rafmacalaba/datause-encoder-data huggingface dataset:
Interestingly, there has been little increase (or change) in the use of improved agricultural inputs. Figure 2.2.1 details the national trends in input use over time for households engaged in agriculture.<sup>4</sup> While the observed fertilizer use rate in 2013/14 (at 6 percent) was slightly higher than that observed for previous years, it has stayed constant at about 5 percent throughout 2005/06‐2013/14. Pesticide use has been higher than that of fertilizer use, but the increase in use recorded in 2009/10 and 2010/11 disappeared, and only 15 percent of households were applying pesticides in 2013/14. In contrast, the proportion of households purchasing improved seed has been much higher, ranging between 17 and 24 percent. Improved seeds/planting materials are used for certain crops such as maize and cassava, and to a lesser extent beans and matooke (Figure 2.2.2). In Section 4 we investigate the potential factors behind these adoption rates.

**Table 2.2. Share of crop income coming from each crop for selected waves**

||||2010/11<br>|<br>||||2011/12<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||National|Central|Eastern|Northern|Western|National|Central|Eastern|Northern|Western|
|Beans|0.17|0.16|0.10|0.16|0.25|0.16|0.18|0.11|0.13|0.21|
|Maize|0.12|0.12|0.17|0.12|0.07|0.17|0.15|0.25|0.16|0.10|
|Matooke|0.16|0.24|0.11|0.02|0.30|0.16|0.25|0.08|0.02|0.34|
|Cassava|0.11|0.12|0.16|0.13|0.03|0.11|0.09|0.15|0.14|0.04|
|Sweet|||||||||||
|Potatoes|0.10|0.12|0.10|0.09|0.07|0.09|0.15|0.11|0.06|0.06|
|Groundnuts|0.07|0.04|0.10|0.07|0.07|0.06|0.02|0.08|0.06|0.05|
|Coffee All|0.05|0.08|0.06|0.01|0.05|0.04|0.08|0.03|0.01|0.05|
|Sorghum|0.04|0.00|0.04|0.09|0.02|0.04|0.00|0.03|0.09|0.02|
|Finger Millet|0.03|0.01|0.05|0.03|0.02|0.03|0.01|0.06|0.04|0.02|
|Simsim|0.03|0.00|0.01|0.08|0.00|0.02|0.00|0.01|0.06|0.00|
|Sunflower|0.01|0.00|0.00|0.04|0.00|0.02|0.00|0.00|0.05|0.00|

Source: Staff calculations using RIGA 2010/11‐2011/12. Note: red indicates a share 10 percent and higher in a given region, green indicates a share between 3 and 10 percent in a given region.

**Figure 2.1. Share of crop income derived from crop sales, bottom 40 percent, 2006‐2012**

<!-- Start of picture text --> 1<br>0.8<br>0.6 2006<br>2010<br>0.4<br>2011<br>0.2<br>2012<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Share of bottom 40 percent<br>Source: Staff calculations using RIGA income aggregates calculated from UNPS 2005/06‐2011/12.<br>crop sales<br>Share of crop income from<br><!-- End of picture text --> 4 The percent of households engaged in agriculture ranged between 75 and 82 percent throughout the 2005/06 ‐ 2013/14 period.

5
# **Table. A.4. Breusch-Godfrey Serial Correlation LM Tests**

**EQ.1a EQ. 2a EQ. 3a** Total Government Expenditure Current Expenditure Capital Expenditure F-statistic (Prob.) 1.51(0.23) 0.66(0.52) 5.71(0.008) Obs*R-sq. (Prob.) 3.29(0.19) 1.52(0.47) 9.88(0.007) _Note: Data source: IMF World Economic Outlook database. 2. p-values in parentheses._

These regression results suggest there is a strong indication that the marginal propensity to spend out of oil revenue is highly asymmetric (see table A.5). The asymmetric adjustment coefficient, as described above is negative and significant in all three regressions, suggesting that expenditure adjustment (or the coefficient on oil revenues) in years of falling oil revenue exceeds the coefficient in years of rising oil revenue. This actually confirms the DO hypothesis and therefore, we call this variable an overhang variable.

