rafmacalaba/datause-encoder-data huggingface dataset:
Following the literature, we may expect shocks affecting the agriculture sector to mainly test poverty related mechanisms (notably, the opportunity cost channel) as they affect more laborintensive commodities and as rapacity effects concern a specific subset of high-value agricultural exports. Alternatively, we may expect shocks affecting the extractive sector to mainly test rapacity channels as they focus on more capital-intensive commodities. As a matter of fact, Table 1 confirms our expectations. Of the 808 estimates focusing on positive agricultural shocks, 648 (80%) test poverty-related mechanism and 151 (19%) test rapacity effects. Of the 951 estimates focusing on negative agricultural shocks, 798 (84%) test poverty-related mechanism and only 5 (0.5%) test rapacity effects, and 139 (15%) test other channels of transmission (including 107 cases of regional destruction).

Table 1: Main Implicitly Tested Mechanism in Estimates According to their Meta-Regression Subgroup

||Agricultural shock (+)|Agricultural shock (-)|Hydrocarbon/Mineral shock (+)|Other shock (+ or -)|Total|
|---|---|---|---|---|---|
|_Mechanism_||||||
|Poverty-related|648|798|82|46|1574|
|Rapacity-related|151|5|423|4|583|
|Other|4|139|38|0|181|
|Not specifed|5|9|0|112|126|

Notes: Number of estimates in the total sample of 2,464 estimates. Other shock (+ or -): estimates of negative or positive pure climatic shocks, estimates of labor market shocks, financial crisis, and estimates of shocks to the drug sector. Poverty-related mechanisms include the dynamism of local economy, food scarcity, budget constrains and water insecurity. Rapacity-related mechanisms include rent capture and the funding of insurgents. Other mechanisms include exodus, grievance, imperfect information, regional destruction (incl. agricultural soils) and state capacity. _Source: Authors’ compilation from MRA database._ Figure 3 presents the proportion of collected estimates in each meta-regression subgroup. Our three main subgroups (AS-, AS+, and HS+) account for 90.2% of the baseline sample of 1,391 regressions and 66.8% of the baseline estimates concern agricultural shocks (71.4% of the total sample of 2,464 regressions). Other shocks only account for 6.5% of the total sample. Interestingly, while selected studies on the agriculture sector investigate equally positive and negative shocks, studies focusing on the hydrocarbon/mineral sector only investigate positive shocks. Detailed information on the types of shocks in each meta-regression subgroup is provided in Figure A1, Table A1, and Tables A5 to A8 in the Appendix.

11
variable in equation (1) rather than **levels** of each variable in each year. Such a first-differenced production function is shown below: where Δ denotes the difference between the level of each variable in a given year from the previous year.<sup>3</sup> By purging the data of the time-invariant level differences in productivity due to unobserved firm-level ability effects, this procedure turns the focus of the regression analysis to whether CIMO participation, θ , affects productivity growth.

To test the efficacy of this approach, the panel data sets from both STPS evaluation studies – the 1991-1993 and 1993-1995 samples plus the 1991-1995 sample of firms that appeared in both studies – were combined and the variables re-defined to ensure that they were comparable over time.<sup>4</sup> Tables 2 and 3 summarize several key outcome variables for the CIMO and control groups.

**Table 2.  Effects of CIMO Participation on Intermediate Outcomes**

|Variable|**Period**|**Effect (double**<br>**difference)**|**Prob.**|
|---|---|---|---|
|Mean private investment (per worker) in training|1991-1993|192.7|0.024|
|Mean private investment (per worker) in training|1993-1995|170.0|0.002|
|Share of firms withqualitycontrol mechanisms|1994-1995|23.1|0.000|

_Source_ : Own calculations based on STPS databases. _Note_ : All estimated effects are statistically significant.

