rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Total Factor Productivity Growth: Average in 1950-1995 and Change During Periods of Reform(%), Seemingly Unrelated Regressions (Percent)

|Recessions Dummy|None||DRECI||DREC2||
|---|---|---|---|---|---|---|
|Country / Period|1950-95|Reforn<br>(change)|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|
|Argentina<br>Bolivia|-1.3<br>(0.123)<br>-1.2<br>(0.14)|9.9<br>(0.002)<br>1.3<br>(0.126)|1.9<br>(0.002)<br>0.3<br>(0.683)|5.4<br>(0.016)<br>1.1<br>(0.108)|2.0<br>(0.000)<br>0.4<br>(0.459)|6.4<br>(0.002)<br>1.6<br>(0.003)|
|Brazil|-0.6<br>(0.548)|-0.3<br>(0.918)|1.2<br>(0.161)|-3.0<br>(0.213)|2.3<br>(0.001)|3.2<br>(0.065)|
|Chile|-1.9<br>(0.064)|4.6<br>(0.000)|1.4<br>(0.148)|2.5<br>(0.029)|4.0<br>(0.001)|-0.7<br>(0.576)|
|Colombia|0.6<br>(0.236)|_0.5_<br>(0.446)|0.3<br>(0.473)|_0.5_<br>(0.476)|2.4<br>(0.000)|-0.6<br>(0.173)|
|Costa Rica|0.4<br>(0.7)|-1.0<br>(0.27)|1.5<br>_(0.115)_|-0.4<br>(0.610)|1.3<br>(0.078)|9.2<br>(0.132)|
|Ecuador|0.3<br>(0.881)|1.7<br>(0.460)|2.3<br>(0.152)|0.3<br>(0.891)|2.0<br>_(0.155)_|4.2<br>(0.029)|
|El Salvador|-1.6<br>(0.03)|1.8<br>(0.072)|0.4<br>(0.484)|-0.2<br>(0.774)|_0.5_<br>(0.571)|1.0<br>(0.234)|
|Guatemala|-0.3<br>(0.509)|0.1<br>(0.804)|1.0<br>(0.029)|-0.7<br>(0.155)|0.9<br>(0.010)|0.3<br>(0.383)|
|Guyana|-1.6<br>(0.048)|3.3<br>(0. 105)|1.2<br>(0.097)|4.2<br>(0.013)|3.0<br>(0.001)|1.6<br>(0.291)|
|Honduras|-0.2<br>(0.792)|0.6<br>(0.588)|0.5<br>(0.379)|0.7<br>(0.362)|3.1<br>(0.000)|-0.3<br>(0.755)|
|Jamaica|-0.7<br>(0.372)|0.5<br>(0.675)|2.0<br>(0.014)|-1.2<br>(0.231)|3.0<br>(0.000)|-0.2<br>(0.749)|
|Mexico|-2.5<br>(0.002)|0.6<br>(0.540)|-0.7<br>(0.294)|0.4<br>(0.594)|0.9<br>(0.346)|-0.3<br>(0.758)|
|Nicaragua|-4.5<br>(0.000)|0.0<br>(0.994)|-1.6<br>(0.276)|0.3<br>(0.873)|0.7<br>(0.644)|1.8<br>(0.349)|
|Paraguay|-1.2<br>(0.074)|-1.1<br>(0.475)|0.1<br>(0.784)|-1.0<br>(0.378)|1.8<br>(0.001)|0.6<br>(0.513)|
|Peru|-1.6<br>(0.086)|1.3<br>(0.400)|0.7<br>(0.332)|1.1<br>(0.306)|1.6<br>(0.046)|-1.5<br>(0.151)|
|Uruguay|0.6<br>(0.369)|0.0<br>(0.995)|2.5<br>(0.000)|-0.8<br>_(0.555)_|2.6<br>(0.000)|-1.8<br>(0.271)|
|Venezuela<br>|-1.3<br>Q1033)<br>|4.0<br> <br>(0.009<br>|0.6<br>(0.345)<br>|2.9<br>(0010)I<br>|3.5<br>(0.0001<br>|-0.4<br> <br>(0.704)<br>|

