rafmacalaba/datause-encoder-data huggingface dataset:
22 rate regime that prevailed from the early 1980s until unification of the exchange rate in 1994, price comparisons at the official exchange rate might suggest China has been protecting farmers. But using the method described above to account for distortions in the market for foreign currency (data for which are available from 1981), a quite different pattern emerges. Huang et al. provide new annual estimates of protection and taxation from 1981 to 2005 for 11 commodities: rice, wheat, maize, soybeans, cotton, pork, milk, poultry, fruit (using apples as a representative product), vegetables (using tomatoes as a representative product) and sugar (both sugar beet and sugar cane). Over their study period, these commodities accounted for between 70 and 90 percent of the total value of agricultural output in China. A summary of their key results is provided in Table 1.

A striking feature of Table 1 is the extent to which producers were directly taxed by agricultural and trade policies in the early reform era. This was particularly the case for staple foods such as rice and maize. Taxation of rice reduced returns to farmers by over 50 percent relative to world price levels. Cotton prices were also strongly depressed, partly because of a desire to have low-cost inputs for the processing sector. Returns to other labor-intensive agricultural products such as vegetables and pork were depressed too, partly as a consequence of restrictions on exports. Returns to import-competing agricultural products were much less seriously depressed, and some of these products, such as sugar and milk, had quite high levels of positive protection. For these products, the goal of self-sufficiency, or at least concerns about vulnerability if grain imports reached double-digit shares of consumption, appear to have resulted in much lower levels of taxation. Assistance to agricultural exportables and to maize and cotton remained strongly negative in the late 1980s, while protection to imported commodities became slightly positive.

In the first half of the 1990s, there were very important changes, including the abolition of the system of compulsory procurement of grains. The weighted average rate of taxation fell by half in this period, to just under 20 percent. However, key commodities such as rice, maize and cotton still had negative rates of assistance. In the late 1990s, the taxation of rice diminished greatly, and taxation of a range of other exportable goods
# **Table 3. Prevalence of catastrophic health expenditures and impact of drug and hospitalization expenditures**

|||**Total health expenditures**|**Drug expenditures**|**Hospitalization expenses**|
|---|---|---|---|---|
|**≥ 10%**|||||
|Q1|N|418,252|239,776|17,923|
||% (CI)|17.3 (16.4; 18.2)|9.9 (9.2; 10.6)|0.7 (0.5; 0.9)|
|Q2|N|383,758|185,115|22,651|
||% (CI)|15.9 (15.1; 16.7)|7.6 (7.1; 8.2)|0.9 (0.7; 1.2)|
|Q3|N|319,463|156,832|10,979|
||% (CI)|13.2 (12.5; 14.0)|6.5 (6.0; 7.0)|0.3 (0.2; 0.4)|
|Q4|N|257,135|86,972|7,774|
||% (CI)|10.6 (10.0; 11.3)|3.6 (3.0; 4.0)|0.3 (0.2; 0.4)|
|Q5|N|148,993|47,220|6,422|
||% (CI)|6.2 (5.7; 6.7)|2.0 (1.7; 2.2)|0.3 (0.2; 0.4)|
|All|N|1,527,601|715,915|65,749|
||% (CI)|12.6 (12.3; 13.0)|5.9 (5.7; 6.2)|0.5 (0.5; 0.6)|
|**≥ 25%**|||||
|Q1|N|53,096|12,299|4,532|
||% (CI)|2.2 (1.9; 2.5)|0.5 (0.3; 0.7)|0.2 (0.1; 0.3)|
|Q2|N|24,438|4,058|2,614|
||% (CI)|1.0 (0.8; 1.2)|0.2 (0.1; 0.3)|0.1 (0; 0.2)|
|Q3|N|13,369|1,563|830|
||% (CI)|0.6 (0.4; 0.7)|0.1 (0; 0.1)|0.03 (0; 0.1)|
|Q4|N|7,379|0|461|
||% (CI)|0.3 (0; 0.2)|0|0.01 (0; 0.04)|
|Q5|N|2,672|0|780|
||% (CI)|0.1 (0; 0.2)|0|0.03 (0; 0.07)|
|All|N|100,954|17,920|9,217|
||% (CI)|0.8 (0.8; 0.9)|0.1 (0.1; 0.2)|0.1 (0; 0.1)|

