rafmacalaba/data-use-mentions-extended huggingface dataset:
While a large body of literature explores the fiscal deficit—money growth—inflation nexus, the empirical evidence remains mixed and contingent on country context. Several factors may influence the timing and strength of this nexus (Agénor and Montiel 2015). If the deficit is financed by bonds rather than money creation, the short-run relationship may be weak. Moreover, if the government resorts significantly to external borrowing, the relationship between the consolidated government deficit and inflation may be weak. Other factors would have to be considered in explaining high inflation in such contexts. Finally, the impact of deficit monetization on inflation may be weakened or delayed in the presence of unstable money demand and slow inflation adjustment due to inertial forces such as sticky wages and price controls.

Evidence from African countries is generally supportive of a positive relationship between fiscal deficits and inflation (Ahmad and Aworinde, 2019). In the case of Ethiopia, a study by Alemu, Mulugeta, and Wassie (2016) finds evidence of a positive impact of the fiscal deficit on inflation in both the short run and long run.

This study contributes to the literature by providing both a theoretical and institutional motivation for the impact of the fiscal sector—including the deficit and public sector borrowing— on inflation. The analysis distinguishes between long-run effects (through adjustment to equilibrium in the fiscal sector) and short-run effects (through the impact of increased borrowing by the central government and state-owned enterprises).

# **External factors of inflation**

External factors play an important role in fueling domestic inflation, notably through imported food and energy products as well as exchange rate movements, although the relative impacts of these factors need to be further explored in future research. For Ethiopia, empirical evidence has shown that rising world food prices exert upward pressure on local food prices (Durevall et al. 2013). However, the transmission of global food prices to domestic prices remains unclear. According to UNCTAD trade statistics, Ethiopia is a net exporter of basic foods, while it is a net importer
capturing (iv) the percentage of the population over 60 covered by pension schemes, (v) the cost of pension spending as a percentage of the country's GDP, (vi) the logarithm of the age of eligibility, (vii) the percentage contribution rate and (viii) the ratio of employer/employee contribution from the World Bank Pensions database (2014).<sup>18</sup>

# **4.2 Results and analysis**

Table 1 presents descriptive statistics for the main variables in the analysis of saving for old age around the world. Column 1 present weighted averages for the pooled sample of all countries (147,692 individuals), while Column 2 and Column 3 present averages for individuals who save for old age (24.8% of the weighted sample from 30,395 individuals) and those who do not (75.2% of the weighted sample, from 117, 297 individuals), respectively.<sup>19</sup> Column 4 also presents the mean difference and asterisks for the level of significance from a test of difference in the means of the variables between the two samples.

The comparison of the figures between individuals who save for old age around the world and those who do not in Columns 2-4 suggests that individuals who save for old age are more likely to be males, residing in an urban region, more likely to have tertiary or secondary education, less likely to have primary education or less, more likely to be in the older age groups and less likely to be in the age range 18-25, less likely to be single or never married, and less likely to have children aged under 15. They are more likely to be in the top three income centiles by country, and less likely to be in the bottom two or the missing income centile. Those who save for old age are more likely to be employed, and less likely to be self-employed, unemployed or inactive.

Savers for old age are
track of their time. Figure 6 gives some indication that this may be the case, revealing a bimodal sleep distribution for recall respondents that is particularly pronounced for women. A significantly greater share of recall respondents, particularly women, report earlier sleep times that would conclude the diary interview — 64 percent of women in the recall arm reported sleep times between 5 pm8:59 pm, versus 54 percent in the smartphone arm. For men, these shares were 50 and 45 percent, respectively. This supports, to some extent, that fatigue may be a key factor. Other factors may be at play as well, however, including potential difficulties among those reporting earlier sleep times to pinpoint in the interview exactly when they went to sleep.<sup>20</sup>

> 20 Also see Kaplan et al., 2020, for a discussion on recalling sleep times using the American Time Use Survey.