Notes: (*) The average for the period 1950-95 is the coefficient on the constant in equation (3), while the change during the periods of Reformn is the coefficient on the variable DREF in equation (3); P-values in parenthesis; DREC is activated when GDP growth is negative; DREC2 is activated when TFP growth (calculated with a capital share of 0.4) is negative. Source: Authors' calculations.
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obtained by aggregating the small area estimates to the national and regional level using population weights. The results are shown in Table 5. The model-based estimates from both approaches are seen to line up with the survey-direct estimates. ELL and SEM both identify the North and South as the regions with the highest rates of poverty.<sup>9</sup> While the two approaches disagree slightly on the ordering of these two regions, the observed difference in the poverty estimates lies well within the 95 percent confidence interval.

More striking is the difference in statistical precision between the two approaches. ELL achieves slightly greater statistical precision than the survey-direct estimates (by virtue of combining the survey with unit record population census data), while the standard errors obtained by the SEM approach are notably larger (between two and three times the ELL standard errors). This difference in precision is in large part due to divergence in number of observations used to estimate the model parameters: 12,271 households in the ELL approach versus 767 PSUs in the SEM approach. The variance of the poverty estimates are largely driven by the variance of the model parameter estimates, which in turn scales with the number of observations used in the respective regression model. As the model adopted by ELL is at the household level, it has 16 times more observations to work with compared to the SEM approach.

||survey-direct|ELL||SEM||
|---|---|---|---|---|---|
|Malawi|0.507 (0.009)|0.498|(0.006)|0.492|(0.023)|
|North|0.543 (0.021)|0.562|(0.018)|0.540|(0.022)|
|Central|0.445 (0.015)|0.438|(0.009)|0.434|(0.024)|
|South|0.555 (0.014)|0.537|(0.008)|0.533|(0.026)|

Table 5: Estimates of poverty from the survey, ELL and SEM The small area estimates of poverty for the two approaches are presented in Figure 2, which shows the two poverty maps side by side. A visual inspection of the two maps shows a striking agreement on the spatial distribution of poverty. Both maps suggest low levels of poverty in the urban areas of Mzuzu, Kasungu, Lilongwe, Zomba, and Blantyre, as well as areas of high poverty in the far north and south of the country.

9SEM slightly underestimates poverty in areas of the Northern region with comparatively low rates of poverty.

24
Table A1: Correlates of “Passing” ACASI Screening Questions

||(1)|(2)|(3)<br>|(4)<br>=1 if passed|(5)<br> SI screening|(6)<br><sup>a</sup>|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Mal|awi|||Liber|ia||
|Years of education|0.011***|||0.017***|-0.012***|||-0.011*|
||(0.003)|||(0.006)|(0.004)|||(0.006)|
|=1 if able to write/read in English||0.046*||-0.037||-0.065**||0.016|
|||(0.025)||(0.038)||(0.029)||(0.044)|
|=1 if has access to mobile phone|||0.009|-0.009|||-0.017|-0.028|
||||(0.023)|(0.024)|||(0.026)|(0.027)|
|R-square|0.006|0.002|0.000|0.013|0.009|0.004|0.000|0.039|
|Overall mean of outcome|0.62|0.62|0.62|0.62|0.70|0.70|0.70|0.70|
|Observations|1,737|1,737|1,737|1,737|1,261|1,261|1,261|1,261|

Note: Columns 1-3 and 5-7 present bivariate regressions. Columns 4 and 8 include all variables in Table 1, but other coefficients are not reported for space. Standard errors clustered at individual level are in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively. a Passing threshold is choosing “yes” for all the five questions in Table 2.

