rafmacalaba/datause-encoder-data huggingface dataset:
# **IV. Robustness Checks**

We conduct several robustness checks. First, we examine protest spillover at the quarterly frequency. Second, we employ the Standard Poisson estimator. Our results are robust to these checks.

We find that the spillovers are weaker when using quarterly frequency. This is true for both measures of protests. Table 4 shows that past foreign protests, even with large social media penetration, are no longer having spillover effects on domestic protests (columns [1] to [3] of Table 4). The coefficients remain positive but are not statistically significant.

In terms of news-based protests, foreign protests are found to have statistically significant spillover effects on domestic protests at all three thresholds of social media penetration (see columns [4] to [6]). This finding could reflect the stronger tendency for news-based protests to spread than actual protests do. However, the coefficients associated with the effects for the last quarter’s discussions about foreign protests are smaller than those of the last month’s (see Table 2 for a comparison). The effect using quarterly frequency tends to “dilute” the effect captured at the monthly frequency.

**Table 4: Contagion, Distance, and Social Media—Quarterly Frequency**

|Zero-Inflated Poisson|Protest|sper million|persons|N|ews-basedpr|otests|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Social Media Penetration Threshold|**> 10%**|**> 20%**|**> 30%**|**> 10%**|**> 20%**|**> 30%**|
|Protest pc (t-1)|0.010***<br>(0.002)|0.010***<br>(0.002)|0.010***<br>(0.002)||||
|News-based protests (t-1)||||0.193***<br>(0.032)|0.413***<br>(0.033)|0.412***<br>(0.035)|
|Inverse-distance-Common Social|||||||
|Media weighted Protest pc*(t-1)|0.039|0.040|0.038||||
||(0.029)|(0.025)|(0.024)||||
|Inverse-distance-Common Social|||||||
|Media weighted news-based<br>protests*(t-1)||||0.440**<br>(0.208)|0.536**<br>(0.237)|0.662***<br>(0.233)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,780|1,108|884|10,395|8,694|8,064|
|Number of countries|67|47|40|165|138|128|
|N_zero|310|174|145|1813|1477|1291|
|Chi-square|11224|7293|6535|14666|8477|8142|
|Loglikelihood|-2805|-2117|-1751|-4286|-3466|-3224|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ capture the number of protests per million persons. Data are from January 2010 to January 2020. _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. Since Facebook was launched in 2004, data in columns [4] to [6] are restricted from January 2004 to December 2019. *denotes foreign.

13
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Performance of individual ports in ECOWAS countries varies (tables 2.7). Ports in Nigeria and Ghana seem to perform significantly worse than others in terms of efficiency. Container dwell times are particularly high, exceeding one month in a number of cases. Abidjan (Côte d‘Ivoire), Cotonou (Benin), Dakar (Senegal), and Lomé (Togo) all present significantly better performance on efficiency. Container dwell time is as low as seven days in Dakar, putting it close to global best practice. Crane productivity in Lomé is 23 tonnes per hour, substantially better than elsewhere, although still short of the international benchmark. The ports that perform best are often those that charge the most. Container handling costs are well above $200 per TEU in Abidjan and Lomé, or about twice the global benchmark. General cargo handling is around $15 per tonne in Abidjan and Dakar, again about twice the global benchmark.

**Table 2.7  Comparative performance across West African ports**

||Abidjan|Apapa|Cotonou|Dakar|Harcourt|Lomé|Tema|
|---|---|---|---|---|---|---|---|
||Côte<br>d‘Ivoire|Nigeria|Benin|Senegal|Nigeria|Togo|Ghana|
|**Performance**||||||||
|Container dwell time (days)|12|42|12|7|30|13|25|
|Truck processing time (hours)|3|6|6|5|24|4|8|
|Crane productivity (containers per hour)|18|12|─|─|─|─|13|
|Crane productivity (tonnes per hour)|16|9|15|─|8|23|14|
|**Charges**||||||||
|Container handling (US$ per TEU)|260|155|180|160|─|220|168|
|General cargo handling (US$ per tonne)|14|8|9|15|8|9|10|

Source: AICD ports database.

