rafmacalaba/datause-encoder-data huggingface dataset:
_Page 13 of 32_ Such a focus on benefits and disregard of risks and robustness may consist, for instance, in the discounting of spatial assimilation problems described by Priemus (forthcoming) elsewhere in this issue. Competition between projects and authorities creates political and organizational pressures that in turn create an incentive structure that makes it rational for project promoters to emphasize benefits and deemphasize costs and risks. A project that looks highly beneficial on paper is more likely to get funded than one that does not.

Specialized private consultancy companies are typically engaged to help develop project proposals. In general, the interviewees found that consultants showed high professional standard and integrity. But interviewees also found that consultants appeared to focus on justifying projects rather than critically scrutinizing them. A project manager explained: "Most decent consultants will write off obviously bad projects but there is a grey zone and I think many consultants in reality have an incentive to try to prolong the life of projects which means to get them through the business case. It is in line with their need to make a profit." The consultants interviewed confirmed that appraisals often focused more on benefits than on costs. But they said this was at the request of clients and that for specific projects discussed "there was an incredible rush to see projects realized." “One typical interviewee saw project approval as "passing the test" and precisely summed up the rules of the game like this: "It’s all about passing the test [of project approval]. You are in, when you are in. It means that there is so much focus on showing the project at its best at this stage." In sum, the UK study shows that strong interests and strong incentives exist at the project approval stage to present projects as favorably as possible, that is, with benefits emphasized and costs and risks deemphasized. Local authorities, local developers and land owners, local labor unions, local politicians, local officials, local MPs, and consultants all stand to benefit from a project that looks favorable on paper and they have little incentive to actively avoid
**Table 4: Estimated yield effects of different predecessor crops for maize and soybeans**

||**2016/17**|**Maize**<br>**2017/18**|**2016-18**|**2016/17**|**Soybeans**<br>**2017/18**|**2016-18**|
|---|---|---|---|---|---|---|
|% after sunflower|-1.405***|-1.158***|-1.161***|-0.665***|-0.440***|-0.441***|
||(0.0713)|(0.0606)|(0.0606)|(0.0574)|(0.0492)|(0.0493)|
|% after cereals|-0.519***|-0.974***|-0.978***|-0.0436|-0.105**|-0.106**|
||(0.0741)|(0.0634)|(0.0634)|(0.0493)|(0.0450)|(0.0450)|
|% after others|-0.796***|-0.583***|-0.585***|0.202***|-0.0155|-0.0165|
||(0.0811)|(0.0677)|(0.0676)|(0.0572)|(0.0458)|(0.0459)|
|% self twice|-0.379***|-0.374***||-0.0441|0.0188||
||(0.0798)|(0.0634)||(0.0590)|(0.0525)||
|% self twice only|||-0.293***<br>(0.0699)|||0.0379<br>(0.0633)|
|% self thrice|||-0.510***<br>(0.0802)|||-0.0350<br>(0.112)|
|No. of VCs|4,767|4,785|4,785|3,811|3,654|3,654|
|R<sup>2</sup>|0.224|0.274|0.275|0.110|0.0875|0.0875|
|Mean dep. var.|3.737|4.144|4.144|2.677|3.071|3.071|
|SD dep. var.|0.663|0.601|0.601|0.635|0.485|0.485|
|Elasticity: twice|-0.0909|-0.104|-0.0563|-0.00756|0.00276|0.00424|
|Elasticity: thrice|||-0.0439|||-0.00123|
|El after sunflower|-0.350|-0.279|-0.279|-0.0908|-0.0662|-0.0663|
|F-test|||||||
|Self 2x=self 3x|||7.61***|||0.29|

_Note:_ Results are from regressions at village council level for two-year periods in columns 1 and 4 (2016-17) as well as 2 and 5 (2017-18) and for the 3-year period 2016-18 in columns 3 and 6. Dependent variable is log of physical yield (dt/ha). The soybeanmaze and maize-soybean rotations are the omitted categories, respectively. A constant as well as mean farm size is included throughout but omitted. Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

18
Risk Index on the available values of legality and using the predicted value of 14.6 for China.

Alternatively, omitting the 98 Chinese firms from all of the subsequent analysis leaves the results and conclusions unchanged.

