rafmacalaba/datause-encoder-data huggingface dataset:
than 20 percent (Table 3). Another 13 percent (US$1 billion) had an applied tariff of above 10 percent. These would be the imports with the most likely potential to increase following an EPA that completely removes all tariffs on imports from EU. However, this also suggests that if the EPA only requires Nigeria to liberalize 80 percent of its imports from the EU, all products with tariffs higher that 10 percent could be excluded from the liberalization schedule. Some of the most “protected” import categories are reported in Table 4. As expected the categories with the highest protection tend to register small import flows. Categories with significant import values and high applied tariffs include non-metallic mineral products from EU facing an average applied tariff rate of 16 percent, miscellaneous food products (14 percent), motor vehicles (11 percent), and processed foods (11 percent).

# **4.  Simulated Impact of Preferential Tariff Liberalization**

The preferential liberalization of Nigerian tariffs with respect to the EU will likely decrease domestic prices and increase Nigeria’s imports from the EU. But at the same time, it can displace imports from other (potentially more efficient) sources and result in losses in tariff revenues and welfare. The use of a rigorous analytical framework is useful to assess these different (competing) effects and estimate the net overall impact. This study uses the Tariff Reform Impact Simulation Tool (TRIST) developed by the World Bank’s International Trade Department to quantify the potential impact of an EPA on Nigeria. Box 1 provides a brief description of the simulation tool.

The simulations are undertaken at the product (tariff line) level. By its comparative static nature, the model allows for comparison of two states of the world: one in which the base values of policy instruments (such as tariffs) are unchanged, and another in which these measures are exogenously changed or shocked, to reflect the policies that are being studied. Concretely, the experiment compares the Nigerian economy in two different environments: one in which an EPA has not yet occurred, and another where the tariffs against imports from the EU have been removed. The model is partial equilibrium and so does not take into account any intra-sectoral linkages and given lack of suitable data we are unable to include substitutability between imports 10
**Table 15: Interaction with Firm Size, El Salvador**

||Percentage<br>perm full-ti<br>since D|change of<br>me workers<br>ec 2019|Decreased to<br>temp w|t number of<br>orkers|Decreased to<br>of opera|tal hours<br>tions|Anticipate<br>arrears on o<br>liabil|falling in<br>utstanding<br>ities|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Received national or local|0.634|0.187|-0.949|-2.015|-1.636|-2.890|-0.051|1.230|
|government support|||||||||
||(0.458)|(0.163)|(1.477)|(1.990)|(1.724)|(2.363)|(1.385)|(1.404)|
|Support X ln of employment size at<br>ES|-0.180|-0.069|0.291|0.425|0.244|0.407|-0.272|-0.704|
||(0.138)|(0.059)|(0.535)|(0.591)|(0.504)|(0.686)|(0.439)|(0.473)|
|Average change in monthly sales<br>compared to one year ago|-0.156|0.265*|-2.837***|-5.765***|-2.229**|-1.005|-2.616***|-3.342***|
||(0.131)|(0.140)|(0.910)|(1.634)|(0.869)|(1.404)|(0.801)|(1.276)|
|Decreased demand for products or<br>services compar to one year ago|-0.038|0.506***|-0.290|-0.126|1.370***|0.965|-0.586|-0.384|
||(0.073)|(0.169)|(0.710)|(1.787)|(0.491)|(0.831)|(0.757)|(0.614)|
|Decreased supply of inputs<br>compared to one year ago|0.090|-0.354**|2.505**|4.525**|0.455|-0.254|-0.723*|-0.011|
||(0.091)|(0.165)|(1.068)|(2.115)|(0.430)|(0.896)|(0.383)|(0.554)|
|Decreased liqdt or cash flow avail.<br>since COVID-19 began|1.119|0.062|-1.471**|-4.727**|-1.218|1.096*|1.081**|1.091|
||(0.736)|(0.130)|(0.729)|(1.923)|(0.789)|(0.561)|(0.487)|(1.071)|
|Overdue on obligations to<br>financial institutions|-0.194|-0.631|-0.154|-1.672**|-0.372|-0.178|1.169*|-0.050|
||(0.172)|(0.473)|(0.470)|(0.761)|(0.590)|(1.160)|(0.668)|(0.620)|
|Ever temporarily closed during the<br>COVID-19 outbreak|-0.091|0.085|-0.653|-0.036|0.128|-0.471|-0.839***|-0.129|
||(0.120)|(0.080)|(0.501)|(0.637)|(0.556)|(0.597)|(0.274)|(0.601)|

