rafmacalaba/datause-encoder-data huggingface dataset:
the bulk of the closures’ effect on the labor market would then be driven by the other main channel we identified, i.e. reduced firms’ profitability and labor demand. However further research possibly using firm level data would be needed to explicitly test for this hypothesis.

The analysis is likely to provide an underestimation of the actual labor market effects of the barriers as it cannot capture the general equilibrium effects nor the dynamic effects (e.g. through lower human capital accumulation) of mobility restrictions. Notwithstanding these limitations, our estimates suggest that the checkpoints have non negligible effects on the West Bank labor market. In our preferred estimation, placing one check-point one minute away from a locality decreases the probability of being employed by 0.41 percentage points, the hourly wage by 6.3 percentage points and the working days by 2.6 percentage points, and increases the hours per working day by 4.3 percentage points. Taking the year 2007 as an example, we estimate that these effects translate into costs of around USD 229 million or 6% of West Bank GDP. Most of these costs are due to lower wages, thus suggesting that the labor markets have adjusted to the restrictions more through prices than quantities.

# **References**

Abadie, A., and J. Gardeazabal (2003). The Economic Costs of Conflict, _American Economic Review_ , 93(1): 113-132.

Angrist, J.D. (1995), The Economic Returns to Schooling in the West Bank and Gaza Strip, _American Economic Review_ , 85:1065-1087.

Angrist, J.D. (1996), Short-run Demand for Palestinian Labor, _Journal of Labor Economics_ , 14: 425-453.

Baum-Snow, N. (2010). Changes in Transportation Infrastructure and Commuting Patterns in US Metropolitan Areas, 1960-2000, _American Economic Review_ , 100(2): 378–82.

Benmelech, E., Berrebi, C. and E. Klor (2011). The Economic Cost of Harboring Terrorism, _Journal of Conflict Resolution_ , XX(X): 1-23.

Blattman, C. and J. Annan (2011) The Consequences of Child Soldiering. _Review of Economics and Statistics,_ vol. 92(4): 882-898.

Bundervoet, T., P., Verwimp and R. Akresh (2009). Health and civil war in rural Burundi, _Journal of Human Resources_ , 2009, 44(2): 536-563.

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# **1 Introduction**

What drives governments with similar revenues to publicly provide very different amounts of goods for which private substitutes are available? Education and health-care are prime examples; in almost all countries, the public and the private sectors simultaneously provide versions of each. Access to them may have profound impacts on growth and welfare. It is important to understand why some governments provide more of the public version while others rely more on the private version. This is especially so since the rich and the poor often consume different versions, with the poor frequently consuming the public version. In this paper, I demonstrate that, in areas with higher median income and greater income inequality, governments allocate less of an exogenous revenue increase to goods with private substitutes (specifically, pre-primary education) and more to goods that have no private substitutes (such as roads and parks).

My central result can be explained by either of two existing theories. The first, “collective choice,” hypothesizes that societies with higher income and more unequal income simply have more people who consume a private sector version of publicly-provided goods. Because they are consuming private versions, they vote for low spending on the public sector counterparts. Under majority voting, the government thus provides less of goods with private substitutes. This is in contrast to government provision of goods like infrastructure, which have no private substitutes. Important models in this vein are due to Barzel (1973), Besley and Coate (1991), Epple and Romano (1996), Glomm and Ravikumar (1998), de la Croix and Doepke (2009), and Gutirrez and Tanaka (2009). The second theory, “political power,” argues that people have control over public policy that is roughly proportionate to their share of income, as opposed to their share of votes. That is, they can buy political influence, even in a democracy. As others grow richer relative to a given person, his ability to make government publicly supply the goods he wants (which might include pre-primary education) declines. This person might have more power under some sets of institutions that even the playing field. Prominent studies in this vein include Pande (2003), Foster and Rosenzweig (2004), Bardhan and Mookerjee (2006), Banerjee and Somanathan (2007), and Bartels (2009). The existing literature says little about the relative importance of the two theories (channels). This is problematic because they do not necessarily 1
# **The Belt and Road Initiative:**

# **Reshaping Economic Geography in Central Asia?***

Julia Bird<sup>1</sup> , Mathilde Lebrand<sup>2</sup> , Anthony J. Venables<sup>1</sup> 1 University of Oxford, 2 The World Bank

**Keywords** : regional integration, transport infrastructure, spatial modeling, economic geography, central Asia. **JEL classification: F12** , F15, R11, R13.

