rafmacalaba/datause-encoder-data huggingface dataset:
**<u>TABLE 2. SOURCES FOR PERFORMANCE DATA IN HEALTH SECTOR DELIVERY</u>**

|**AREA**|**ISSUE**|**KEY SOURCES**|
|---|---|---|
|**E**|Budget processes|° Public Expenditure and Financial Accountability indicators<br>° Focus groups with public officials, recipient institutions, and civil society<br>° Interviews with public officials, recipient institutions, and civil society|
|**RESOURC**<br>**EMENT**|Budget leakages|° Public Expenditure Tracking Surveys<br>° Public Expenditure Reviews<br>° Focus groups with public officials, recipient institutions, and civil society<br>° Interviews with public officials, recipient institutions, and civil society|
|**DGET AND**<br>**MANAG**|Payroll leakages|° Public Expenditure Tracking Surveys<br>° Public Expenditure Reviews<br>° Household surveys<br>° Focus groups with public officials and health workers|
|**BU**|In-kind leakages|° Public Expenditure Tracking Surveys<br>° Quantitative Service Delivery Surveys<br>° Facility surveys<br>° Focusgroups withpublic officials, recipient institutions, and health workers|
||Job purchasing|° Official administrative records combined with facility surveys<br>° Focus groups with public officials<br>° Governance and Anti-Corruption Country Diagnostic surveys|
|**DERS**|Physician<br>credentials|° Official licensing records<br>° Professional bodies|
|**PROVI**|Health worker|° Quantitative Service Delivery Surveys<br>° Surprise visits<br>° Direct observation|
|**IVIDUAL**|absenteeism|° Facility records<br>° Focus groups with facility heads and patients<br>° Interviews with facility heads and patients|
|**IND**|Health worker<br>performance|° Direct observation<br>° Vignettes<br>° Facility surveys<br>° Focus groups with facility heads, health workers, and patients<br>° Administrative records|
|**HEALTH**<br>**FACILITIES**|Facility<br>performance|° Official administrative records<br>° Facility records<br>° Facility surveys|
|**INFORMAL**<br>**PAYMENTS**|Under-the-table<br>payments to<br>individuals|° Household surveys<br>° Facility exit surveys<br>° Focus groups with providers/patients and health workers<br>° Interviews with providers/patients and health workers<br>° Governance and Anti-Corruption Country Diagnostic surveys|
|**UPTION**<br>**PTIONS**|Perceptions of<br>corruption|° Governance and Anti-Corruption Country Diagnostic surveys|
|**CORR**<br>**PERCE**|Institutional quality|° WB Country Policy and Institutional Assessement (CPIA)|

Source: Authors.

13
variation in domestic prices.<sup>5</sup> International price changes have been particularly strong drivers of food inflation in Iraq and West Bank and Gaza, accounting for over 50 percent of the food inflation, followed by Egypt, Djibouti and the United Arab Emirates (over 40 percent of the food inflation). Except for Tunisia and Algeria, exchange rate depreciation has been a minor factor in domestic food inflation. The pass-through is smaller but is still sizable, varying between 0.2 and 0.4 percent, for a large group of MENA countries, including Morocco, Jordan, Syria, Iran, Yemen, and all GCC countries other than United Arab Emirates. This indicates a high degree of vulnerability of households to international food price increases in virtually all MENA countries.

Over the past 50 years, most countries, even those that actively intervene in food markets, have come to depend on private markets for the physical distribution of food (Akiyama et al., 2001). This is less true in MENA, where governments often play a significant role in the procurement, storage, and distribution of wheat. And many countries have explicit policies determining the level of strategic reserves the government is required to have on hand at any given time. Overall, storage capacity in the region is on average six months of consumption and estimated ending stocks are five months (World Bank, 2012). Plans are already well underway to expand storage capacity in selected countries by an additional 5 months on average.

While there are different levels of private participation in wheat storage and distribution in the region, recent analysis suggests that the costs of port logistics, storage, inland transport (to the mills) and management are on average $42 per metric ton of wheat or 12.5 percent of the cost of a ton of wheat at current prices. The largest components of these costs are port logistics ($13 per metric ton) and management ($14 per ton), which includes such costs as product loss, cost of capital, and overhead. The average cost of storage for the region ranges between $1.5 and $3.5 per metric ton per month, which compares favorably with storage costs in the Netherlands and the United States, both large handlers of traded grain, where storage costs run about $2.00 per metric ton per month (World Bank, 2012).

