rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>3: robustness with fast-moving, country-specific time trends</u>**

||(1)<br>anyonset|(2)<br>anyonset|(3)<br>anyonset|(4)<br>major onset|(5)<br>major onset|(6)<br>major onset|
|---|---|---|---|---|---|---|
|P|0.037**|0.035**|0.033**|0.010|0.006|0.007|
|x (t)|(0.017)|(0.015)|(0.016)|(0.011)|(0.010)|(0.010)|
|P t1|-0.015|-0.016|-0.015|-0.015*|-0.015*|-0.014*|
|x (-)|(0.020)|(0.020)|(0.020)|(0.009)|(0.008)|(0.008)|
|2|0.008|0.003|0.008|0.008|0.006|0.009|
|Px (t-)|(0.019)|(0.017)|(0.018)|(0.009)|(0.007)|(0.008)|
|P t|0.007|0.011|0.017|-0.021|-0.005|-0.003|
|m ()|(0.026)|(0.023)|(0.025)|(0.015)|(0.011)|(0.012)|
||-0.007|-0.021|-0.023|0.015|0.003|0.004|
|Pm (t-1)|(0.026)|(0.022)|(0.022)|(0.016)|(0.014)|(0.014)|
|P t2|0.001|0.001|0.010|0.004|0.001|0.004|
|m (-)|(0.025)|(0.017)|(0.020)|(0.016)|(0.010)|(0.011)|
||-0.015|-0.016|-0.027|-0.007|-0.009|-0.024**|
|MP (t)|(0.023)|(0.022)|(0.024)|(0.011)|(0.011)|(0.012)|
|MP t1|-0.043|-0.031|-0.052|-0.038|-0.026|-0.031|
|(-)|(0.055)|(0.048)|(0.050)|(0.038)|(0.038)|(0.042)|
||0.068|0.057|0.093|0.059|0.053|0.068|
|MP (t-2)|(0.070)|(0.057)|(0.066)|(0.051)|(0.054)|(0.060)|
|Trade with|-0.084|-0.030|-0.080|-0.038|-0.015|-0.036|
|neigh. (t-1)|(0.059)|(0.054)|(0.057)|(0.041)|(0.033)|(0.041)|
|Controls|YES|YES|YES|YES|YES|YES|
|Observations|3,428|3,425|3,425|3,812|3,809|3,809|
|R-sq. (within)|0.156|0.072|0.157|0.064|0.027|0.066|
|Countries|114|114|114|115|115|115|
|Shocks|logP|logP|logP|logP|logP|logP|
|Weight|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|
|Time trends|YES|NO|YES|YES|NO|YES|
|Sum Px|0.030*|0.022**|0.025|0.003|-0.003|0.002|
|Sum Pm|-0.00003|-0.009|0.003|-0.002|-0.002|0.005|
|Sum MP|0.010|0.010|0.014|0.013|0.018|0.013|

_Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; controls include any conflict since 1946 and war border (t-1)._ 28
We can further assess the extent of elite capture and mistargeting of NAIVS over time in our study villages in Arusha compared to those in Kilimanjaro by running the main specification in Alatas et al. (2019): where Yij is a dummy that indicates whether household i in village j reported being a NAIVS beneficiary, Relatedij is a dummy that takes the value of 1 if the head of household i in village j reports holding the position or being related to the DALDO, WEO, VEO, VVC member, Village Chair or Hamlet chair, and Eligibleij is a dummy that indicates whether household i in village j is eligible in the 2008/09 season for the NAIVS program (i.e. had not used improved inputs prior to the start of NAIVS and had cultivated less than 1 Ha of land). We use baseline and follow-up data from the Villager survey and report the OLS estimates of Equation 3 in Table 6. We run separate regressions for the 2008/09 and 2010/11 seasons in Arusha (columns 1 and 2, respectively) and Kilimanjaro (columns 3 and 4, respectively).

In Arusha villages, being related to government officials or influentials is not correlated with being a NAIVS beneficiary in either 2008/09 season (column 1) or 2010/11 season (column 2). In both columns, the coefficient β1 is small and not statistically different from zero at conventional levels. In contrast, political connections played a role in voucher distribution in the 2008/09 season in Kilimanjaro (column 3), consistent with the findings of Pan and Christiansen (2012), Kilic et al. (2015) from Malawi and Mason et al. (2017) from Zambia. Column 3 reports that being related to government officials or influentials increased the probability of receiving vouchers by 3.2 percentage points, an increase of 60 percent over the mean of 5.4 percent among non-eligible individuals that were not related to government officials or influentials. By 2010/11, however, the elite capture is no longer significant. Table 6 confirms the results that elite capture was only present in Kilimanjaro, especially in the 2008/09 season, but not in Arusha.

