rafmacalaba/datause-encoder-data huggingface dataset:
1999a,b<sup>7</sup> ). Because entry implies sunk costs (Sutton 1991) and generally occurs at a scale that is lower than the minimum efficient scale (MES), a larger entry size is a signal of commitment and self-confidence and makes both the occurrence of wrong entry decisions and the risk of failure due to diseconomies of scale less likely.

Moreover, a larger start-up size is positively correlated with other factors, such as lower credit constraints and a higher technological capability, which are predictors of a higher likelihood of survival and better post-entry performance. Therefore, a larger start-up size can be considered a reliable indicator of better chances of survival of a newborn firm.

In contrast, a vast number of papers have found (conditional on survival) a negative relationship between start-up size and post-entry growth, thus rejecting Gibrat’s Law<sup>8</sup> (Gibrat 1931; Hall 1987; Hart and Oulton 1996; Sutton 1997; Lotti, Santarelli, and Vivarelli 2003, 2009). This evidence means that smaller entrants with a sub-optimal entry size and with a higher risk of early failure (see above) must grow to survive and reach the MES as soon as possible. However, it is worth emphasizing that the (negative) relationship between size and growth has been found to be significant within the sub-sample of new entrants that struggle to survive (Lotti, Santarelli, Vivarelli 2003). Once market selection is accounted for, long run analyses have shown that a convergence towards Gibrat-like behavior can be detected among the surviving most efficient firms (Lotti, Santarelli, and Vivarelli 2006, 2009; Daunfeldt and Elert 2013). In other words, once small entrants have succeeded in approaching an efficient scale of production, their growth dynamics increasingly resemble a stochastic process in which size and growth are independent.

A firm’s age consistently turns out to be positively correlated with survival (that is, the hazard rate is decreasing with age; see Fackler, Schnabel, and Wagner 2013) and negatively with growth (Evans 1987; Dunne and Hughes 1994; Calvo 2006; Coad, Segarra, and Teurel 2013). Experienced, mature firms are more able to address market dynamics and 12
speed of adjustment: entrepreneurs’ human capital, market competition and infrastructure quality have improved rapidly, while progress in restraining government expropriation and limiting political connections has largely stagnated. Perhaps underscoring the rising importance of government control in private sector development, we find that politicallyunconnected entrepreneurs have experienced declining status in the society.

An implication of our research is on the efficiency of the dual-track approach to reforms (i.e., the co-existence of the plan track and the market track for governing resource allocations of firms). Having the plan and/or government track has the benefit of compensating political losers in the initial stage of transition, and likely has facilitated the whole reform process (Lau, Qian and Roland 2000). But once such arrangement is institutionalized, government officials and connected entrepreneurs--which may also endogenously expand in strength and in number-could form entrenched interest groups, and block further reforms that would establish de jure institutions that protect property rights of non-connected private entrepreneurs (Acemoglu, Johnson and Robinson, 2005). Our research thus suggests that leveling the playing field for connected and non-connected firms remains a daunting task for China’s further development.

# **References**

- Acemoglu, Daron, Simon Johnson, James A. Robinson. 2001. “The Colonial Origins of Comparative Development: An Empirical Investigation.” _American Economic Review_ , 91, pp.1369–1401.

- Acemoglu, Daron and Simon Johnson. 2005. "Unbundling Institutions." _Journal of Political Economy_ , 113(5), 949-95.

- Acemoglu, Daron, Simon Johnson, James Robinson, 2005. “Institutions as the Fundamental Cause of Long-run Growth,” NBER wp 10481, the _Handbook of Economic Growth_ , Elsevier, North-Holland.

- Akcigit, U. S. Baslandze, F. Lotti. “Connecting to power: Political connections, innovation, and firm dynamics.” _Mimeo, University of Chicago._

- Aidis, Ruta and Tomasz Mickiewicz. 2006. "Entrepreneurs, Expectations and Business Expansion: Lessons from Lithuania." _Europe-Asia Studies_ , 58(6), 855-80.

- Allen, Franklin; Jun Qian and Meijun Qian. 2005. "Law, Finance, and Economic Growth in China." _Journal of Financial Economics_ , 77(1), 57-116.

- Ang, James S; Yingmei Cheng and Chaopeng Wu. 2014. "Does Enforcement of Intellectual Property Rights Matter in China? Evidence from Financing and Investment Choices in the High-Tech Industry." _Review of Economics and Statistics_ , 96(2), 332-48.