Wheat consumption rates are high in MENA, and because of the large government presence in domestic markets, wheat price shocks have important budgetary consequences. The World Bank (2011c) estimates that the rise of food prices in MENA in 2010 increased the import bill by 0.6 percent of GDP with grains making the largest contribution, followed by edible oils, sugar and meat. Oil importers are expected to be hardest hit with an estimated increase in the import bill of 1.2 percent of GDP, and with half of the increase attributed to the impact of higher grain prices. The expected increase in the import bill of the developing oil exporters as a result of higher food product prices is estimated at 0.8 percent of GDP with grain accounting for slightly less than half the increase.

> 5 Based on calculated pass-through rates (World Bank, 2011c).

5
and con...scation of cocaine from equation (36), which can be seen as a proxy of gains in the e¢ ciency of law enforcement authorities. The ...nal value of �t for 2013 is found using the average amount of assets con...scated in the Colombian economy from 2010 to 2014, an observable quantity, divided by the average illicit income from cocaine and common crime, as in the RUII measure of equation (39), e giving �2013 = 0:2. We then work backwards using the growth rate of qt from 1997 to 2013 to impute the values of the fraction of assets con...scated by Colombian authorities between 1998 and 2012.

The calibrated transition equation comes from equation (34) where we replace the depreciation rate and the marginal propensity to save and subtract the assets con...scated by Colombian authorities after 1997. This gives us the following dynamic equation for aggregate asset laundering Substituting equation (48) into this last equation and regrouping terms we get where 11998 is an indicator variable that takes the value one for 1998 onwards and zero otherwise.

This equation is similar to a perpetual inventory equation for capital accumulation of the form Kt = (1 � �) Kt�1 + It where It denotes gross investment in period t while Kt is the capital stock. Assets laundered depreciate at the same rate as licit capital which is similar to the term (1 � �) Kt�1 while the term It is equal to the amount of income generated from illicit assets laundered RIRt�1ALt�1 plus the term �0:17 � �t�1 � 11998� � RUIIt�1 that represents the amount of undetected illicit income saved by illicit households in the economy in period t � 1 and which enters the licit economy in period t, increasing the laundered capital stock in that period.

We can also obtain a decomposition of assets laundered from undetected income from cocaine and from common crime using equation (49) and replacing total illicit undetected income RUIIt with the variables RUICOCt or RUICt from equations (37) and (38) respectively and assuming that �t is the same for both types of illicit activities.

Calibration in itself is closely attached to theoretical deep structural parameters of a given model and since ALt is an unobservable quantity we cannot check whether the calibration works in the sense of reproducing an observed time series. Nonetheless it has the appeal of being consistent with the theory and can give us a trajectory consistent with the fundamentals of the Colombian economy.

# 8.3 Kalman Filter

The empirical methodology of the Kalman ...lter uses not only the transition equation (44) but also the measure equation (45) based on a state-space time invariant representation. In what follows we 40
# **7. Appendix**

## **Appendix Table 1. Person who usually decides on large household purchases**

||Variable||(1)|(2)|t-test|
|---|---|---|---|---|---|
|||N|_Wife _|_Husband_|_(1)-(2)_|
||Respondent alone||0.102|0.074|0.028***|
|Benin|Respondent and husband/partner|2660|0.44|0.197|0.243***|
||Husband/partner alone||0.459|0.729|-0.271***|
||Respondent alone||0.019|0.035|-0.015***|
|Burkina Faso|Respondent and husband/partner|4945|0.166|0.155|0.011|
||Husband/partner alone||0.815|0.81|0.004|
||Respondent alone||0.055|0.026|0.029***|
|Burundi|Respondent and husband/partner|3580|0.619|0.629|-0.01|
||Husband/partner alone||0.326|0.345|-0.019*|
||Respondent alone||0.126|0.118|0.009|
|Cameroon|Respondent and husband/partner|2658|0.381|0.281|0.100***|
||Husband/partner alone||0.492|0.601|-0.109***|
||Respondent alone||0.232|0.209|0.023|
|Comoros|Respondent and husband/partner|783|0.301|0.36|-0.059**|
||Husband/partner alone||0.466|0.43|0.036|
||Respondent alone||0.078|0.04|0.038***|
|Côte d'Ivoire|Respondent and husband/partner|1924|0.285|0.287|-0.002|
||Husband/partner alone||0.637|0.674|-0.036**|
||Respondent alone||0.128|0.105|0.023***|
|Congo, Dem.<br>Rep.|Respondent and husband/partner|4303|0.468|0.487|-0.019*|
||Husband/partner alone||0.404|0.408|-0.003|
||Respondent alone||0.08|0.09|-0.010**|
|Ethiopia|Respondent and husband/partner|6009|0.698|0.716|-0.018**|
||Husband/partner alone||0.222|0.194|0.028***|
|Gambia,The|Respondent alone|1282|0.054|0.031|0.023***|