However, our results also suggest that there was a structural break around 1984. Before 1984, the marginal propensity to spend out of rising oil wealth was larger than one (the spending coefficient in good years is for total public expenditure is 1.49), confirming that at least before 1984 there is an indication for VE.

|**Table. A.5. Evidence for**|**an asymmetric adjustment of pub**<br>|**lic spending to oil revenu**<br>|**e in Nigeria.**<br>|
|---|---|---|---|
||**EQ.1a**|**EQ. 2a**|**EQ. 3a**|
||<br>TotalGovernmentExpenditure|<br>CurrentExpenditure|<br>Capital Expenditure|
|**Before 1984**||||
|Good years, defined as||||
|d(oilrev_r )>0|1.49|0.88|0.56|
|Bad years, defined as||||
|d(oilrev_r )>0|2.01(=1.49+0.52)|1.19(=0.88+0.31)|0.79(=0.56+0.23)|
|**After 1984**||||
|Good years, defined as||||
|d(oilrev_r )>0|0.86(=1.49-0.63)|0.58(=0.88-0.30)|0.19(=0.56-0.37)|
|Bad years, defined as<br>d(oilrev_r)>0|1.38(=1.49+0.52-0.63)|0.89(=0.88+0.31-0.30)|0.42(=0.56+0.23-0.37)|

30
# **5.5 Accounting for Atmospheric Circulation**

Although the Hakkarainen filter removes annual and seasonal changes from CO2 concentrations, it does not account for region-scale changes in atmospheric circulation that can alter measurements by several parts per million, even if local emissions remain constant (Weir et al. 2021). By implication, some of our FUA trend results may reflect regional atmospheric circulation effects rather than changes in local emissions. We test for this effect by creating two groups within 100 km of each FUA centroid: grid squares inside the FUA and those lying outside. If regional circulation plays an important role, then trend results should be very similar for the two groups. Formally, we estimate separate regressions for 100-km-radius grid squares inside (I) and outside (O) of FUA j:

- (8) Inside Regression: (2I) CO2ijtI = γ0I + γ1jIt+ εitjI

(9) Outside Regression: (2O) CO2ijtO = γ0O + γ1jOt+ εitjO The relevant null hypothesis holds that atmospheric circulation explains a significant result for γ1jI. The null hypothesis is rejected if: sign(γ1jI) ≠ sign(γ1jO) or p(γ1jI) ≤ p(.05); p(γ1jO) > p(.05) To summarize, regional atmospheric circulation is rejected as the source of a significant change in an FUA concentration anomaly if the outside change parameter has the opposite sign from the inside parameter and/or the inside parameter is significant while the outside parameter is not.

Table 7 provides evidence for 335 FUAs from our estimation sample that have populations greater than 100,000 and inside change parameters with p ≤ .05. Among the corresponding outside change parameters, 92 (27.5%) have sign reversal (sign( γ1jI ) ≠ sign( γ1jO )). Among the 243 outside parameters that do not have sign reversal, 136 (56.0%) are not significant at 95% confidence. In summary, the null hypothesis is rejected for 228 (92 + 136) (68.1%) of the 335 urban areas in the sample.
FDI provisions have become more common in recent agreements, which make up the “third wave” of PTAs that began in the 1990s (see Table 1 for a partial list of PTAs whose texts include the liberalization of cross-border financial flows). At the same time, while the incidence of inclusion of FDI disciplines in PTAs is increasing, most of the active PTAs still do not contain any explicit investment provisions, and the majority of those that do fail to make specific commitments, delineate timelines for implementation, and ensure compliance. Notable exceptions include many US free trade agreements, several EU agreements, and a few South-South PTAs.

Table 1: “Deep integration” <u>provisions</u> of various PTAs

||Invest-<br>ment|Services|Standards|Compe-<br>tition|Customs<br>cooperation|IPR|Dispute<br>settlement|
|---|---|---|---|---|---|---|---|
|_North-South_||||||||
|US-Jordan|Yes|Yes|No|Yes|Yes|Yes|Yes|
|US-Chile|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|US-Singapore|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|US-Australia|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|US-CAFTA|Yes|Yes|Yes|No|Yes|Yes|Yes|
|US-Morocco|Yes|Yes|Yes|No|Yes|Yes|Yes|
|NAFTA|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|EU-South Africa|No|No|No|Yes|No|Yes|Yes|
|EU-Mexico|Yes|Yes|Yes*|Yes|Yes*|Yes|Yes|
|EU-Chile|Yes|Yes|Yes*|Yes|Yes|Yes|Yes|
|Euro-Med.|No|No|No|Yes*|No|Yes|Yes|
|Japan-Singapore|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Canada-Chile|Yes|Yes|No|Yes|Yes|No|Yes|
|_South-South_||||||||
|MERCOSUR|Yes|Yes|Yes|Yes|Yes|No|Yes|
|Andean Comm.|Yes|Yes|Yes|Yes|Yes|No|Yes|
|CARICOM|Yes|Yes|Yes|Yes|Yes|No|Yes|
|AFTA|Yes|Yes|Yes|No|Yes|No|No|
|SADC|No|n.a.|n.a.|n.a.|Yes|Yes|Yes|
|COMESA|Yes|Yes|Yes|Yes|Yes|No|Yes|
|Chile-Mexico|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Source:_ World Bank (2004). _Note:_ While EU agreements mention cooperation in most of the subject areas, only those in which specific commitments are undertaken receive a “Yes” rating. (*) denotes areas where implementation steps are to be agreed on at a later date. Euro-Med. agreements include bilateral PTAs between the EU and developing countries in the Mediterranean basin—EU-Algeria, EU-Tunisia, etc.—that serve as predecessors to a unified EU-Mediterranean Free Trade Area to be implemented by 2010.