* This project was supported by the World Bank’s Global Solutions Group on Territorial Development under its program on spatial productivity, with financial contributions from UK DFID. Thanks to participants in the authors’ workshop, Washington DC, September 2018
Table 3. _Variable description_

|**_Variable_**|**_Description_**|
|---|---|
|SICKNESS|= 1 if trader reported any eye irritation, headache, dizziness, vomiting, diarrhea, fever,<br>convulsion, shortness of breath or skin irritation; = 0 otherwise|
|PERCEPTION|= 1 if trader reported “Some, Large or Fatal effects” for short- or long-term health<br>impacts of pesticides; = 0 otherwise<br>|
||**_Exposure_**|
|PESTAMT|Pesticide amount in stock (kilograms)|
|PWHOIab|Percentage of pesticide stock that is classified as WHO category Ia or Ib (extremely or<br>highly hazardous)|
|PWHOII|Percentage of pesticide stock that is classified as WHO category II (moderately<br>hazardous)|
|PWHOIII|Percentage of pesticide stock that is classified as WHO category III (slightly<br>hazardous)|
|PWHOU|Percentage of pesticide stock that is classified as WHO category U (unlikely to present<br>any acute hazard in normal use)|
|PWHOUnk|Percentage of pesticide stock that is not classified by WHO (dropped in regression)|
|BUSYEAR|Number of years in business|
|TIMEDAY|Average number of hours spent in shop per day<br>**_Socio-economic_**|
|NUTRTON|Nutritional ratio of weight/height|
|AGE|Age in years|
|TRNGSAFE|= 1 if trader received training on pesticide use and safe handling; = 0 otherwise|
|PROTLOAD|= 1 if protective measures were taken during loading; = 0 otherwise|
|SUPPLIER|= 1 if the main source of pesticide use and safe handling information is from the<br>supplier; = 0 otherwise<br>**_Interaction terms_**|
|PIabPIITRN<br>PIabPIIPROT|Product of pesticide toxicity (PWHOIab + PWHOII) and TRNGSAFE<br>Product ofpesticide toxicity (PWHOIab+ PWHOII) andPROTLOAD|
||**_Other controls_**|
|CHITTAGONG<br>COMILLA|= 1 if the trader is located in the district of Chittagong; = 0 otherwise<br>= 1 if the trader is located in the district of Comilla; = 0 otherwise|
|JESSORE|= 1 if the trader is located in the district of Jessore; = 0 otherwise|
|RAJSHAHI|= 1 if the trader is located in the district of Rajshahi; = 0 otherwise|
|RANGPUR|= 1 if the trader is located in the district of Rangpur; = 0 otherwise|
|DHAKA|= 1 if the trader is located in the district of Dhaka;= 0 otherwise|

# _Estimation results_

Model (4) was initially estimated as a bivariate probit with health effects as an endogenous dummy variable. Full information maximum likelihood estimates of the parameters are given in Table 4. The estimated value of ρ = − .0691 leads to a Wald test statistic of ( − .110) 2 = .121 < χ ,12.010 = .271, thus the null hypothesis of ρ = 0 is not rejected and the two equations can be estimated separately. We continue to present the individual results in Table 4.

