rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Democracy and levels of urbanization**

|Regression<br>|12<br>non-democracies<br>|13<br>democracies<br>|14<br>Interaction<br>|
|---|---|---|---|
|dates available|1975-95<br>|1975-95<br>|1975-95<br>|
|Ln(Y)<br>|2.49**<br>|1.62**<br>|1.68**<br>|
|(Ln(Y))<sup>2</sup>|-0.15**<br>|-0.093**<br>|-.096**<br>|
|Ln (Yag/Y)|-0.22**<br>|-0.20**<br>|-0.19**<br>|
|Ln(Ymanuf/Y)|0.34**<br>|-0.058<br>|0.050<br>|
|Africa*pre80|1.65**<br>|-- droppeda--<br>|0.39<br>|
|Africa*post80|2.12**<br>|2.93**<br>|1.20**<br>|
|Africa*pre80*ln(Y)|-0.23**<br>|-0.039**<br>|-0.073*<br>|
|Africa*pre80*ln(Y)|-0.28**<br>|-0.44**<br>|-0.17**|
|Ln(Noeduc)<br>|-1.51**|-0.096**<br>||
|(Ln(Noeduc))<sup>2</sup>|0.27**<br>|0.033**<br>||
|Ln(secondary)|0.17**<br>|0.015<br>||
|Ln(RUW)|0.16**|0.32**|0.28**<br>|
|CivilRight*ln(RUW)|||-0.021**|
|Sivard|0.018|0.093||
|Adj. R<sup>2</sup>|.875<br>|.820<br>|.851<br>|
|N|166|99|303|

a. There were no countries classified as "Free"<sup>in Africa before 1980.</sup> Dependant variable is log of the level of urbanization.<sup>Notes: See Table 2.</sup> Thus where civil and political rights are low,<sup>urbanization should be much less affected</sup> by urban rural wage differentials. As a result,<sup>the convergence between rural and urban</sup> wages should proceed more slowly in restricted<sup>civil environments.</sup> Using Freedom House data on political and civil<sup>liberties, we tested whether urbanization</sup> is in fact more closely related to economic incentives<sup>in freer countries. Regression 14</sup> shows the coefficient on an interactive term<sup>that allows RUW to change with the level of</sup> civil liberties to be significantly negative. This<sup>implies that as civil liberties decrease,</sup> urbanization occurs with much less rural-urban wage<sup>equalization. An alternative</sup> formulation (not reported here) uses dummies<sup>for the various levels of civil liberties and</sup> confirms this result: it shows that in countries<sup>with a very low degree of civil liberties,</sup> rural urban wage differentials are no longer<sup>significantly associated with urbanization. In</sup> these countries, then, it would appear that<sup>the lack of civil liberties weakens the</sup> relationship between economic incentives and<sup>urbanization. Results are very similar if</sup> we use political liberties instead.

# **_Ethnic diversity and urbanization_**

Easterly and Levine (1997) showed that ethnic<sup>diversity helps explain many of the public</sup> policy choices of African countries - public<sup>policy choices that in turn account for much</sup> of Africa's poor growth performance. We ask<sup>the question of whether these policy</sup> choices may have affected urbanization, over<sup>and above their impact on income.</sup> What we found was that the ethnic variables were<sup>not generally helpful in explaining</sup> urbanization beyond what income could explain.<sup>In other words, most of the ethnic</sup> 18
# **Section 3. Newly Registered Companies and Exports**

Rwanda’s exports have grown significantly in recent years, and have kept within the 10‐15 percent range of GDP. What has been the contribution of newly registered companies to this increase in exports?

To provide some context, the bulk of exports in Rwanda emanate from a handful of companies and products.<sup>15</sup> Nearly 90 percent of exports are commodities traded on international markets. The remainder of exports largely (about 90 percent) go to Burundi and the Democratic Republic of Congo. These two export markets are distinct and have little overlap, and in recent years both markets have received new entrants.

Fresh capital injections through privatizations are likely to boost commodity exports. The privatization of large tea and coffee estates (Rwandex in 2009, Kitabi tea factory in 2009, and Mata tea factory in 2011) as well as the entry of other players into the sector is likely to have buoyed the productivity and value of commodity exports. A number of new entrants in the mining sector, which contributed the lion’s share of new taxable employment, have had a similar effect.

