is 6 years. When we consider the effects of additional control variables, the sample is slightly smaller and varies across specifications depending on data availability. The data sources and geographical

> 9 We prefer this method of filtering the data over the alternative of simply taking quinquennial or decadal average **s** ince our method avoids the unnecessary introduction of noise into the timing of the distribution data and the other variables we consider. Since one of the most interesting of these, income growth, is very volatile, this mismatch in timing is potentially problematic.

13
Pseudo Maximum Likelihood (PPML), assuming the conditional mean of imports can be modelled as described: The gravity-type variables are self-explanatory but further details including data sources can again be found in Annex 3. We use the average applied tariff between country-pairs for product _k_ . _PTA_ is a dummy variable indicating whether a bilateral route is covered by any of the main preferential agreements to which the US and New Zealand are party, namely NAFTA, CAFTA, and other ‘closer economic relations’ (CER) agreements.<sup>21</sup>

# **5.2  Results**

We obtain a rich set of results that illustrate how natural and policy barriers affect seaborne trade flows. By linking the first-stage tobit estimates with second-stage PPML estimates, we are able to disentangle direct and indirect effects (through transport costs) and trace out how these change across different samples.

As a point of reference, we start by estimating equation (3) using actual MTC data, as if maritime transport costs were exogenous to trade flows (Table 4, col. 1-3). All variables exhibit the correct sign and most of them are significant at the 1 percent level. Notice that the coefficient on contiguity is also correctly signed for the specific sample of exclusively seaborne imports, since surface transport (road trucking) is arguably the most efficient transport mode for countries sharing a border. This result confirms a pattern often stated in the literature, namely that the importance of maritime transport decreases significantly when two trading partners share a common border (Hummels, 2007).

However, the partial effect of maritime transport costs may be biased because of the reverse causality arising from the fact that larger volumes shipped are associated with lower average costs. In order to obtain an unbiased estimate in the trade gravity equation, we use predicted values of MTCs from the first stage (col. 4-6). From the specifications with predicted

> 21  New Zealand has a CER agreement with Thailand; the United States has CERs with Australia, Chile, Jordan and Morocco, respectively.

19
**Appendix 6.  Comparison of the November 2017 WFP Survey to the 2014 Household Budget Survey**

||2014 Hous<br>_________<br>|ehold Budget<br>___________<br>|Survey<br>_______|Novemb<br>__________<br>|er 2017 WFP Su<br>____________<br>|rvey<br>______|
|---|---|---|---|---|---|---|
||Lower|Upper||Lower|Upper||
|Variable|Bound-<br>95% CI|Bound-<br>95% CI|Mean|Bound- 95%<br>CI|Bound- 95%<br>CI|Mean|
|Own House|0.776|0.880|0.828|0.459|0.570|0.515|
|Rent House|0.070|0.165|0.118|0.224|0.373|0.299|
|HH Size|6.73|7.51|7.12|9.50|10.53|10.02|
|Share relying on Food<br>Coping|0.040|0.123|0.082|0.851|0.901|0.876|
|Number of Days Eating<br>Staples Last Week|6.99|7.00|7.00|5.92|6.11|6.01|
|Number of Days Eating<br>Pulses Last Week|3.38|4.21|3.80|2.74|3.32|3.03|
|Number of Days Eating<br>Vegetables Last Week|3.91|5.03|4.47|2.11|2.67|2.39|
|Number of Days Eating<br>Fruits Last Week|1.20|1.71|1.45|0.74|1.08|0.91|
|Number of Days Eating<br>Proteins Last Week|4.24|5.24|4.74|1.26|1.99|1.63|
|Number of Days Eating<br>DairyLast Week|4.00|5.04|4.52|1.99|2.53|2.26|
|Number of Days Eating Fats<br>Last Week|6.76|6.97|6.87|5.05|5.46|5.25|
|Number of Days Eating<br>Sugars Last Week|6.64|7.00|6.82|5.25|5.69|5.47|
|Share Living with Improved<br>Water|0.347|0.561|0.454|0.177|0.332|0.254|
|Share with Access to<br>ElectricityNetwork|0.674|0.877|0.776|0.000|0.010|0.005|
|Share Relying on Solar<br>Energy|0.000|0.044|0.022|0.444|0.657|0.550|
|Share Relying on Generator<br>Energy|0.005|0.050|0.027|0.120|0.349|0.235|

Notes: This table compares variables common to both the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone. The 2014 HBS is the last known population estimates of these variables for Yemen. Consistent with reports of humanitarian and news agencies on the ground in Yemen, the WFP estimates report significantly worse welfare statistics and access to basic services relative to the2014 HBS before the conflict.