It is important to emphasize that Table 1 is not representative of all the 229 active PTAs, and relying on it to make any generalizations about the content and relative strength of investment provisions for all agreements in force is likely to be misleading. There are several reasons why obtaining information on the investment provisions of many PTAs is a challenging task. First, the texts of many agreements are not easily available, and therefore it is difficult to estimate how many PTAs even mention investment as a potential area of cooperation and integration. Second, PTAs 3
Hence, our analysis focuses primarily on outcome measures obtained from the final follow-up survey, though we briefly discuss intermediate results also.

Finally, the discussion below also explains our qualitative results from the three focus groups. Note that the focus groups were held after we analyzed the quantitative data and were designed and directed at understanding mechanisms and heterogeneity of some of our quantitative findings.

# _6.1. Financial Knowledge_

The first outcome of interest is financial knowledge. While our intermediate CATI surveys did ask questions on financial knowledge, we could only ask very basic questions and do not detect any significant differences between treatment and control groups in these surveys.

However, in the final face-to-face survey, we were able to ask more comprehensive questions, and further were able to distinguish between general financial literacy and content specific financial literacy. General financial literacy refers to questions that test financial concepts that were not at the forefront of the soap opera storyline, such as separating business and personal expenses, probabilities in lotteries (although gambling was highlighted, less attention was paid on probabilities), and writing down budgets. In contrast, content specific financial literacy refers to questions that focused on knowledge of hire purchase agreements, which was the central contract through which the main soap character overspent and ended up with unaffordable high monthly payments.

Table 4 presents our findings. Column 1 shows no significant differences between treatment and control groups on general financial literacy, but significantly higher scores on content specific financial literacy in column 2 – compared to 42.5% in the control group, 47% of respondents in the treatment group answered these questions correctly, a 4.5 percentage point increase. Comparing across subgroups of gender, ex-ante financial literacy, schooling, and ex-ante income status, we do not see significant differences on these literacy measures.

_6.2 Debt Management_ 18
B-4 ;

- @ For individual pollutants, we find generally high correlations across intensities based on output value, value added and employment. At a purely 'mechanical' level, we therefore find little to distinguish the three sets of intensity measures as bases for pollution projection. However, basic economic reasoning does suggest that employment-based intensities may be preferable for pollution projection in developing countries. The logic is as follows: (1) Effective environmental regulation is thought to be quite income-elastic, although careful empirical work on cross-country data has yet to be done; (2) Sectoral pollution is thought to be quite responsive to effective environmental regulation in many cases; (3) Most crosscountry econometric studies of sectoral labor demand find relatively high wage elasticities; (4) From (1)-(3), we can conclude that both sectoral pollution and sectoral labor demand will rise substantially as we move from richer (highwage, high-regulation) to poorer (low-wage, low-regulation) economies. Since pollution and employment vary in the same direction, the variation in pollution intensity with respect to employment (P/E) may well be less than variation in pollution per unit of output. Very preliminary tests on U.S. and Indonesian sectoral data for water pollution provide support for this hypothesis, showing much higher variation for value-based intensities than for employmentbased estimates.

- @ We have uncovered what looks like an "iron law" of pollution intensity for all pollutants and levels of aggregation: Sectoral intensities are always exponentially distributed, with a few highly intensive sectors and many which have very low intensities. High-intensity sectors differ markedly across pollutants (see below), but the exponential pattern persists. The implication for applied work is clear: Pollution projections should always be done with the most disaggregated data available. The resulting gains in accuracy are often quite striking.

- @® Although the phrase "pollution intensive" is commonly applied to industry sectors, it can be quite misleading. We find a very diverse pattern of sectoral intensity correlations across pollutants. Intensity correlations are sometimes high within similar classes (e.g., nitrogen
MINIMUM WAGES IN CAMBODIA 27

- wage bites hard: Introduction of minimum wages to a low wage sector.” _Journal of the European Economic Association_ , 1(1): 154–180.

