rafmacalaba/datause-encoder-data huggingface dataset:
## **Place-based policies**

For the second set of policies, I study whether place-based policies that reallocate formal firms in the city can effectively increase welfare and reduce informality rates. The intervention consists of increasing the commercial floor space employed by formal firms in different parts of the city. I consider two sets of policies; the first one consists of increasing the commercial floor space in the center of the city, and the second one in the outskirts. Figure 9 plots the locations in which the Government implements the policy; in total, there around 250 treated census-tracts in both parts of the city. The goal is to compare policies that reallocate formal firms to the outskirts vs. transit shocks that connect informal workers with formal jobs.

Figure 10 plots the results of the intervention. In panel A, the Government increases commercial floor space in the central locations, and in panel B in the remote areas. The results suggest that it is more effective to intervene in the central locations than in the outskirts. For instance, if the Government increases the commercial floor space by around 40% in the central areas, the policy generates similar welfare gains to the transit shock that I studied. On the other hand, as shown in panel B, it is very ineffective to reallocate firms to the outskirts. Even if the Government increases the commercial floor space by a substantial proportion, 60%, it only increases welfare by 0.18%, which is significantly lower than the one obtained by the new subway line that connected informal workers with formal jobs. Moreover, in the latter case, the allocative efficiency margin and the externality component explain a very small fraction of the total gains.

There are two main explanations for this result. The first one is that since most of the formal firms locate in the CBD, the agglomeration forces are minimal in the outskirts. The second one is that these locations are very unproductive in terms of the productivity scale parameters, especially for formal firms. Hence, reallocating firms to the outskirts generate negligible welfare gains. In general, the results imply that it is more effective to connect informal workers with formal jobs by transit lines than to move formal firms to the city’s remote areas through de-agglomeration policies.

# **7 Conclusion**

This paper has examined the welfare gains from transit improvements in developing countries, considering the allocative efficiency margin. The mechanism that it studies is whether workers reallocate from the informal to the formal sector. I find that transit infrastructure that facilitates commuting may generate additional welfare gains by improving the market access of the informal labor force to formal employment.

From an empirical perspective, the paper exploits a transit shock in Mexico City that connected poor and remote areas with the center of the city. The main finding is that informality rates decrease in the locations that experienced the shock relative to other areas in the city. This result implies that workers reallocated to firms with higher TFPR, thereby increasing welfare to a larger extent 35
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 2. Trading across borders in West African countries**

|Country<br>**C/L**|**Documents**<br>**to export**<br>**(number)**|**Time to**<br>**export**<br>**(days)**|**Cost to export**<br>**($ per**<br>**container)**|**Documents**<br>**to import**<br>**(number)**|**Time to**<br>**import**<br>**(days)**|**Cost to import**<br>**($ per**<br>**container)**|
|---|---|---|---|---|---|---|
|Burkina Faso<br>L|11|41|2,262|11|49|3,830|
|Mali<br>L|7|32|2,075|10|37|2,955|
|Niger<br>L|8|59|3,545|10|64|3,545|
|West Africa, landlocked|9|44|2,627|10|50|3,443|
|West Africa, coastal|7|24|1,263|8|28|1,514|
|Sub-Saharan Africa|8|34|1,942|9|39|2,365|

_Source:_ World Bank, Doing Business 2011. _Note:_ C= coastal; L= landlocked. Documents to export (import): The total number of documents required per shipment to export (import) goods. Documents required for clearance by government ministries, customs authorities, port and container terminal authorities, health and technical control agencies, and banks are taken into account Time to export (import): The time necessary to comply with all procedures required to export (import) goods. If a procedure can be accelerated for an additional cost, the fastest legal procedure is chosen. Cost to export (import): The cost associated with all procedures required to export (import) goods. Includes the costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal-handling charges, and inland transport.

**Figure 6. Shares of different factors affecting the time and cost of importing through gateways in West Africa**

a. Cost b. Time <!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Border Administrative Transport Port Border Administrative Transport Port<br>Source: AICD ports database; Teravaninthorn and Raballand 2009.<br>Note : Ports data are based on indicators from 2006–07.<br>Share (%) Share (%)<br><!-- End of picture text --> Similarly, the lengthy _travel times_ to import and export from Niger are associated with ports’ inefficient operations. In the Cotonou-Niamey corridor, delays in the Port of Cotonou are responsible for more than 70 percent of the time required to import into Niamey, as the port operates over 200 percent of its capacity. It is estimated that the delays cost around $180 per container; the average pre-berth waiting 10
# **5.4 Impact of Low Reliability of Logistics on Firms Competitiveness**