# **IV.   Analysis**

In this section we describe the tests of our hypotheses and the results from these tests. Our main hypothesis is that foreign debt allows firms which reside in countries with poor legal systems to overcome borrowing constraints in their local markets and thus to achieve their preferred level of debt financing. Our starting point is a descriptive examination similar to LaPorta et al. (1997) of external equity and debt capital that firms use and the relation to legal environment. We aggregate to the country level and compare external capital ratios with a country’s legal environment.<sup>6</sup>

## **A. Main Results**

Our sample of 1689 firms from 9 East Asian countries spans the range of legal systems well: three countries have an English legal origin (Hong Kong, Malaysia and Singapore), two have a German legal origin (South Korea and Taiwan), three have a French legal origin (Indonesia, Philippines and Thailand) and one has a socialist legal origin (China). Table 1 presents some descriptive statistics. Among the three primary legal origins the distribution of firms is fairly even ranging from 397 firms of French legal origin to 749 of English legal origin.

The next column shows the legality index for each country. Unsurprisingly, the highest average legality measures are found among countries with an English legal origin (average of 18.2), then among countries with a German legal origin (average of 15.8), and finally among countries with a French legal origin (average of 11.0). China has a legality index that is roughly the same as the average of the German legal origin countries.

> 6 Country level data based on median cash-flow yields qualitatively similar results to those presented in Table 1.  Finally, using assets instead of sales did not make any difference in the results.

10
- 18 -

are not employed. Self-employees are used as a baseline in the analysis. For household asset ownership, only 6 percent in the sample own private cars. Motorcycles and bicycles are more prevalent: 17 percent for the former and 13 percent for the latter. These modes of transport can potentially be an alternative to informal public transport.

**<u>Table 2. Summary Statistics</u>**

|Variable|Abb.|Obs<br>|Mean|Std. Dev. Min<br>|Max|
|---|---|---|---|---|---|
|Number of trips made per week|_Q_|3,133|4.477|<br>1.687<br>1|15|
|Total travel costs paid per trip (MGA)|_P_|3,133|837.612|966.144<br>2|24000|
|Trip purpose (dummy variable):||||||
|Go to work||3,133|0.459|<br>0.498<br>0|1|
|Business||3,133|0.023|<br>0.149<br>0|1|
|School||3,133|0.129|<br>0.335<br>0|1|
|Shopping||3,133|0.245|<br>0.430<br>0|1|
|Personal reasons||3,133|0.064|<br>0.244<br>0|1|
|Family visit||<br>3,133|0.063|<br>0.242<br>0|1|
|Hobby||3,133|0.010|<br>0.101<br>0|1|
|Escort for others||3,133|0.003|<br>0.054<br>0|1|
|In-vehicle time (minute)|TIME|<br>3,133|50.167|<br>36.471<br>1|280|
|<br>Access time (minute)|ACC|3,133|9.444|<br>9.375<br>1|180|
|<br>Egress time (minute)|EGR|3,133|15.738|<br>19.535<br>1|180|
|Number of transfers made|TRS|3,133|1.271|<br>0.659<br>0|7|
|Transport mode: Only within city bus||3,133|0.753|<br>0.431<br>0|1|
|Only suburban bus|dSUBU|3,133|0.191|<br>0.393<br>0|1|
|<br>Both|dBOTH|3,133|0.048|<br>0.214<br>0|1|
|Dummy variable for users within CUA|dCUA|3,133|0.564|<br>0.496<br>0|1|
|<br>Users perception: Unsafe|dUNSAFE|3,133|0.191|<br>0.393<br>0|1|
|Uncomfortable|dUNCOMF|<br>3,133|0.210|<br>0.407<br>0|1|
|Individual effects:||||||
|Income: < 200k||3,133|0.243|<br>0.429<br>0|1|
|200-300k||3,133|0.304|<br>0.460<br>0|1|
|300-400k||<br>3,133|0.185|<br>0.388<br>0|1|
|400-500k||3,133|0.119|<br>0.324<br>0|1|
|500-750k||3,133|0.079|<br>0.270<br>0|1|
|750k-1 mil||3,133|0.036|<br>0.187<br>0|1|
|> 1 mil||3,133|0.034|<br>0.181<br>0|1|
|Dummy variable for female users|dFEMALE|3,133|0.527|<br>0.499<br>0|1|
|Age: Users above 56 years old|dAGE56|3,133|0.071|<br>0.256<br>0|1|
|No. of household members|SIZE|3,133|4.425|<br>1.723<br>1|22|
|Dummy variable for household head|dHHH|<br>3,133|0.413|<br>0.492<br>0|1|
|<br>Occupation: Self employed||3,133|0.389|<br>0.488<br>0|1|
|<br>Employed||3,133|0.318|<br>0.466<br>0|1|
|<br>Unemployed||3,133|0.226|<br>0.418<br>0|1|
|Other||3,133|0.068|<br>0.251<br>0|1|
|Asset ownership: Car||3,133|0.063|<br>0.243<br>0|1|
# **APPENDIX VI – Regression Results**