37
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 10 Monetary aggregate (M2) as a share of GDP and GNI per capita, 2007**

<!-- Start of picture text --> Percent<br>Low-income cut-off<br>150<br>100<br>R 2 = 0.06<br>50<br>0<br>4 5 6 7 8 9 10<br>Log(GNI per capita)<br>Figure 11 Private sector credit as a share of GDP and GNI per capita, 2007<br>Percent<br>Low-income cut-off<br>100<br>75<br>R 2 = 0.22<br>50<br>25<br>0<br>4 5 6 7 8 9 10<br>Log(GNI per capita)<br>Monetary aggregate M2 / GDP<br>Private credit / GDP<br><!-- End of picture text -->

**Figure 11 Private sector credit as a share of GDP and GNI per capita, 2007**

_Source:_ IMF International Financial Statistics.

Corporations and governments in countries with high domestic interest rates have higher incentives to access external financing.<sup>11</sup> It is difficult to gauge the importance of this due to the poor quality of data on domestic interest rates in most developing countries, particularly relating to the cost of

> 11 Christensen (2005) reports that the implicit interest rate on domestic debt exceeds that on external debt by a wide margin in several low-income countries in Sub-Saharan Africa. However, this calculation is based on external debt owed to private and official creditors, the latter being highly concessional.

14
arbitration, and 74% negotiation. In the case of negotiation, 80% of firms recover the full value, while in case of court action 84% of the firms do not recover anything. Similar figures arise when we look at disputes with suppliers: among the 435 firms reporting at least one dispute with suppliers, only 23% rely on court action; 5% firms use arbitration, and 65% use negotiation. Overall, this evidence seems to suggest that when firms have disputes, they prefer to negotiate and to avoid relying on the assistance of third-parties, in line with Johnson, McMillan and Woodruff (2002). This could suggest that the quality of legal institutions is not crucial given that only few firms rely on court action. However, it could also be that firms rely seldom on courts since they anticipate that the cost will be too high.

To gain a better understanding of the role of legal enforcement, we use firm-level survey information on the relation between the firm and the local judiciary. Firms are asked to evaluate the likelihood that the legal system will uphold contracts and property rights in business disputes in a scale ranging from zero to one. We call this variable ( _Property_right_ ); the median value is 80%, suggesting that most firms in our sample do not consider the legal system as a major constraint to doing business.<sup>11</sup> This variable is defined at the firm-level and therefore reflects how the firm _perceives_ the quality of legal institutions. This is a nice feature of our data since it is likely that the effect of institutions on firm activity also depends on the characteristics of the firm (i.e., size).

Lastly, we include a set of variables to test the robustness of previous theories, in particular, that trade credit is related to collateral values and customer creditworthiness. We include two direct measures of collateral value: First, the percent of sales made to

> 11 Our results are robust to the inclusion of _Law_predictability_ , which measures the predictability of laws or regulations that materially affect the operation and growth of business; however, this variable is missing for about 1/3 of firm observations.

18
2

# **I. INTRODUCTION**

Frustrated with the perceived inefficiency of directly-managed budget-financed public hospitals, governments around the world have looked for new models of hospital organization. Some have resorted to outright privatization. Many have opted instead to keep their hospitals in the public sector but to grant them greater autonomy in their decisionmaking; at the same time, these autonomized hospitals are given a financial stake in the outcomes of their decisions. This policy of autonomization has often been combined with a pro-competition policy, forcing public hospitals to compete with one another for publiclyfinanced patients; some have been forced to compete with private hospitals too. In some countries, the policy of autonomization has been pushed a stage further, with hospitals being turned into public corporations. These corporatized hospitals enjoy a higher degree of autonomy, but are also made to face a higher degree of financial risk; they are, however, not private entities.

While there is considerable cross-country variation in the precise model used – the emphasis on competition, the areas in which hospitals are given autonomy, and the degree to which hospitals and their staff are exposed to financial risk – it would appear that the trend worldwide is firmly toward granting public hospitals some degree of autonomy. The field has already seen three multi-country edited volumes: Govindaraj and Chawla‘s (1996) study covers Ghana, India, Indonesia, Kenya and Zimbabwe; Preker and Harding‘s (2003) volume covers Australia, Ecuador, Hong Hong, Malaysia, Singapore, Tunisia, New Zealand, and the UK; Saltman et al.‘s (2011) recent volume covers the Czech Republic, England, Estonia, Israel, the Netherlands, Norway, Portugal, and Spain. Stand-alone studies have covered several other countries, including: Brazil (La Forgia and Couttolenc 2008); Chile (Mendez