rafmacalaba/datause-encoder-data huggingface dataset:
# **V. DATA**

The sample data are collected from 45 fixed-line and mobile operators in 18 African countries. Data on the number of fixed-line telephone subscribers are available in ITU database. EMC database contains the number of mobile subscribers on an operator basis. The mobile data are also disaggregated between prepaid and monthly-based users. There is no such disaggregated data available for fixed-line subscription. The current analysis assumes that all fixed-line users are charged on a monthly payment basis, because the majority of fixed-line operators rely on the post paid contracts. However, it is noteworthy that the prepaid system is technically applicable for fixed-line services as well. In fact, there are a few cases in the sample where the prepaid fixed-line telephone services are available. For example, the Tanzania Telecommunications Company Limited (TTCL) offers both prepaid and post paid tariffs for fixed-line telecommunications.

The market share _MSjt_ is calculated by dividing the net change of the number of subscribers with contract _j_ in 2005 by the potential market size. The potential market is defined by the total population who do not subscribe to any telephone services at the beginning of the sample period. For simplicity, the potential market size is measured by total population minus total subscribers at the end of 2004.<sup>25</sup> In case the number of subscribers to a particular subscription plan declines, the market share is assumed to be a sufficiently small but strictly positive number. It aims to avoid the logarithm of negative numbers.

The price and produce characteristics data are collected from the websites of individual telephone operators in the region. Potentially, more than one tariff schedule may be offered by a telephone operator. If it is the case, one of the lowest packages, meaning a plan with the minimum free minutes and/or the lowest monthly fee, is taken. This criterion is not perfect to capture the whole pricing structure but is expected to help standardize heterogeneous price attributes across carriers to a certain extent. Figure 11

> 25 There is no available data on gross increases/decreases in subscribers. In the Africa’s context, however, it seems that few people have changed their telephone carriers.

26
# 3 GHANA COCOA Figure

## Production, Prices, Income

<!-- Start of picture text --> 00 tons, C/MT, x .2 mill C (Thousands)<br>8.<br>6<br>4f<br>2<br>1947 1952 1957 1962 1967 1972 1977 1982<br><!-- End of picture text -->

### crop years

> - Total output + real price i real income

Income is real price times production
**Table 5.1 Income, traffic and guarantees for projects in full operation**

|**Project**|**Year**|**Income**<br>**(UF)**|**Minimum**<br>**Income**<br>**Guarantee**<br>**(UF)**|**Traffic**<br>**(vehicles or**<br>**passengers)**|**Minimum**<br>**Traffic**<br>**Guarantee**|**Payments by**<br>**government'**<br>**(UF)**|
|---|---|---|---|---|---|---|
|El Melon Tunel|1997|164,475||1,102,137|762,000|-2,838|
||1998|174,720||1,251,648|769,500||
|Nogales-Puchuncavi Road|1997|5,927|7,125||---|**1,198**|
||1998|39,306|42,100|594,322|---|2,794|
|Santiago-San Antonio Highway|1997|355,402|---|3,913,131|||
||1998|523,387|453,440|6,486,136|||
|Access to AMB airport|1997|70,365||4,137,640|||
||1998|82,846|40,818|4,594,894|---|-1,711|
|La Madera Road|1997|66,257|---|369,356|221,000||
||1998|73,846|---|414,182|228,000||
|Airport Diego Aracena of Iquique|1997|26,797|16,315|240,429|||
||1998|39,982|16,968|340,018|||
|Airport El Tepual of Puerto Montt|1997|17,536|---|89,579|---||
||1998|51,731|21,734|246,373|||

Notes: The Unidad de Fomento (UF) is an indexed monetary unit used in contracts. In December 1998, it corresponded to $14,685.39 in Chilean pesos, or US$ 31.00 at the prevailing exchange rate. For airport concessions the number of users corresponds to passengers, while for roads and highways it is the number of vehicles. Some concessions were not in full operation and therefore the minimum income or traffic guarantee was not applicable.

a Negative payments indicate a transfer from the concessionaire to the governnent as a result of a revenue sharing clause. The figures in the column do not include subsidies or transfers determined at the beginning of the concession. They are only due to minimum income guarantees or revenue sharing arrangements.