22
All models account for the clustering of children by primary sampling units. Results are presented as changes in the risk of stunting (relative to 1) given by . Models are estimated using R’s “geeglm” package and expected conditional probabilities (predicted probabilities) are computed using the “Zelig” package. ee<sup>ββ</sup>

# **6 Results**

Model 1a draws on data from 693 infants between 0 and 5 completed months of whom 11.7 percent are stunted (Figure 24). The direction and magnitude of the association for each of the predictors is consistent in both the unadjusted and adjusted model. Infants with low birth weight (LBW) have 3.3 times the risk (adjusted RR) of stunting compared to infants with a normal birth weight, LBW infants have a predicted probability (PP)<sup>47</sup> of stunting of 21.3 percent compared to 6.6 percent among normal birth weight infants. To a lesser degree, maternal short stature and a low body mass index are also associated with an increased risk of stunting (adjusted RR of 1.7 and 1.9, respectively). Infants whose mothers attended at least 4 antenatal care visits are almost half as likely to be stunted compared to infants whose mothers did not attend at least 4 visits (adjusted PP of stunting of 7.3 vs 12.8 percent, respectively). Only one postnatal factor is associated with stunting: Infants with access to improved drinking water are 0.44 times as likely to be stunted compared to infants without access to improved drinking water (adjusted PP of stunting of 7.5 vs 17.2 percent, respectively). Only one socio-economic predictor - whether the mother is married - is significantly associated stunting. Married women are almost half as likely to have infants who are stunted than single women, all else equal.

43
The last two columns in Table 4.1 report the estimates obtained on the cross-sectionally demeaned data. Accounting for time effects raises the magnitude of the PMG estimates of the long-run parameters **--** and, to a lesser extent, their standard errors too. The long-run coefficient on the return variable becomes now significant. The **MG** estimates are now of the correct signs, but only that on the return variable is significant; their values are again affected **by** a few large outliers (Costa Rica, Ireland and Saudi Arabia). As before, the long-run parameter restrictions are not rejected **by** the Hausman tests. Finally, the average speed of adjustment rises as well, especially in the case of the **MG** specification, and the model continues to account for a large portion of the variation in the de-meaned data..

Table 4.2 turns to the subsample of high and upper-middle income countris, for which the risk-return model might be expected to provide a better characterization of portfolio diversification than for the overall country sample. This expectation is bome out **by** the PMG estimates of the long run coefficients in the table, all of which are correctly signed and statistically significant. In particular, the return variable now carries coefficients much larger in absolute value than those in Table 4.1 above. Furthermore, the **MG** estimates carry also the correct signs and are fairly close to their PMG counterparts, although they remain imprecise.

The Hausman test statistics in Table 4.2 fail to reject the constraints on the long-run coefficients. The average speed of adjustment is somewhat lower than in the full-country sample when using the raw data, and higher when using the demeaned data, although the **SBC-** determined ARDL dynamics are still unstable in four countries, while some others (e.g., Singapore) display very slow adjustment. As before, the short-run coefficients show significant lagged effects
pollution will be used as an instrument in what follows. Although coal-fired power plants create large amounts of pollution, the largest sources of pollution according to the Global Carbon Project in Hannah Ritchie and Rosado (2020) are oil and gas. For our instrument to be effective, the location of the power-plants should not be dependent on the economic development of the closest city. As argued earlier, there are several justifications for using the power sector as an instrument for pollution. Power stations in Chile plug into regional grids and are hence isolated from demand that is specific to cities. The location of a

> 4see `http://generadoras.cl/english` for more details.

> 5 `https://uk.practicallaw.thomsonreuters.com/w-019-3060?transitionType=Default&contextData=(sc.Default) &firstPage=true`

> 6New plants are restricted from being around residential and commercial areas. In this sense, the location of the plant is independent of a city’s economic activity. This is a function of Law 19300, which requires an environmental impact assessment.

> 7Law No. 20,257 could in principle serve as an instrument in the analysis, but would likely be negatively correlated with pollution.