ECOWAS needs better transshipment links. Although Abidjan has enjoyed some success as a container transshipment center, it has suffered in recent years from internal strife and problems relating to the ownership of operating rights to the container terminal. It is clear that the West African coast requires another major transshipment center. One testament to this is that the major carriers engaged in West African container trade, Maersk Line and its affiliate Safmarine, now use the port of Malaga, Spain, as their hub for West African container trade. They relay West African cargo moving to or from Europe and Asia there, and handle other key trades there as well. As a general rule, transshipment traffic tends to become a casualty as ports reach capacity constraints, as has recently occurred in several of these hubs. Nigerian ports play an important role in the transshipment of liquid cargo—mainly oil. Liquid cargo transshipment has been concentrated around the oil exporters, with Nigeria being the largest in the continent.

33
# **Table** **<u>6: Impacts on Business Performance</u>**

||Z score<sup>a</sup>|Z score<br>sales<sup>b</sup>|<br>Z score<br>profits<sup>c</sup>|<br>Sales<br>(U$S)|<br>Sales (U$S)<br>winzorized|Profits<br>(U$S)|<br>Profits<br>(U$S)<br>winzorized|
|---|---|---|---|---|---|---|---|
||||Datapoole|d for all follow-|upsurveys|||
|**Panel A: Full sample**||||||||
|Treatment 1: BRC|0.008|0.023|-0.008|37.055|19.685|-0.996|1.863|
||(0.029)|(0.031)|(0.031)|(63.793)|(43.448)|(9.663)|(7.238)|
||0.789|0.453|0.794|0.561|0.651|0.918|0.797|
||_0.864_|_0.604_|_0.907_|_0.642_|_0.715_|_0.918_|_0.797_|
|Treatment 2: BRC + TPIN|0.032|0.050|0.013|143.657|44.525|2.571|4.407|
||(0.040)|(0.044)|(0.041)|(113.816)|(58.201)|(12.255)|(9.787)|
||0.433|0.254|0.749|0.207|0.444|0.834|0.653|
||_0.864_|_0.507_|_0.907_|_0.331_|_0.592_|_0.918_|_0.746_|
|Treatment 3: BRC + IS +<br>BBA|0.106***|0.118***|0.094***|224.849***|130.636***|26.698***|22.977***|
||(0.028)|(0.030)|(0.029)|(69.022)|(40.630)|(9.316)|(6.924)|
||0.000|0.000|0.001|0.001|0.001|0.004|0.001|
||_0.001_|_0.000_|_0.005_|_0.003_|_0.002_|_0.007_|_0.001_|
|Control group mean|0.000|-0.000|-0.000|731.126|668.128|159.195|152.474|
|Sample size|10,900|10,900|10,900|10,900|10,900|10,900|10,900|
|p-value: Treat1 = Treat 2|0.558|0.543|0.608|0.362|0.672|0.777|0.795|
|p-val: Treat 1 = Treat 3|0.001|0.002|0.001|0.007|0.008|0.005|0.002|
|p-val: Treat 2 = Treat 3|0.063|0.125|0.048|0.485|0.130|0.050|0.053|
|p-value test of equality|0.000|0.001|0.001|0.008|0.007|0.010|0.003|
|<br>p-value test of equality of treat|ment effects over|time||||||
|Treatment 1|0.817|0.939|0.498|0.868|0.857|0.398|0.598|
|Treatment 2|0.833|0.713|0.848|0.284|0.759|0.761|0.621|
|Treatment 3|0.200|0.336|0.182|0.740|0.346|0.310|0.028|

Notes: Data pooled for all four follow-up surveys. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. The Z score index is constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p- values and q-values are reported below standard errors ( _q-values_ in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score includes different measures of sales and profits including weekly outcomes, non-winzorized outcomes, and inverse hyperbolic sine. Variables are detailed in b and c.

b Z-score of sales includes five variables. Those are: last month revenues in U$S, last week revenues in U$S, last month revenues in U$S winzorized at 99th percentile, last week revenues in U$S winzorized at 99th percentile, and the inverse hyperbolic sine of the last month revenues in U$S.

c Z-score of profits includes five variables. Those are: last month profit in U$S, last week profit in U$S, last month profit in U$S winzorized at 99th percentile, last week profit in U$S winzorized at 99th percentile, and the inverse hyperbolic sine of the last month profit in U$S.

