rafmacalaba/datause-encoder-data huggingface dataset:
|Arab donor|0.49|0.51|0.52|0.49|0.51|0.50|0.34|0.47|
|---|---|---|---|---|---|---|---|---|
||(1.26)|(1.33)|(1.35)|(1.28)|(1.33)|(1.00)|(0.69)|(1.22)|
|Western donor|0.06|0.09|0.10|0.11|-0.03|0.11|-0.21|0.08|
||(0.14)|(0.21)|(0.24)|(0.24)|(-0.07)|(0.19)|(-0.38)|(0.19)|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Governorate FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Constant|-<br>10.95***|-10.99***|-11.14***|-10.88***|-11.72***|-<br>10.17**<br>*|-<br>10.11***|-10.68***|
||(-5.12)|(-5.40)|(-5.36)|(-5.24)|(-5.17)|(-4.40)|(-4.08)|(-5.07)|
|Observations|329|329|329|329|329|219|219|329|
|R2|0.25|0.25|0.26|0.25|0.27|0.31|0.31|0.25|

_Notes: Dependent variable is a dummy variable for whether a contract is overspent. PCF indicates dummy variables for all connected firms. PCF1 captures firms connected to the inner circle of elites that controls the CDR board. PCF2 includes firms of all political elites. Regression model uses robust standard errors; The table shows beta coefficients and t-statistics in parentheses; Significance levels: * p<0.10, ** p<0.05, *** p<0.01._ We test the economic significance of these results and specify an additional model to understand whether those contracts are overspent by larger margins. We calculate the log value of a project's cost overruns, that is, the discrepancy between the amount of the initially awarded contract and the actualized expenditures. Using this discrepancy as a dependent variable, Table 8 shows that contracts supervised by connected consultants or executed by connected contractors are not overspent by a larger margin. Model 2 indicates that frequent interactions between a contractor and supervisor is weakly associated with higher cost overruns. Model 7 shows that projects designed by PCF1 designers, while already more likely to be overspent, are associated with significantly larger cost overruns. Projects designed by PCF2 designers, by contrast, are associated with lower actualized costs vis-à-vis the initial contract value.

Table 8: Regression results on cost overruns

|**Hypothesis**|**H2.1**|**H2.2**|**H2.3**|**H**|**2.4**||**H2.5**||
|---|---|---|---|---|---|---|---|---|
|**Discrepancy**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|Supervision contains<br>design|-0.08<br>(-0.07)||||||||
|Repeated interactions<br>contractor & supervisor||1.64*|||||||
|||(1.96)|||||||
|Supervisor PCF1|||1.78*||||||
||||(1.89)||||||
|Supervisor &<br>Contractor PCF1||||0.89<br>(0.83)|||||
|Supervisor PCF2 &<br>contractor PCF1|||||-5.23**||||
||||||(-2.52)||||
|Designer PCF1||||||2.83**|||
|||||||(2.27)|||
|Designer PCF2|||||||-3.90**||
||||||||(-2.15)||

16
and household-level networks.<sup>1</sup> However, in the _Progresa-Oportunidades_ setting, many beneficiary communities are very close to each other, thus spillovers may occur not only within, but also across, villages.

To investigate the presence of neighborhood effects, we combine data from the experimental evaluation of the program with information on the geo-referenced locations of the villages benefitting from it. We focus our analysis on the secondary school participation decisions of program-eligible children, which is the primary short-run outcome of the intervention and the key requirement associated with the largest component of the in-cash transfer.

We use a simple empirical framework that allows us to disentangle the effects of the incentives resulting from the program eligibility of the household (and the village it resides in) from the indirect effects arising from the local density of program recipients at the level of areas surrounding targeted villages. In particular, we exploit the randomized evaluation design and the clustered spatial distribution of the villages in our sample in order to identify the causal effects of program externalities generated by those neighboring villages selected in the experimental treatment group. Next, we investigate whether spillovers arise in this setting because of social interactions between program beneficiaries or as a result of other changes associated with variations in the local density of the program across areas surrounding villages.