Table 6 also complements the findings in Table 2 and Figure 3 that targeting improved over time, especially in Arusha villages. For a well targeted program, one would expect a positive correlation between eligibility and being a beneficiary. In the 2008/09 season, however, being eligible for the NAIVS program was actually negatively correlated with self-reported beneficiary status (column 1). By 22
explain the global inflation synchronization. Trade linkages increase the exposure of domestic inflation to foreign shocks and may lower import prices. Auer et al. (2017a) find that the expansion of GVCs has not only been an important contributor to the change in inflation dynamics, and it has become a key channel through which global economic slack influences domestic inflation, by making domestic inflation more sensitive to the global output gap.

There is strong evidence of the existence of international inflation spillovers that generates inflation synchronization through trade linkages. Auer and Mehrotra (2014), in the analysis most closely related to ours, find that both headline inflation rates and producer prices move more closely together between Asian-Pacific region economies that trade more with one another.<sup>3</sup> They show that in closely integrated supply chains, any shock to domestic production costs or exchange rates could be easily passed through to economies in the supply chain, affecting intermediate prices in other economies. More recently, Auer et al. (2017b) show that input-output linkages account for half of the global component of producer price inflation and that this synchronization across countries is driven primarily by common sectoral shocks and input-output linkages amplify the co-movement by propagating those shocks. Andrews et al. (2018) use data for 22 OECD countries over the period 1995 to 2014 to provide evidence that rising participation in GVCs has placed downward pressure on producer price inflation, by increasing the ability of firms to substitute domestic inputs with cheaper foreign equivalents. The mechanism at work is a downward pressures on unit labour costs (raising productivity and reducing wages) in the importing country.

Regarding the the policy relevance of inflation synchronization across countries, monetary policy is the most concerned. As supply chains represent a direct link between foreign marginal costs and domestic production costs, their prevalence has a significant effect on the extent to which optimal monetary policy is inward-looking (Auer et al., 2017b). In other words, this rise the question of to what extent central banks control domestic inflation dynamics (Carney, 2017). For this reason Central banks of the mayor advanced economies now pay attention to global developments when setting domestic monetary policy (Bernanke, 2007; Draghi, 2015; Carney, 2015).

> 3In Auer and Mehrotra (2014) the Asia-Pacific region includes Australia, China, Chinese Taipei, India, Indonesia, Japan and Korea.

6
The PEN data distinguish between moderate and severe shocks, and so far we have focused on severe shocks, for two reasons: first, a critical aspect of future climate change is the higher frequency and severity of extreme events; second, looking at the data and frequency of shocks, moderate shock might by some respondents be interpreted as what could more appropriately be classified as normal income variations. For example, while 11% of the households reported a severe income shock, 31% reported a moderate income shock, bringing the total incidence of income shocks to 42%. Yet, a brief analysis can shed light on how different coping mechanisms depends on the magnitude of the shock.

A comparison of the most common coping responses by severity of the shock, across poverty categories, is given in Figure 5. Harvesting of environmental products is more frequently mentioned for the moderate shocks compared to the severe ones: 17% vs. 13%. This pattern holds across all poverty categories. Among the active coping responses (i.e. excluding “do nothing”), 22% stated that harvesting more forest or other wild products is their primary response to a moderate income shock.

<!-- Start of picture text --> Total 13%<br>17%<br>Inc & asset rich 16%<br>21%<br>Inc poor & asset rich 11% 16%<br>Inc rich & asset poor 19% 24%<br>Inc & asset poor 11%12%<br>0% 5% 10% 15% 20% 25% 30%<br>Severe shock Moderate shock<br><!-- End of picture text -->

**Figure 5: Share of households using harvesting environmental products as primary coping strategy after an income shock**

Harvesting more environmental products is not “quick and easy cash” in response to a shock. If it was, it would have been fully exploited by the household even without experiencing a shock. Rather, the result suggests that it is particularly suitable for dealing with moderate income shortfalls.