52
# **1. Introduction**

Firm entry and exit – Schumpeterian "creative destruction" – is critical for the continued dynamism of the modern economy. Though evidence has linked entrepreneurship and economic growth in developed countries,<sup>1</sup> we have scarce evidence that such a relationship exists in developing countries. For instance, to what extent does formal sector entrepreneurship contribute to job creation in countries with regulatory, legal, and tax barriers to entry and exit? What factors are related to firm survival in a low-income country? In this paper, we examine job creation and destruction, at both new and incumbent firms, in Cote d'Ivoire, ranked 169 of 181 countries in the 2011 Doing Business "Ease of Doing Business" ranking (where 1 indicates the most favorable business environment) (World Bank, 2010a).

We use a new and unique database that includes the complete census of registered firms in Cote d'Ivoire from 1976 through 1997 to answer these questions.<sup>2</sup> This includes data collected at the time of registration (ownership and sector) as well as annual reporting of total employment. Unlike comparable studies that rely on survey data or manufacturing censuses, we broaden existing studies to compute over time comprehensive formal sector entry and exit rates, as well as growth, contraction, and survival at incumbent firms. Moreover, since we use a census of formal sector firms that extends beyond the manufacturing sector, we can analyze the impact of macroeconomic and other shocks on sector distributions.

First, we examine business dynamics in Cote d'Ivoire. We decompose trends in job creation and growth into entry of new firms and expansion of incumbent firms, and job destruction into exit and contraction. We also examine the distribution of firms by size, sector, and ownership. We find that job creation by new firms is quite high at 8% on average, but the number of jobs added by new firms is small in absolute terms. Next, we use a discrete time duration model to examine the relationship of GDP growth, reform episodes, and firm survival. We find that, on average, a 1% increase in GDP growth lowers the likelihood of exit by 1.7%, while trade liberalization and fiscal adjustment episodes raised the overall likelihood of exit but attenuated the probability of exit for small and medium sized firms relative to large firms. A

> 1 For example, see Haltiwanger et al. (2009a); Klapper et al. (2006); Djankov et al. (2002).

> 2 Regulatory changes related to the modernization of the Registrar of Companies in 1998 prevent us from extending the time series.