13
Table 2: Impact on Adolescent Gender Equitable Attitudes

||Gender E<br>Stan|Full-Index<br>quitable A<br>dardized I|ttitudes<br>ndex|Educ<br>Attitu<br>Sub-I|ation<br>des<br>ndex|Women<br>Attitu<br>Sub-I|'s Role<br>des<br>ndex|Sub-<br>Masculi<br>Attit|Index<br>ty Norms<br>udes|
|---|---|---|---|---|---|---|---|---|---|
||All<br>(1)|Boys<br>(2)|Girls<br>(3)|Boys<br>(4)|Girls<br>(5)|Boys<br>(6)|Girls<br>(7)|Boys<br>(8)|Girls<br>(9)|
|Panel A: Endline||||||||||
|_CHOICES_|0.051**|0.029|0.075***|0.115***|0.137***|0.026|0.089**|-0.029|0.031|
||(0.02)|(0.03)|(0.03)|(0.04)|(0.05)|(0.04)|(0.03)|(0.03)|(0.03)|
||[0.03]|[0.43]|[0.01]|[0.00]|[0.01]|[1.00]|[0.01]|[0.41]|[0.34]|
|_EMB (marginal)_|0.019|-0.016|0.043|-0.011|0.021|-0.052|0.032|-0.005|0.054**|
||(0.02)|(0.03)|(0.03)|(0.04)|(0.05)|(0.04)|(0.05)|(0.03)|(0.03)|
||[0.67]|[1.00]|[0.19]|[1.00]|[1.00]|[0.26]|[0.99]|[1.00]|[0.05]|
|Control group mean|-0.00|-0.02|0.01|-0.04|0.02|-0.01|0.01|0.00|0.00|
|Adjusted R<sup>2</sup>|0.07|0.06|0.08|0.07|0.08|0.04|0.04|0.02|0.02|
|<br>Observations|2,643|1,285|1,358|1,285|1,358|1,285|1,358|1,285|1,358|
|ANCOVA|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Panel B: Midline||||||||||
|_CHOICES_|0.066***|0.064**|0.073**|0.075|0.104*|0.030|0.116***|0.039|0.047|
||(0.02)|(0.03)|(0.03)|(0.05)|(0.06)|(0.04)|(0.04)|(0.03)|(0.03)|
||[0.01]|[0.04]|[0.02]|[0.17]|[0.08]|[0.89]|[0.00]|[0.20]|[0.14]|
|_EMB (marginal)_|-0.002|-0.002|-0.014|-0.019|-0.012|-0.013|0.013|-0.041|-0.026|
||(0.02)|(0.03)|(0.03)|(0.05)|(0.04)|(0.03)|(0.04)|(0.03)|(0.03)|
||[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|[0.26]|[0.47]|
|Control group mean|-0.01|-0.05|0.03|-0.08|0.09|-0.00|0.02|-0.01|-0.00|
|Adjusted R<sup>2</sup>|0.06|0.07|0.06|0.10|0.11|0.06|0.06|0.02|0.02|
|<br>Observations|1,987|939|1,048|939|1,048|939|1,048|939|1,048|
|ANCOVA|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: * significant at 10% level ** significant at 5% level *** significant at 1% level (1) CHOICES=1 if adolescent was invited to attend the CHOICES training (assigned to CHOICES only or CHOICES+EMB treatment groups). EMB=1 if adolescent was in CHOICES+EMB group, effectively giving the marginal impact of the EMB program in addition to the CHOICES program. (2) ANCOVA estimation controls for the level of the outcome variable. All regressions include district fixed effects and a set of controls measured at baseline: dummy for whether household has multiple adolescents and age of respondent.

(3) Standard errors are clustered at the community level and are reported under the coefficient in parentheses. Sharpened q-values that correct p-values for the false discovery rate (FDR) are in square brackets. A sharpened q-value of 1.00 indicates the null hypothesis is not rejected at any level of FDR.

(4) Outcomes: Gender Equitable Index is a standardized index of 14 gender-progressive statements the respondent agrees with (index standardized as per method from Kling et al. 2007). Higher scores indicate more gender-equitable attitudes. The education sub-index, women's role sub-index, and masculinity norms sub-index are constructed from (mutually exclusive) statements in the Gender Equitable Index.