37
# **References**

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19
34

**Table 6:  Radio Access and Education Inputs**

||Student-<br>teacher<br>ratio|Textbook<br>-pupil<br>ratio|Absent/Total<br>teachers|Classroom<br>number|Proportion of<br>active<br>classrooms<br>with teachers|Level of<br>PTA<br>activity|
|---|---|---|---|---|---|---|
|Community|-1.656|-0.505|-0.00856|-0.0978|-0.133|1.444|
|radio stations<br>received by<br>village (#)|(0.23)|(0.39)|(0.55)|(0.52)|(0.58)|(0.39)|
|Village|1.81|-.046|-.012|.041|.113|.902|
|population<br>(thousands)|(0.02)|(0.69)|(0.07)|(0.75)|(0.45)|(0.48)|
|Observations<br>|196|196|199|195|198|199|
|_R_<sup>_2_</sup>|0.59|0.32|0.43|0.37|0.47|0.51|

**Note** :  Commune-clustered _p_ -values in parentheses.  Regressions control for commune fixed effects.  Control variables are the same as in the base specification (column 2) of Table 3.  With isolated exceptions, none are significant.  Only village population and community radio stations are reported here.

In fact, in Table 6, no determinants of school inputs seem to matter, consistent with the government‘s claim to allocate resources on a strict per capita basis, or with the possibility that allocation is purely random. The only correlate that is significant in even two of the specifications is village population. Both significant results, however, are intuitive: larger villages with larger student populations have higher student-teacher ratios (classroom size is approximately two pupils larger). On the other hand, they are also more attractive to teachers: teacher absenteeism is somewhat lower in larger villages.

A further test of the accountability channel is to ask whether households with greater access to community radio have greater knowledge of government policies relevant to literacy. Prior research emphasizes the impact of media on government responsiveness through its effects on citizen knowledge (e.g., Besley and Burgess 2002; Strömberg 2004), but data limitations have prevented researchers from examining whether this is indeed the case.<sup>13</sup> The Benin household 13 Snyder and Stromberg (forthcoming) is an exception; they estimate effect of newspaper access on
firm size – especially for Shanghai listed firms – we use the average total assets in 2010-12 to predict the probability of being connected within each exchange. In doing so, we remove most of the ex-ante difference in size between connected and unconnected firms (Appendix Figure 4).

We find a positive and significant impact of the Stock Connect program for firms in each stock market (Table 8). Among firms listed in Shanghai, the ratio of equity raised over assets increased by about 7 p.p. more for connected firms relative to unconnected firms in 2015 (Figure 6, Panel A). The cumulative difference was 17 p.p. in 2020 (Figure 6, Panel B). Among firms listed in Shenzhen, the ratio of equity raised over assets increased by 22 p.p. for connected firms relative to unconnected firms in 2016 (Figure 6, Panel B), and the cumulative difference was 42 p.p. in 2020 (Figure 6, Panel B). Since firms listed in Shanghai are, on average, larger than firms listed in Shenzhen (Appendix Figure 4), the fact that connected firms in Shenzhen reacted more than connected firms in Shanghai is consistent with our size-related results.<sup>29</sup>

# **4.3 Investment Activity**

To examine the effect of internationalization on firms’ investment activity, we focus on capital expenditures (capex), spending on acquisitions, R&D, and cash and short-term investments.<sup>30</sup> While cash and short-term investments are measured as stock values each year, capex, acquisitions, and R&D are flows, so the changes in those variables are not easily comparable.