2
Table C8: Profile of the poor across urban versus rural areas

||||**Poor b**|**y DOU**|||**Po**|**or by D**|**B**||
|---|---|---|---|---|---|---|---|---|---|---|
||All<br>(1)|Urban <br>(2)|center <br>(3)|Urban <br> cluster <br>(4)|Urban<br> Rural <br>(5)|Urban <br>(6)|Core <br>(7)|Suburb <br>(8)|Town <br>(9)|Other<br> Rural<br>(10)|
|**Demographic:**|||||||||||
|Household size|5.70|6.37|6.36|6.37|7.48|7.37|7.39|6.88|7.94|6.49|
|Age of household head|43.15|42.49|42.79|42.37|40.27|40.53|41.11|36.95|44.21|42.12|
|Household head is male|0.81|0.81|0.77|0.83|0.81|0.79|0.75|0.80|0.82|0.83|
|No education|0.33|0.40|0.38|0.41|0.49|0.45|0.39|0.41|0.55|0.47|
|Primary complete or incomplete|0.35|0.44|0.43|0.44|0.44|0.45|0.46|0.50|0.37|0.43|
|Secondary complete or incomplete|0.27|0.15|0.18|0.13|0.07|0.10|0.14|0.08|0.08|0.10|
|Tertiary complete or incomplete|0.05|0.01|0.02|0.01|0.00|0.01|0.02|0.01|0.00|0.00|
|**Employment (of household head):**|||||||||||
|Not in labor force|0.14|0.15|0.18|0.14|0.14|0.14|0.14|0.14|0.13|0.15|
|Unemployed|0.02|0.04|0.07|0.03|0.02|0.03|0.05|0.02|0.02|0.02|
|Employed in Agriculture|0.44|0.46|0.21|0.57|0.73|0.62|0.46|0.67|0.71|0.62|
|Employed in Industry|0.13|0.11|0.16|0.08|0.04|0.06|0.09|0.05|0.04|0.07|
|Employed in Services|0.28|0.24|0.38|0.18|0.07|0.15|0.26|0.11|0.09|0.13|
|**Access to basic services**|||||||||||
|Improved water|0.80|0.85|0.91|0.82|0.59|0.66|0.76|0.63|0.61|0.73|
|Improved sanitation facility|0.40|0.24|0.29|0.22|0.16|0.18|0.23|0.14|0.18|0.22|
|Access to electricity|0.64|0.44|0.63|0.36|0.20|0.29|0.39|0.24|0.25|0.31|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line.

90
TABLE 5. INITIAL FREEDOM VS OTHER INITIAL CONDITIONS Dependent variable: **_LIBERALIZATION_**

||**Two-Stage**|**s Least Squares**|**Ordinary L**|**east Squares**|
|---|---|---|---|---|
||**(1)**|(2)|(3)|(4)|
|**_CONSTANT_**|0.25***|0.31***|0.23***|0.30***|
||(3.81)|(4.29)|(4.88)|(5.74)|
|**_LIBERALIZATION_**<br>**_(-1)_**|0.70***|0.69***|0.70***|0.69***|
||(18.16)|(18.03)|(18.05)|(18.19)|
|**_FREEDOM_**|-0.00|-0.001|||
||(0.48)|(0.22)|||
|**_COHESIDEOL_**|0.02***|0.02***|0.02***|0.02***|
||(2.62)|(2.68)|(2.62)|(2.69)|
|**_AID_**|0.004***|0.004***|0.004***|0.004***|
||(3.90)|(4.26)|(5.20)|(5.71)|
|**_WAR_**|-0.05***|-0.05***|-0.05***|-0.05***|
||(2.66)|(2.94)|(2.73)|(3.10)|
|**_INITFREEDOM_**|0.01**_**_**|**0.01Q****|**0.01*****|**0.01*****|
||(2.55)|(2.34)|(3.32)|(3.45)|
|**_YEARPLANN_**|-0.002**|-0.002***|-0.002***|-0.002***|
||(2.16)|(2.79)|(2.71)|(3.61)|
|**_LOCATION_**|0.04**|0.04*|0.04**|0.03*|
||(2.34)|(1.89)|(2.16)|(1.81)|
|**_STATE_**|-|-0.04*|-|-0.04***|
|||(1.93)||(3.19)|
|**Adjusted R-squared**|0.91|0.92|0.91|0.92|
|F-**statistic**|163.0***|150.3***|191.2***|172.0***|
|**White test for**<br>**heteroscedasticity**|55.67*|62.01|53.52**|57.49***|
|**Number of observations**|125|125|125|125|

Absolute value of t-statistic in parenthesis. *, **, and *** mean that the coefficient is significant Respectively at 10%, 5% and 1 % level of confidence. The t-statistics in equation (4) and ( 5) are Obtained using the White heteroscedasticity-consistent standard errors.