10
# 2 **Transaction data from the Trucking portal**

Our main source of data is the confidential transaction-level data on trucking from a logistics company we call JDL. The data set covers 487,406 transactions that took place on its platform between January 2017 and February 2021. These transactions hauled about 8.8 million metric tons of freight. The platform was open to truckers and their customers, and a bidding process was followed leading to the ultimate transaction. Each transaction recorded its origin and destination at administrative/census code level 4 (town/village level), the types of goods transported, truck characteristics including its length, capacity, and type, and most importantly transport charge paid by the customer and weight carried by each truck. Though JDL maintains a fleet of trucks, the platform attracted many other truckers and customers beyond its own customer base. All transactions were facilitated by JDL for a small fee. About 5 percent of the transactions in the data set involve JDL’s own trucks. The main analysis of the paper is based on transactions that took place before the COVID-19 lockdown on March 24, 2020. After light cleaning, we have about 470,000 transactions in the data set during this pre-COVID-19 period. The data during and after lockdown due to COVID-19 are utilized to track changes in transport costs during the pandemic period. The COVID-19 period data consists of 7,344 observations. We complemented these transactions data with distance, travel time, and crow fly distance using google distance API. This data set is further augmented with night light data from the VIIRS (Visible Infrared Imaging Radiometer Suite) which improved upon the data from the DMSP satellites.<sup>8</sup> The trips in the data set cover nearly all corners of India ensuring very wide geographical coverage. About 31 percent of trips either originate or end in 7 main Indian cities highlighting the importance of economic density in trade flows.<sup>9</sup> Though the amount of freight hauled in this
# **Abstract**

This paper provides estimates of the economic impact of initial adoption of genetically modified (GM) cotton and of its potential impacts beyond the few countries where it is currently common. Use is made of the latest version of the GTAP database and model. Our results suggest that by following the lead of China and South Africa, adoption of GM cotton varieties by other developing countries – especially in Sub-Saharan Africa – could provide even larger proportionate gains to farmer and national welfare than in those firstadopting countries. Furthermore, those estimated gains are shown to exceed those from a successful campaign under the WTO’s Doha Development Agenda to reduce/remove cotton subsidies and import tariffs globally.

## **JEL codes: D58, F17, Q16, Q17**

**Key words:** GMOs, cotton biotechnology, computable general equilibrium modeling, economic welfare, subsidy and tariff reform

## **Contact author:**

Kym Anderson Development Research Group The World Bank Mailstop MC3-303 1818 H Street NW Washington DC 20433 USA Phone +1 202 473 3387 Fax +1 202 522 1159 kanderson@worldbank.org
are roughly in line with 2014 levels in log terms and somewhat higher in level terms (see Figure 25).<sup>10</sup> However, unlike with agriculture, there is not a clearly discernible secular trend, with growth and even overall productivity levels fluctuating significantly on a year-to-year basis (see Figure 26).

Figure 25. Cumulative change in weighted average log labor productivity, 2014-2021 (mining and quarrying; constant 2010 prices) <!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 26. Annual change in weighted average log labor productivity, 2014-2021 (mining and quarrying; constant 2010 prices) <!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation Entry* Exit*<br>Log-changes<br><!-- End of picture text -->

Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Fluctuations in labor
# _Quality Gap with the Best_

Did Thailand’s expansion of access to secondary and tertiary education, just as other come at the price of lower quality of education? The government has begun reforming the education system. The agenda includes upgrading teacher training, modernizing curricula to meet international standards, operating 175 Local Education Authorities in line with the 1999 National Education Act, promoting school accountability towards parents, and raising mandatory schooling to 12 years by 2015. While there are no data to assess trends in the quality of education over the last decade or so, it is possible to compare Thailand to other countries in terms of test scores. At first glance, it seems that international comparisons of test scores confirm the weaknesses in knowledge and creative thinking. The 2003 Program for International Student Assessment (PISA) found that 15 year-old Thais ranked 36<sup>rd</sup> in mathematics, in science and in reading out of 40 countries.<sup>18</sup> By contrast, their Korean counterparts ranked second in mathematics and in reading and fourth in science. For mathematics, this translates into more than one in two Thai failing to progress beyond the most basic proficiency level compared to only one in twenty Korean. The 2003 PISA results were similar compared to the first assessment in 2000, perhaps indicating that the ongoing education reforms have not yet born fruit.

