rafmacalaba/datause-encoder-data huggingface dataset:
> implications of the findings. The principal conclusion is that only one economic geography variable-local economic diversity-has significant, consistent, and substantial cost-reduction effects. Hence, diversity matters.

# **I.** MODEL OF INDUSTRY LOCATION

# **1.1** Analytic Framework

The analytic framework to examine location of manufacturing industry primarily draws on recent findings from the NEG literature. In the 'new economic geography' literature Krugman (199la, 199 lb) and Fujita et al. (1999) analytically model increasing returns, which stem from mostly pecuniary externalities.<sup>3</sup> They emphasized the importance of supplier and demand linkages and transportation costs. Firms prefer to produce each product in a single location given fixed production costs, and firms also prefer to locate their production facilities near large markets to minimize transportation costs.

Drawing upon Fujita and Thisse (1996) and Fujita (1989), we model firms to benefit from externalities arising from being co-located with other firms. If a(x, y) is the benefit to a firm at x obtained from a firm at y, and fly) denotes the density of firms at each location ye **_X_** then, 3 NEG's approach bears a strong resemblance to Marshall (1890) and Weber (1929) in many ways. However, unlike its predecessors, new economic geographers place less emphasis on technology spillovers as a source of externalities than on labor pooling and specialized suppliers. Krugrnan (1991) argued that externalities from technology spillovers are difficult to measure, and therefore, cannot be modeled. Instead, he argued that under increasing returns to scale and imperfect competition, pecuniary externalities have clear welfare effects due to the variety of market size effects (i.e., each firm's monopoly power can affect the production function of other firns through buying and selling in the market) (Krugman, 1993). By focusing on pecuniary externalities (or rent spillovers) rather than technology spillovers, NEG tries to focus the general discussion on externalities.

4
to the trigger level of imports for SSM tier _i= 1, 2, 3_ , we have the following complementary slackness condition: We adopt the 2001 benchmark year as the baseline level of imports. Therefore, in our subsequent analysis, countries are permitted to apply a tier 1 safeguard tariff once imports reach 110% of their 2001 levels. With the Q-SSM, we assume that—when imports reach but do not exceed a trigger level—the duty is adjusted to keep imports at that trigger level<sup>8</sup> . The full duty permitted at a given trigger level is imposed only when imports exceed the specified trigger level. Attention then focuses on whether the next higher trigger is reached and the next higher duty imposed.

Table 2 reports on the _power_ of the SSM tariff (i.e., 1 + the _ad valorem_ tariff rate) for both the Quantity-based and Price-based SSM scenarios. The Q-SSM columns relate to the tier 1 and tier 2 tariffs applied to imports _from all sources_ , while the P-SSM columns report the _bilateral changes_ in power of the SSM tariff under the P-SSM regime. In this section, we focus on the Q-SSM. For example, the mean power of the tier 1 SSM tariff in China is 9.7% higher than its value (simply 1.0) in the baseline (No-SSM) regime.

When _cif_ prices are unchanged, a one percentage point change in the _power of the SSM tariff_ translates directly into a one percentage point change in the domestic price of imported wheat. In the absence of the SSM, this tariff – and hence the power of the tariff – is unchanged. However, when the SSM is present, all regions except for Other East Asia (where the SSM is always non-binding) show a positive mean change in the power of the tier-1 SSM tariff, ranging from 2.9% in MENA to 10.7% in Brazil, where domestic production is extremely volatile. Only China and Brazil invoke the tier-2 SSM tariff; the tier-3 tariff is not utilized in our simulations.<sup>9</sup>

> 8 An alternative, and potentially much more trade-restrictive, scenario would involve imposing the full duty permitted whenever imports reached the trigger in the past twelve months, even if this results in imports falling below the trigger.