21
1.0%, respectively<sup>4</sup> (Ministério da Saúde, 2013). However, in our sample of 2,000 children ages 3 to 5 from 76 poor rural communities, pre-program growth indicators showed that 42% of children were stunted, and 5% of children wasted, suggesting that our study population more closely reflected the national average. Nationally representative statistics on child development outcomes other than growth were not available at the time of this study. However, standardized child screening tests from our baseline survey showed that around half the children in the sample were at risk of delays in fine motor skills and problem resolution, and more than 20% scored below age-appropriate levels in communication (Bruns et al, 2010).<sup>5</sup>

Baseline descriptive statistics of the study population are presented in Table 2. Most children in the sample lived in poor households, dependent on subsistence agriculture or informal employment, and 10 percent of children in the sample were orphaned, primarily single parent. Caregivers had only 3.3 years of education on average, 38% could not read or write, and 51% of caregivers did not speak Portuguese (the country’s official and primary language for education). Furthermore, cultural practices and norms may have played a role in limiting caregivers’ interactions with children and thus children’s opportunities for cognitive development and school readiness. At baseline, only about half of caregivers reported reading, drawing objects or playing games with children at home.

While there is a multiplicity of demand side constraints that may limit investments in human capital of preschool aged children, in 2008 the supply of pre-primary education services was virtually inexistent in rural Mozambique. Existing preschools were concentrated in urban and more affluent areas, and available national estimates put the proportion of children enrolled in preschool at the time of the intervention at about 4%.<sup>6</sup> To help address the lack of child development services for preschool aged children in rural Mozambique, the non-governmental organization Save the Children began implementing a community based preschool program in three districts of the Gaza Province starting in 2008.<sup>7</sup> The program focused on early stimulation, emergent literacy and numeracy instruction and psychosocial support, with the objectives of improving children’s cognitive, social, emotional, and physical development, and facilitating transition to primary

> 4 The DHS also showed that nationally, 91.7% of newborns were breastfed, and the median duration of exclusive breastfeeding was 3.5 months, well below the recommended 6 months. The situation in Gaza is comparable. 5 With no country specific reference group, we compare to available reference populations.

> 6 Starting in 2014, a national preschool program rolled out to 84,000 children in 600 communities.

> 7 The preschool model was initially piloted in 12 communities starting in 2005. Based on this initial experience and thanks to additional financial resources, the model was scaled up to 30 new communities in early 2008.

6
# **II. LAND LEASING AND URBAN INFRASTRUCTURE FINANCE: THE CASE OF CHINA**

Land leasing in China involves the up-front sale of long-term occupancy and development rights. The practice was introduced on an experimental basis in 1987 in Shenzhen and other coastal cities, as part of the de facto decentralization of China’s fiscal system. Up to that time, public authorities allocated land administratively and land use was free. The land-leasing reforms were intended in part to stimulate locally led economic development, by allowing cities to attract foreign investment by providing stable land occupancy rights to investors. From the start, land leasing was tied to infrastructure investment. Land leasing provided a potentially large source of income, whose revenues were to be invested primarily in infrastructure systems, further enhancing cities’ competitive position for economic growth.

In 1988, China’s constitution, which previously had prohibited all types of land transfer, was amended to permit land leasing while retaining public ownership of land. In 1990, the State Council formally affirmed land leasing as public policy. By 1992, Shanghai and Beijing had adopted land leasing as local practice, and it began to spread.<sup>3</sup> Like most of China’s economic development and fiscal reforms, the wave of land leasing moved from coastal experimental cities to Shanghai and the capital, then westward to the rest of the country.

Land leasing has been a key element of China’s fiscal decentralization In China, the central government retains all tax policy authority over local governments. Municipalities cannot change tax rates, introduce new taxes of their own design, or scrap dysfunctional local taxes. They need higher-level governmental approval for adjustments in user charges. As initiated in Shenzhen and applied elsewhere, land leasing was an attempt by municipalities to gain control over a revenue source genuinely within their control. Until very recently municipal governments have been free to assemble and sell land at their discretion, constrained only by the expansion of urban boundaries approved in their master plans.