# **VI.1 Stage 1 - Country-Pair Fixed Effects**

(implies dropping all time-invariant country-pair characteristics)

||**TELECOMMS**<br>**EQUIPMENT**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1990)**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1993)**|
|---|---|---|---|
|**Dependent variable**|**prtelcoexp**|**prmedexp**|**prmedexp**|
|**lngdprep**|0.21090<br>(0.4173487)|0.3942407<br>(0.5618693)|0.5083504<br>(0.5634355)|
|**lngdppcrep**|-2.701938***<br>(0.8502425)|0.684101<br>(0.5857651)|0.5994565<br>(0.5887835)|
|**lngdppar**|0.0973794<br>(0.33824599|-3.488452***<br>(0.539633)|-3.626826***<br>(0.5437054)|
|**lngdppcpar**|-1.076191<br>(0.9102524)|2.593148***<br>(0.5397502)|2.693682***<br>(0.5352667)|
|**lndistwces**|n/a|n/a|n/a|
|**comlang_off**|n/a|n/a|n/a|
|**contig**|n/a|n/a|n/a|
|**colony**|n/a|n/a|n/a|
|**lleither**|n/a|n/a|n/a|
|**isleither**|n/a|n/a|n/a|
|**WTO**|0.7473382***<br>(0.1429587)|0.1681899*<br>(0.0958562)|0.1479443<br>(0.0952302)|
|**FTA**|-0.6485442<br>(0.3994121)|-0.4973737**<br>(0.2020673)|-0.6413389***<br>(0.1901858)|
|**MRA(****_sector_) **|-0.0571326<br>(0.1990434)|0.5530708<br>(0.4278304)|0.4329727<br>(0.4099194)|
|**HR(****_sector_) **|Dropped by Stata|-0.3705425<br>(0.3933758)|0.0646127<br>(0.3581467)|
|**HR(****_sector_)oecd_sgp**|0.5816262**<br>(0.2273636)|0.3479144*<br>(0.1923466)|0.3922432<br>(0.2399145)|
|**HR(****_sector_)_dev**|-0.073575<br>(0.0844198)|-0.7103974***<br>(0.1234612)|-0.7088038***<br>(0.1233057)|
|**Pseudo-R**<sup>**2**</sup>|0.4091|0.4641|0.4646|
|**No. of observations**|10,347|19,188|19,188|

*** significant at the 1% level ** significant at the 5% level

* significant at the 10% level

37
economic activity in the West Africa Economic Monetary Union (WAEMU).<sup>8</sup> For example, in 2009, GDP in Cote d‟Ivoire was US$ 23 billion, as compared to US$ 15 billion in Ghana, US$ 13 billion in Senegal, and US$ 8 billion in Burkina Faso (World Bank, 2010c). Furthermore, the country's relatively high GDP per capita (US$ 1,674 in 2009) suggests opportunities for private sector development and growth exist.