# _Inventories and Hedging Costs_

A fragmented transit chain and variance in processing time not only causes delays but also causes uncertainty and unpredictability. This increases the logistics cost for economic operators who are willing to pay premium for reliable logistics solution or need to maintain high inventories For maritime transport, a standard deviation of 20% of transport time increases transport costs by nearly 45% (Frankel 1999). Although difficult to quantify, the non-transport costs may be even more for shippers. Dobberstein _et al._ (2005) show that in Asian emerging markets, non transport-related logistics costs were 10% of the GDP and this was almost equal to the extent of transport costs in total logistics costs. Allen _et al._ (1985) demonstrate that increasing transit time and variance in transit time causes higher inventories and ultimately higher logistics costs.<sup>32</sup> Due to uncertainty, companies need to maintain high safety stocks in order to avoid any shortage of raw materials or intermediate products. Spoornet,<sup>33</sup> the main railway company in Southern Africa, undertook a customer survey to assess its customer concerns. Reliability was considered as the prime concern before predictability, whereas time and speed were ranked respectively 7<sup>th</sup> and 8<sup>th</sup> in this list. In the textile industry, product quality, reliability and time to supply goods from developing countries to Europe or the US are as important as the price.

Fafchamps _et al._ (2000)<sup>34</sup> also demonstrate that the incidence of delayed deliveries has a strong positive effect on inventory holdings. Based on a sample of firms, the authors find that Zimbabwean firms hedge delivery risk by building input inventories. In developing

> 32 Baumol (1970) in his model had already raised this issue of the impact of uncertainty on inventories. Consequently, exporter/importer wishes to pay more or may wait for one or two additional days in transit to have more reliable deliveries. See futher sections for the application of the model.

> 33 Interview with B. Le Roux on March, 6th 2004, former CEO of Spoornet.

> 34 Fafchamps, M., Gunning, J.W., and Oostendorp, R. (2000), “Inventories and Risk in Manufacturing,” _The Economic Journal,_ Number 110, pp.861-893.

30
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_Implementation Science_ , 5 (69): 1-9.

22
interest to remember that the first loan extended by the World Bank in its early days of existence was not to a developing country, but to France in May of 1947 ($250 million reconstruction credit), followed by similar reconstruction loans to the Netherlands, Denmark, and Luxembourg. It was not until March 1948 that the first loan to a developing country was approved---to Chile for $16 million in support of financing a hydroelectric plant and production of agricultural machinery. It was indeed after the US had taken over the task of reconstruction of Western Europe through the Marshal Plan that the architects of the Bretton Woods system turned their attention to matters of development and financing the developing world.

Though the international role of the dollar has evolved over time, US monetary policy has set the tone for global monetary conditions for most of the post-war era, while US balance of payments deficits have served as the engine of global liquidity expansion, determined endogenously in a complex interaction with the operation of offshore Eurodollar markets. Between 1944, the year the Bretton Woods system was established, and 2008, the United States has run a deficit on its current account of the balance of payments for 36 years and for every year since 1992. This has led to amassing liabilities to the rest of the world totaling $7.37 trillion (according to some estimates), with about 50 percent accumulated over 2003–07 alone--- years when the world economy experienced an economic growth boom fueled by high credit expansion and liquidity. But until the mid-1960s, the US current account remained broadly in balance, as the combination of a healthy trade surplus and income on US overseas investments provided the necessary resource space to pay for the three key levers of US international monetary power: overseas military expenditure, foreign aid, and private investment abroad (see Figure 1). However, this balance between resource availability and commitments to foreign economic relations began to unravel in the mid-1970s as the trade account turned negative and continued to grow in a big way, reaching $840 billion in 2006. It has been only the financial crisis of 2008 and its fallout of deep economic recession that has served to narrow the US trade deficit over the past two years to an estimated current level of $406.5 billion<sup>5</sup> .

> 4 Barry Eichengreen, “The Dollar Dilemma” _Foreign Affairs_ , September/October 2009.

> 5 Based on 2009:H1 data of $ 203.25 bn. reported by BEA.

4
Table <u>12: Fixed efects estimation results: GDP</u>

|**Variable**|(1)<br>(2)<br>**Coefcient**|
|---|---|
|OPEN|-0.177<sup>_∗_</sup>|
||(0.025)|
|GNIREL|0.649<sup>_∗∗_</sup>|
||(0.071)|
|N|3772<br>3820|
|R<sup>2</sup>|0.030<br>0.158|
|F|50.67<br>84.46|

Significance levels : _†_ 10% _∗_ 5% _∗∗_ 1% _Note:_ Standard errors are in parentheses. The dependent variable is expressed in natural logarithms.