Whereas China’s 1994 fiscal framework reforms (see Chapter 2) have been described as a form of fiscal re-centralization, due to the increase in central government’s share of shared taxes, revenue-sharing arrangements for land leasing moved in the opposite direction. Originally, the central government’s share of landleasing revenues was set at 60 percent. The split subsequently was modified to 40:60 for central and local governments, respectively, then to 32:68 and 5:95, before all land-leasing revenues were assigned to municipal governments as part of the 1994

> 3 The speed with which land leasing was adopted can be seen from Shanghai’s records.  Between 1988 and 1991, 12 land leases were granted in Shanghai.  The total rose to 201 in 1992 and 3,000 in 1993.  (Fu, 1996)

4
_Low-Income Countries’ Access to Private Debt Markets_

**Table 4 OLS Estimates of equation (2) over the period 2003-07**

|_Capital flow_|Β|2|Β3||R<sup>2</sup>|SER|N|
|---|---|---|---|---|---|---|---|
|Debt and equity|0.489***|(0.068)|-0.037***|(0.008)|0.348|0.900|130|
|Debt|0.586***|(0.084)|-0.020**|(0.009)|0.336|1.005|110|
|Equity|0.368***|(0.078)|-0.046***|(0.014)|0.269|0.942|125|
|Bank loans|0.387***|(0.086)|-0.011|(0.010)|0.152|1.076|109|
|Bond issuance|0.402***|(0.132)|-0.020*|(0.012)|0.317|0.978|67|
|FDI|0.379***|(0.074)|-0.046***|(0.014)|0.306|0.898|125|
|Portfolio equity|0.278|(0.183)|0.008|(0.034)|0.144|1.678|61|
|ODA|-1.013***|(0.070)|-0.016*|(0.010)|0.836|0.767|107|

|||_Commo_|_n sample peri_|_ods:_||||
|---|---|---|---|---|---|---|---|
|Debt|0.574***|(0.082)|-0.020|(0.012)|0.334|1.005|105|
|Equity|0.296***|(0.064)|-0.041***|(0.009)|0.284|0.788|105|
|Bank loans|0.361***|(0.124)|-0.001|(0.014)|0.119|0.977|63|
|Bond issuance|0.430***|(0.116)|-0.020|(0.013)|0.355|0.915|63|
|FDI|0.176*|(0.094)|-0.037***|(0.010)|0.311|0.737|63|

_Notes:_ ***, ** and * denote significant at the 1%, 5% and 10% levels, respectively.

Estimated standard errors shown in parentheses.

SER = standard error of the regression; N = number of countries in sample.

ODA = net ODA (Official Development Assistance) disbursements.

Potential creditors might also consider indicators of the business climate and economic policies in assessing the risks of lending to low-income countries. For example, the “Doing Business” rankings produced by the World Bank has gained prominence in the business media. Low-income countries tend to have lower rankings, on average. In 2007 low-income countries accounted for 24 of the lowest 30 rankings. Kenya had the highest ranking among low-income countries, ranking 72<sup>nd</sup> out of 178 countries. This suggests that the relatively poor investment climate in low-income countries might have an 19
allocation, but we will be able to control effectively for φ in the productivity model.<sup>10</sup> Finally, note that identification of φ requires that the selection into cotton is affected by the same unobservables that affect cotton productivity. In principle, it would be possible to argue that there are additional unobservable factors that affect the selection into cotton. We extend our results to the case where these unobservables in section 5.1.

Plugging in the estimates of φ in (3), the productivity model is This modified difference-in-differences approach is consistent with entry and exit into cotton farming.