19
government borrowing than budgeted. Pinto, Drebentsov, and Morozov (2000b) attribute some 65 percent of the net new borrowing by the federal government during 1996 and 1997 to these tax arrears and the resulting cash revenue shortfalls.

A last point: could falling oil prices have worsened Russia’s government debt dynamics and contributed to the crisis? The answer has two parts: first, oil prices did not influence government debt dynamics significantly before the 1998 crisis. The reason was that many of the taxes on oil were specific, rather than ad valorem; besides, oil companies had managed to consistently show low profits, regardless of price level. But after the crisis, oil export taxes were brought back in early 1999 and increased substantially in 2000 as oil prices took off, establishing a close link between the primary fiscal surplus at the federal government level and oil prices.<sup>14</sup> Second, the impact of oil price changes on the fiscal accounts pales in comparison with nonpayments. Table 3 presents relevant data for 1996-98.

**Table 3: Oil prices, implicit subsidies and Federal government borrowing**

||1996|1997|1998|
|---|---|---|---|
|Oil Revenues/ GDP|4.1|3.4|2.6|
|Implicit subsidies (% GDP)|4.9|3.6|6.9|
|New net borrowing of the federal government (% GDP)|7.4|5.7|9.2<sup>a/</sup>|
|Average Oilprice (Urals, US$/bbl)|20.1|18.3|11.8|

Source: Oil prices and revenues from Min Fin, Rosstat, IMF; rest of the data from Pinto, Drebentsov and Morozov (2000b). a/ Reflects the large depreciation of the ruble after August 17, 1998.

The table makes the point convincingly. Oil prices fell by 35 percent in 1998 relative to 1997 with a significant negative impact on the current account balance as we shall see below; but oil revenues declined by less than 1 percentage point of GDP while implicit subsidies rose by a whopping 3.3 percentage points of GDP.

> 14 IMF (2000a) attributes the big fiscal improvement in 2000 to discretionary tax policy changes, of which the re-introduced and expanded export taxes on oil were probably the most important component. But prior

13
-3-

disequillbrla In food and feed grain markets. The system works as follows: In the fall when major crops are harvested, the predominant public agency In food supply operations, CONASUPO, can estimate with some accuracy the supply available from domestic productlon over the next year. Combining this information with estimates of food deinand at existing prices produces an estimate of excess supply or demand, and hence an indication' of the quantities to offer for export or to order for Import. Any errors In the Initial estimates of surplus or shortage (at existing prices) can be met by varying the leve' cf government held Inventories. Slnce the system provides no Incentive for private Investmnents in storage facilities or the holding of Inventorles, and even though trade In basic foods is ro longer a government monopoly, the g-wernment supply adjustment mechanism Is still an essential part of the food distributio;. system.

# Ill MEXAGWKTS Model Structure

The objective of this model Is to provide a simulatlon tool at the disaggregated level of Individual agricultural commodity markets that will permit experiments exploring the effect on those markets of policies at the domestic macroeconomic or international (i.e., trading partner) macroeconomic and sectoral levels. The effects are to be transmitted by changes In variables that are specified as exogenous determinants of quantitles demanded or supplied. In turn, the values of these linkage variables are determined In upstream models In an experimental framework of recurslve causation. The structure of this framework Is given In Flgure 1. Note that MEXAGMKTS recelves values of linkage variables from both the Mexican macroeconomic and the US (and rest of the world) agricultural markets models.
Although the Sundarbans’ ecological importance and uniqueness are recognized by Bangladesh, India and the international community, and its conservation is an obligation under international conventions and treaties, the region is currently threatened by a number of natural factors and human actions. The area is still in active formation as its rivers change their course. Eastward meandering of the Ganges and Brahmaputra Rivers over time is affecting sedimentation and reducing freshwater inflows significantly. In addition, water supplies, sedimentation, and the region’s topography and hydrology have all been affected by human actions, such as construction of upstream dams, embankments to protect land from tides, over-exploitation of mangrove timber, urban and industrial pollution, and mangrove clearing for agriculture and aquaculture. Local human-induced losses will probably continue, and climate change is likely to aggravate current problems.