We find evidence of a positive effect of the local frequency of participants in the program over short distances (0–5 km) on secondary school participation decisions, which tend to quickly dissipate at larger distances (5–10 km). With estimated effects of respectively one or two or more treated villages in the neighboring area on secondary school enrollment of 6.1 and 8.0 percent, this spillover effect does not increase linearly with the number of treated villages. But the magnitude of the indirect effect of the program is substantial when compared to the direct effect of own village treatment of 9.7 percent.

Crucially, these spatial externalities appear to exclusively affect children from beneficiary households; there is no evidence of such effects for children in the control

> 1. Other recent examples from the literature include Duflo and Saez (2003) who examine the take-up of retirement plans within academic departments and Kuhn et al. (2011) who study spillover effects of lottery winnings within Dutch postal codes.

4
In Section 4 we perform the estimation of the model by presenting an econometric strategy that uses differences in location entrepreneurship rates and earnings in Cameroon to identify the structural parameters of the theoretical model. We also present the structural estimates by cities and by sectors. In Section 5, we perform a set of counterfactual simulations to evaluate the impact of public policies on entrepreneurship and informality across cities and industries in Cameroon. Section 6 concludes while additional results and technical details are relegated to the appendix.

# **2 Facts about Firm Informality, Location and Constraints**

Our structural and empirical analysis requires three key pieces of industry-level and city-level information: (i) measures of absolute geographic concentration; (ii) measure of specialization of cities (places), and (iii) measures of formal, and informal entrepreneurship. We discuss our data and these measures, and take a first look at Cameroon’s geographical concentration of industries, specialization of cities, firm productivity, the constraints hindering the creation of new firms (formal and informal), and how these constraints vary across sectors and spatial locations. This will allow us to uncover important features of the data used for this study and assess the empirical relevance of the model predictions for Cameroon’s cities and industrial sectors.

## **2.1 Data**

Our primary data sources are the Cameroon National Survey of Employment and Informal Sector (henceforth eesi) and the General Enterprise Census (henceforth gec). The eesi is at the household level and covers the years 2005 and 2010. This dataset will be used to measure and characterize formal and informal entrepreneurship. The General Enterprise Census is at the firm level and is only for the year 2009. We are using the gec dataset to measure the absolute geographic concentration of industries, and the specialization of cities.

### **2.1.1 General Enterprise Census data**

The gec provides detailed micro-geographic information on all formal and informal establishments with fixed location, including the 4-digit nace (Statistical classification of economic activities) classification, the number of employees of the establishment, turnover, as well as information on the business environment. Table 1 presents the 2009 distribution of formal and informal establishments and jobs in Cameroon (by region and by industries). As can be seen, the bulk of jobs (68 percent) and firms (85 percent) are in the commerce and services sectors. Manufacturing industries represents roughly 13 percent in terms of firm counts and 18 percent in terms of jobs. Economic activities 5
De Mel, S., D. McKenzie, and C. Woodruff. (2013): “The demand for, and consequences of, formalization among informal firms in Sri Lanka.” _American Economic Journal: Applied Economics_ 5 (2): 122–50.

De Mel, S., D. McKenzie, and C. Woodruff. (2010): “Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto” in _International Differences in Entrepreneurship_ , J. Lerner and A. Schoar (eds.), pp.63-87, 2010.

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Levy, S. (2008): “Good Intentions, Bad Outcomes: Social Policy, Informality and Economic Growth in Mexico”, _Washington: Brookings Institution Press_ Maloney, W. (2004): “Informality Revisited”, _World Development_ 32(7): 1159-78 McKenzie, D. (2012): “Beyond Baseline and Follow-up: The Case for more T in Experiments”, _Journal of Development Economics_ , 99(2): 210-21.

Perry, G., Maloney, G. Arias, O. Fajnzylber, P. Mason, and A., Saavedra, J. (2007): “Informality: Exit and Exclusion”. World Bank Latin America and Caribbean Studies: World Bank, Washington D.C.

World Bank (2015): Doing Business 2015: Going Beyond Efficiency.