> 9 It is also of interest to know how the SSM tariff would change if only a single region utilized the SSM. In separate simulations, not reported here, we have undertaken the stochastic simulations numerous times, in

15
_Table 8: Heterogeneity in loss size by plot characteristics_

|**Dependent variable:**Percent of harvest lost|(1)<br>Pooled|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Mali|(5)<br>Niger|(6)<br>Nigeria|
|---|---|---|---|---|---|---|
|Any hired labor on plot|-5.120***<br>|-3.632<br>|-3.179<br>|-0.707<br>|-4.162***<br>|-9.870*<br>|
||(1.762)|(2.236)|(3.237)|(1.977)|(1.454)|(5.065)|
|Any inorganic fertilizer used|-8.073***|-7.744***|-2.133|-15.47***|-4.415**|-17.28***|
||(1.512)|(1.885)|(1.885)|(2.563)|(1.955)|(4.320)|
|Any organic fertilizer used|0.707|3.476*|-3.111**|-4.282**|-6.589***|-8.291**|
||(1.315)|(1.842)|(1.522)|(2.106)|(1.183)|(4.103)|
|Plot is irrigated|3.450|1.126|9.046|-20.15***|4.865|14.80*|
||(3.063)|(3.636)|(14.07)|(6.356)|(4.255)|(8.736)|
|Plot is intercropped|-7.513***|-3.781|-1.550|-6.306*|-6.367***|-36.05***|
||(1.754)|(2.329)|(2.454)|(3.550)|(1.773)|(4.865)|
|Plot is owned|-1.945|-2.300|6.057*|-2.874|-2.586|-3.114|
||(1.556)|(2.017)|(3.122)|(3.702)|(1.645)|(4.379)|
|Log plot area (ha)|-2.565***|-2.782***|-4.610***|-0.255|-0.599|0.394|
||(0.410)|(0.497)|(1.109)|(0.686)|(0.427)|(1.383)|
|Plot topographic wetness index|0.263|0.576*|0.0928|0.993***|0.0383|0.0686|
||(0.206)|(0.301)|(0.434)|(0.344)|(0.249)|(0.451)|
|Plot elevation (m)|-0.00727***|-0.00650***|-0.0250***|-0.0179|-0.00721|0.00584|
||(0.00214)|(0.00228)|(0.00404)|(0.0187)|(0.0108)|(0.00892)|
|Urban household|4.463|12.26***|-3.747|5.014|1.584|-5.863|
||(2.772)|(2.829)|(3.387)|(6.587)|(2.659)|(5.539)|
|Household engaged in livestock farming|-2.395|1.289|0.157|3.039|-9.035***|-2.391|
||(1.733)|(2.353)|(1.820)|(3.219)|(2.203)|(4.460)|
|Household has access to electricity|2.388|1.600|3.479|-1.266|-2.625|5.226|
||(1.946)|(2.219)|(4.478)|(2.336)|(2.222)|(4.404)|
|Constant|64.21***|53.17***|61.56***|55.52***|84.30***|104.4***|
||(6.177)|(7.224)|(9.642)|(10.15)|(6.901)|(10.39)|
|Observations|30,845|14,896|2,059|7,130|6,071|689|
|Crop FE|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Results from OLS regressions with the share of pot<br>weighted to be nationally representative. Standard errors i|YES<br>ential harvest lost as outcom<br>n parentheses. *** p<0.01, *|NO<br>e variable. Base ca<br>* p<0.05, * p<0.1|NO<br>tegory for crop fi|NO<br>xed effects is 'oth|NO<br>er crop'. The esti|NO<br>mates are|

29
# _2. Additionality_

The traditional issues of additionality are important in evaluating emergency programs. Any central government subsidy to the municipal governments can lead to a "crowding out" of existing resources. If we suppose, for example, that the municipal governments had planned to implement a number of projects generating a certain amount of employment, it is difficult to be sure that an employment subsidy as envisaged in Trabajar or Manos a la Obra doesn't merely free up municipal resources for other purposes with no net gain in employment. In Colombia, for example, municipal investment programs are approved yearly at the central government level and, for the first year of the emergency program, additionality could be guaranteed. However, in subsequent years, municipal governments can redirect investment resources and offset the impact of the program.<sup>17</sup> Theoretically, this is potentially a very serious issue and there is little existing evidence that would assure that it isn't important in practice. Nor does Bank experience with additionality issues suggest ready contracting mechanisms that would eliminate the problem. One impractical solution is careful monitoring of all municipal budgeting and investment decisions. A second is simply that that projects be kept effectively separate from municipal finances, perhaps through complete financing by the central government. This, however, eliminates much of the leverage arising from municipal contributions.