New entrants from across the region boosted non‐commodity exports. Non‐commodity exports increased by 82 percent in value from US$ 48.4 million in 2011 to US$ 88.0 million in 2012, mainly due to an increase in the export of milling products, beverages, iron, and steel. New entrants from across the region, such as SteelRwa and Bakhresa, together with more seasoned Rwandan companies such as Inyange and Bralirwa (which registered long before the reforms), significantly boosted the increase in exports. The bulk of these non‐traditional exports went to the Democratic Republic of Congo and Burundi (National Bank of Rwanda 2013).

In sum, the contribution of newly registered companies to the increase in exports would appear to be significant, particularly in non‐commodities. The absolute magnitude of this contribution is an area for further study.

> 15 Relying on (Gathani & Steolinga, Understanding Rwanda's Agribusiness and Manufacturing Sectors, 2012) and (Gathani & Stoelinga, Understanding Rwanda's Export Sector, 2012). figures cited in this section refer to cleaned customs figures for the year 2010.

29
# **2. Macroeconomic Fundamentals**

- 2.1 _Public Finances and External Vulnerability_

Figure 1 depicts trends in the general government’s (center plus states consolidated) gross fiscal deficit and debt as percentages of GDP in 1985/86-2002/03. It shows that the gross fiscal deficit averaged 9 percent of GDP before the crisis. It then fell sharply during the period of high growth and fiscal restraint that marked the Eighth Plan period (1992/93-1996/97), but resumed growing equally sharply after 1997/98, returning to the 9-10 percent of GDP range during the Ninth Plan period (1997/98-2001/02).

<!-- Start of picture text --> Figure 1: General Government Deficit and Debt Stock (% GDP)<br>1985/86-2002/03<br>Debt stock /GDP<br>12.0 Fiscal deficit /GDP 90.0<br>11.0<br>80.0<br>10.0<br>70.0<br>9.0<br>8.0 60.0<br>7.0<br>50.0<br>Ninth Plan<br>6.0 Eighth Plan<br>(97/98-01/02)<br>(92/93-96/97) 40.0<br>5.0<br>4.0 30.0<br>85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02<br>Fiscal deficit Debt stock<br><!-- End of picture text --> Source : Budget Documents, RBI, Ministry of Finance, IMF and Staff Estimates Figure 1 also shows that the ratio of general government debt rose from approximately 58 percent of GDP in 1985/86 to 85 percent in 2002/03.<sup>8</sup> This debt was the equivalent of $440 billion with a rupee-to-foreign currency mix of 86:14 compared to 77:23 on the eve of the 1991 crisis. Debt of central public enterprises was another 10 percent of GDP, and guarantees by the center and states, the latter largely in support of borrowing by loss-making public enterprises in power and irrigation, amounted to 12 percent of GDP.

With fiscal deficit levels in the 10 percent of GDP range and government debt more than 25 percentage points of GDP higher than the mid-1980s, public finance fundamentals could be considered worse than in the pre-1991 crisis period. Table 1 shows trends in public finance and external vulnerability indicators. Data values over 1985/86-1989/90, the five years preceding the 1991 crisis, serve as a benchmark. The 1990s are divided into the period of rapid growth, 1992/93-96/97, which coincided with the Eighth Plan (shown in the table as 8<sup>th</sup> Plan avg.); and the slowdown, 97/98-01/02, which coincided with the Ninth Plan period (9<sup>th</sup> Plan avg.).<sup>9</sup>

> 8 Throughout this paper, the component of general government debt denominated in foreign currency is valued at current exchange rates.

> 9 This division of the time periods is used throughout the paper.

4
similarities of content, available slots, and their time of the day (among other logistical factors) in order to secure the comparability of listeners. This selection criteria alleviates the risk of confounding factors having an influence on the variables of interest.

# **_III. Data_**

# **_A. Sampling Framework_**

This study was conducted in two regions of Western Senegal: Thiès and Diourbel. The two regions were selected based on an exhaustive review of national radio signals, density of weekly local markets called _Loumas_ , and proximity to the capital. Both were also among the eight Senegalese regions selected by Lighting Africa as priority targets for its information campaign.

Thiès and Diourbel are the most populated regions in Senegal after the Dakar region, with 1.7 million and 1.4 million inhabitants, respectively. Although they have the highest population density after Dakar<sup>19</sup> both regions remain greatly agricultural: about 52 percent and 43 percent of households live from agricultural production in Thiès and Diourbel, respectively, close to the country average (50 percent).