However, this pessimistic reading of Thailand’s performance can be challenged. Of the 40 PISA countries, 33 are OECD member states. In fact, Thailand consistently outperforms not just Indonesia, whose per capita national income is less than half of Thailand’s, but also Tunisia, Brazil and Mexico whose income levels are 20 percent to 300 percent higher than Thailand. The results of the 1999 Trends in International Mathematics and Science Study (TIMSS) show a similar pattern.<sup>19</sup> TIMSS is more evenly balanced among high-, middle- and low-income countries. It includes only 13 industrialized countries out of the 38
predict stock price responses to elections during the post-Suharto era, nor stock price responses to news about President Wahid’s impeachment.<sup>8</sup> In addition, we use data on the appointment dates of the Suharto appointed mayors from MartinezBravo et al. (2013). We supplement the data they have made publicly available with data we collected ourselves from the Government of Indonesia’s Official Directories at Cornell University. We also construct new data on entry regulation from presidential decrees issued in 1993, 1995, and 2000. We create a (stringent) entry regulation indicator variable that equals 1 if an industry is completely closed to investments or closed unless the firm in question meets certain conditions and zero otherwise. Industries that are reserved for small businesses are not considered as regulated.

# **4 The Impact of Political Turnover on Manufacturing Firms**

# **4.1 Characteristics of Politically Connected Manufacturing Firms**

Even though the 86 Suharto family firms account for only 0.4 percent of firms in our sample, they matter for macroeconomic performance. As Table 1 (panel A) shows, Suharto family firms accounted for 1.3 percent of jobs and exports, 2.9 percent of all imports, and 3.8 percent of output in 1996. The larger group of firms with broadly defined political connections (presented in panel B) comprised 1.1 percent of all firms, employed 4.4 percent of all manufacturing workers, and produced 15.8 percent of total manufacturing output. They also accounted for 5.0 percent of manufacturing exports and for 12.7 percent of all imports. Politically connected firms were thus among the larger firms and were oriented toward production for domestic consumption. By 2000 the aggregate contributions of politically connected firms had diminished somewhat. Suharto connected firms nonetheless remained important and continued to account for 13.4% of all output.

Table 2 shows the average characteristics of politically connected and nonconnected firms and compares the difference between these averages during the
employees in using a new machine. Table 1 summarizes the approved activities over a period of two years for one of the programs we wanted to evaluate.

Among firms which had previously received grants, one of the most successful cases was a windgenerated electricity producer. It used a matching grant of $8,000 to develop the feasibility studies that allowed it to enter into a joint-venture with a European firm and obtain an investment of US$1 million. It took four years after funding for the project to become commercially operational, and within the first year of operation the firm was already supplying 14 percent of all the electricity consumed in the two districts it services. This project serves as a possible example of multiple levels of impact, with the firm receiving greater profits, consumers receiving better quality electricity service (brown-outs and blackouts were reduced), and the environmental benefits through clean energy provision. Even in this case it is not possible to ascertain absolutely whether or not this would have happened without the matching grant, but it is certainly the type of case where multiple market failures and externalities seem apparent, and public benefit from the grant seems likely. Another two examples of former grant recipients concern investment in worker and firm training. A civil engineering firm used the grant to help fund quality certification training for its workers, while a communications firm used the grant to develop an employee training scheme. Both firms said that they had been unsure about the effectiveness of such training, but after seeing the results from the grant funding, they had subsequently invested more of their own funding in continuing this activity.