Cote d'Ivoire grew rapidly after achieving independence in 1960, and by 1974, nominal GNI per capita rose from 190 to 450 CFA (over 300% in real terms). This expansion was primarily due to growth in the agricultural sector as the country benefitted from high international prices of coffee and cocoa, for which the country is a major exporter. In addition, the country's investment code during this period and relatively stable government and policies encouraged foreign capital investment in agribusiness activities (Tuinder, 1978). By 1978, the World Bank declared, "[t]he so-called „Ivorian Miracle‟ has not been a matter of luck..." and the country's political stability and favorable economic environment led international organizations to believe that the country could be the first African country to achieve "developed" status (Tuinder, 1978).<sup>9</sup> However, with the collapse of coffee and cocoa prices and the oil shock in 1979, the Ivorian economy experienced a severe annualized contraction of 12% in 1980 and entered a prolonged recessionary period that persisted through most of the 1980s. For most of this period, GDP growth was less than 3% and private credit (as a percentage of GDP) sharply declined from about 30% to less than 20%.

After defaulting on commercial bank loans in 1983, the country undertook a series of foreign debt reschedulings with both official and private creditors. For the next decade, Cote d'Ivoire instituted a series of trade, fiscal, and monetary reforms. First, from 1985 to 1987, the government removed a series of import quotas, in order to increase competition and reduce prices. The government removed quantitative restrictions and unified effective rates of protection across intermediate and final goods, which reduced the competitive advantage of domestic manufacturing firms vis-à-vis foreign exporters (Harrison, 1994).

> 8 Authors‟ calculation based on IMF World Economic Outlook nominal GDP data.  Cote d‟Ivoire also accounts for close to 4% of Sub-Saharan Africa nominal GDP and more than 20% of ECOWAS‟ output (authors‟ calculations based on IMF data).

> 9 From 1960 until his death in 1993, the country was led by Houphouet-Boigny.

7
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- **b. Hidden costs of selected water utilities, percentage of revenues**

<!-- Start of picture text --> Zambia<br>Malawi<br>Mozambique<br>Namibia<br>Lesotho<br>South Africa<br>Madagascar<br>Botswana<br>Resource-Rich<br>LIC-Fragile<br>LIC-NoFragile<br>MIC<br>SSA<br>0 50 100 150 200 250<br>Percentage of revenues<br>Losses Under-pricing Collection inneficiencies<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ LIC = low-income country; MIC = middle-income country; SSA = Sub-Saharan Africa.

## **Challenges**

The government has consistently allocated significant resources to the sector despite challenging conditions. In particular, the provision of rural water in Botswana is more costly than in an average SubSaharan Africa country. This is first because Botswana’s large, sparsely settled rural areas often include vast spaces between settlements, making economies of scale difficult to realize. Second, due to high evaporation rates and a deep water table the costs of water provision are relatively high. Most of the rural supply comes from boreholes that are often quite deep (in some cases, hundreds of meters, as the layer of the Kalahari Desert can be quite thick). Exploration and operation of such deep wells is difficult and expensive (World Bank 2010).

The ongoing reforms of the water sector pose a big challenge to the WUC, which will take over the water supply for the whole country. The capital investments needed to continue providing high-quality services are not negligible. Expanding access to flush toilets and improved latrines in the years to come is a major challenge, as is keeping pace with the urbanization trend and reducing the practice of open defecation.

# Power

## **Achievements**

Botswana more than doubled its electrification rate between 2006 and 2008, pushing it from 22 percent to 50 percent; a remarkable achievement (table 9). According to the National Development Plan (NDP), the target is to reach 80 percent national power access and 60 percent rural access by 2016. Between 2004 and 2007, rural access to electricity also doubled, to 44 percent; which is a major improvement even though still falling short of national targets.