25
**Table 3. The Effects of Decentralized Reforms for Chinese SOEs (1980--89)**

Dependent Variable=ln(Value Added Per Employee)

|_Variables_|T<br>|he Level Mo<br>|del<br>||The Growth<br>|Model<br>|
|---|---|---|---|---|---|---|
||(2) OLSa|(3) FEb|(4) RE|(5) OLS|(6) FE|(7) RE|
|ln(k)|0.404***<br>(0.014)|0.303***<br>(0.018)|0.319***<br>(0.017)|0.404***<br>(0.014)|0.305***<br>(0.018)|0.321***<br>(0.017)|
|In(number of employees)|0.012<br>(0.012)|-0.056<br>(0.045)|0.027<br>(0.023)|0.011<br>(0.012)|-0.089**<br>(0.045)|0.019<br>(0.023)|
|Share of management personnel|-0.533***<br>(0.203)|-0.240<br>(0.254)|-0.308<br>(0.232)|_-0.555***_<br>(0.203)|-0.237<br>(0.253)|-0.309<br>(0.231)|
|Share of engineers|1.588***<br>(0.269)|-0.141<br>(0.362)|0.484<br>(0.323)|1.620***<br>(0.268)|-0.100<br>(0.361)|0.524<br>(0.323)|
|Share of "other workers"|-0.140<br>(0.147)|-0.359<br>(0.242)|-0.281<br>(0.205)|-0.126<br>(0.147)|-0.274<br>(0.241)|-0.210<br>(0.205)|
|Firm age|0.018***<br>(0.001)|0.049***<br>(0.013)|0.023***<br>(0.003)|0.018***<br>(0.001)|0.043***<br>(0.013)|0.022r<br>(0.003)|
|(Firm age)<sup>2</sup>|0.000***<br>(0.000)|-0.001*******<br>(0.000)|0.000***<br>(0.000)|0.000***<br>(0.000)|-0.001***<br>(0.000)|0.000***<br>(0.000)|
|INPUT PRICE: the average input price<br>relative to the market input price|-0.192**<br>(0.090)|-0.079<br>(0.077)|-0.071<br>(0.076)|-0.199**<br>(0.090)|-0.092<br>(0.077)|-0.085<br>(0.076)|
|Marginal profit retention rate|0.114**<br>(0.047)|0.109**<br>_(0.055)_|0.118**<br>_(0.051)_|0.112**<br>(0.047)|0.109**<br>(0.055)|0.117**<br>(0.051)|
|Dummy: AUTONOMY_EVENTUALLY|||-0.088*<br>(0.048)|||-0.1 14**<br>(0.048)|
|Dummy: autonomy|0.045**<br>(0.022)|0.101***<br>(0.023)|0.110***<br>(0.023)|0.019<br>(0.032)|0.060**<br>(0.025)|0.067***<br>(0.025)|
|autonomy * year||||0.009<br>(0.007)|0.029***<br>(0.007)|0.026***<br>(0.006)|
|S_PLANQUAN|0.049<br>(0.035)|-0.154***<br>(0.035)|-0.135***<br>(0.033)|0.039<br>(0.035)|-0.167***<br>(0.035)|-0.148***<br>(0.033)|
|Dummy: W_DISCRETION_EVENTUALLY|||-0.021<br>(0.045)|||-0.023<br>(0.045)|
|Dummy:W_DISCRETION, managerialwagediscretion|0.163***<br>(0.030)|0.147***<br>(0.027)|0.156***<br>(0.027)|0.077<br>(0.049)|0.076**<br>(0.036)|0.082**<br>(0.036)|
|W_DISCRETION * year||||0.034**<br>(0.015)|0.034***<br>(0.012)|0.035***<br>(0.012)|
|Dummy: CRS_EVENTUALLY|||0.195***<br>(0.064)|||0.193***<br>(0.065)|
|Dummy: CRS|0.026|-0.004|-0.001|0.029|0.018|0.027|
||(0.044)|(0.035)|(0.035)|(0.053)|(0.039)|(0.040)|
|CRS * year||||0.003|-0.022|-0.020|
|||||(0.019)|(0.016)|(0.016)|

18
information available through either a public credit registry or a private credit bureau.”<sup>39</sup> Development of these two financial institutions should encourage lending by facilitating the use of a broad range of movable assets as collateral, increasing the rights of creditors in case of bankruptcy, and reducing informational asymmetries. The coefficient of correlation between the log of the private credit to GDP ratio and this measure of the quality of financial institutions suggests that it is indeed the case: it is equal to 0.52, statistically significant at the 1% level.