For other approved matching grants the additionality needed to justify the government support is not as clear, particularly taking into consideration the information available to firms on the benefits of these investments and their lack of constraints in accessing them. In fact, according to a survey commissioned by the government in one of the countries where we started an evaluation, approximately 25 percent of the firms receiving the matching grant stated that they would have pursued the
**Figure 15: Cargo Dwell Time by Frequency of Deliveries, 2011**

<!-- Start of picture text --> 12<br>10.32<br>10<br>7.74<br>8<br>6<br>4<br>2<br>0<br>< 10 deliveries ≥ 10 deliveries<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

# **Analysis by Market Structure**

Figures 16 and 17 present dwell time by the level of competition in the sector. In Figure 17, the categories 0, 1, and 2–5 competitors are aggregated into ―monopoly-oligopoly,‖ and the categories 6– 20 and more than 20 competitors are aggregated into ―competition.‖ Importers in monopoly-oligopoly situations experience a two days significantly shorter dwell time than importers in competitive situations (t(223.564) =2.694; p = 0.003).<sup>9</sup> . In fact, rational importers in monopoly-oligopoly situations are likely to have shorter dwell time because they seek to minimize their logistics costs (long dwell time generally translates into higher logistics costs) in order to optimize their profits.

**<u>Figure 16: Cargo Dwell Time, by Number of Competitors, 2011</u>**

<!-- Start of picture text --> 12<br>9.92<br>10 9.41<br>8.35<br>8<br>6.00 5.90<br>6<br>4<br>2<br>0<br>0 competitor 1 competitor 2-5 competitors 6-20 competitors more than 20<br>competitors<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

> 9 This also holds when comparing average dwell time of each
Table 2: Evidence on Gender Effects on Household Expenditures, continued

|Study|Methodology and Data<br>Source|Outcome<br>Variables|Measure<br>of<br>Female<br>Resources|Effect<br>of<br>Increase<br>in<br>Female Resources|
|---|---|---|---|---|
|Kennedy and<br>Peters (1992)|Cross-sectional data,<br>Kenya and Malawi|Expenditure<br>shares|Female versus<br>male headed<br>households|In female-headed<br>households, smaller<br>expenditures on alcohol<br>and a larger expenditure<br>share for food.|
|Khandker<br>(2005)|Quasi-field experiment<br>in Bangladesh|Total<br>expenditures,<br>schooling|Woman's<br>borrowing<br>versus man's<br>borrowing|In households where the<br>woman has borrowed, food<br>as well as non-food<br>expenditures are larger.<br>Also a positive effect on<br>girls' schooling.|
|Lundberg,<br>Pollak, and<br>Wales (1997)|Natural experiment with<br>child allowance, U.K.|Expenditure<br>shares|Woman's<br>income share|Expenditure on children's<br>and women's clothing<br>increases relative to men's<br>clothing.|
|Phipps and<br>Burton (1998)|Cross-sectional data,<br>Canada|Expenditure<br>shares|Woman's<br>income share|Expenditures on child care,<br>children's clothing,<br>women's clothing, and food<br>increase.|
|Rubalcava,<br>Terual
review of the effectiveness of mental health promotion interventions for young people in low and middle income countries. BMC Public Health, 13(1), 1.

Becker, B. E., and Luthar, S. S. (2002) “Social-Emotional Factors Affecting Achievement Outcomes Among Disadvantaged Students: Closing the Achievement Gap.” _Educational Psychologist, 37_ (4), pp. 197–214 Blattman, C., J. C. Jamison and M. Sheridan (2015) “Reducing Crime and Violence: Experimental Evidence on Adult Non-Cognitive Investments in Liberia”, _SSRN Working Paper No. 2594868_ Blum, R. and M. Ireland (2004) “Reducing Risk, Increasing Protective Factors: Findings from the Caribbean Youth Health Survey”, _Journal of Adolescent Health_ , 35, pp. 493-500 Bradley, D., P. Noonan, H. Nugent and B. Scales (2008) _Review of Australian Higher Education: Final Report_ , Department of Education, Employment and Workplace Relations, Canberra 13