34
|Table 7: Effe|Treatment vs<br>control<br>ct on Female PP|Treated<br>clusters vs<br>control<br>PP Vote|Treated<br>households<br>only vs<br>control|Control<br>households<br>in treated<br>clusters vs<br>households<br>in control|
|---|---|---|---|---|
||households<br>T-C|clusters<br>TN-CN|clusters<br>T-CN|clusters<br>CTN-CN|
||(1)|(2)|(3)|(4)|
|Panel A: Treatment|||||
|Treatment (T)|-0.056|-0.157***|-0.146***|-0.218***|
||[0.041]|[0.052]|[0.047]|[0.077]|
|R-squared|0.166|0.175|0.196|0.285|
|Panel B: T1vs T2|||||
|Importance of voting (T1)|-0.023|-0.130*|-0.114*|-0.202**|
||[0.052]|[0.069]|[0.065]|[0.091]|
|Importance of voting and secret balloting (T2)|-0.093|-0.190***|-0.183***|-0.246***|
||[0.057]|[0.057]|[0.055]|[0.090]|
|R-squared|0.171|0.179|0.201|0.286|
|N. Observations|1,133|1,133|974|299|
|Mean dependent variable among CN|0.950|0.950|0.950|0.950|
|_P-value_(T1= T2)|0.328|0.403|0.346|0.652|
|_P-value_(F-test for joint significance of T1and T2)|0.264|0.005|0.004|0.017|

Note: The dependent variable takes the value 1 if a woman reports having voted for PPPP in the February 2008 elections and was verified as having voted. The symbols *, **, *** represent significance at the 10, 5 and 1 percent respectively. All specifications include village fixed effects and the following controls: number of registered female voters, whether woman has a NIC or CNIC, level of schooling, age, zaat status, TV access, number of children between 0-5, total land owned, index of mobility and whether woman seeks advise from "Pir". Standard errors are reported in parentheses below the coefficient and are clustered at the geographic cluster level.
further complicated in programs that are “complex in costs,” which is determined not only by the number of cost ingredients, but also by the proportion of total cost explained by few cost ingredients. Third, we explore how various biases – including “recall bias”, much explored in relation to consumption but little in this literature, and “pilot bias”, wherein pilot programs are likely to have higher costs but potentially also higher impacts – are likely to lead to biased cost-effectiveness estimates. These challenges, added to the external validity of impact estimates, which has been much discussed elsewhere, point to the need for governments and their advisors to carry out significant contextualization of cost estimates before drawing conclusions about the relative cost-effectiveness of programs. We conclude with some discussion and recommendations for the way forward.

# **II. Errors in the Estimation of Impact**

Cost-effectiveness analysis typically uses point estimates of impact to calculate costeffectiveness: For example, a girls’ scholarship program in Kenya has an estimated impact of 0.27 standard deviations in student learning, with a cost per girl per year of $19.51, so the estimated cost-effectiveness is 1.38 standard deviations in student learning per $100 spent. Every point estimate, however, has a confidence interval, the size of which varies depending on the statistical power of each underlying evaluation. Imprecision in the estimation of impact means that it is possible that although one program may appear more cost-effective than a second when using point estimates to calculate cost-effectiveness, the relative costeffectiveness may change – or the difference become trivial – if the variance around the two point estimates is taken into account.

We first demonstrate the extent to which this occurs in practice by re-ordering the relative cost-effectiveness for a sample of 14 education programs with student learning impacts, using the upper and lower bounds of the 90% confidence interval around the impact estimate and observing how this affects the relative ranking of the cost-effectiveness of these programs. Table 1 presents the original cost-effectiveness results of these 14 learning programs, alongside the results of this sensitivity analysis. We use data from J-PAL (2014a), who conduct cost-effectiveness analyses of 27 education interventions across Africa and Asia, all of which aim to increase student test scores, and apply our sensitivity analysis to the 14 of these programs found to have a significant impact at the 10 percent level. Table 1 ranks the programs in order of descending cost-effectiveness, measured in terms of the additional standard deviations in test scores produced by each program for a cost of $100. The impact on learning is measured in standard deviations of test scores, intended to allow comparison 4
the extremely high values from the roaring twenties, are now found all over the Internet and in many magazines and newspapers. But their origin goes back to Piketty’s 2001 book on top incomes in France.