CHE: catastrophic health expenditures; Q1-Q5: quintiles 1, 2, 3, 4, and 5; N: number (population) CI: confidence intervals 14
# **Cambodia**

Table 8a: Returns to levels of education - Employed for wages

||1997|2007|Change (%)<br>2007-1997|
|---|---|---|---|
|_Basic controls only_||||
|- Primary|0.124|0.330|_166.1_|
|- Junior Sec./primary|0.087|0.265|_204.6_|
|- Senior Sec./ primary|0.129|0.462|_258.1_|
|- Tertiary/primary|0.349|1.097|_214.3_|
|_Basic controls + Industry dummies_||||
|- Primary|0.127|0.173|_36.2_|
|- Junior Sec./primary|0.063|0.128|_103.2_|
|- Senior Sec./ primary|0.119|0.208|_74.8_|
|- Tertiary/primary|0.361|0.761|_110.8_|

# Table 8b: Returns to skill over time – Employed for wages

||1997|2007|Change (%)<br>2007-1997|
|---|---|---|---|
|_Basic controls only_||||
|- Lower Sec. and above/ primary and below|0.174|0.518|_197.7_|
|- Upper Sec. and above/ Low sec. and below|0.154|0.595|_286.4_|
|- At least some tertiary/Secondary and below|0.190|0.939|_394.2_|
|_Basic controls + Industry dummies_||||
|- Lower Sec. and above/ primary and below|0.146|0.256|_75.3_|
|- Upper Sec. and above/ Low sec. and below|0.155|0.301|_94.2_|
|- At least some tertiary/Secondaryand below|0.302|0.630|_108.6_|

17
address problems of recall bias, particularly in low-literacy populations, as well as the possibility of obtaining an inaccurate picture of time use patterns owing to seasonality. Methods that recognize multiple aspects of time use have greater potential for measuring well-being. In developing country settings, in particular, the energy intensity of activities, the increased work intensity owing to multi-tasking of productive and domestic activities, and the status and vulnerability associated with different types of work all combine to make the measurement of time a more nuanced effort than accounting for quantity alone. Addressing these issues in a cost-effective manner is essential if time use is to be captured in large-scale surveys that go beyond laboratory or small experimental studies.

We believe that broadening the scope of time use research to capture both quantity and quality of time use will provide richer insights into gendered time use patterns and trends, and that the best path to this goal will be through combining mainstream time use data collection methods with promising methodological innovations from other disciplines. Lessons from psychological approaches, in particular, are useful in discerning whether activities contribute to a person’s greater sense of well-being.

Approaches from the biomedical sciences can arrive at reasonable estimates of energy expenditure, which is important in determining the nutrition and health status of poor populations. As biomedical sensors become cheaper to use in field settings, such efforts may be useful in informing the development of technologies that reduce drudgery, particularly for women in their domestic roles. Careful documentation and comparison of these approaches will help to advance the multidimensional measurement of time, in order to identify what time constraints—whether actual or multidimensional—contribute most to gender inequality, and what policies and interventions can be implemented to relieve those constraints.

24
One issue in the literature is that there are no good measures of the contribution of R&D to technological improvements as they are reflected by the fluctuations of aggregate production. Patents might be an indicator of the inventive activity but they are not very explicit about the degree of the e↵ect of R&D on macroeconomic fluctuations. Griliches (2000) argues that patent applications are usually taken early during research processes in expectation of long run gains. As a result, there is lag between granting a patent and actual innovation. Lach and Schankerman (1989) point out that advancements in science and technology have a direct impact on R&D spending.<sup>6</sup> We argue that potential shocks identified from fluctuations of R&D expenditures (investment) reflect precicely technological innovations resulting from R&D activities. Griliches (1979) proposes the introduction of the stock of knowledge, approximated by past R&D expenditures, as an input in the production function. This idea is also implemented by Doraszelski and Jaumandreu (2007), who assume a linear accumulation equation for the stock of knowledge in order to estimate production functions and retrieve productivity and its relation with R&D at the firm level.