We study again the differences between connected and unconnected firms. We run the baseline difference-in-differences specification (Equation 1) using the following dependent variables in turn: capex over total assets, acquisitions over assets, R&D over assets, and cash and short-term investments over assets. Assets are measured as of 2012. We report the estimated

> 29 In unreported regressions we found that smaller connected firms within the Shanghai and Shenzhen events were more reactive than larger ones, which is consistent with the pattern shown in Table 6 for all firms.

> 30 We use the Worldscope definition for each variable, as detailed in Appendix Table 4.

20
Table 1 shows the timber **revenues** of timber exporting developing countries (excluding China, Argentina, Chile and the Near East). The total exports for non-coniferous logs plus sawnwood plus wood based panel was US$9.02 billion in 1991. In addition, wood furniture and wood manufacturers, exports from developing countries (excluding the countries mentioned earlier) are estimated to account for around US$1.64 billion.11 Thus the value of the total timber export revenues for logs, sawnwood, wood based panels, wood furniture and wood manufactures from developing countries was around **US$10.66** billion in 1991.

Timber certification is likely to affect the share of tropical timber imports destined to locations where consumer awareness for TC is high and where TC initiatives are proliferating. Table 2 indicates that around 70% of tropical timber is imported by developing countries and Japan and not by Western developed countries where demand for certified timber appears to be strong. The concern over environmental issues has not reached the vast majority of consumers in developing countries and hence certified timber is not likely to be in high demand in those markets, at least in the short to medium-run.

Developing countries likely to be most affected by TC are those that significantly rely on tropical timber exports and have a large share of their exports destined to countries that are developing TC initiatives (such as in Western Europe and North America). Table 2 shows that the two largest exporters of tropical timber non-manufactured products are Malaysia and Indonesia; trade accounts for a large part of their production. In 1990, Malaysia's exports of logs and timber products accounted for about 75% of timber production and for Indonesia the same portion was 60%. Other countries with high export shares are: Congo (62%), Cote d'Ivoire (57%), Gabon (78%), Ghana (49%) Liberia (64%), and Papua New Guinea (60%). Table 2 also shows that eighty two percent (82%) of global exports of tropical timber originate from Asia, particularly Malaysia and Indonesia, which together account for about two-thirds of world tropical timber exports.

"Calculatd **from** UN **Thade** Statistics.

16
Figure 3: Distribution of sum of first 3,400 draws: `invnorm(uniform())` versus `rnormal()` <!-- Start of picture text --> invnorm(uniform()) vs rnormal() in Stata 10.1<br>Seeds 1/100000; 3400 observations<br>−.1 −.05 0 .05 .1 −.1 −.05 0 .05 .1<br>Mean of 3400 draws from invnorm(uniform()) Mean of 3400 draws from rnormal()<br>SD:.01712 (theory: .01715) SD:.01111 (theory: .01715)<br>Kurtosis 3 (theory: 3); Skewness 6.9e−03 (theory: 0) Kurtosis 2.97 (theory: 3); Skewness −.124 (theory: 0)<br>40 40<br>30 30<br>20 20<br>Density Density<br>10 10<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> invnorm(uniform()) vs rnormal() in Stata 12.1<br>Seeds 1/100000; 3400 observations<br>−.1 −.05 0 .05 .1 −.1 −.05 0 .05 .1<br>Mean of 3400 draws from invnorm(uniform()) Mean of 3400 draws from rnormal()<br>SD:.01712 (theory: .01715) SD:.01715 (theory: .01715)<br>Kurtosis 3 (theory: 3); Skewness 6.9e−03 (theory: 0) Kurtosis 3 (theory: 3); Skewness −.011 (theory: 0)<br>40 40<br>30 30<br>20 20<br>Density Density<br>10 10<br>0 0<br><!-- End of picture text --> Histograms of the distribution across 100,000 initialization seeds of the mean of the first 3,400 normal draws in Stata. Upper panel shows results using Stata 10.1; lower panel shows Stata 12.1. Left histograms show results using `invnorm(uniform())` ; right histograms show `rnormal()` .

13
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