# **Conclusion:**

Most emergency programs- workfare programs, conditional transfers, or training programs- employed to date include an investment component, either an infrastructure

> " As a particularly perverse twist, if the municipality cuts funding to direct or in-kind transfers (comedores populares etc) in order to pay for intermediate inputs for investment projects, we may be taking away the safety net during the crisis to finance future consumption. My thanks for this thought to David Rosenblatt, LCSPR (1999)

20
190 of its municipal development projects, covering about 3,000 municipalities, reports significant improvements in urban public management in recent years. Half of the projects surveyed had substantial results in financial management. Large municipalities—such as Kazan, Maputo, and Tianjin—unified accounts and integrated financial management across their large organizations. Half also achieved substantial results in enhancing revenue mobilization, updating tax records, expanding the coverage of cadastres or land registers, and improving tax collections. Weaker results for eight projects in Brazil, Indonesia, Mozambique, and Zimbabwe arose from political reluctance by some municipalities to raise taxes. Only six projects wanted to “bring municipalities to market.” Colombian municipalities were particularly successful in establishing a local credit market, complete with recognized credit ratings of active municipalities; some became able to issue municipal bonds for the first time. Only five projects wanted to stimulate private finance of municipal services and only one (in Colombia) yielded substantive results through private funding of water, gas, and solid waste services in several municipalities. The less-successful projects promoted privatization of solid waste operations in Sri Lanka and Uzbekistan; these did not go far, given poor financial performance and uncertain regulatory environments (World Bank 2009). In metropolitan cities, the quality of public services delivered is far better than that provided in the rest of the country. The coverage of basic water and sewer services is higher, health clinics are more accessible, and the scope of services provided is broader. This has been explicitly recognized in countries such as Colombia, where the large cities have been given more expenditure responsibility and autonomy (Bahl Linn & Wetzel 2013).

City managers have essentially four major sources of finance to choose from: own resources (budget and balance sheet); grant funds; loans; and public-private ventures. Each method of financing city expenditures has associated costs and benefits, and no single approach dominates the others at all time. In recent years, innovative financing to finance municipal investments by leveraging domestic capital have emerged.

# _Own Revenue_

Using own resources (budget and balance sheet) is the simplest solution because there usually are no strings attached. However, the amounts spent for public services provided in most metropolitan areas are much larger than own-source revenues of local governments, which means that much of the job of financing local services is left to the intergovernmental transfer system and to vertical programs. Some policy analysts see this as an inevitable outcome in developing countries and stress the need to sharpen the structure of transfers so that they can better match the goals that have been set for them.

Buenos Aires, São Paulo, and Bogotá are examples of metropolitan areas that have done well with own revenue mobilization. But in most developing countries, cities are less successful.

7
expansion of the city causing increased economic activities and jobs in the urban periphery. All these factors have increased the demand for urban transport services and have put a strain on the existing transportation infrastructure. There has been an under-investment in the urban transport sector and the projected investment in this sector for major cities in Morocco including Greater Casablanca is around 320 billion MAD over a time horizon of 10 years,<sup>2</sup> i.e. 32 billion MAD per year.<sup>3</sup> The public urban transit system remains highly inadequate and overcrowded. Several public and private operators faced bankruptcies in the past decade (World Bank, 2016) and this has exacerbated the poor service quality in public urban transport. New tram lines became operational since 2012, but the market share of this mode was only 2% in 2014 while the cost of its implementation is high. According to World Bank (2016), a Bus Rapid Transit (BRT) option would have incurred only 29% of the tramway cost.