Within the two regions, we identified weekly local markets and searched for villages within a radius of 8 km to those markets. Those villages were considered proximate enough that a villager without access to transportation could visit the market and return to the village during daylight in one day. We only considered villages with approximately more than 100 inhabitants and national radio signal. Among the 169 eligible villages, we randomly selected 150 to be part of the experiment.

At baseline, enumerators visited two villages per day, completing as many surveys as possible with a minimum of 40 surveys per village. The village center was identified as the starting point for the listing in each village, which was typically the community center or village leader’s office, and the exercise systematically worked through proximate residential properties. A household was defined as a group of individuals living together and putting together part or all of their resources to meet their basic needs (housing and food in particular). To be eligible for the study, households were required to either own a mobile phone or have direct access to one.

While power calculations suggested that 50 villages per treatment arm and 20 households per village would be a large enough sample to be able to detect an economically meaningful effect, we aimed for a substantially larger number of surveys per village because of potentially high survey non-response rate at end line due to the employed survey methodology described below.

> 19 With 256 and 294 inhabitants per km2; against 65 inhabitants/km2 on average in Senegal.

10
## **Figure 1: Breakdown of youth population aged 15 to 24 in LAC, 2010**

<!-- Start of picture text --> Youth<br>(aged 15 - 24)<br>92.2 million = 100%<br><!-- End of picture text --> <!-- Start of picture text --> Study only<br>34%<br>Work only<br>33%<br>Study & Work<br>12%<br>Ninis<br>19%<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015)

# **3. Stylized facts: The evolution of ninis in Latin America**<sup>**6**</sup>

Having identified that 1 in 5 Latin American youth are both out of school and out of work, we analyze how this rate has changed over the past 20 years, to understand whether the problem is becoming more or less severe, as well as how individual countries’ trends vary around this mean trend. Figure 2 presents the evolution of ninis aged 15 to 24 years in Latin America between 1992 and 2010, in both absolute terms and as a proportion of the relevant age demographic. The figure shows that the unweighted average _share_ of ninis has decreased modestly over that period, from 23.4 percent in 1992 to 19.3 percent in 2010, which amounts to a decline of 4.1 percentage points. Over the same period, however, the _number_ of ninis rose slightly from 16.4 million to 18.2 million, amounting to an increase of 14 percent (or 1.8 million individuals) in spite of their decline relative to the 15 to 24 year-old population. This trend of a declining share but rising number of ninis is driven by high population growth for this age group during the 1990s.

> 6 Although for most of the analysis in this report the latest year included is 2010, more household surveys became available as the study was being completed. To confirm the report’s findings, the key statistics presented here were updated to circa 2013, and all of the messages remain unchanged. For instance, the long term trend in the decline in the share of ninis remains reaching 19 percent in 2013, while the absolute number of ninis declines only marginally to less than 18 million in that same year. The most recent statistics are included in country-specific briefs and are available from <u>https://openknowledge.worldbank.org/handle/10986/22349.</u>

6
list of the areas where we would look for impact, by collecting panel data through repeat surveys:

- Learning outcomes—are more children able to read, write, and do arithmetic in villages that received an intervention? What is the relative impact of the three types of interventions?

- What are the immediate channels through which learning outcomes might have changed?: more regular child attendance, increase in child enrollment into schools, greater teacher attendance, hiring of additional teachers in schools, greater resource availability in schools, volunteers working with schools and children, parents paying greater attention to their children’s learning;

- What are the indirect channels of change in incentives and institutions?: Gram Pradhan/VEC takes more interest in schools (meets with teachers, visit schools, etc.); VEC gets more active—hires _shiksha mitras_ , organizes volunteers; other forms of community engagement in and contributions to education;

- Does impact depend upon pre-existing socio-economic conditions in villages? We will attempt to address this question using available village-level data from the Indian Census.

We began collecting new data in March 2006, six months after the interventions were launched, to evaluate the short-run impact on child enrollment and learning, VEC activity, and other forms of community engagement in education. We aim to have the midterm evaluation report ready by August 2006. In September 2006, one year after the interventions were launched, we plan to collect another round of data for the impact evaluation, informed by what we learn from the mid-term evaluation. We aim to have the final report on the impact evaluation ready by March 2007.

How long does impact, if any, last? That is, how self-sustaining is local collective action once outside facilitation is removed? To address this important question, we would like to plan for a longer-term study, and return to these villages 2-3 years after the interventions to study how things evolve over time.