Methodological and political changes went hand in hand: suddenly household surveys, which for decades were the bread-and-butter of economists working on inequality (including myself), were shown wanting as they failed to fully capture this new phenomenon: the ineluctable rise of the top...and the stagnation of everybody else.

A reader who knows Thomas Piketty from this literature will expect to find a book that discusses issues of income concentration. He or she will not be disappointed. Here they all are, described and explained more clearly than ever. However, this is not the only important part of the book. The key contribution is Piketty’s analysis of capitalism. The book provides “a general theory of capitalism.” Issues of inequality are only one, no doubt important, facet of that general theory. Piketty’s unstated objective is nothing less than the unification of the theory of economic growth with the theories of functional and personal income distributions, and thus a quasi complete description of the functioning of a capitalist economy.

The book is organized in four parts and 16 chapters. The four parts are as follows: first, some “clearing of the decks”, which consists mostly in the introduction of definitions, national accounts identities and relationships to be used later; second, focus on the capital-income ratio and functional distribution of national income; third, inequality in inter-personal distributions of wages, property incomes and wealth; and fourth, policy recommendations. Capital, as the title suggests, is at the center of the book. It is a huge and extremely rich book. Suffice it to say that it covers two to three centuries of empirical data on capital and output, national income distributions, the rate of return on capital, inflation, inheritance flows and more for the most important rich economies (France, UK, United States, and somewhat less Germany, Japan, Sweden, and Canada). Its range is immense: from the exchange rate of the _livre tournois_ on the eve of the French revolution to the 2013 Cyprus financial crisis; from the capitalized values of slaves in the Southern USA to the Chinese private foreign holdings today; from the percentage of the population with the right to vote in France under Bourbon Restoration to today’s incarceration rates in the US. In addition, this 950-pages long book is accompanied by an enormous online technical annex (http://piketty.pse.ens.fr/fr/capital21c) which contains all the underlying data used in the book, tables, graphs, references and the summary of the essential points. So by using less than 1⁄2 of 1 percent of the total space of Piketty’s book and annex, this review will attempt to provide an exposition and assessment of the book’s key points and messages.

3
10 software and a polygon overlay technique to match PSUs from the DHS to districts and UWPs from the 2009 Local Government Engineering Department (LEGD) UWP-level map. After identifying each PSU’s UWP, we matched it with a UWP-level population density from census data from Bangladesh Bureau of Statistics (2002) and Bangladesh Bureau of Statistics (2012) to create our independent variable, the interaction of PSU-level open defecation with the log of UWP-level population density. The 1999 and 2004 DHS were matched to the 2001 population census of Bangladesh; the 2011 DHS was matched to the 2011 population census.<sup>11</sup> Thus, each PSU is matched to a highly disaggregated measure of population density. Because the DHS surveys are repeated, nationally representative cross sections that do not form a panel of PSUs, it is often the case that a given UWP is not represented in more than one round of the DHS surveys. Therefore, the smallest geographic unit for which we can include a is the district.

**Independent variable.** Our independent variable of interest is the interaction of the log of UWP-level population density with the fraction of households in a PSU that defecates in the open; this is the same for each child in a given survey round and PSU.

**Dependent variable.** The dependent variable in this analysis is the height-for-age _z_ - score of children under five, using the WHO 2006 reference of healthy children. For the Bangladesh analysis, we no longer use infant mortality as a dependent variable. With a sample less than four percent as large as in the international analysis of section 3, we are unable to precisely identify effects on infant mortality, a low probability binary outcome, using district fixed effects. Sample size is less of a constraint for continuously-distributed, normalized height-for-age, which is routinely studied in samples of this size (e.g. Spears, 2013). The appendix presents evidence that supports an interactive effect of sanitation and population density on infant mortality. Appendix table 7 presents results for infant mortality that use fixed effects for region, rather than district fixed effects, and repeats falsification

> 11In a small number of cases, area was not available from the census, so we computed population density by dividing census population by area from LEGD data.