23
and economical significance. Notice that the point estimator of _β_ 1 in specification 4 is significantly smaller than the one presented in Table 4, as one would expect if test scores were driven by both initial subject-specific and general skills. Once the effects of general skills are removed, the association between one SD higher test scores in Grade 6 is about 0.25-0.34 SD on test scores in 9th and 12th grades (as opposed to the 0.49-0.52 SD reported in Table 4). The implied estimates for general skills at Grade 6 on future test scores is about 0.2 SD and highly significant–see row 2 in columns 1 and 2 and row 1 in columns 3 and 4 in Table 7. These results suggest that test scores at Grade 6 are a strong predictor of future education trajectories with the persistence working through the positive link between initial and future subject-specific skills and through the positive effects of initial general skills on future education outcomes.

Finally, we go back to the ENILEMS-ENLACE panel and look at average ENLACE test scores in mathematics and Spanish (at the end of secondary school) by university field of studies (see Figure 5). If ENLACE captures specific skills and students tend to self-select to university degrees that match better their stock of skills, one would expect to observe that students enrolled in majors that require higher math (Spanish) skills, have higher test scores in this subject. Importantly for our story, engineering students tend to have higher test scores in mathematics than in Spanish, while the opposite is true for Social Science students. We interpret this pattern as complementary evidence that ENLACE indeed captures the specific skills it is designed to measure. Furthermore, to our knowledge, this is the first piece of evidence about how university students sort by ability into fields of study in Mexico.

# **5 Conclusions**

Using the Mexican census-based standardized test ENLACE, we construct a longitudinal dataset tracking students’ completion of and test scores in Grades 6, 9, and 12. Our analysis shows that higher test scores at Grade 6 have a large and significant relationship with the student’s likelihood of finishing lower- and upper-secondary school on time and, 23
years 2010 and 2011. The panel A of table 5 presents the results of a placebo exercise where we assume that the refugees in 2014 had been there in 2011 and use 2010 as the pre-treatment control year to estimate the IV regressions. Unfortunately, data are not available to test for long-term trends. There is evidence of different trends for net sales and profits when the full sample is used as shown in table 5. However, once we limit the sample to eastern Turkey, the placebo treatment only remains statistically significant for net sales. In the case of net sales, the real treatment has a considerably larger coefficient than the placebo treatment, suggesting that it is upward biased, but the true effect is still positive. Nevertheless, we remain cautious about interpreting the results from net sales given the difficulty of finding a region where the common trend assumption seems to hold.

Table 5: IV placebo estimates

||New frms|New foreign<br>frms|Share of<br>foreign frms|Gross profts<br>per capita|Net sales<br>per capita|
|---|---|---|---|---|---|
|Panel A: Full sample||||||
|Refugees (x10,000)|0.0112*|-0.0244|0.0002|43.3222***|589.5904***|
||(0.0063)|(0.0176)|(0.0007)|(14.0182)|(111.4231)|
|Observations|162|162|162|162|162|
|Panel B: Regional||||||
|Refugees (x10,000)|0.0122|-0.0337|0.0002|25.0731|438.8429***|
||(0.0095)|(0.0280)|(0.0010)|(18.8053)|(132.5547)|
|Observations|58|58|58|58|58|

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Source:_ Authors’ own analysis results from data described in text. _Note:_ All models include a year-specific log distance control, province and year fixed effects. Robust standard errors in parenthesis. Data are from 2010 and 2011 for 81 provinces in Panel A and 29 provinces in Panel B. The refugee variable is the number of refugees in the province in 10,000s. The first-stage F-test for the instrument in IV regressions is 12.44. For the regional sample the F-test is 6.45.

# **6.2 DD results**

While the IV methodology provides our preferred estimates, we also estimate the effects using the DD method by defining south-eastern provinces of Turkey as the treatment group and the unaffected north-eastern provinces as the control group. Since refugees begin moving out to the rest of Turkey in 2014, we define the years 2009 to 2011 as the pre-treatment years and the years 2012 and 2013 as the treatment years. While closely 17
identify spatial variations in methane emissions that are not already captured by the IFPRI and EDGAR data?