- Ayyagari, Meghana; AsliDemirgüç-Kunt and Vojislav Maksimovic. 2008. "How Important Are Financing Constraints? The Role of Finance in the Business Environment." _The World Bank_

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and 108 rural health centers in the nine control districts. The surveys included general facility assessments, health provider interviews, and direct clinical observation of consultations with children under-5 and adults aged 40 and above. When present, a single health house affiliated with each rural health center was randomly selected for assessment.

In the catchment area of each of the chosen health centers, two villages were randomly selected to be included as a sampling unit for the household survey.<sup>8</sup> Within these villages, 4,345 households with women who had live births in the 24 months before the survey were randomly selected for the household survey. In a third of the catchment areas in each district, households with adults aged 40 and above were also selected for a total of 1,668 such households.

A follow-up survey was conducted from March to July 2018 in the same health facilities and the same villages, using the same survey tools. The only change in survey methodology concerned the direct observations. In the baseline survey, less than 30% of the target number of consultations have been observed because few patients arrived in the facilities in the winter months, and care was mainly provided through home visits. Therefore, the follow-up facility-based survey was conducted in the spring and summer months, and the enumerators also observed consultations provided through home visits.

In six rural health centers included in the baseline sample, a follow-up survey was not conducted because they were under renovation, closed, or downgraded to health house status. Those catchment areas were removed from the analysis sample. Table A2 in the appendix presents sample characteristics and some key outcomes from the baseline survey of the analysis sample.

# Empirical specification

Our primary empirical specification is the following difference-in-differences model used for estimating the impact of the PBF intervention on outcomes of interest:

> impact on results. If the program had immediate impacts, this would diminish the differences between baseline and follow-up surveys and our estimated impacts.

> 8 In catchment areas with a health houses, one of the villages was selected from the list of villages directly served by the health house selected into the sample.

8
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Prevailing tariffs are insufficient to recover huge operating costs and long-run marginal costs**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US cents per kilowatt-hour<br><!-- End of picture text --> Source: Rosnes and Vennemo 2009. Note: LMRC = long-run marginal costs; historical costs as of 2007.

# Information and communication technologies

## **Achievements**

The ICT revolution has found fertile ground in Mali. The number of people with access to a mobile line grew by 300 percent between 2004 and 2007, allowing Mali to catch up with peers with similar income level and positioning Mali among the best performers in West Africa. These improvements are also reflected in an impressive record of customer satisfaction, low end-user prices, and the development of international corridors of fiber optics to connect with neighbors (table 13). The network of fiber optic with West African peers guarantees the country parallel and independent access to the landing stations of the submarine cable in Senegal and Côte d’Ivoire, allowing Mali to overcome its landlocked condition.

How has this remarkable progress taken place? Since 1998, the development of the mobile market was made possible by institutional reforms including a sound sector policy, the creation of a regulatory body, and the progressive liberalization of the telecommunications market. The results have been striking. In the mobile market, subscriptions went from 10,000 in 2000 to more than 2.5 million in 2007 and mobile penetration rose from less than 7 percent in 2005 to almost 36 percent in 2008—the highest penetration rate in West Africa outside of Nigeria. Annual average growth was the highest in West Africa at 75 percent per year (table 14). GSM coverage remains relatively low when compared to other lowincome countries (see table 13), which reflects Mali’s challenging geography and demography.

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The intuition underlying our difference-in-differences approach is that in “normal” times, if availability of external finance matters for outward FDI, the ratio of outward FDI in financially vulnerable sectors to outward FDI in other sectors should be larger in countries characterised by high financial development (holding other factors constant).<sup>5</sup> This implies that during a credit crisis, interruption of access to external finance should have a greater negative impact on the relative volume of outward FDI in financially vulnerable sectors in deep financial systems than in shallow financial systems since, in the latter countries, availability of external finance is expected to be less of a determinant of the outward FDI performance of financially vulnerable sectors in normal times. Hence, as long as financial development is important for outward FDI, a credit crisis ought to have a larger negative influence on outward FDI flows in more financially vulnerable sectors in more financially developed countries. By focusing on this specific relationship, we deepen the likelihood that our results reflect the causal impact of credit conditions on outward FDI.