Climate change poses a number of threats to the Sundarbans, including rise in sea level, rise in air and water temperature, and change in the frequency and intensity of precipitation and storms (Alongi 2008). Among these threats, increased saltwater intrusion from sea level rise may pose one of the greatest challenges. (For example, see Dasgupta et al. 2015a, b, c, d; SRDI 2010, UK DEFR 2007; IWM 2003; Peterson and Shireen 2001; SRDI 2000; Karim et al. 1990.) Healthy mangroves need daily fluxes from both ocean and fresher water. The Sundarbans is already facing a serious shortage of fresh water during the dry season (October to May) because some major distributaries of the Ganges feeding Sundarbans are currently moribund. Anticipated alteration of riverine flows from the Himalayas and an increase in sea level will intensify salinity intrusion as climate change continues. The associated increase in aquatic salinization will inevitably change the hydrological regime of the Sundarbans and alter its forest ecology.

3
7

# **3. Descriptive Analysis**

# **_3.1 Firms’ perception of corruption in Mauritania_**

In 2005, some 303 of the 361 firms surveyed (84 percent) admitted paying bribes to government officials, while only 58 firms (16 percent) in the sample denied it. These shares are similar to those found by Svensson (2003) for Ugandan enterprises surveyed in 1998 (81 percent). Yet, only 18 percent of firms in Mauritania consider corruption as a major or severe constraint for their business operations and growth, while 44 percent of firms rate access to finance a major/severe constraint. Firms consider tax rates (32 percent), anti-competitive practices by informal businesses (31 percent), and electricity and access to land (26 percent) as major/severe barriers to their growth and operations (Figure 2). Breaking down the sample into formal and informal firms, the results show that approximately 18 percent of formal firms perceive corruption as a major/severe obstacle to growth, while this share is around 12 percent for informal firms.