Table A2: Effect of ACASI on Choosing “Don’t know” or “Refuse to answer” in Placebo Questions

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Farm work<br>(past year)|Market visit<br>(past week)|Maize/Rice<br>(next week)|Meat<br>(next week)|
|**Panel A. Mal**|**awi**||||
|SI|0.013***|0.013**|-0.080***|-0.000|
||(0.005)|(0.005)|(0.020)|(0.022)|
|FTFI mean|0.005|0.007|0.266|0.294|
|Observations|1,737|1,737|1,737|1,737|
|**Panel B. Lib**|**eria**||||
|SI|0.003|0.002|0.017*|-0.011|
||(0.002)|(0.002)|(0.009)|(0.019)|
|FTFI mean|0.000|0.000|0.020|0.132|
|Observations|1,261|1,261|1,261|1,261|

Note: Regressions include individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

25
Figure 23: Cost and poverty reduction of four CCT scenarios aimed at small children <!-- Start of picture text --> A. Absolute gain per HH by market income decile B. Absolute gains per HH by demographic<br>structure<br>21,000 12,000<br>18,000<br>9,000<br>15,000<br>12,000<br>6,000<br>9,000<br>6,000 3,000<br>3,000<br>0<br>0 No children No children, 1 child 2 children 3+ children<br>1 2 3 4 5 6 7 8 9 10 or elderly with elderly<br>CCT Version 5 CCT Version 5<br>CCT Verison 6 CCT Version 6<br>KHR/month<br>KHR/month<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **6. Conclusion**

This paper performs a fiscal incidence analysis for Cambodia using the CEQ approach. The present analysis relies on the 2019/20 Cambodia Socio-Economic Survey (CSES) and 2019 fiscal data. The CEQ approach is a tool that helps assess the redistributive effect of a fiscal system and its individual interventions, offering invaluable insights for policy. Moreover, it offers a platform to simulate policy scenarios and assess their distributional implications.

The 2019 fiscal system in Cambodia, per the CEQ model, reduces inequality yet lowers incomes of poor households in the short-term. Note, these results predate the substantial increase in cash transfers to poor and vulnerable households in response to the COVID-19 pandemic. The informal sector accounts for a large share of the economy in Cambodia, hence taxes are largely collected indirectly. Poorer households pay indirect taxes as part of the purchase price of goods and services, which reduces their purchasing power, even with tax exemptions on necessities. In the absence of offsetting transfers, their net cash position is negative on average. Short-term poverty can increase following execution of fiscal policy because direct transfers are too small to compensate for the impact of indirect taxes on poor and vulnerable households. Note, households also benefit from in-kind services from spending on health, education, and infrastructure, but they do not receive these services in cash and therefore do not increase their incomes today. However, it is possible for fiscal policy to reduce poverty in the long run through investments in human capital and infrastructure. Other components of spending not captured in the CEQ exercise, such as public infrastructure, can make the overall system more progressive through their indirect effects on inclusive economic growth.

Cambodia is not alone among developing countries in seeing a short-term poverty increase due to fiscal policy. Several peer lower middle-income countries also experience net poverty increases due to their 31
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16
# The Causal Impacts of Child Labor Law in Brazil: Some Preliminary Findings

## _Caio Piza_

The World Bank Development Research Group Contact email: <u>caiopiza@worldbank.org</u>

## _André Portela Souza_

Professor of Economics at Sao Paulo School of Economics (EESP-FGV) Contact email: <u>andre.portela.souza@fgv.br</u> Keywords: Child labor ban, child labor, participation rates, and treatment effects. **JEL** : J08, J22, J23.
# **_3.4  Biodiversity_**

Livestock are having widespread direct and indirect impacts on biodiversity, via the increasing demand for and consumption of livestock products. These impacts differ in kind, magnitude and geographic spread around the globe (Table 8, from Reid et al., 2009). Most are negative, although there are some positive impacts.