Our model of Grand Casablanca is highly aggregated spatially due to data limitations on geographically detailed land and labor markets. Nevertheless, we calibrate a model of modal split in commuting that explains the user choices among private car, motorcycle, shared taxi service, public bus and tram. The distribution of these choices among the modes determines endogenously the equilibrium road congestion, the travel times by mode and the equilibrium in-bus and in-tram crowding. Out of the five vehicle types, four modes (private car, motorcycle, taxi and bus) share the same aggregate road capacity and impose delays on each other causing congestion which lengthens travel time and increases fuel consumption due to lower speeds. In Greater Casablanca, a taxicab carries 5.5 passengers on average, while the average bus occupancy is reported as106 passengers. In contrast, a motorcycle carries one passenger-driver, and a private car 1.4 passengerdrivers on average. The average tram occupancy is reported as 324 passengers per two connected tram cars and the average capacity (seating and standing) of a tram is 454 passengers. In carequivalent units in our baseline, adjusting for the load caused by the four vehicle types, a persontrip by shared taxi places an almost three times lower load on road capacity than does a persontrip by private car or motorcycle, while a person-trip by bus places a load that is almost 38 times lower than that of a private car.

Travel by bus or tram is more time consuming due to lower speed, passenger aggress-egress times to and from stations and waiting times at stations. Adding buses or trams have several effects.

> 2 The World Bank Report No. 1214332-MA, 2017

> 3 MAD: Moroccan Dirham. The average exchange rate in 2014, our baseline year, was 8.5 MAD per US $.

3
- 51 -

# Table 19: EARNINGS FUNCTION (InW2 ) - ALL EMPLOYEES - CORRECTED FOR SELECTION BIAS

||(1|)|(2|)|(3|)|
|---|---|---|---|---|---|---|
|Intercept|10.210|(74.2)|10.182|(73.2)|10.329|(52.6)|
|Years of Primary Schooling|.046|(2.7)|<br>.041|(1.7)|<br>.031|(1.2)|
|Years of Secondary Schooling|.143|(7.1)|<br>.131|(5.3)|<br>..27|(5.2)|
|Years of Higher Schooling|.151|(4.9)|<br>.123|(2.9)|<br>.130|(3.2)|
|Years of Current Job Experience<br>|.085|(6.6)|<br>.085|(6.6)|<br>.084|(6.5)|
|(Years of Current Job Experience)<sup>2</sup>|-.0015|<br>(3.3)|<br>-.0014|<br>(3.3)|<br>-.0015|<br>(3.2)|
|Years of Previous Job Experience<br>|.041|(1.7)|<br>.038|(1.6)|<br>.040|(1.7)|
|(Years of Previous Job Experience)<sup>2</sup>|-.0012|<br>(0.8)|<br>-.0010|<br>(0.6)|<br>-.0012|<br>(0.7)|
|Years of Apprenticeship|.012|(0.6)|<br>.014|(0.7)|<br>.014|(0.7)|
|Years of Secondary VTE|.063|(2.5)|<br>.059|(2.0)|<br>.064|(2.2)|
|Years of Post-Secondary VTE|.141|(1.6)|<br>-.039|(0.3)|<br>-.008|(0.1)|
|Years of Teacher Training|.099|(1.4)|<br>.102|(1.1)|<br>.088|(1.0)|
|CEPE|-|-|.051|(0.3)|<br>.031|(0.2)|
|BEPC|-|-|-.019|(0.1)|<br>-.016|(0.1)|
|Secondary VTE Diploma|-|-|.124|(0.6)|<br>.115|(0.6)|
|Post-Secondary VTE Diploma|-|-|.713|(1.9)|<br>.634|(1.6)|
|TeacherDiploma|-|-|.147|(0.5)|<br>.148|(0.5)|
|OtherDiploma|-|-|.286|(1.0)|<br>.290|(1.0)|
|Sex (1-female)|-|-|-|-|-.079|(0.8)|
|Non-Ivorian|-|-|-|-|-.289|(3.0)|
|SeasonalJob|-|-|-|-|-.567|(2.8)|
|Abidjan|-|-|-|-|-.064|(0.7)|
|RuralAreas|-|-|-|-|-.042|(0.3)|
|Lambda|-.333|(5.4)|<br>-.313|(4.9)|<br>-.258|(2.5)|
|R2|.57||.57||.59||
|R2|.56||.56||.57||
|F|54.16||36.20||29.71||