Table 2 confirms the findings of the previous CIMO studies that the program improved intermediate outcomes of participating firms relative to the control group. In both the 1991-1993 and 1993-1995 periods, CIMO had a positive and significant effect relative to the control group in terms of raising training investments per worker; CIMO firms invested an average of 170-190 pesos more (in 1994 constant pesos) in training per worker than non-CIMO firms. The table also shows that a higher proportion of CIMO firms (23 percent) adopted quality control systems throughout the 1994-1995 period, as compared to the control group. As discussed earlier in Section II, the CIMO program sought to influence these two intermediate outcomes so as to improve performance and productivity of participating firms.<sup>5</sup> Table 3 reports the simple means of alternative measures of labor productivity for CIMO and non-CIMO firms in 1991, 1993 and 1995, and separates the samples into ones that 10
reduction in high school dropout rate; 14-26 percent reduction in special education placement; higher grade completion rates in 2<sup>nd</sup> , 3<sup>rd</sup> and 4<sup>th</sup> grades; higher scores on achievement tests; higher attendance; and less age-grade distortion. They include significant effects on economic potential: significantly higher monthly earnings; higher rates of home ownership; higher rates of school completion; and 18 percent chance of employment at age 19 (Filp 1983; Feijo 1984; Pontieri _et al_ . 1981; Barnett 1995; Kagitcibasi _et al_ . 1987). In order to make meaningful comparisons we build on this and use a Mincerian model for age-earning profile to make benefitcost scenarios. In all these scenarios the benefits are based on minimum estimates as there are many other side benefits that we did not attempt to estimate.

_Scenario I: Preschool program_ . We look at an early childhood development intervention program that is based on pre-school education and use the above success indicators, and assuming the cost per child is $1,000. Since the returns to schooling are different across countries and among countries, we looked at different levels (within the ranges in Psacharopoulos and Patrinos 2004): 5 percent return to a year of schooling in industrial countries, and 10 to 30 percent in very poor countries. We assume a discount rate of 10 percent.

_Scenario II: Preschool plus nutrition and health_ . This scenario assumes the settings of scenario I and adds health and nutrition interventions before and during pre-school years. Benefit-cost calculations were conducted based on international studies of the effects of providing nutritional supplements and health interventions on survival, enrollment and achievement rates, and reducing dropout rates. We assume cost increases to $2,000 a child.

_Scenario III: Preschool, nutrition, health and classroom interventions in first two grades_ . This scenario assumes the settings of scenario II and adds more interventions (teacher training, additional material, breakfast, health). We assume a cost of $3,000 a child.

In addition to these scenarios Table 4 includes similar benefit-cost calculations based on different comparable interventions. It is clear that the ECD interventions surpass other investments. A review of the literature suggests that early interventions are very cost-effective. Early interventions have the best payoff (Carneiro and Heckman 20054; Figure 1).

12
# **Table 2 – Determinants of Closing Error**

# _OLS Regression_

||Ethiopia|Tanzania|Nigeria|Pooled|
|---|---|---|---|---|
|CR Area (acres)|0.020|‐0.291**|0.096|‐0.032|
|CR Area<sup>2</sup>|0.166|‐|‐0.005|‐|
|CR Area<sup>3</sup>|‐0.039**|‐|‐|‐|
|Number of Corners|‐0.032***|0.006|‐0.004|‐0.013***|
|Slope (clinometer)|0.014***|‐|‐|‐|
|_Treecover:_|||||
|Partial|‐0.003|‐0.052|‐0.128|‐0.143***|
|Heavy|0.183|‐0.163|0.331|0.021|
|_Weather:_|||||
|Mostly Cloudy ‐ Rainy|‐0.014|‐0.128**|0.036|‐0.048|
|Constant|2.298***|2.118***|1.597***|2.238***|
|N|1765|1908|485|4158|
|R2|0.015|0.007|0.019|0.008|

*p<.1; ** p<.05; *** p<.01

33
- Ferriani, F., Oddo, G., 2019. More distance, more remittance? Remitting behavior, travel cost, and the size of the informal channel. Econ Notes 48, e12146.

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15
# Is predicted data a viable alternative to real data?<sup>_∗_</sup>

Tomoki Fujii<sup>_†_</sup> Roy van der Weide<sup>_‡_</sup>

**JEL classification c odes:** C20, C53, I32.

**Keywords:** Prediction; Double sampling; Survey costs; Poverty.

> _∗_ This study has been funded by the World Bank’s Knowledge for Change Program for which the authors are grateful.

> _†_ Singapore Management University. Email: tfujii@smu.edu.sg

> _‡_ World Bank. Email: rvanderweide@worldbank.org
-21-

determining the need for the survey and obtaining the first results was about ten days, and the incremental cost of the survey was US $330, exclusive of Bank staff-time input. The Bank staff-time input was three weeks and the cost was around US $6000. Further, as the WDR 1990 indicates, changes in the living standards of the poor can be assessed by movements in proxy variables such as producer prices for small farmers, real wages for unskilled labor, the relevant cost-of-living indices, and per capita expenditures on basic social services. Useable information about these variables can often be obtained fairly quickly.