19
27 regressions control for whether countries have competitive elections, highlighting the complementary nature of additional institutional arrangements that facilitate collective action by citizens. Table 4 reports only the coefficients from the competitive elections and collective action variable from each regression.

The first panel of Table 4 indicates that when the fraction of parties that are programmatic in a country goes from zero to one, both gross primary and secondary school enrollment rise by approximately 6 percentage points; bureaucratic quality rises by .64 (approximately one-half of a standard deviation); and corruption rises by one point (more than two-thirds of a standard deviation). Private investment rises more modestly, by 1.5 percentage points, or 18 percent of a standard deviation, an effect that is not quite statistically significant.

The second panel examines programmatic parties from a different perspective, using a dummy variable that equals one if the main government and opposition parties are programmatic. If a country has a programmatic ruling party, but no opposition party or a non-programmatic opposition, it is coded as zero for the dummy variable, though it receives a positive score for the average programmatic variable. The effects are nearly the same, except that private investment is significantly higher, while the magnitude of the corruption effect is somewhat lower.

The theory predicts that the age of the largest government party at the time the leader takes office (party age less leader years in office) should only have an effect in nondemocracies; in democracies, citizens have the opportunity to act collectively through other vehicles, including parties other than the largest government party. The last three panels document that this is the case. The first of these looks at all country-years, both those with and without competitive elections. Ruling party age matters only for bureaucratic quality and
|log (Capital)i,s,t−1|-0.0354*|-0.00405|-0.0191***|-0.00397|-0.0149***|-0.00266|
|---|---|---|---|---|---|---|
||(0.0171)|(0.00296)|(0.00514)|(0.00274)|(0.00381)|(0.00220)|
|TFPR (TL)i,s,t−1|-0.0423**|0.00658|-0.0220|-0.00673|-0.0105|-0.00369|
||(0.0191)|(0.0268)|(0.0167)|(0.0164)|(0.0165)|(0.0126)|
|Constant|1.447***|1.134***|1.306***|1.137***|1.253***|1.130***|
||(0.153)|(0.0234)|(0.0466)|(0.0181)|(0.0385)|(0.0170)|
|Level of winsorization|None|None|1%|1%|5%|5%|
|Firm fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry-year fixed|||||||
|effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|73,768|69,770|73,768|69,770|73,768|69,770|
|R-squared|0.599|0.809|0.783|0.830|0.819|0.840|

Robust standard errors clustered at the industry level in parentheses. Dependent variable displayed at the top of each column: CD denotes markups derived assuming a Cobb-Douglas production function, while TL denotes markups derived assuming a translog production function. *** p<0.01, ** p<0.05, * p<0.1

# Propensity score matching and the impact of foreign acquisition

The non-random selection of acquisition targets by foreign acquirors is a potential source of endogeneity that may bias estimates under our baseline specification: Foreign acquirors may disproportionately target firms with greater market power or, more precisely, firms on more upward trajectories with respect to market power. To control for the non-random selection of acquired firms, we apply propensity score matching and reweighting to construct a counterfactual by matching acquired firms with similar firms that were not acquired. By combining propensity score reweighting with the difference-in-differences estimator, we estimate the average treatment effect on the treated (ATT), which compares the actual post-acquisition markup of a target firm and the situation had the firm not been acquired (Stiebale & Vencappa, 2018).

A probit regression is used to calculate propensity scores for the likelihood of acquisition by a foreign acquirer controlling for industry and year fixed effects. According to the coefficients from the probit estimation reported in Table 8, output is a significant predictor of foreign acquisition regardless of specification. Total factor productivity is also associated with a higher propensity to be acquired when the specification accounts for all available information before the acquisition (Columns 3-4).