# **4.3 Measures of sector-specific financial vulnerability**

As a first indicator of sector-specific financial vulnerability ( _FVs_ ), we use the Rajan and Zingales (1998) measure of external dependence (ED). They calculated a sector’s need for external finance as the fraction of capital expenditures that were not financed with cash flows from operations for a sample of publicly traded US firms in the 1980s. For each firm, the ratio was averaged over the 1980s and the final ED measure corresponds to the sector median. The key assumption underlying the validity of their ED proxy is that the ranking it generates across sectors is stable across countries because a sector’s need for external finance is intrinsically linked to sector-specific, but country-invariant, technological characteristics. By using U.S. data on publicly traded firms, Rajan and Zingales (1998) increase the likelihood that they correctly identify a sector’s technological demand for external financing. Large firms typically face fewer financing obstacles than small firms and if there is any country in which firms’ actual use of external finance reflects their desired level, the United States is perhaps the closest one can find given the sophistication of its financial system.

Firms which rely on external finance to conduct their day-to-day trading operations or invest in new growth opportunities at home can be expected to be those which need external financing to expand abroad. The establishment of a foreign affiliate requires substantial purchases of new (e.g. land, building, machinery) or existing foreign fixed assets. It is also plausible that any product which entails high R&D, marketing or distribution costs at home will similarly involve large customisation, marketing and distribution fixed costs when produced

> 39Data, definitions, and more information can be found at http://www.doingbusiness.org/data/exploretopics/getting-credit Both indexes have been rescaled on a common 1-10 scale (worst to best). Given that data are only available for the years 2005 and 2006, we use the average size of each index over these two years to construct our institution-based measure.

17
because of male unemployment rate. The female unemployment has increased since 2009.. Labor force participation rate is special concern among the female population. Decreasing from 28 percent in 2000 to 22 percent in 2018, FLFP reflects the absence of urban job creation for women, and probably discourages many women from FLFP altogether (Figure 2.9).

**Figure 2.8: Unemployment rate has decreased for men, not for women** Percentage of the active population

**Figure 2.9: Labor force participation rates** Percentage of the working age population

<!-- Start of picture text --> 16 80%<br>12 60%<br>Total Total<br>8 Male 40%<br>Male<br>Female<br>Female<br>4 20%<br>Source: Estimates based on the Enquête Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force Nationale sur l’Emploi (ENE; Labor Force<br>Survey) Survey)<br>2000 2003 2006 2009 2012 2015 2018 2000 2003 2006 2009 2012 2015 2018<br><!-- End of picture text --> The sectorial distribution of female employment may help explain the declining FLFP rate. In 2000, the manufacturing sector represented 18 percent of total female employment. In 2014 it accounted for just 11 percent (Figure 2.10) as women lost jobs in apparel. This is different from the experience of many developing countries (Artuc et al., 2019). Female employment in agriculture, however, remained about the same. The share of women in the service sector overtook manufacturing. The main point is that urban job creation has not been enough to offset rural migration and the increasing working-age population. Women encountered fewer working opportunities while opportunities for men in a few capitalintensive sectors expanded.

# **Figure 2.10: Share of manufacturing in female employment considerably decreased, while agriculture remained steady and services expanded** Percentage of female employment

7 | P a g e
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# Understanding the Sources of Spatial Disparity and Convergence: Evidence from Bangladesh<sup>1</sup>

Forhad Shilpi The World Bank Key Words: Spatial Disparity, Neighborhood Correlation, Mixed E¤ects Model, Infrastructure, Sorting, Regional Convergence JEL Classi...cation: O18; R13; R23

> 1 I would like to acknowledge superb research assistance from Ieva Zumbyte. All remaining errors are mine. The views expressed in this paper are those of the author’s and should not be attributed to World Bank or its member countries.
# _Table 3_ . Soil Suitability Classification Summary