Note: Absolute t - values in parentheses.
# **III.Program**

As part of the government of Pakistan’s commitment to the reduction of gender disparity in education, it initiated a conditional cash transfer program targeted to female students in Punjab. Under the program girls receive a stipend of Rs. 200 per month (approximately US$ 3.5) if they are enrolled in grades 6 to 8 in a government school and maintain a minimum attendance of 80 percent. The stipend is paid in quarterly payments to the student via a postal money order. To give this amount context, consider that a day’s unskilled wage for a male in these areas is estimated to be Rs. 60. Thus an unskilled male who works 30 days a month can expect to receive Rs. 1800 a month. The stipend, then, amounts to one-ninth of the average monthly income for an unskilled worker.

The program was announced in the last quarter of 2003. The government did not consider randomization politically feasible and therefore used district-level literacy rates for the population aged 10 and over according to the 1998 census (the most recent census at the time) to determine which districts would be eligible to receive the stipend program. Districts with literacy levels below 40 percent were deemed eligible. Those with literacy rates higher than 40 percent were ineligible. These districts and their literacy rates are reported in Table 2. Implementation of the program reportedly did not begin in full until the second quarter of 2004.

# **IV. Data**

The data used in this paper come from three waves of the Learning and Educational Achievement in Punjab Schools (LEAPS) survey which was conducted in 112 villages of the Punjab province of Pakistan. The villages are located in Faisalabad (center), Rahim Yar Khan (south) and Attock (north) districts. Sample villages were randomly chosen from a list of villages with at least one private school according to the 2000 census of private schools. The survey 6
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Power Outages (country-level)**|0.31|-0.27|-0.92|0.38|
||(0.11)**|(0.05)***|(0.20)***|(0.26)|
|Firms’ age|-0.01|0.07|-0.01|-0.08|
||(0.05)|(0.06)|(0.06)|(0.05)*|
|Firms’ Status|2.79|3.81|2.26|-0.33|
||(1.00)**|(1.68)**|(0.83)**|(0.70)|
|Gender|-0.95|-1.14|1.50|-1.78|
||(1.94)|(1.90)|(3.24)|(4.49)|
|Log(Labor(t-3)+1)|3.31|0.68|0.40|3.94|
||(0.81)***|(0.72)|(1.42)|(1.59)**|
|Log(Output(t-3)+1)|-0.62|-0.76|-0.79|-0.45|
||(0.49)|(0.40)*|(0.89)|(0.55)|
|Transport|0.60|0.87|0.47|0.27|
||(0.37)|(0.37)**|(0.50)|(1.14)|
|Exchange rate growth(t-1)|-0.21|0.19|-0.02|-0.54|
||(0.01)***|(0.02)***|(0.02)|(0.06)***|
|Trade openness(t-1)|-0.03|0.28|0.44|-1.11|
||(0.04)|(0.03)***|(0.05)***|(0.07)***|
|Generator Use|1.26|-1.42|3.54|1.96|
||(0.91)|(1.18)|(1.66)**|(3.58)|
|Electricity losses(t-1)|-0.88|-0.91|0.06|-1.44|
||(0.04)***|(0.09)***|(0.05)|(0.09)***|
|GDP per capita(t-1)|0.00|-0.00|-0.01|0.02|
||(0.00)|(0.00)**|(0.00)*|(0.00)***|
|GDP growth(t-1)|-0.11|-0.04|-0.33|1.15|
||(0.08)|(0.04)|(0.04)***|(0.25)***|
|Rule of law growth(t-1)|-0.17|-0.08|-0.17|-0.10|
||(0.03)***|(0.01)***|(0.03)***|(0.04)**|
|R2|<br>0.31|<br>0.26|<br>0.27|<br>0.52|
|_N_|3,218|1,656|999|563|
|# Countries|39|39|39|39|
|- Fixed Effects: Country,Year|Yes|Yes|Yes|Yes|