36
training courses are given by firm personnel. External training describes formal training courses given by external agents. Most of the training is provided by private institutions.

Table 2: Distribution of the Human Ca ital Variables

|Variable|**1993**<br>Mean<br>S|<br>td.Dev.|**1999**<br>Mean<br>S|td.Dev.|
|---|---|---|---|---|
|Training|38.24%|-|43.33%|-|
|In-housetraining|19.69%|-|23.33%|-|
|Extemaltraining|18.56%|-|20.00%|-|
|Yearsofeducation|9.10|3.98|9.82|3.25|
|Seniority|15.40|11.51|14.82|10.60|

_Source:_ Author's calculations based on ENTRAM-ENESTYC.

Table 3 shows the percentage of training according to various factors such as gender, location of firm, and size of firm. It is apparent that male workers received more training than females in both 1993 and 1999, in both external training and training in general. In 1993 and 1999, the percentage of males and females who received in-house training was about the same, but the percentage of females was slightly higher in both Table 3: Distribution of Training variables

|||**1993**|||**1999**||
|---|---|---|---|---|---|---|
|Variable|Training|**In-house**<br>Training|**External**<br>Training|Training|**In-house**<br>Training|**External**<br>Training|
|FemaleWorkers|34.13|19.91|14.22|41.47|23.91|17.55|
|Male Workers|39.92|19.59|20.32|44.32|23.02|21.30|
|North|34.61|18.65|15.97|42.21|23.89|18.32|
|Center|43.87|22.92|20.95|45.29|23.66|21.63|
|South|37.24|14.01|23.23|31.58|13.34|18.24|
|Mexico City|32.62|16.19|16.43|43.48|24.01|19.47|
|Micro Firms|14.73|8.82|5.90|10.37|4.42|5.95|
|Small Firms|23.43|9.12|14.31|29.34|12.39|16.95|
|MediumFirms|35.60|14.25|21.35|47.03|21.84|25.19|
|Large Firms|51.68|31.57|20.11|58.37|34.03|24.34|

_Source:_ Author's calculations based on ENTRAM-ENESTYC.

7
# **Appendix Table A3d: Impact on Individual Marketing Practices (ITT)**

||(1)|(2)|
|---|---|---|
||Consulted with Former Customers|Offered Discount to<br>Loyal/Bulk Customers|
|Assigned Handbook|0.007<br>(0.024)|-0.027<br>(0.032)|
|Assigned Handbook & Movie|0.042|0.041|
||(0.026)|(0.034)|
|Assigned Handbook & Assistance|0.026|0.055|
||(0.025)|(0.034)|
|Assigned All Three|0.057**|0.067**|
||(0.027)|(0.034)|
|R-squared|0.098|0.144|
|N|2204|2205|
|Dependent Variable Mean in Control Group|0.142|0.358|
|F-test (p-value): Book = Book & Movie|0.197|0.041|
|F-test (p-value): Book = Book & Assistance|0.468|0.014|
|F-test (p-value): Book = All Three|0.070|0.005|
|F-test (p-value): Book & Movie = Book & Assistance|0.570|0.703|
|F-test(p-value): Book & Move = All Three|0.614|0.461|

**Notes:** This table presents impact analysis (Intent-to-Treat) for individual marketing practices across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level). Details on business practices can be found in Appendix Table A10.
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Policy ReseaRch WoRking PaPeR 4846

# **Abstract**

The apparently inexorable rise in the proportion of “missing girls” in much of East and South Asia has attracted much attention amongst researchers and policymakers. An encouraging trend was suggested by the case of South Korea, where child sex ratios were the highest in Asia but peaked in the mid-1990s and normalized thereafter. Using census data, we examine whether similar trends have begun to manifest themselves in the two large populous countries of this region, China and India. The data indicate that child sex ratios are peaking in these countries, and in many sub-national regions are beginning to trend towards less masculinization. This suggests that, with continuing vigorous efforts to reduce son preference, the “missing girls” phenomenon could be addressed in Asia.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to study socio-political processes and development outcomes. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The first author may be contacted at mdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4. Firms’ Access to Finance**