- **Magruder, Jeremy R.** 2013. “Can minimum wages cause a big push? Evidence from Indonesia.” _Journal of Development Economics_ , 100(1): 48–62.

- **Maloney, William, and Jairo Mendez.** 2004. “Measuring the impact of minimum wages: Evidence from Latin America.” In _Law and Employment: Lessons from Latin America and the Caribbean_ . , ed. James J. Heckman and Carmen Pag ́es, 109–130. University of Chicago Press.

- **Millea, Meghan J, Jon P Rezek, Brian Shoup, and Joshua Pitts.** 2017. “Minimum wages in a segmented labor market: Evidence from South Africa.” _Journal of Labor Research_ , 38(3): 335–359.

- **Neumark, David, JM Ian Salas, and William Wascher.** 2014. “Revisiting the minimum wageemployment debate: Throwing out the baby with the bathwater?” _ILR Review_ , 67(3 ~~s~~ uppl): 608–648.

- **Neumark, David, Wendy Cunningham, and Lucas Siga.** 2006. “The effects of the minimum wage in Brazil on the distribution of family incomes: 1996–2001.” _Journal of Development Economics_ , 80(1): 136–159.

- **Neumark, David, William L Wascher, et al.** 2007. “Minimum wages and employment.” _Foundations and Trends⃝_ R _in Microeconomics_ , 3(1–2): 1–182.

- **Stewart, Mark B.** 2004. “The employment effects of the national minimum wage.” _The Economic Journal_ , 114(494): C110–C116.

- **The World Bank.** 2017. “Cambodia: Sustaining strong growth for the benefit of all.” The World Bank.

- **The World Bank.** 2019. “Cambodia future jobs.” The World Bank.

Appendix A. Additional Results
Table 6: Second-Stage Enrollment Equation: Determinants of the _δj_

||OL|S|I|V|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Government:|||||
|FPE|-.012|.039|-.039|-.072|
||(.018)|(.013)|(.037)|(.056)|
|KCPE|.002|.001|.003|.003|
||(.0006)<sup>_∗∗∗_</sup>|(.0006)<sup>_∗∗_</sup>|(.0007)<sup>_∗∗∗_</sup>|(.0009)<sup>_∗∗∗_</sup>|
|_σ_<sup>_m_</sup><br>_j_ <sup>(25% least educated)</sup>|.445<br>(.139)<sup>_∗∗∗_</sup>||-1.727<br>(.123)<sup>_∗∗∗_</sup>||
|_σ_<sup>_m_</sup><br>_j_ <sup>(25% most educated)</sup>||.761<br>(.060)<sup>_∗∗∗_</sup>||-1.413<br>(.162)<sup>_∗∗∗_</sup>|
|Private:|||||
|FPE|.068|.057|.080|.093|
||(.016)<sup>_∗∗∗_</sup>|(.012)<sup>_∗∗_</sup>|(.025)<sup>_∗∗∗_</sup>|(.032)<sup>_∗∗∗_</sup>|
|KCPE|.0005|.0004|.0008|.0008|
||(.0002)<sup>_∗∗∗_</sup>|(.0002)<sup>_∗∗_</sup>|(.0002)<sup>_∗∗_</sup>|(.0002)<sup>_∗∗∗_</sup>|
|_σ_<sup>_m_</sup><br>_j_ <sup>(25% least educated)</sup>|.117<br>(.042)<sup>_∗∗∗_</sup>||-.452<br>(.052)<sup>_∗∗∗_</sup>||
|_σ_<sup>_m_</sup><br>_j_ <sup>(25% most educated)</sup>||.201<br>(.028)<sup>_∗∗∗_</sup>||-.370<br>(.047)<sup>_∗∗∗_</sup>|
|Obs.|69513|69513|69513|69513|

The table reports estimates of Equation (5), the second step of the Bayer-Timmins procedure. Standard errors are reported in parentheses. The dependent variable is the district-sector-time fixed effect from the conditional logit in Table (3). _σj_<sup>_m_</sup> is the share of pupils from the 25% least (most) educated households choosing school type _j_ in market _m_ at time _t_ , and KCPE is the average exam score for schools of type _j_ in market _m_ . Intercepts included, but not reported. To see how marginal effects are calculated, consider the effect of an increase in KCPE in government schools by _h_ units. This increases each _δgov,t_<sup>_m_by</sup><sup>_β_0</sup><sup>_× h_units.ThemarginaleffectofachangeinKCPEinpublic</sup> school is then given by the change in probability of enrolling in public school with and without the increase in _P rob_ ( _yi,gov,t_ =1; _δg_<sup>_m_</sup> _ov_<sup>_′_</sup> _<u>,t</u>_<sup>)</sup><sup>_−P rob_(</sup><sup>_yi,gov,t_=1;</sup><sup>_δ_</sup> _<u>g</u>_<sup>_m_</sup> _ov,t_<sup>)</sup> KCPE (divided by _h_ ), namely _h_ . The marginal effects are averaged across the sample. All standard errors are bootstrapped to account for estimation uncertainty in _δ_<sup>ˆ</sup> _j,t_<sup>_m_andclusteringat</sup> the district level.