Table 13: Granger causality estimation results

|**Variable:**|FDI|OPEN|FDI|GDP|
|---|---|---|---|---|
|L.FDI|0.497<sup>_∗∗_</sup>|0.004<sup>_†_</sup>|0.435<sup>_∗∗_</sup>|0.016<sup>_∗∗_</sup>|
||(0.022)|(0.002)|(0.022)|(0.003)|
|L2.FDI|0.129<sup>_∗∗_</sup>|0.000|0.094<sup>_∗∗_</sup>|0.001|
||(0.024)|(0.002)|(0.023)|(0.003)|
|L3.FDI|0.099<sup>_∗∗_</sup>|0.004<sup>_†_</sup>|0.052<sup>_∗∗_</sup>|0.007<sup>_∗_</sup>|
||(0.020)|(0.002)|(0.020)|(0.003)|
|L.OPEN|0.517<sup>_∗_</sup>|0.781<sup>_∗∗_</sup>|||
||(0.241)|(0.023)|||
|L2.OPEN|-0.151|-0.021|||
||(0.293)|(0.028)|||
|L3.OPEN|-0.083|0.038<sup>_∗_</sup>|||
||(0.221)|(0.017)|||
|L.GDP|||0.886<sup>_∗∗_</sup>|1.080<sup>_∗∗_</sup>|
||||(0.150)|(0.023)|
|L2.GDP|||-0.086|-0.224<sup>_∗∗_</sup>|
||||(0.212)|(0.031)|
|L3.GDP|||-0.116|0.048<sup>_∗_</sup>|
||||(0.147)|(0.021)|
|N|2100|2176|2112|2188|
|R<sup>2</sup>|0.895|0.959|0.901|0.997|
|F|107.206|305.264|115.034|4849.023|

Significance levels: _†_ 10% _∗_ 5% _∗∗_ 1% _Note:_ Standard errors are in parentheses. The dependent variable is expressed in natural logarithms.

# **B.4 Specification tests of the baseline model**

Table 14 provides a summary of some simple specification tests of the baseline model. For ease of comparison, column (1) repeats the results of estimating equation (2) with three-step FGLS (shown 55
Combatting Forest Fires in the Drylands of Sub-Saharan Africa: Quasi-Experimental Evidence from Burkina Faso Tung Nguyen Huy<sup>a</sup> , Guigonan Serge Adjognon<sup>b</sup> , Daan van Soest<sup>a</sup>

> _aDepartment of Economics and Tilburg Sustainability Center, Tilburg University, The Netherlands_

> _bDevelopment Impact Evaluation Department (DIME), The World Bank Group, Washington D.C._

_Keywords:_ `forest conservation, forest fires, deforestation, synthetic control method.` _JEL Codes:_ `Q23, Q56, Q28, O13.`

> _⋆_ We thank the Climate Investment Fund for their financial support, and Burkina Faso’s Forest Investment Program (FIP) for their generous collaboration in the implementation of this research. We thank Charles Palmer, Patricio Dalton, Hideki Shimada, participants of the 2019 Bioecon and the 2020 EAERE conferences for their constructive comments and suggestions; this paper also benefitted from the careful and constructive comments by colleagues from the World Bank (and especially Arianna Legovini, Dan Rogger and Mirko Serkovic), and computational reproducibility was verified by DIME Analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank Group, or the governments they represent. All remaining errors are our own. Please send all correspondence to d.p.vansoest@tilburguniversity.edu.
28 be supported from the top, but they cannot be managed from the top. They must grow from the grass roots, if they are to survive on their own strength.

Box 2.2. A Success Story in **Rural** Credit Cooperatives: Taiwan's Township Farmer Associations The Taiwanese cooperative system consists of 280 Township Farmer Association **s** erving vitually all farming families. Largely as a result of this scheme, farm productivity in Taiwan is among the highest in the world. Interest rates charged by the associations are positive and cover the cost of funds. By 1970 these associations were mobilizing 30 percent more funds from members than they were relending A 1992 study carried out by Ohio State University rates the Taiwanese scheme verv highly on four important criteria: number of people served, participants' transaction costs, loan recovery, and number of deposits to agriculture. The surplus, which continues to expand, is being on-len **t** o other sectors of the economy.