Variants of this difference-in-differences approach have been employed to investigate the detrimental financial effects of the 2008-2010 global financial crisis on international trade (Bricongne et al., 2012; Chor and Manova, 2012).<sup>6</sup> However, we are the first study to implement it to examine the influence of tight credit conditions on FDI during this period. This is possible thanks to our access to a unique, and under-exploited,<sup>7</sup> database on sector-specific real greenfield manufacturing FDI. From a broader perspective, our study contributes to the limited literature on the effects of source countries’

> 5Engaging in FDI involves large upfront fixed costs related to market research, the modification of products to meet foreign tastes or regulatory requirements, or the establishment of distribution and servicing channels. Some of these costs may have to be incurred once and may not apply for follow on investments. However, crucially, each new FDI project also involves establishing or purchasing a production facility in the destination country. Firms with limited internal funds should rely heavily on external finance to engage in FDI since they can finance internally a small fraction only of the fixed costs of FDI. It can thus be expected that outward FDI in financially vulnerable sectors is more sensitive to access to external finance (which depends on financial development) than outward FDI in other sectors.

6More broadly, this approach, initially suggested by Rajan and Zingales (1998), has been used to study the impact of structural cross-sectional differences in financial development on international trade. See for example Beck (2002, 2003), Amiti and Weinstein (2011), or Manova (2013).

> 7Using “ _FDI Markets_ database” as our search string, we obtained 55 results only in Google Scholar.

2
# **Figure S16. Comparing estimates of minimum quality care conditional on drugs availability and low absence.**

<!-- Start of picture text --> A1. Diarrhea: Drugs available B1. Diarrhea: Low absence<br>100 100<br>TZA TZA<br>MOZ<br>SLE MOZ KEN SLE<br>80 80<br>NER<br>NER<br>KEN<br>60 60<br>UGA<br>40 40<br>UGA<br>NGA<br>NGA<br>MDG MDG<br>20 20 TGO<br>TGO<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Pr. of min. quality standards (%) Pr. of min. quality standards (%)<br>A2. Pneumonia: Drugs available B2. Pneumonia: Low absence<br>100 100<br>90 90<br>TZA<br>80 80 KEN<br>TZA<br>70 70<br>KEN<br>MOZ<br>60 60<br>SLE SLE MOZ<br>50 50 TGO<br>40 NGA TGO 40<br>NER MDG MDG<br>UGA<br>30 30 NER<br>NGA<br>20 20 UGA<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Pr. of min. quality standards (%) Pr. of min. quality standards (%)<br>A3. Malaria: Drugs available B3. Malaria: Low absence<br>100 TZA 100 SLE<br>MOZ TZA<br>SLE<br>MDG UGA<br>80 80<br>NER MDG<br>NGA UGA<br>TGO<br>60 60<br>TGO NERMOZ<br>NGA<br>40 40<br>20 20<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Pr. of min. quality standards (%) Pr. of min. quality standards (%)<br>Pr. of min. quality standards: No absence (%)<br>Pr. of min. quality standards: Drugs available (%)<br>Pr. of min. quality standards: No absence (%)<br>Pr. of min. quality standards: Drugs available (%)<br>Pr. of min. quality standards: No absence (%)<br>Pr. of min. quality standards: Drugs available (%)<br><!-- End of picture text --> 62
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# _5.I. Assessing the consistency of measurements_

To assess these consistency conditions, we rely on the indicators estimated by the Asian Development Bank (1997). These indicators cover tariffs (average tariff/cubic meter, average monthly bill, technical efficiency measures such as water losses, operational characteristics (number of hours/day when service is available, purchasing power of the population in relation to their water bill, consumption, subsidies, share of households with water meters, access to public fountains, salary of the directors) and measures of performance such as accounts receivable and number of employees per 1000 connection).

Relying on accounts receivable and number of employees per 1000 connection as performance indicators yields two ranking (one for each indicator).