36. LSMS-type surveys or Integrated SDA Surveys which include small samples but a detailed questionnaire (as opposed to the Priority SDA Survey which includes a large sample with a limited number of questions -- referred to in paragraph 34) could be used to collect data about the likely changes in the consumption (of public and private goods), income, and employment of different socio-economic groups. An LSMS survey can take roughly 12 to 18 months and cost on average US $500,000, but the survey can be focussed on issues of immediate concern, and can be performed using existing government survey personnel and apparatuses, thereby reducing costs and producing results quickly. In Jamaica, for example, a first round LSMS survey consisted of adding-on a questionnaire to the existing labor force survey conducted every quarter by the government. The questions pertained to consumption expenditure, health, education, housing, ownership of durable goods, and anthropometrics and did not include other modules such as agricultural production and migration. As a result, the additional cost of obtaining the relevant data (which fed into the design of the Human Resource Development Program) was only about US $ 75,000 and took 2 to 3 months of
Most of our specifications exclude Italy, which does not have a nationwide minimum wage. As a robustness check, models [7] and [8] (like models [1a]-[1c] in Table 3B) assign to Italy a minimum wage at the level of 38.5% of the average wage in industry and services (the mean level for our sample of Eastern and Southern European countries) and include a dummy for Italy. The share of non-contracted employees in Italy‘s labor force (as of 2006) was by 8 to 10 percentage points below the level predicted by the model with this hypothetical minimum wage level (this effect of absence of a minimum wage is stronger than in Table 3B because this time the Gini index is controlled).

# **4 Macro determinants of informal employment in Western and Northern Europe**

Table 5 presents seemingly unrelated regressions estimating the institutional and other macro determinants of the shares of informal dependent employment and informal selfemployment, as well as the total share of unemployed and discouraged workers in the [extended] labor force of Western European and Nordic countries. Countries covered include the UK, Ireland, Netherlands, Belgium, France, Germany, Switzerland, Austria, Norway, Sweden, Finland, and Denmark. For most countries we have three observations which refer to 2004-2005, 2006-2007, and 2008-2009 (see details in Table 1); for France, we miss the first of these biannual cycles, whilst for Austria we miss the last. Austria aside, the most recent data for each country come from a survey implemented during the economic crisis (late 2008 and/or early 2009, except for Ireland where it was a year later).

With few exceptions, the explanatory variables are the same as for Eastern and Southern Europe. The _Rule-of-Law_ indicator is not used for Western and Northern Europe. New variables (see Annex 1 for details) include:

- (i) _SAT_GOV_ – average satisfaction with the national government (on a 0 to 10 scale), from the respective round of the ESS (used only in specification{2})

- (ii) _GOV_EXPsq_ – government expenditures (consumption and transfers) at all levels as percentage of GDP, squared /100<sup>20</sup>

- (iii) _BUS_FREE:_ Business Freedom – the Heritage Foundation (2010) index (on a 0 to 100 scale) which measures „the ability to start, operate, and close a business‖ and is negatively related to the overall burden of regulation as well as to the efficiency of government in the regulatory process.

> 20 This variable is a simple transformation of the Heritage Foundation (2010) Government Spending index: the latter equals 100 – 3  GOV_EXPsq; the transformation has been applied to facilitate interpretation of the results.

19
**Table 7.** Correlation between risk measure and risky sexual behavior

||HIV in|cidence|STI pre|valence|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Risk-loving|0.139<sup>***</sup>|0.119<sup>***</sup>|0.037<sup>***</sup>|0.034<sup>**</sup>|
||(0.038)|(0.035)|(0.013)|(0.014)|
|Mean risk averse|0.095|0.095|0.022|0.022|
|Baseline controls|No|Yes|No|Yes|
|Observations|555|555|662|662|

Note: Sample of control group individuals aged 18-32 at baseline who responded to the hypothetical risk aversion question. See notes to tables 4 and 5 for definitions of HIV incidence and STI prevalance. Risk-loving is an indicator variable taking the value 1 for respondents who, at baseline, prefered a fixed amount of money equal or above 250 maloti instead of a lottery with 50% chance of winning 500 maloti, and 0 for respondents who prefered a fixed amount of money below 250 maloti instead of a lottery with 50% chance of winning 500 maloti. The control variables are STI status at baseline and all household characteristics listed in panel B, table 2. All regressions include village fixed effects. Robust standard errors in parentheses, clustered at the village level. *** 1% , ** 5% , * 10% significance.
Despite the reduction of observations in the last two quartiles of checks and balances, the picture is broadly the same. The scatter plots in Figure 2 confirm the negative relationship.