Previous literature using global firm-level data finds that SMEs face greater financing obstacles than large firms and small firms use less external finance, especially bank finance.<sup>6</sup> Improvements in the functioning of the formal financial sector can therefore reduce financing constraints for small firms and others who have difficulty in self-financing or in finding private or informal sources of funding. To analyze firms’ access to finance in Africa, we use data from Enterprise Surveys<sup>7</sup> , which cover more than 130,000 firms in 127 countries. Data are collected across the world using consistent questionnaires and sampling methodology. Data is typically collected on a 3 to 4 year rotation in each major World Bank region.<sup>8</sup> On average, percent of enterprises in African countries with a bank account (across all firm size groups) is comparable to or greater than the percent of enterprises with a bank account in all other developing economies. For instance, 83% of small and 94% of medium sized enterprises in Africa report having a bank account as compared to 87% of small and 93% of medium sized enterprises in other developing economies.

Yet, firms in Africa have notably limited access to external funding.<sup>9</sup> Enterprise Surveys data show that on average, only 22% of enterprises have a loan or a line of credit. In comparison, the average of enterprises with a loan or a line of credit in other developing economies excluding Africa is 43%. Like elsewhere, we find that small firms in Africa are at a relative disadvantage in accessing external credit (Figure 7). In Africa, 45% of firms in Africa cite access to finance as a major constraint to growth. However, a higher percentage of small firms report access to finance as a major constraint relative to medium and large enterprises.

> 6 See, for example, Beck, Demirguc-Kunt & Maksimovic (2005); Beck & Demirguc-Kunt (2006); Beck, DemirgucKunt, Laeven, Maksimovic (2006), and Beck, Demirguc-Kunt and Maksimovic (2008).

> 7 The Enterprise Surveys indicators are available at: www.enterprisesurveys.org . 8 All samples are drawn following a stratified random selection. The standard strata for every economy are sector of activity, firm size, and geographical location. Under geographical location the stratification aims at having representativeness in the main economic centers of each country. Firm size is stratified into small (5-19 employees), medium (20 to 99), and large (100 and more).

> 9 Africa includes Sub-Saharan African countries only as there were no North African countries covered in Enterprise Surveys using the global methodology as of May 2012.

13
<!-- Start of picture text --> Figure 16. Impact of lower VAT and PIT rates on poverty and inequality<br>A. Gini Coefficient B. Poverty Headcount rate<br>(US$5.50 PPP)<br>0.380 18 15.0<br>15.4 14.8<br>16<br>0.370 13.9<br>14<br>0.360 12.1 11.8<br>12<br>0.350 10<br>0.340 8<br>6<br>0.330<br>4<br>0.320<br>2<br>0.310 0<br>Market income + Disposable Consumable<br>0.300<br>pensions income income<br>Market Disposable Consumable Final Income<br>Income + Income Income 2016 Baseline<br>Pensions<br>VAT decline to 18%<br>Baseline<br>PIT decline to 10%<br>Simulation PIT rate=10% & VAT 18%<br>Decline in both PIT and VAT<br><!-- End of picture text --> <u><mark>Source: World Bank estimates based on Romanian HBS (2016).</mark></u> If the purpose of these reforms was to reduce poverty and inequality, a much more efficient way of achieving this objective would have been to keep the PIT rate constant and to increase targeted social transfers. Such an alternative would not only have been less costly, but also more redistributive. To illustrate this point we undertook a final simulation in which we assumed that the PIT was kept at 16 percent, and that half of the cost of the reduction to 10 percent was instead spent on higher social transfers (Figure 17). Such an alternative would not only have halved the impact on the fiscal deficit, it would have also reduced poverty by 1.4 percentage points (compared to only 0.6 percentage points with lower PIT and VAT rates) and reduced inequality by 0.006 Gini points, as opposed to an increase with a lower PIT.