Table 4

|-<br>Fbim|Act wnRe>x<br>cvabl1;|_J._<br>_!j_|_V_<br>OO|
|---|---|---|---|
|Almaty|5.4|34|13.9<br>33|
|Apia|s.d.|s.d.<br>|15.8<br>35|
|Bandung||11|7.7<br>24|
|Bangkok|2|22|4.6<br>12|
|Beijing|0.1|2|27.2<br>46|
|Bishkek|7.7|41|6.9<br>21|
|Calcutta|_1.5_|16|17.1<br>38|
|Cebu|1.9|21|9.3<br>27|
|Chennai|5.8|36|25.9<br>45|
|Chiangmai|1.2|15|2.9<br>9|
|Chittagong|10|42|27.7<br>47|
|Chonburi|1.6|19|2.6<br>7|
|Colombo|3.2|27|7.3<br>22|
|Davao|_0.5_|7|6.2<br>18|
|Delhi|4.5|32|21.4<br>42|
|Dhaka|11|43|18.5<br>41|
|Faisalabad|12|45|25<br>43|
|Hanoi|0.1|2|13.3<br>31|
|Ho Chi Minh|3.4|2.9|6.4<br>20|
|Hong Kong|4|30|2.8<br>8|
|Honiara|5.4|34|10.7<br>29|
|Jakarta|1|11|5.9<br>16|
|Johor Bahru|2.5|25|1.2<br>4|
|Karachi|16.8|46|8.4<br>25|
|Kathmandu|4.5|32|15<br>34|
|Kuala Lumpur|0.5|7|1.1<br>2|
|Lae|3|26|17.1<br>38|
|Lahore|7|40|5.7<br>15|
|Male|1|11|7.6<br>23|
|Mandalay|0.2|6|6.3<br>19|
|Manila|6|37|9.8<br>28|
|Medan|0.1|2|4.9<br>13|
|Mumbai|19.7|47|33.3<br>48|
|Nuku'alofa|1.5|16|16<br>36|
|Penang|2|22|4.4<br>11|
27

# 7. Parking Charges

Parking charges can also be interpreted as an instrument to reduce transport sector externalities as it could discourage driving through increasing the costs of car use. Moreover, in areas where parking charges are levied, it could lead to an increase in the use of public transportation (Acutt and Dodgson, 1997). Feeney (1989) shows that an increased parking cost and decreased availability may have five major effects on motorists: (1) change their parking location, (2) change starting time of their journey, (3) change the mode used, (4) change trip destination and (5) abandon the trip altogether. Willson and Shoup (1990) estimate that an increase in parking charges for all employees in government offices in Ottawa, Canada not only led to a 20% reduction in single car trips, but also forced a model shift. Through simulation studies of five British cities, Dasgupta et al. (1994) demonstrate that doubling the parking charges reduces car share of central area trips by 13%, from 56% to 43%.

# 8. Policy Implications

## _8.1 Single or multiple policy instruments?_

As there is no single policy that fits all prescriptions when it comes to designing appropriate fiscal instruments to combat transport sector externalities, many cities around the world, particularly in developing countries, are experiencing difficulty in trying to determine appropriate sets of policy instruments to reduce the transport sector externalities. Existing literature (e.g., Molina and Molina, 2004) suggests that urban air pollution originating from transport activities cannot be solved through one specific strategy; instead, it requires a mix of policy measures that best suit each city’s specific circumstances. For example, as marginal decisions to travel are directly affected by a set of taxes and charges such as fuel taxes, road pricing, and other road usage related charges, imposition of fuel taxes alone may only account for some externalities but not all. Although fuel taxes, to some extent, can be justified as road use charges, they are
off, better-educated peers, or lack of social capital so that poor traders are not seen as welcome group trade members.

# **6. References**

Aker, Jenny C., Michael W. Klein, Stephen A. O'Connell & Muzhe Yang, 2014. Borders, ethnicity and trade. _Journal of Development Economics_ (107), pages 1-16.

Bas, Maria, Ana Fernandes & Caroline Paunov, 2022. “How Resilient Was Trade to COVID19?” _World Bank Policy Research Working Paper_ 9975. Washington, DC: World Bank. Bensassi, Sami & Joachim Jarreau, 2019. "Price discrimination in bribe payments: Evidence from informal cross-border trade in West Africa," _World Development_ 122, pages 462-480. Bensassi, Sami, Joachim Jarreau & Cristina Mitaritonna, 2019. Regional integration and informal trade in Africa: Evidence from Benin's borders. _Journal of African Economies_ 28(1), pages 89-118.

Bernard, Tanguy & David J. Spielman, 2009. “Reaching the rural poor through rural producer organizations? A study of agricultural marketing cooperatives in Ethiopia.” _Food Policy_ 34, Issue 1, February 2009, Pages 60-69.