16
<!-- Start of picture text --> 0.60% —- Average Forecast<br>90% forecast interval<br>S 0.50% 50% forecast interval<br>oF ° — Brazil Legal Amazon<br>a 0.40%<br>©<br>LLv i<br>0.30% HH<br>S<br>S<br>J i<br>N<br>(0.20%<br>-i<br>v<br>2<br>~ '<br>® 0.10%<br>v i<br>v2021 (0.37% ~ 0%)<br>2 0.00% —<br>y iy \<br>n iN \.<br>v i WA<br>2 -0.10% a i<br>= N Low ambitiousness<br>5 ' ae Ambitious<br>c<br>& -0.20% High ambitiousness<br>2000 2004 2008 2012 2016 2020 2024<br><!-- End of picture text -->
We then turn to the third and final dimension of child development covered in this study: socioemotional and behavioral development. The survey included instruments based on the Strengths and Difficulties Questionnaire (SDQ), in which mothers assess children’s behavioral and emotional problems along five dimensions: emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and prosocial behavior problems.<sup>8</sup> While all five sub-scales are typically used when screening for disorders, broader subscales can be used for analytical purposes. This involves combining the emotional and peer subscales into an internalizing subscale and the conduct and hyperactivity subscales into an externalizing subscale (Goodman, Lamping, & Ploubidis, 2010). Internalizing behaviors are negative behaviors focused inward, and can include fearfulness, social withdrawal, and excessive thoughts, feelings and behaviors focusing on physical symptoms such as pain, weakness or shortness of breath (somatic complaints). In contrast, an externalizing behavior is directed outward toward others. Bullying, vandalism, and arson are examples of externalizing behaviors. Studies have found the Strengths and Difficulties Questionnaire (SDQ) to be a good predictor of children’s scores on the same instrument one year later (Hasan et al., 2013).

Figure 4 relates the count of difficulties with maternal education (completed years). In this case, there are some interesting differences in the strength of the gradient. Prosocial difficulties (considerate of others people feelings; sharing with other children; helpful if someone is hurt, upset or ill; kind to younger kids; volunteering to help others) are the most strongly associated with maternal education, followed by internalizing and externalizing difficulties, respectively. Illiterate mothers have kids with prosocial difficulties scores 0.23 standard-deviations (s.d.) higher than kids whose moms are literate. The corresponding numbers for internalizing and externalizing difficulties are 0.08 and -0.02 s.d., respectively. These scores were standardized using the age specific group within the full ECED sample. Appendix Table A5, Panel B, reports the descriptive statistics.

The second instrument we employ is the Early Development Instrument, a comprehensive mea-

> 8As noted in Pradhan et al. (2013), the SDQ is an informant-based assessment of a child used internationally. It has been translated to Indonesian by Wiguna and Hestyanti (2012).

10
_Ways Out of Povertv - Diffusing Best Practices and Creating Capabilities_ _8_ OPEC during the last 30 years. So, despite massive population growth, the relative scarcity of natural resources has shrunk. Nor is possession of resources the key to growth. Botswana (diamonds) and Chile (copper) have benefited from their resource endowments, but the possession of natural resources can also be a curse.<sup>21</sup> The "curse of oil" characterizes the fact that countries endowed with high priced, large volumes of oil are not growing much or are even shrinking. Look at Nigeria, Saudi Arabia, and Venezuela.

**Figure 4: Intelligence is scarce-natural resources abundant**

<!-- Start of picture text --> sO -A4 WMd GOP p Cept. W. (R.d S t-)<br>4Co 1900 - 100 -IWeWdahWC..dt.pro r w d me)<br>300<br>200<br>150<br>100-<br>60 ~~~~~~~~~~~~~TePrice of Commnodiloes<br>0 .<br>60 - A = \ a Thae 00a<br><!-- End of picture text --> Source: The _Economist_ (Commodity Price Index); World Bank WDI Database.

**Human capital.** Even investment in education does not easily translate into superior growth.<sup>22</sup> Educational progress in Africa was much faster relative to its starting point than in East Asia, but did not bring higher growth. Educational improvements overall in the developing world in fact coincided with declining growth rates during the last 40 years. Detailed household surveys also suggest that educational levels are not critical in explaining who escapes from poverty.

**Access to resources** and **capital.** None of this is to say that finance, investment, natural resources, or education do not matter. They do, but by themselves they are not enough. And on the positive side, it has become easier to move finance into good investment opportunities. Trade barriers have been lowered, so it has become easier to acquire capital goods that embody new technologies. And the education levels needed for income growth in poor countries do not appear to be too difficult to achieve.

> **_21_** Gelb et al., 1988; Sachs and Warner, 2001. _22_ Pritchett, 1996.