As in Korea and Japan, the Taiwanese system was initiated with U.S. assistance soon after the Second World War (1948). A Sino-American Joint Commission on Rural Reconstruction (which became the Council of Agriculture after U.S. involvement ended in the 1970s) was established to help promote economic growth. The program initially used targeted subsidies but later began to loosen up. Apart from rural finance, important objectives of the commission were to carry out comprehensive rural land reform and to promote more participatory runal government by establishing farm cooperatives. These institutional changes acted as a catalyst in channeling the already high rural savings of a progressive Chinese rural community to support agricultural growth.
Despite that heterogeneity, research using household data shows that across the region, the educational achievement of Latin America’s indigenous people lags distantly behind the achievement of Latin America’s non-indigenous people. In Guatemala, indigenous adults have about half the years of schooling that non-indigenous adults have. In Mexico, adults in indigenous municipalities have completed 3 years of schooling while adults in non-indigenous municipalities have completed 8 years of schooling. In Peru, indigenous adults have on average completed 6 years of schooling while non-indigenous had completed 9 years of schooling. Explanations for the lower schooling achievement of indigenous students include the quality of the educational environment at home and interactions of greater poverty with rural residence, neglect of indigenous languages and failure to accommodate linguistic diversity (Herrera Lara 1999; McEwan 2004 **)** .

Using mathematics and Spanish reading test scores from indigenous and non-indigenous students in Guatemala, Mexico and Peru, we attempt to distinguish the quality of indigenous schooling from the quality of indigenous schools. We first use feasible generalized least squares (FGLS) estimate production functions for Spanish and math exam scores that control for various student-, family- and school-level factors. Family- and student-level factors proxy a student’s cognitive ability. We then use Oaxaca-Blinder decompositions to separate the portion of the test score gap between indigenous and non-indigenous students that is due to family and school factors, and the portion that cannot be explained using observed background indicators.

Our results are generally consistent across the three countries. In every country, the test score gap between indigenous and non-indigenous students is about twice as large in Spanish as 3
# **E.14 Results for Pre-specified Families in Ebola Sample**

**Table E.14:** Pre-specified Families in Ebola Sample

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|General utilization|0.000|0.074<br>|0.128<br>|0.036|2857|
||(1.000)|(0.040)<sup>_∗_</sup>|(0.049)<sup>_∗∗∗_</sup>|(0.042)||
|||[0.188]|[0.110]|[0.397]||
|Maternal utilization index|0.000|0.058|0.232|_−_0.055|595|
||(1.000)|(0.086)|(0.112)<sup>_∗∗_</sup>|(0.097)||
|||[0.379]|[0.143]|[0.488]||
|Health outcomes index|0.000|0.034|0.085|_−_0.006|3183|
||(1.000)|(0.062)|(0.069)|(0.074)||
|||[0.379]|[0.283]|[0.886]||
|Satisfaction index|0.000|0.108|0.106|0.109|3183|
||(1.000)|(0.055)<sup>_∗_</sup>|(0.068)|(0.061)<sup>_∗_</sup>||
|||[0.188]|[0.235]|[0.144]||
|Health service delivery index|0.000|_−_0.056|_−_0.033|_−_0.064|1819|
||(1.000)|(0.066)|(0.088)|(0.070)||
|||[0.329]|[0.459]|[0.397]||
|Clinic quality index|0.000|0.311|_−_0.023|0.565|160|
||(1.000)|(0.236)|(0.301)|(0.264)<sup>_∗∗_</sup>||
|||[0.205]|[0.459]|[0.144]||
|CDPE index|0.000|0.264|0.232|0.288|320|
||(1.000)|(0.108)<sup>_∗∗_</sup>|(0.155)|(0.132)<sup>_∗∗_</sup>||
|||[0.188]|[0.235]|[0.144]||
|Contributions to clinic index|0.000|0.173|0.292|0.080|320|
||(1.000)|(0.127)|(0.142)<sup>_∗∗_</sup>|(0.148)||
|||[0.205]|[0.143]|[0.488]||
|Water and sanitation index|0.000|0.167|0.137|0.190|3183|
||(1.000)|(0.086)<sup>_∗_</sup>|(0.101)|(0.096)<sup>_∗_</sup>||
|||[0.188]|[0.264]|[0.144]||
|Economic outcomes index|0.000|0.151|0.065|0.219|3183|
||(1.000)|(0.087)<sup>_∗_</sup>|(0.106)|(0.105)<sup>_∗∗_</sup>||
|||[0.188]|[0.425]|[0.144]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A47