Blekking, Jordan, Nicolas Gatti, Kurt Waldman, Tom Evans & Kathy Baylis, 2021. “The benefits and limitations of agricultural input cooperatives in Zambia.” _World Development_ 146, 105616.

Bonadio, Barthélémy, Zhen Huo, Andrei A. Levchenko & Nitya Pandalai-Nayar, 2021. “Global Supply Chains in the Pandemic,” _Journal of International Economics_ , 133, 103534.

26
observations  provide the most positive results for decreasing extreme poverty by 2030: median value of 4.6 percent or 371 million people in relative and absolute terms, respectively. In contrast, the simulations derived by combining data from the 1980–2014 base period with growth rates of per capita consumption provide the worst predictions for global poverty reduction by 2030: median value of 8.9 percent or 724 million people in relative and absolute terms, respectively.

Non-resource-output oriented economies, however, are predicted to achieve and even surpass the 3 percent poverty target by 2030. Although extreme poverty is expected to diminish in relative terms, in several simulation exercises, resource-output oriented economies could see an increase in absolute poverty over the period 2015–2030. Note that there is significant variability in the predicted future poverty outcomes for resource-output oriented countries, implying that given recent historical economic episodes, it is hardly possible to predict precise estimates of poverty rates in these economies. Moreover, poverty is expected to decline in the period 2015– 2030 in relative terms in low-output, middle-output, and high-output growth country categories. Nevertheless, poverty in low-output growth economies is expected to increase in absolute terms. The results show that the high dispersion—low precision—in the simulation outcomes denotes some inability to predict poverty outcomes in countries classified as low-output growth economies. The model simulations predict that high-output growth economies will quickly alleviate poverty below the 3 percent level before 2030. Noticeably, the simulations show a low degree of uncertainty in the expected poverty outcomes of countries with high-output growth rates.

The second main result indicates that changes in relative income inequality are predicted to be on the positive side, meaning that in general, the Gini coefficients across the studied country classifications are predicted to decrease on average over the period 2015–2030. Across this 2015– 2030 horizon, our estimates of the Gini coefficient at the global level are expected to decline between 0.7 and 1.9 Gini points (in the Gini scale of 0–100). The simulation outcomes suggest some degree of uncertainty such that non-resource-output oriented countries and low-output and high-output growth countries could either decrease or increase their income inequality levels across the period 2015–2030.

The third significant result indicates that the B40 of the income distribution is, on average, predicted to grow faster than the mean and higher percentiles over the 2015–2030 horizon and across the studied country aggregations. There are a few simulations, however, with median values Page **29** of **45**
# **Overall Impact of Taxes and Spending on Poverty and Inequality**

In Cambodia, the overall fiscal system reduces inequality. Bearing in mind the caveat that top income households are often underrepresented in household surveys—a common feature of most surveys, especially in developing countries—the fiscal system in Cambodia reduces inequality by 1 percentage point. Inequality, as measured by the Gini coefficient, falls between _market income_ and _final income_ (Figure 8). Before any fiscal interventions, the _market income_ Gini index is 32.4 percent. Once direct taxes and direct transfers are considered, the Gini index reduces slightly to 32.2 percent at _disposable income_ . Indirect taxes and subsidies have limited effect, and their consideration leaves the Gini index of _consumable income_ remain at 32.2 percent. “In-kind” transfers from health and education, on the other hand, have the largest effect on inequality with _final income_ Gini at 31.4 percent. This reflects the fact that in-kind transfers represent a significant share of pre-fiscal income and proportionally benefit lowerincome households more relative to those from the upper end of the income distribution.

Figure 8: Gini index before and after fiscal interventions in Cambodia <!-- Start of picture text --> 34<br>33<br>32.4<br>32.2 32.2<br>32 31.4<br>31<br>30<br>Market income Disposable Consumable Final income<br>income income<br>Gini index (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

While Cambodia does reduce inequality through taxes and transfers, the degree of redistribution is small in international comparison. Figure 9 demonstrates that the redistributive effect of fiscal policy (including in-kind transfers) is low in Cambodia. Some lower-middle-income countries achieve inequality reduction of up to 9 percentage points from the pre-fiscal level. When in-kind transfers are excluded, the redistributive effect in Cambodia is even lower. This is because of the very low impact of direct transfers on inequality.

17