# 5 Results

Our benchmark productivity results are reported in Table 7. Columns (1) and (2) report estimates of equation (1), that is, a productivity model that does not control for unobservables such as It, ηht and φht. In these regressions, we use data from the three main cotton provinces, the Central, the Eastern, and the Southern provinces. The main findings indicate that small farms are more productive; there is also evidence in favor or decreasing returns to scale in cotton since there is a negative association between the size of land allocated to cotton and cotton yields per hectare. The negative association between farm size and household agricultural productivity has long been established in the literature (Feder, 1985; Benjamin, 1994). In addition, households with male heads are more productive in cotton, as are larger households. Assets (such as ploughs or livestock) are positively associated with yields. The effects of inputs such as basal and top-dressing fertilizers are not as strong as expected.<sup>11</sup> The dynamics of cotton productivity are closely linked to the dynamics in market

> 10We also consider the possibility of estimating different selection models in different years and in different provinces. This would control for idiosyncratic provincial effects in cotton adoption. We report results in the next section.

> 11One reason for these result is that both basal and top-dressing fertilizers are actually used in maize more than in cotton. See the discussion below for more details.

18
References: Blayney D. P. (2005) "Disease-Related Trade Restrictions Shaped Animal Product Markets in 2004 and Stamp Imprints on 2005 Forecasts" - United States Department of Agriculture Electronic Outlook Report.

Burns A, D. van der Mensbrugghe, H. Timmer (2006) “Evaluating the Economic Consequences of Avian Influenza” Global Development Finance, World Bank, June 2006 Moore T. and N. Morgan. (2006) Council for Agricultural Science and Technology (CAST). “Avian Influenza: Trade Issues” CAST Commentary QTA 2006-2.

Olsen B, V. Munster, A. Wallensten, J. Waldenström, A. Osterhaus, R. Fouchier (2006) “Global Patterns of Influenza A Virus in Wild Birds”, _Science_ 312, 5772, 384-388 United Nations Food and Agriculture Organization. Animal Health Special Report, Avian Influenza Bulletins, various issues.

United Nations Food and Agriculture Organization. 2005. “Update on the Avian Influenza Situation” FAO AIDE News, Issue 35.

U.S. Foreign Agricultural Services – Global Agriculture Information Network Report, Poultry Market, various issues.

25
DID and RD. The results show that PAE had a positive, though not statistically significant, effect on math and Spanish learning outcomes among students with an age-grade distortion while the effect was positive and significant among students without a distortion. This has two important implications: (1) the improvements in average learning outcomes brought about by PAE were not the outcome of an increase in results among the better-off students while reducing learning outcomes among the relatively worse-off, and (2) students with relatively better starting conditions seem to benefit more from the changes introduced by a low-stakes accountability and diagnosis interventions such PAE. Arguably, improving learning outcomes among students with more challenging starting conditions requires more comprehensive interventions, combining PAE’s diagnosis with specialized pedagogical interventions.

# **5 Conclusions**

In 2009, the state of Colima identified 108 public primary schools that had obtained the lowest learning outcomes as measured by the national standardized student assessment, ENLACE. In early February 2010, the state governor announced the “performance status” of selected schools: schools which performed below an arbitrary cut-off were automatically enrolled in a mandatory school improvement program known as PAE. The program, however, was discontinued during the 2011-2012 school year.

Following two alternative strategies to identify the effects of PAE on learning outcomes, a difference-in-difference and a regression discontinuity design, the paper shows that PAE increased test scores by 0.12 standard deviations only a few months after program launch. The size of the effect aligns with other studies evaluating the impact of low-stakes accountability interventions on test scores. Although this effect remains one and, to a lesser extent, two years after program implementation, our results show no additional impact attributable to the implementation of _pedagogical interventions_ or interventions intended to change major inputs in the learning production function as oppose to a marginal change in effort. The effects are homogeneous across boys and girls; however, learning outcomes among students with disadvantaged initial conditions, proxied by age-grade distortion, improved only marginally (statistically not different from zero) as a result of the intervention.

The fact that the PAE program was halted after only 18 months of implementation suggests that the main intervention of the program was circumscribed to the public announcement made by state authorities, followed by detailed information provided to the schools about the test scores of their students, the activities connected to the design of a school improvement plan, and close support provided by the program’s technical advisers.

17