26
that differences in land rights arrangements had any impact on the ability to cope with these shocks. Although many of the factors affecting households' land nght preferences are as predicted, what is most surprising is the evidence of "learning" whereby households who experienced more stable land rights are likely to favor them.

# **4.1 Agricultural investment**

The impact of land tenure arrangements on households' propensity to invest is widely seen in the literature as a test of the investment-enhancing impact of higher levels of tenure security (Feder et al., 1988; Besley 1995, Li et al., 1998). With respect to China, most applications focus on short term investment. Without data on long-term investment, many studies have resorted to short-term measures such as the use of specific inputs such as fertilizer or organic manure.'<sup>4</sup> Also, to the extent that the observed vanation in tenure secunty is endogenous, i.e. adopted in response to higher payoffs from land-improving investments, it may suffer from biased estimates. Both of these shortcomings can be addressed with our data. First, we have data on long-term investment. Second, the fact that laws affecting tenure security in Guizhou were made at the provincial level, provides exogenous variation **in** tenure security. To the extent that more secure property rights (such as the policy of the "two nos") have been adopted in other villages outside Guizhou where the benefits from such a policy would be particularly high, the econometric estimates of the impact of this arrangements as obtained by our study would provide a lower bound of the true effect.

To make inferences on determinants of household investment, we use a specification that follows Feder et al. (1987) where overall levels of agricultural investment are a function of household characteristics, in particular endowments and access to credit markets and other mechanisms for smoothing consumption, and policy variables. In addition, as farmers' investment decisions are likely to depend on the aggregate level of investment undertaken by the village, we include village level investment as one of the right hand side variables and adopt a two-stage approach. Formally, we estimate

# _(1) J, =a+XI*,+6X,+_ **_yZ,+_** _P,_ **_+ei_**

where _1',_ is either (m the probit specification) a dummy equaling one if household i made investments in agriculture during the period and zero otherwise or (in the tobit specification) the actual amount of investment made. Xi are vectors of household characteristics, **Zi** are endowments of labor and land and **Pi** are land policy variables. Agricultural investment used for this study include households investment

> **1'** To the extent that these "investments" may Just compensate for failure to investment **in** the past (possibly due to low tenure secunty) or are correlated with soil quality, a factor that is generally unobserved, doing so can give nse to erroneous conclusions

14
<!-- Start of picture text --> Costa Rica<br>"precios"<br>100<br>filtered<br>original<br>80<br>60<br>40<br>20<br>0<br>20<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 5: Smoothed results for the average of the “precios” keyword in Costa Rica. The smoothed series is computed using the Christiano-Fitzgerald filter with all stochastic cycles at a periodicity lower than 3 months and higher than 12 months removed.

13
even greater for firms with both a female owner and female manager at 0.44 percent reduction in contrast to 0.24 percent reduction for firms that have all male owners and male managers. These results are largely retained when using the full sample that includes large firms. An interpretation of this result is that economic growth may benefit the more vulnerable or less well off in a society and female owned or managed firms are less susceptible to crime.

# _3.5 Governance and per Capita Economic Growth Interaction_

We use ICRG’s Quality of government indicator as a measure of governance. The interaction term between Quality of Government and Economic growth is positive and statistically significant at 5 % as shown in column 1 of table 3. Economic growth has a negative and significant association with crime losses experienced by small and medium sized firms at the sample mean of the governance indicator. However, this relationship turns insignificant at the sample maximum of the governance indicator. At the sample minimum level of governance, the association of economic growth with crime losses has a larger magnitude while retaining significance, when compared to the mean level of governance. A 1 percent increase in economic growth is associated with an approximately 0.28 and 0.43 percent reduction in crime losses for the sample mean and minimum quality of governance respectively. These results are largely retained when extending the sample to large firms. One interpretation is that better governance is a substitute for the crime-reducing effect of economic growth.

# _3.6 Voter Turnout and per Capita Economic Growth Interaction_

Social disorganization theory indicates that factors that diminish the effectiveness of informal social controls increase criminal activity (Kelly, 2000). Here we consider one indicator of social 17