Reid et al. (2009) (from which much of this section is drawn) distinguish four sets of impacts, or syndromes, that depend on the inherent productive potential and the current intensity of the relevant livestock system. These syndromes are simplifications of the real world, but they illustrate the inter-related nature of impacts of livestock on biodiversity. The first of the four is the extensive dryland syndrome, where rangelands are contracting to make way for cropping and settlement, with significant impacts on biodiversity. In the areas where grazing livestock is profitable enough to slow this development, then livestock can actually protect biodiversity in these landscapes. Invasive species can be a problem in some areas, and bushlands and shrublands may be replacing grasslands (Asner et al., 2004). The second syndrome is centered on dryland key resources such as towns, markets, riverine areas and wetlands, where unusually high numbers of people and livestock gather. In such places, livestock grazing is heavy, wildlife are all but excluded, and only plants tolerant of heavy grazing thrive. The landscapes tend to be highly fragmented, and they may contain a wide array of non-native plant species. This syndrome can be found in specific spots in all the drylands of the world. The third, a syndrome of wetter environments, is the deforestation / reforestation syndrome, where the presence of pastures indicates that there has been massive biodiversity loss in the conversion from tropical rain forest; or alternatively, the pastures constitute a valuable source of biodiversity in the cases where abandoned pastures are being reforested in the temperate regions. The fourth syndrome occurs in forests and woodlands that are used even more intensively. In this syndrome, invasive species are pervasive, there are no wildlife, nutrient pollution is common, and biodiversity is simplified and homogeneous from place to place. This represents almost complete replacement of the native ecosystem, with strong and negative implications for biodiversity. Reid et al. (2009) note that while accurate estimates are highly problematic, the impacts of livestock on biodiversity vary widely depending on the situation, and in general the negatives outweigh the positives.

43
**Table 10:   Out-of Sample Inclusion and Exclusion Targeting Errors**

|**Clas**|**sificatio**|**n Trees**|**Logi **|**stic Reg**|**ression**|**Ra**|**ndom F**|**orest**|**B**|**oosted**|**Trees**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Incl.|Excl.|Budget|Incl.|Excl.|Budget|Incl.|Excl.|Budget|Incl.|Excl.|Budget|
||||||**_Moza_**|**_mbique_**||||||
|0.21|0.63|0.28|0.13|0.71|0.20|0.13|0.71|0.20|0.13|0.71|0.20|
|0.21|0.63|0.28|0.22|0.59|0.30|0.22|0.59|0.30|0.21|0.58|0.30|
|0.21|0.63|0.28|0.30|0.47|0.40|0.30|0.47|0.40|0.30|0.46|0.40|
|0.52|0.26|0.62|0.39|0.35|0.50|0.39|0.36|0.50|0.39|0.35|0.50|
|0.52|0.26|0.62|0.50|0.27|0.60|0.50|0.27|0.60|0.51|0.28|0.60|
||||||**_Ma_**|**_lawi_**||||||
|0.13|0.76|0.18|0.13|0.70|0.20|0.13|0.70|0.20|0.13|0.69|0.20|
|0.16|0.68|0.23|0.21|0.57|0.30|0.21|0.57|0.30|0.21|0.57|0.30|
|0.32|0.47|0.41|0.30|0.46|0.40|0.31|0.46|0.40|0.30|0.46|0.40|
|0.39|0.38|0.49|0.40|0.36|0.50|0.41|0.36|0.50|0.40|0.36|0.50|
|0.43|0.34|0.53|0.51|0.27|0.60|0.50|0.26|0.60|0.50|0.26|0.60|
||||||**_Zim_**|**_babwe_**||||||
|0.19|0.67|0.23|0.15|0.69|0.20|0.15|0.70|0.20|0.15|0.68|0.20|
|0.19|0.67|0.23|0.24|0.57|0.30|0.24|0.58|0.30|0.24|0.57|0.30|
|0.35|0.42|0.43|0.33|0.45|0.40|0.34|0.46|0.40|0.33|0.46|0.40|
|0.40|0.38|0.47|0.43|0.34|0.50|0.43|0.34|0.50|0.43|0.34|0.50|
|0.40|0.38|0.47|0.53|0.24|0.60|0.53|0.25|0.60|0.53|0.25|0.60|
||||||**_Tan_**|**_zania_**||||||
|0.07|0.84|0.10|0.06|0.83|0.10|0.06|0.83|0.10|0.06|0.83|0.10|
|0.15|0.71|0.20|0.13|0.69|0.20|0.13|0.69|0.20|0.13|0.69|0.20|
|0.27|0.53|0.35|0.22|0.56|0.30|0.21|0.56|0.30|0.22|0.57|0.30|
|0.36|0.43|0.44|0.30|0.44|0.40|0.31|0.45|0.40|0.30|0.44|0.40|
|0.42|0.35|0.51|0.40|0.34|0.50|0.40|0.34|0.50|0.40|0.34|0.50|
|0.48|0.30|0.56|0.50|0.24|0.60|0.51|0.25|0.60|0.51|0.25|0.60|
||||||**_Za_**|**_mbia_**||||||
|0.15|0.74|0.19|0.14|0.72|0.20|0.14|0.72|0.20|0.15|0.72|0.20|
|0.28|0.57|0.34|0.23|0.59|0.30|0.23|0.60|0.30|0.23|0.60|0.30|
|0.28|0.57|0.34|0.32|0.48|0.40|0.32|0.49|0.40|0.32|0.49|0.40|
|0.28|0.57|0.34|0.42|0.39|0.50|0.42|0.39|0.50|0.43|0.40|0.50|
|0.60|0.23|0.67|0.52|0.29|0.60|0.52|0.28|0.60|0.53|0.29|0.60|