# **7.  S5P CH4 Value Added**

We assess the “value added” of SP5 CH4 measures in a two-step exercise. First, we regress those measures on IFPRI field extents and EDGAR CH4 emissions. Table 3 shows that both variables contribute significantly to explaining SP5 CH4 variations. The regression residuals are the “value added” of S5P – the component of spatial variation in methane emissions not explained by field extents or EDGAR emissions.<sup>12</sup>

# **Table 3:  Determinants of local SP5 CH4 anomalies**

Dependent variable: CH4 Anomaly Percentile

||OLS|Robust|Cluster|
|---|---|---|---|
|Field Extent|0.199***|0.199***|0.199***|
|Percentile|(21.92)|(21.24)|(4.96)|
|EDGAR Emissions|0.400***|0.400***|0.400***|
|Percentile|(44.36)|(44.94)|(10.74)|
|Const.|20.34***|20.34***|20.34***|
||(57.28)|(58.31)|(8.61)|
|R<sup>2</sup>|0.3247|0.3247|0.3247|
|Obs.|19,755|19,755|19,755|
|t statistics in parentheses:<br>* p<0.05 ** p<0.01 *** p<|0.001|||

Figure 7 displays the global distribution of the value-added component. Large positive outliers (colored light and dark red) denote areas where S5P CH4 anomalies are much higher than would be predicted with knowledge of field extents and EDGAR emissions alone. The converse is true for large negative outliers.

> 12  It may also be useful to interpret this as a three-step process.  The first step performs separate regressions of S5P CH4 anomalies and EDGAR emissions on IFPRI field extents.  The second step computes residuals for both regressions.  The third step regresses the S5P residuals on the EDGAR residuals and computes the residuals from that exercise.  These residuals incorporate the S5P CH4 anomalies that are not explained by EDGAR residuals, after both variables have been purged of field scale effects.

16
such a context.

Table 4: Government spending multiplier over both sub-periods

|Quarters|1|4|8|12|20|Peak|
|---|---|---|---|---|---|---|
||||**1970:Q1**|**-1990:Q4**|||
|Impact multiplier|0.44*|-0.07|0.11|0.17|0.09|0.44*(1)|
|Govt. spending|1.00*|0.40*|0.24*|0.18|0.13||
|Net tax revenue|-0.64*|-1.18*|-0.50*|-0.08|0.07||
|Cumulative multiplier|0.44*|0.10|0.12|0.23*|0.52|0.66(30)|
||||**1991:Q1**|**-2010:Q4**|||
|Impact multiplier|0.70*|0.85*|0.80*|0.79*|0.40*|0.92*(2)|
|Govt. spending|1.00*|1.08*|0.89*|0.59*|0.26||
|Net tax revenue|0.01|-1.08*|-1.33*|-1.02*|-0.46||
|Cumulative multiplier|0.70*|0.80*|0.82*|0.91*|1.08*|1.09*(24)|

Notes: This table reports spending multiplier and responses of government spending and net tax revenue to a government spending shock over the two sub-periods. The multiplier is the median, and * denotes that 0 does not belong to the confidence region. The last column reports the maximum multiplier and numbers in brackets denote the quarters in which the maximum is observed.

## **3.3.2 Sub-period tax revenue multiplier**

The impact of a net tax revenue shock on GDP over the two sub-periods is reported in Table 5. In response to an increase in net tax revenue of $1, GDP declines by $0.38 upon impact during the first sub-period and by $0.21 during the second sub-period. The peak response in the second sub-period is equal to -$0.51, while in the first sub-period, it is equal to the response upon impact.

For the cumulative multiplier, the same patterns are observed. The decline in GDP is larger in the first sub-period than in the second sub-period. The response of net tax revenue may explain this different impact on GDP. After the shock, net tax revenue declines steadily in the second sub-period, while the decline in the first sub-period tends to be faster.

# **3.4 Comparison of the results with existing findings**

To my knowledge, most of the papers examining empirical evidence of the impact of fiscal policy shocks in Canada do not explicitly report numerical results. Rather, they report the IRFs on graphs, making any quantitative comparison difficult. Therefore, the quantitative comparison is limited to theOwyang et al. (2013) study.

## **3.4.1 Comparison with Owyang et al. (2013)**

Table 6 reports the cumulative multiplier associated with spending increases in both studies. Owyang et al. (2013) estimate spending multiplier, contingent on the state of the 15