**Figure 2: Major or severe obstacles to growth, whole sample (% of firms)**

<!-- Start of picture text --> 50<br>44<br>40<br>32 31<br>30 26 26<br>23<br>20 18 18 18 16 14<br>8 8<br>10 6 4 3 3<br>0<br>Access to financeAnti-cmpttn,informalsTax rates ElectricityAccess to landTax administrationCustoms and trade regTransportationSkil / educ of workersCorruptionMacro instabilityPolitical instabilityJuducial systemTelecommunicationsCrime, theft, disorderBus lic, oprtnl procLabor reg<br>growth<br>% firms reporting major or severe obstacle t<br><!-- End of picture text --> _Source: Mauritania ICS, 2006._ The low perception of corruption in Mauritania stands out when comparing it with other countries. The share of formal businesses that identifies corruption as a major/severe impediment to do business in Mauritania (18 percent) is much lower than in neighboring countries such as Mali (49 percent), Senegal (40 percent), or other SSA countries like Cameroon (53 percent). It remains nevertheless higher that in middle-income countries such as South Africa (16 percent) and most OECD countries. If obstacles to business
||No. of stud<br>Elementar|ents<br>y|**Table 33: I**<br>Secondary|**V Estimation **|**(8)**<br>University||No. of sick|HH members|
|---|---|---|---|---|---|---|---|---|
|D|-0.261|(0.519)|0.334|(0.458)|-0.007|(0.165)|-0.613|(0.500)|
|lnHH size|1.206|(0.162) ***|0.441|(0.143) ***|0.133|(0.052) ***|0.279|(0.156) *|
|Adult share|-4.644|(0.340) ***|-0.869|(0.300) ***|0.210|(0.108) *|0.086|(0.327)|
|HH head sex: Male|-0.017|(0.195)|-0.157|(0.172)|0.214|(0.062) ***|0.230|(0.188)|
|HH head education: Elementary|-0.022|(0.140)|-0.082|(0.123)|0.035|(0.045)|-0.255|(0.135) *|
|HH head education: Secondary|-0.698|(0.228) ***|0.106|(0.201)|0.101|(0.073)|-0.131|(0.220)|
|HH head education: University|-0.257|(0.463)|-0.086|(0.408)|0.480|(0.147) ***|-0.712|(0.445) *|
|Appliance ownership: Refrigerator|0.016|(0.212)|0.133|(0.187)|0.053|(0.067)|-0.030|(0.204)|
|Appliance ownership: Color TV|0.190|(0.210)|0.298|(0.186) *|0.025|(0.067)|-0.313|(0.202)|
|Appliance ownership: Radio|0.029|(0.130)|-0.205|(0.115) *|0.015|(0.041)|-0.137|(0.125)|
|Appliance ownership: Gas stove|-0.596|(0.219) ***|0.275|(0.193)|0.010|(0.070)|0.147|(0.211)|
|Appliance ownership: Washing|||||||||
|machine|0.069|(0.153)|0.138|(0.135)|-0.029|(0.049)|0.321|(0.147) **|
|Gas use for cooking|0.145|(0.147)|-0.084|(0.130)|0.079|(0.047) *|-0.147|(0.142)|
|Power use for lighting|-0.110|(0.223)|-0.133|(0.197)|-0.034|(0.071)|0.395|(0.215) *|
|Tap water for cooking|0.209|(0.142)|-0.134|(0.125)|-0.006|(0.045)|-0.128|(0.136)|
|Constant|-0.124|(0.775)|-4.108|(0.684)***|-5.167|(0.247)***|-4.027|(0.746)***|
|Obs.|911||911||911||911||
|R-squared|0.497||0.090||0.040||||
|Wald stat|59.54||6.40||2.50||1.81||
|Sargan overidentifying restriction test|0.75||3.23 *||2.61||1.26||
|Hausmanexogeneity test|21.17*|**|8.13*|**|9.20*|**|15.96*|**|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

52
as it surveys every establishment that has a fixed location and operates an economic activity. The ensuing microdata is comprehensive and detailed with respect to key establishment characteristics such as location, registration and legal status, age, ownership and management, industrial classification, and employment size. The census covers both formal and informal businesses. An establishment is considered to operate in the formal sector if it is registered with the Rwanda Revenue Authority (RRA) and maintains regular operational accounts.<sup>12</sup> Table 1 summarizes the key features of the census data from these Sub-Saharan African countries. We now turn to the description of each of the datasets. In Burkina Faso, 99,261 establishments hire about 256,000 workers in 2015. The corresponding figures are 88,144 establishments and 430,000 workers in 2008 (Cameroon); 638,234 establishments and 3.4 million workers in 2013 (Ghana), and 154,236 establishments and 493,000 workers in 2013 (Rwanda). Establishments with foreign equity or state ownership are rare in number and employ a small number of workers. Notable exceptions are the employment shares of foreign-owned establishments in Cameroon (17 percent), and stateowned enterprises in Ghana (15 percent) and Rwanda (14 percent).

Manufacturing production involves a small fraction of the establishments and the workforce in the economy ranging from 7 percent (Ghana) to 17 percent (Burkina Faso) and 8 percent (Rwanda) to 21 percent (Burkina Faso), respectively. It also involves a limited number of foreign-owned establishments, but they play a bigger role as sources of manufacturing employment. In Burkina Faso, they constitute about 1 percent of the establishments and 7 percent of the employment in manufacturing. The corresponding figures are 5 and 25 percent in Cameroon, 1 and 7 percent in Ghana, and 1 and 17 percent in Rwanda. Likewise, state-owned establishments are less than 1 percent of manufacturing establishments. However, they contribute a small fraction to manufacturing employment; about 1 percent in Burkina Faso, Cameroon, and Ghana, and 4 percent in Rwanda.

> 12 National Institute of Statistics of Rwanda (NISR). The Establishment Census, 2014. June 2015.

8