# **5.3 Access to forest resources and resource degradation**

Environmental income plays an important role in the livelihoods, both to support current consumption, reduce vulnerability and potentially make some asset poor households move above the poverty line. It is therefore relevant to see how the physical access to environmental resources vary and how the resource base is changing. If rural households, particularly the poorest, experience a decline, this might jeopardize the future potential for environmental income to buffer the impact of climate change. We address this by asking: _Do the income and/or asset poor have poorer physical access to forest resources and is their resource base more exposed to degradation?_ 26
63

# **Emission Improvements and Fuel Economy**

Based on U.S. data, it is estimated that a well run I/M program is capable of significant emissions reductions, on the order of 25% for HC and CO and about 10% for NOx. The NOx reductions are less significant and are mostly the result of lower tampering rates, as at present there has been no focused effort to specifically design I/M programs to identify and correct NOx problems. If FRG proceeds with implementation of its low-NOx I/M proposal, it will be the first major exception.

It is important to note that emission reductions start out slowly and gradually increase over time because I/M programs tend to retard the overall deterioration rate of fleet emissions. Maximum benefits are achieved by adopting the I/M program as early as possible.

Fuel savings have been attributed to improved vehicle maintenance practices associated with an effective I/M program. Values reported in the literature range from 0 - 7%. In calculating cost effectiveness the U.S. EPA uses a figure of 3.5%.

# **Impact On Tampering and** Fuel **Switching**

Over the last few years, the U.S. EPA has been collecting data on the occurrence of tampering and misfueling to assess the magnitude of the problem as these activities can increase hydrocarbon emissions tenfold and carbon monoxide emissions twentyfold. As a result, it would only take a small percentage of the vehicles with these problems to greatly increase average fleet emissions. Surveys by the U.S. EPA have shown that nearly one in five vehicles had at least one emission control disablement and that a significant number of vehicle owners switched fuels. Fortunately, many European countries have adopted tax policies which make prices of unleaded gasoline equal to or less than leaded variety; this should restrain the tendency for fuel switching which occurred in the United States.

Investigations by the U.S. EPA suggest that I/M programs can significantly reduce tampering and misfueling rates (Table 34). With the exception of Exhaust Gas Recirculation (EGR) system disablements, I/M can cut tampering and misfueling rates virtually in half. As I/M programs in the United States have mostly addressed HC and CO problems, it is not surprising that these programs had no significant effect on tampering of EGR systems, which are designed to reduce NOx emissions. By minimizing tampering and misfueling of newer model cars which use catalysts to control NOx emissions, I/M programs should prove effective in reducing NOx emissions. Tampering surveys conducted by various state agencies in the United States suggest that older vehicles exhibit higher rates of tampering than newer vehicles. Furthermore, the underhood visual inspection portion was found to be effective in identifying many elements of a vehicle emission control system which may have been tampered with or modified.
sons from 1988 to 2002, and (ii) the intergenerational persistence in education for daughters remained largely unchanged (or weakened marginally) over the same time period, and (iii) low to moderate levels of positive selection on unobservables cannot explain away the observed educational persistence in 2002, especially for sons.

# **3.4. Estimating the Causal Effect of Parental Education**

The evidence presented so far in Tables 1‐3 show interesting patterns in the intergenerational link in educational attainment in rural China. The evidence that parental education exerts strong effect on children’s (especially sons’) education in 2002 raises the possibility that the much‐discussed increase in the cross‐sectional inequality following the economic reform in China is being reinforced and reproduced by increasingly stronger intergenerational link in educational attainment. However, the evidence presented so far in Tables 1‐3, while suggestive and useful, cannot provide answer to the question whether the parental education has a causal effect on children’s education. The AET (2005) sensitivity analysis assumes that the selection on unobservables is positive, and that the OLS estimates in column (7) of Table 2 are biased upward. However, the estimated effect from multivariate regressions as in column (7) of Table 2 above can be biased upward or downward because of unobserved heterogeneity, and it is not possible to pin down the direction of bias from a priori arguments. Another important issue is measurement error and the resulting attenuation bias. The importance of tackling measurement error is now well‐appreciated in the literature on intergenerational income mobility. One might argue that the extent of measurement error is likely to be less in self‐ reported schooling data compared to measurement error in income data (for a discussion on measurement error in income and consumption data in developing countries, see Deaton (1997)). However, there is substantial evidence that measurement error in reported schooling data is a 21
**_Energy demand models for policy_** **_<u>formulation</u>_**

# **NEMS [National Energy Modeling System]**

The National Energy Modeling System (NEMS) was designed and primarily used by the US Department of Energy for preparing the Annual Energy Outlook. It is a model of energy-economy interaction that is used to analyze the functioning of the energy market under alternative growth and policy scenarios. The model uses a time horizon of about 25 years (up to 2030 for the present version).