28
Policy Research Working Paper 10151

# **Abstract**

How the enforcement of competition regulation of information and communications technology affects growth depends on how well firms adapt to competitive pressure. This paper tests this empirically using Peruvian firm-level data matched to a compilation of information and communications technology regulations and competition enforcement cases over 10 years. Based on the theoretical dispersion in markups, the paper shows that by increasing productivity, leaders in a market can avoid the effects of competition while maintaining market share. However, much depends on the regulatory structure, which affects productive firms differently depending on how long they have been in business. Highly productive older firms translate regulations that make processes more complex (such as raising quality standards) into more productivity; productive younger firms benefit more from simplifying rules that facilitate competition through lower entry barriers and improved operating conditions. This feature is consistent across different segments of the information and communications technology sector.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cjooste@worldbank.org, tarayavechkit@worldbank.org, aurrutiaarrieta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> 29<br><!-- End of picture text --> <!-- Start of picture text --> .oe-<br>1 .0:<br>1.04<br>1.03 .,<br>1.01<br>1.00<br>198° 1989 1990 1991 1992 1993 1991<br>!-...-(PGRO ---- fYGRDREL --- YGR0AOHR<br><!-- End of picture text --> rigure 6A: Output growth in the base<sup>case with new money</sup> (GNPGRO), with more debt relief<sup>(YGRDREL) and</sup> without an- debt oinlution (YGRCAOHR) <!-- Start of picture text -->
1.(000<br>0.975<br>0.9500<br>0.925  /  ,,  ~  ~  ~~~~~~~~~~~~~~~~~- - - -<br>0.875<br>-988  1989  1990  1991  1992  1993  1994<br>lE  YDSRATIO -. YDSCAOHR<br><!-- End of picture text -->

Figure 6B: Exchange rate misalignment<sup>in base case (YDSRATIO)</sup> and in no-debt-solution case<sup>(YDSCAOHR).</sup>
There is no evidence whatsoever that ride-hailing activity changed differentially in locations nearer to our control locations, providing (even more) confidence in the choice of control locations underlying our baseline estimates.

# **6. Conclusion**

In this paper, we have empirically investigated whether ride-hailing services are a substitute or complement for mass rapid transit (MRT) in the specific case of Jakarta, one of the largest and most heavily traffic congested cities in the world. To do so, we exploited the phased and staggered opening of Jakarta’s MRT system, employing an event study design to estimate the impacts of the system’s Phase 1 opening on ridehailing activity within the immediate neighborhoods of newly opened MRT stations. We find that the system’s opening led to large increases in the volume of ride-hailing trips in the immediate neighborhoods of newly opened MRT stations, while at the same time reducing the average distance of ride-hailing journeys to and from these station locations. These results hold for journeys made on regular weekdays, weekends, during rush hours and during non-rush hours alike. And, they are more prominent in the vicinity of newly opened MRT stations that were not already served by Jakarta’s BRT system. For these locations, the opening of the MRT increased their rapid public transit options at the extensive margin, whereas it only did so at the intensive margin for MRT station locations that were already served by the BRT.

Taken together, our results strongly suggest that ride-hailing and public transport are complements, with the new ride-hailing trips associated with the MRT system’s opening providing important first- and lastmile connectivity, both for trips made for commuting and non-commuting purposes. These effects are very localized. They are primarily driven by the changes in _Gojek_ -activity within 50 m of the newly opened stations. The motorbike-based ride-hailing in Jakarta, much more flexible in terms of being able to pick up and drop off customers as close as possible to their desired pick-up or drop-off location compared to the mostly car-based ride-hailing activity analyzed in earlier studies (notably Gonzalez-Navarro _et al._ , 2021), is an important explanation of this more localized effect. It is very likely to carry over to other developing country city contexts, where ride-hailing services are also primarily motorbike-based.

Our results carry several important potential policy implications. The strong complementarity between the MRT system and ride-hailing services suggests that, far from being a threat, ride-hailing services help boost demand for mass rapid public transport in Jakarta with positive implications for its long-run financial viability, as well as for the overall mobility of both workers and households within the Jakarta metro area. Especially for those that cannot afford their own vehicle, live in places without reliable public transport 25
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22 Cottani, J. D., Cavallo and S. Kahn (1990). “Real Exchange Rate Behavior and Economic Performance in LDCs.” _Economic Development and Cultural Change._ 39(1): 61-76.

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