38
_15_ 44.

44. Many developing countries have active solar water heating industries. Solar water heaters for residential and small commercial installations are manufactured locally in Israel and Jordan where as many as 1 in 5 homes use solar water heating. The SWH industries in Morocco, Tunisia, Egypt, Senegal, and Zimbabwe fabricate as well as import some collector components. Both India and Nepal have sound SWH industries supplying residential and commercial systems. Australia, Israel, France, USA, and Japan are major exporters of SWH technology and large dorr.estic markets exist in both Australia and the USA.

<!-- Start of picture text --> Industrial Sector SWH Application<br>IRR: Kenya Brewery Fuel Oil Retrofit<br>SWYH Internal Rate of Return<br>40%<br>30%<br>20%-<br>1 0 % o%<br>0% I 10 15 20 I ._ * 25 30 * 35 . 40 _ 45<br>Fuel Oil Economic Cost ($/bbl)<br>Source: See Annex<br><!-- End of picture text --> Figure 6 Solar Hot Water Fuel Oil Boiler Retrofit 45. Industrial applications of solar water heating in developing countries are limited to a few large pilot plants in the Middle-East and a few other countries. The brief analysis below, based on an ESMAP study of the potential for solar water heating in Kenya, illustrates that low oil prices can severely curtail the economic advantages of industrial SWH.

46. An ESMAP SWH study for Kenya, originally assessed at 1985 fuel prices, was recently reevaluated at borde.. prices of August, 1986.16/ The border price of fuel oil fell from US$ 16.75 to US$ 11.25/bbl over this period. Because all of the industrial SWH installations originally

> 16/ Joint UNDP/World Bank ESMAP Solar Water Heating Study, Kenya, December 1986. Assumptions and results of **this report are** summarized **in the** Annex.
distribution which lies to the left of the energy requirement estimate is the FAO’s estimate of the proportion of the population with inadequate access to food.

The FAO method has been widely critiqued by the research community (Svedberg, 1999, de Haen et al. 2011, Smith and Subandoro 2007, Fiedler et al. 2012a). The criticisms reflect concerns about the reliability of the three components that go into the FBS-CV calculations, whether the CV is the best measure of dispersion to use, and whether it should be kept constant over time (de Haen et al. 2011). Further, the FBS-CV method relies on two different sources for two moments of the distribution (the mean and the spread). It is a method open to the same criticisms made about measures of global poverty that rely on GDP for the consumption mean and on household survey data for the variance (see the critique in Chen and Ravallion 2010).

Methodological problems aside, the method explicitly aims to compare only across countries, but not within countries, and so does not help national governments determine which areas or population groups are at risk of hunger.

The FAO calculates the CV of food availability directly for only for a limited number of countries and rarely updates this number (see footnotes 3 and 4). As such, differences in hunger estimates across countries and over time are mainly driven by FBS data. While the focus of this paper is not on the reliability of these national estimates of food availability, of note are three concerns about these estimates of food availability. First, food availability is a residual, so any errors in reported production, trade, and stocks will affect the estimates of national food availability. Second, for grain crops the production and trade data are potentially reliable, since it is feasible to measure production with sample plots, with satellite and aerial mapping and so forth, but the same is not true for root crops (potatoes, sweet potatoes, and cassava are especially important food sources for the poor in some countries) whose yield cannot be observed remotely. Moreover, there are complex relationships between production and what is fed to animals and what is retained for seed, which affect the amount left over for human consumption. Studies suggest that there can be substantial errors in root crop food balance sheet data (Horton 1988). Finally, among the grain crops, storage data are especially problematic for rice, which is mostly privately stored (including by producers on farm), making it very difficult to ascertain the amount of rice available for human consumption in a particular period (Timmer 2009).

An alternative approach to the FBS-CV method is to follow the lead of nutritionists, who typically rely on either an observed-weighed food method or a 24-hour recall (Gibson 2005). The latter uses multiple 5