The model is fairly detailed and explicitly represents the economic decision making at various levels (production, consumption, etc.) as well as technologies. The demand analysis component is divided into four modules (residential, commercial, industrial and transport) and each module captures the diversity at the regional level to a great extent (see table 7).

Table 7: Demand representation in NEMS

|Energy<br>activity|Categories|Regions|
|---|---|---|
|Residential<br>demand|Sixteen end-use services<br>Three housing types<br>Thirty–fourend–use technologies|Nine Census<br>divisions|
|Commercial<br>demand|Ten end–use services<br>Eleven building types<br>Ten distributed generation technologies<br>Sixty–fourend-use technologies|Nine Census<br>divisions|
|Industrial<br>demand|Seven energy–intensive industries<br>Eight non–energy–intensive industries<br>Cogeneration|Four Census regions,<br>shared<br>to<br>nine<br>Census divisions|
|Transportation<br>demand|Six car sizes<br>Six light truck sizes<br>Sixty–three<br>conventional<br>fuel-saving<br>technologies<br>for light–duty vehicles<br>Gasoline,<br>diesel,<br>and<br>thirteen<br>alternative–fuel<br>vehicle technologies for light-duty vehicles<br>Twenty vintages for light-duty vehicles<br>Narrow and wide–body aircraft<br>Six advanced aircraft technologies<br>Medium and heavy freight trucks<br>Thirty–sevenadvancedfreight trucktechnologies|Nine Census<br>divisions|

Source: EIA (2003).

79
_40 How_ **_Fdw ha,_** _as.u. Indumy Gam"?_

# TABLJ 15: PzatCzNT CHANCE _iN_ JNDUIIRIAL ENzaGY IRoDucTTY roFO 15 BUSNcuS, 1980/85

||ToWl|Stale|Collective+|
|---|---|---|---|
|1|23.53|16.97|150.62|
|2|7.S0|16.30|126.31|
|3|4.67|11.10|-93.67|
|4|26.19|24.35|122.74|
|s|49.S1|35.18|112.14|
|6|68.93|56.51|99.42|
|7|20.09|16.89|16.72|
|a|7.98|-18.53|89.39|
|9|11.34|0.57|133.03|
|10|27.63|4.41|174.33|
|11|28.34|19.16|30.88|
|12|21.60|6.94|31.71|
|_13_|-53.40|-44.64|-70.45|
|14|379.90|n.a.*|-340.43|
|15|15.76|14.53|0.78|
|Totl|35.41|26.42|45.60|

Souem: Table 11, 12, and 13. Baund on calculatins **using** publishe raher than derived daft for brmach OVIO. *Branches 13 and 14 should be meged in **a** revinsd ca1ulatio underlying outt dat for thee two bncls ame jumbled.
suming area, implying that the seed has to be transported there for crushing.

Of the five cottonseed crushing companies in Uganda, four were visited for this study in May 2009.<sup>**9**</sup> Statistics related to their key characteristics are re‐ ported in Table A1.

# **Table A1**

**Summary Statistics of Uganda’s Cotton By‐Products Industry (Four Compa‐ nies)**

|**_I_**|**_II_**|**_III_**|**_IV_**|
|---|---|---|---|
|**I. Nature of operation**||||
|**Permanent employees**<br>4|4|100|4|
|**Temporary employees**<br>21|25|100|26|
|**Capacity (tons/day)**<br>10|50|200|18|
|**Year operation began**<br>1993|1998|2000|2005|
|**Type of operation**<br>Free standing|Free standing|Free standing|Part of a ginnery|
|**II. Crushings (tons of seed)**||||
|**2007**<br>1,100|—|10,000|2,900|
|**2008**<br>1,600|2,800|5,000|3,600|
|**2009**<br>2,500|5,000|10,000|4,800|
|**III. Prices paid for cotton seeds (UG shs pe**<br> <br>|**r kg)**<br>|||
|**2007**<br>400|220|200|420|
|**2008**<br>480|475|450|465|
|**2009**<br>225|410|300|325|
|**IV. Prices received from cotton oil (000 UG**<br> <br>|**shs per 20‐liter c**<br>|**ontainer)**<br>||
|**2007**<br>37|35|37|55|
|**2008**<br>62|58|54|65|
|**2009**<br>60|58|51|58|
|**V. Cotton meal sold (tons)**||||
|**2008**<br>800|1,400|500|1,800|
|**2009**<br>—|—|350|2,400|

**Notes:** ‘—‘ implies not reported. **Source:** Vasilaky (2009).

Three of the companies employ about 30 workers on average (both tempo‐ rary and permanent) while the fourth employs 200. The daily crushing capacity ranges from 10 to 200 tons of seed. Three facilities can crush other seeds (mainly

> **9** The four companies interviewed were: Busunju Oil Millers (Wakiso District); MM Holdings (Nakasero District); Agro Nile Industries Ltd. (Jinja District); and Agencies Ltd. Cotton Ginneries, Oil Millers, Importers, Exporters (Iganga District).