_Row-wise summary; binary variables, where 1 indicates soil property falls within given suitability class._

|||**MA**|**PS**|||**AF**|**SIS**||
|---|---|---|---|---|---|---|---|---|
||S1|S2|S3|N|S1|S2|S3|N|
|pH|0.35|0.64|0.01|-|0.00|0.99|0.01|-|
|CEC|0.15|0.08|0.35|0.43|0.14|0.08|0.75|0.03|
|Organic Carbon (%)|0.27|0.37|0.34|0.02|0.38|0.51|0.11|0.00|
|Salinity (ECD)|1.00|-|-|-|1.00|-|-|-|
|Slope (%)|0.23|0.29|0.27|0.21|0.23|0.29|0.27|0.21|
|Overall Class|0.13|0.75|0.12|-|0.07|0.88|0.06|-|
|N|106|634|100|0|57|736|47|0|

Benchmarked against the preferred method of plot-level soil testing, there is evidence that the geospatial data fails to adequately distinguish soil suitability levels. AFSIS-based classification results in more intense clustering of observations in the central, moderately suitable class (88 percent), with only 7 percent classified as highly suitable and 6 percent as marginally suitable. While 81 percent of observations are mapped to the same suitability class regardless of whether MAPS or AFSIS soil data informs the classification, the suitability class of 19 percent of plots varies with the source of soil data utilized (see Figure 2), which will have economic implications for the perceived production frontiers for these households.

||||**MAPS**||
|---|---|---|---|---|
|||S1|S2|S3|
|**IS**|S1|**34**|23|3|
|**AFS**|S2|72|**607**|57|
||S3|0|4|**43**|

_Figure 2._ Suitability Classification Matrix; MAPS- and AFSIS-based classifications.

A unique feature of using this method is the ability to identify specific constraints to suitability for each class. According to MAPS-based suitability classifications, cation exchange capacity is an overarching limiting factor, with 43 percent of plots being classified as _not suitable_ for that particular property. The perceived constraints differ when AFSIS-based classifications are made. For example, AFSIS-based classifications suggest that only 3 percent of plots have _not suitable_ levels of cation exchange capacity (compared to 43 percent when using MAPS-based data). Annex Table A1 identifies the limiting factors for each MAPS-based suitability class separately, enabling an assessment of what interventions would be most effective in increasing the suitability of plots from one level to the next.

15
In this section we deploy the tools descriped above to verify the strength of the instruments in the decadal regressions of our previous paper. We began with the following standard cross-country growth regression:

**(7) YCt =Po** + P1 **YC,t-k +** P2'Xct +C + Yt + **VCt**

where **Yct** is log-level of per capita GDP in country c at time t, YC,t-k iS its lag k years ago (k=1 0 years in our application using decadal data) and Xc, is a set of control variables which are measured as averages over the decade between t-k and t. We will consider trade volumes and measures of institutional quality among the variables in X.

Subtracting lagged income from both sides of the equation gives the more conventional formulation in which the dependent variable is growth, regressed on initial income and a set of control variables. The disturbance term in the regression consists of an unobserved country effect that is constant over time, **jc,** an unobserved period effect that is common across countries, **-yt,** and a component that varies across both countries and years which we assume to be uncorrelated over time, vct.

We adopted the approach of Caselli, Esquivel and Lefort (1996), which is to estimate equation (7) in differences, using appropriate lags of the right-hand side variables as instruments. In particular, we estimate the following regression: (8) **yct yc,t = (Yc,t-k Yc,t-2k)+p2(Xct -Xc,t-k)+(Yt** Yt-*)+(Vct **VC,t-k)** This is nothing more than a regression of growth on lagged growth, and on changes in the set of explanatory variables. Or, subtracting lagged growth from both sides of the equation, we have changes in growth from one decade to the next as a function of initial growth and changes in the explanatory variables. Our identifying assumption is that while trade volumes and institutional quality may be correlated with the contemporaneous and lagged shocks to GDP growth (E[Xdtvc,..t-]#0 for s2O), they are uncorrelated with future shocks to GDP growth, (E[(XYdv,,t+]=O for s>0). We therefore instrument for lagged growth using the log-level of per capita income at the beginning of decade t-2k, and we instrument for the changes in trade and institutional quality using their levels at the beginning of decade t-k.

16