[Table 2 about here]

[Figure 2 about here] Finally, Table 3 shows the averages of infrastructure quality per quartile of quality of governance and checks and balances. Three indicators that proxy the quality of infrastructure in transport, energy and telecommunications were selected: paved roads as a percentage of total roads, electric power system losses as a percentage of total power outputs, and faults per 100 main fixed lines per year. The latter two indicators were rescaled such that higher values mean higher quality.<sup>4</sup> The quartile analysis reveals that infrastructure quality is positively correlated with institutional quality. These findings are true for all the indicators, but the changes from quartile to quartile are more dramatic for paved roads as a percentage of total roads. Figure 3 corroborates these results by displaying the scatter plots with a positively sloped prediction line for all combinations of both infrastructure and institutional quality indicators. Interestingly, the shape of the probability density functions for the infrastructure quality indicators have significant differences. While there are very few observations recording low quality values for the energy and telecommunications sectors, the points for the transport sector show much more variance.

[Table 3 about here]

[Figure 3 about here]

# **3. A Review of the Literature**

Numerous papers have been written about the contribution of public investment to economic growth and poverty reduction, but the relationship, though generally positive, has not been conclusive.<sup>5</sup> Part of the reason can be attributed to the difficulty in differentiating between types of public investment. For example, one would expect the impact to be contingent upon the kind of public investment, the amount of investment, the initial stock of public capital, the economic context in which investment occurs (Anderson et al., 2006), as well as the quality of the investment.

> 4 Further details about the choice of measures are provided in Section 4.

> 5 Comprehensive surveys of the literature can be found in Estache (2006), Romp and de Haan (2007) and Straub (2008). A more recent contribution addressing many of the limitations of the previous studies is Calderón and Servén (2011).

4
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18
**Table 33. Annualized Profits in Benin and Malawia**

|||B|enin||||
|---|---|---|---|---|---|---|
||Mean|S.d.|Min.|Max.|Median|N|
|Profit rate on annual sales|3.9%|18.9%|-114.8%|<br>57.7%|_5.5%_|_559_|
|Annualprofit($)b|1338.81|5537.11|-9163.71|<br>68593.59|119.05|559|
|||M|alawi||||
||Mean|S.d.|Min.|Max.|Median|N|
|Profitrate on annual sales|14.1%|<br>18.2%|-80.3%|57.1%|14.9%|549|
|Annual profit ($) b|6140.68|17989.06|-4173.45|172689.50|1142.11|545|

**a** Profit = Gross margin - variable costs - operating costs - wage bill.

**b** At the time of survey, exchange rates were 1 US $= CFA 600 and I US $ _=_ MK 45.

These average profit rates mask a great deal of variation across traders. A large proportion of surveyed traders -- 20% -- appear to be making losses. On the other hand, some traders appear to be making astronomical profits. It is unclear how much of this variation is due to measurement error, but the extent of it suggests that profits from trade are likely to be very variable. Agricultural trade is a risky venture.

Further analysis of absolute levels of annual profits broken down by firm assets and gender provides evidence of significant returns to working capital as well as to social capital in both countries. Median profits nearly double between working capital terciles in Benin and nearly triple in Malawi. Returns to social capital are also high in both countries, though somewhat lower than that of financial capital. In contrast to Malawi, profits are nearly half for traders in the middle tercile compared to the bottom tercile. This suggests that the one-person enterprises, typically of women retailers, are more profitable than the slightly larger firms. As firms grow beyond the threshold level of 2 employees, profitability then increases. Finally, as expected, male traders have considerably higher profit levels than women in both countries.

The figures below confirm that across countries for each type of firm asset and gender, traders in Malawi have considerably higher profit rates, defined as the ratio of annual profits to the value of annual sales, than their counterparts in Benin. What is also revealing is that, unlike the case of absolute profit levels, there is no clear pattern of the impact of assets on profit rates.

58