**_Page 29_**
Lithuania and Ukraine - show that access to funds is a more important barrier for female business owners than their male counterparts. In survey data from Ukraine, women were more likely than men to cite financial constraints as an important obstacle to running their business. These financial constraints also included greater under-capitalization at start up and less ability to raise external funds.

There is some evidence against the discrimination story in the US and Canada. In studies with larger sample size than that of Fay and Williams (1993), only modest or negligible differences were found in the way that bank loan officers perceived gender in business loan applications (Buttner and Rosen, 1989; Wilson et al., 2007). Similarly, the terms of loans grated to male and female business owners did not vary significantly in a Canadian national study (Riding and Swift, 1990). Once one controls for the size and age of the venture and the particular industry, gender differences in access to and terms of finance (including rates of loan approvals and collateral requirements) tend to disappear (Coleman, 2000; Orser et al., 2006). Using data from the _Survey of Small Business Finances_ in US, Cavalluzzo and Wolken (1998) find that after controlling for personal wealth (in the form of home ownership, home equity, personal net worth etc.), differences in denial rates between male and female-owned firms are not statistically significant. Similarly, in a study on micro and small enterprises in Trinidad and Tobago, Storey (2004) tests for the presence of racial and gender discrimination in bank loans. They take the reported refusal to grant loans to groups, when all other indicators of credit-worthiness are taken into account, as evidence of discrimination and find neither application nor denial rates differ significantly by gender once all other indicators of credit-worthiness are taken into account.

_If discrimination does not explain loan decisions, why does the average female entrepreneur start business with less capital or have less access to bank finance than men?_ Significant differences in male and female access to finance may be accounted for by differences in other characteristics affecting their credit worthiness including human capital factors and personal wealth. For instance, women may have on average more difficulties in securing a loan than men because they concentrate in the sectors such as services, which need less capital (Verheul and Thurik, 2001). Since banks typically lend on the basis of “hard assets” such as plant and equipment, of which service businesses 7
**Table 3. Tests of significance of groups of explanatory variables in urban-rural regressions**

|Sector|Variables|dfl|df2|F statistic|Probability|
|---|---|---|---|---|---|
|Rural|Educationofheadofhousehold|_5_|129|7.80|0.0000***|
||Educationofspouse|6|129|1.97|0.0738*|
||Occupationofhead|6|129|12.65|0.0000***|
||Typeofhousing|2|129|14.00|0.0000***|
||Mainsourceofwater|2|129|9.69|0.0001***|
||Typeofsanitaryfacility|2|129|15.64|0.0000***|
||Region|6|129|26.20|0.0000***|
|Urban|Educationofheadofhousehold|5|55|4.01|0.0036***|
||Educationofspouse|6|55|3.10|0.0110**|
||Occupationofhead|6|55|2.90|0.0157**|
||Typeofhousing|2|55|10.76|0.0001|
||Mainsourceofwater|2|55|17.17|0.0000***|
||Typeofsanitaryfacility|2|55|4.12|0.0216**|
||Region|5|55|10.29|0.0000*|

Source: Regression analysis of per capita expenditure using 1998 VLSS Note: The dependent variable is log of per capita expenditure. * coefficient is significant at the 10% level, ** at the 5% level, and *** at the 1% level.

occupational categories (political leaders/managers, professionals/technicians, and clerks/service workers) are significantly better off than households in which the head is not working. On the other hand, there is no statistically significant difference between the expenditure of farm households and households with non-working heads (see Table 2). This somewhat counterintuitive finding probably reflects the fact that non-working heads include retirees as well as a disproportionate number of urban workers who can "afford" to look for work.

In urban areas, households whose head is a leader/manager are significantly better off than those with non-working heads, while those whose head is an unskilled worker are significantly worse For this reason, a set of dummies for employer of the household head were not included in the predictive regressions.

16