Ordinary least squares regression is the estimation approach, with standard errors clustered at country-level in parentheses. The variable **Power Outages (country-level)** corresponds to the number of outages in a typical month at the country level. The dependent variable is the companies' share of exports in total sales. We include control variables (firms' age, firms' status, manager gender, labor and sales three years ago, the quality of transport service, the real exchange rate, trade openness index, firms' use of generators, firms' size, electricity losses during transmission and transport, GDP per capita, GDP growth, rule of law growth). Country-level variables are lagged by one year to avoid reverse causality between them and the dependent variables. Country and year fixed effects are introduced in all the estimates. Small companies are those employing fewer than 20 workers, medium-sized companies employ between 20 and 99 workers, while the number of workers in large companies is greater than 99. * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.

**Table 11. The effect of electricity access on service firms’ exports (% total sales) by size.**

|Dependent variable:**Exports % total sales**|||||
|---|---|---|---|---|
||Overall Sample|Small Firms|Medium Firms|Large Firms|
||(1)|(2)|(3)|(4)|
|**Treatment**|2.74|2.69|6.81|-7.26|
|||||25|
<!-- Start of picture text --> 51<br>FtoSE&I SE& ItoF<br> to U (F) corresponds i<br>i;<br>FtoSE FtoO SEtoF OtoF<br>FtoI FtoALL ItoF ALLtoF<br>Controlling inflows to Formal<br>7b. Controlling outflows from Formal<br>F FtoU F UtoF<br>41 39 37 35 33 31 29 27 25 41 39 37 35 33 31 29 27 25 corresponds to the steady state size of U (F) derived from equation 4 i<br>UtoSE SEtoU<br>UtoI UtoALL ItoU ALLtoU<br>O=Out of the Labor Force, I=Informal Salaried, SE=Informal Self-employed, ALL= SE, I and F (when predicting U),<br>.<br>Figure 7. Actual and predicted Size of unemployment and formal sector<br>UtoF UtoO FtoU OtoU to U (F) i<br>Controlling inflows to Unemployment<br>U UtoI&SE U SE&ItoU<br>7a. Controlling outflows from Unemployment<br>5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0<br>Note: U (Actual unemployment rate) and F (Actual size of the formal sector) are constructed using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002. These series have been averaged per quarter. U (F) to resulting of applying the 1983-2002 average transition rate of all the possible flows except for the transition rate from U (F) to to the steady state size of U (F) derived from equation 4 resulting of applying the 1983-2002 average transition rate of all the possible flows except for the transition rate from ALL=O, U, I and SE (when predicting F)<br>2002 2002<br>2001 2001<br>2000 2000<br>1999 1999<br>1998 1998<br>1997 1997<br>1996 1996<br>1995 1995<br>1994 1994<br>1993 1993<br>1992 1992<br>1991 1991<br>1990 1990<br>1989 1989<br>1988 1988<br>1987 1987<br>1986 1986<br>1985 1985<br>1984 1984<br>1983 1983<br>2002 2002<br>2001 2001<br>2000 2000<br>1999 1999<br>1998 1998<br>1997 1997<br>1996 1996<br>1995 1995<br>1994 1994<br>1993 1993<br>1992 1992<br>1991 1991<br>1990 1990<br>1989 1989<br>1988 1988<br>1987 1987<br>1986 1986<br>1985 1985<br>1984 1984<br>1983 1983<br><!-- End of picture text -->
# **Emerging Economies’ versus Advanced Countries’ Investment Impact in Africa**

Kene Ezemenari<sup>*</sup> The World Bank kezemenari@worldbank.org Esubalew Alehegn Tiruneh<sup>*</sup> Birmingham - Southern College eatirune@bsc.edu Evelyn Wamboye<sup>*</sup> Pennsylvania State University efw10@psu.edu _Keywords_ : Foreign direct investment, labor productivity, employment, Africa, BRICS, G7 JEL Classification: E22, F21, F43, F63, O57 _____________

> *The authors are grateful to Taye Mengistae and Punam Chuhan-Pole for their constructive comments on an earlier version of this paper.