Notes: The table reports inclusion and exclusion out-of-sample errors based on the optimal models from Table 9. Inclusion errors are defined as the proportion of non-stunted children labelled as being stunted. Exclusion errors are defined as the proportion of stunted children labelled as being non-stunted. The budget is the proportion of total children reached. The assumption is that every child is either given some fixed amount of support or is not provided anything.

33
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 13. Benchmarking ICT indicators**

|||Unit|Low-income<br>countries, nonfragile|**Mali**|Middle-income<br>countries|
|---|---|---|---|---|---|
||||2005|2005|2007/08<br>2005|
|GSM coverage|% populat|ion under signal|48|18|22<br>97|
|International bandwidth|Mbps/cap|ita|6|2.2|17<br>30|
|Internet|subscribe|rs/100 people|0|—|0.8<br>2|
|Landline|subscribe|rs/100 people|1|0.6|0.7<br>9|
|Mobile phone|subscribe|rs/100 people|15|7|35.4<br>87|
|_US dollars_||**Mali**<br>2007|Countries without<br>submarine cable|Countries with subm<br>cable|arine<br>Other developing regions|
|Price of monthly mobile ba|sket|11.0|11.1|11.12|9.9|
|Price of monthly fixed-line|basket|13.1|13.6|13.58|nav|
|Price of 20-hour Internet p|ackage|56.5|68.0|47.00|11.0|
|Price of a call to US per m|inute|0.34|0.86|0.48|0.66|
|Price of an intra-Africa cal|l per minute|0.29–0.33|0.7|0.57|non applicable|

Source: Mali 2005 data together with benchmarks are taken from the AICD Database. Mali 2007/8 is compiled from a variety of World Bank sources in order to give a sense of the progress made during the last four/five years (including Information and Communications for Development database). Source: Ampah and others 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available. n.a. = not applicable. Numbers reported in the table as of 2007.

**Table 14. Mali’s skyrocketing mobile teledensity**

|Subscribers per 100 people||||||
|---|---|---|---|---|---|
|Country|2005|2006|2007|2008|Average annual growth|
|Benin|14.10|19.20|23.7|27.7|25.50|
|Burkina Faso|5.00|7.60|11.5|14.4|42.84|
|Ghana|12.10|19.30|29.4|34.6|43.17|
|Mali|6.70|12.90|21.1|35.4|74.63|
|Niger|2.70|4.40|6.7|8.5|47.37|
|Nigeria|14.40|24.40|34.7|44.2|46.35|
|Togo|8.10|10.70|13.2|15.6|24.55|

Since 1992, private flows to Mali’s ICT sector have increased from almost zero to $19.3 million between 2000–03 and to $30.8 million between 2004–08. In 2001, Sonatel/ France Telecom received a license to provide fixed-line, mobile, and internet services. Ikatel entered the market in 2002 and has offered mobile services since 2003. In 2006, Ikatel became Orange Mali. Malitel has served the mobile sector since 1999 and had a market share of 20 percent in 2008. Also, a universal access fund was created to support roll out of services in rural areas.

Prices for telephony remain modest in Mali. End-user prices for international telephony are below those of low-income peers mostly as a result of the roaming agreements that have created cross-country competition despite the existence of a domestic market duopoly (see table 11). Consumer satisfaction has 34