Throughout our analysis, we use US data on investment in R&D, adjusted GDP and employment for R&D activities. The data on R&D investment and adjusted GDP is provided in the satellite account which is developed jointly by the Bureau of Economic Analysis (BEA) and the National Science Foundation (NSF). Data on domestic employment of R&Dperforming companies is provided by NSF.<sup>7</sup> As shown in figure 1, R&D investment is on average 2.7 percent of nominal GDP and is characterized by the peaks of the mid 1960s,

> 6Work by Rosenberg (1969, 1974), Pakes and Schankerman (1984) and Grilinches, Hall and Pakes (1988) also stresses the importance of past technological improvements as factors of current R&D.

> 7In adjusted GDP, contrary to GDP reported in NIPAs, R&D is treated as investment rather than expense. An extensive discussion about the data on R&D investment is presented in section 6.

9
Table 1a: Financial loan data for Indian manufacturing <u>plants</u>

||1989|1994|2000|2005<br>2010|
|---|---|---|---|---|
||A|. Raw count of d|ata observations a|fter basic pruning|
|Total|127,786|197,992|244,339|116,175<br>135,270|
|Organized sector|33,356|42,649|25,571|35,101<br>33,077|
|Urban areas|22,899|29,024|15,296|20,379<br>19,323|
|Rural areas|10,457|13,625|10,275|14,722<br>13,754|
|Unorganized sector|94,430|155,343|218,768|81,074<br>102,193|
|Urban areas|40,584|53,262|131,652|40,062<br>49,818|
|Rural areas|53,846|102,081|87,116|41,012<br>52,375|
||B. Estimate|d number of plan|ts in India using s|ample weights from Panel A|
|Total|13,350,304|12,120,050|16,978,168|16,938,433<br>17,797,958|
|Organized sector|75,442|93,035|99,101|104,581<br>118,150|
|Urban areas|54,675|64,921|62,835|63,044<br>72,750|
|Rural areas|20,767|28,114|36,266|41,537<br>45,400|
|Unorganized sector|13,274,862|12,027,015|16,879,067|16,833,852<br>17,679,808|
|Urban areas|2,823,783|2,967,126|5,048,512|4,901,061<br>7,396,214|
|Rural areas|10,451,079|9,059,888|11,830,554|11,932,791<br>10,283,594|
||C. Share o|f raw data observ<br>|ations from Panel<br>of financial loans|A that report positive levels|
|Total|0.34|0.28|0.19|0.38<br>0.28|
|Organized sector|0.90|0.90|0.90|0.90<br>0.89|
|Urban areas|0.92|0.91|0.91|0.91<br>0.91|
|Rural areas|0.88|0.88|0.88|0.88<br>0.87|
|Unorganized sector|0.13|0.11|0.10|0.16<br>0.09|
|Urban areas|0.14|0.12|0.11|0.14<br>0.08|
|Rural areas|0.13|0.10|0.10|0.17<br>0.10|
||D. Shar|e of weighted ma<br>positive|nufacturing plants<br>levels of financia|from Panel B that report<br>l loans|
|Total|0.10|0.09|0.07|0.08<br>0.06|
|Organized sector|0.85|0.87|0.90|0.90<br>0.89|
|<br>Urban areas|0.86|0.89|0.92|0.92<br>0.91|
|Rural areas|0.82|0.82|0.87|0.86<br>0.85|
|Unorganized sector|0.10|0.09|0.06|0.08<br>0.06|
|Urban areas|0.11|0.12|0.09|0.11<br>0.07|
|Rural areas|0.10|0.08|0.05|0.07<br>0.05|
|||E. District count|where financial lo|ans are observed|
|Organized sector|352|358|315|346<br>345|
|Unorganized sector|342|362|356|357<br>334|

Notes: Descriptive statistics taken from the Annual Survey of Industries (ASI) and National Sample Survey (NSS). Positive levels of financial loans are taken to be where loans and/or interest is reported.
Promoting Democracy in Fragile States constantly ranks at the very bottom of the United Nations Human Development Index, and the study areas were especially poor and underserved by infrastructure.

The 2011 election season arrived with much reason to worry about the quality of electoral participation in the countryside. The country still relied on UNMIL as the primary security and infrastructure provider. The Liberian national police, for example, provided very patchy coverage and were frequently accused of corruption by watchdog organizations and ordinary Liberians (International Crisis Group, 2012). UNMIL was limited in the extent to which its mandate permitted its engagement in the countryside, however, leaving much of rural Liberia completely uncovered by state level institutions. In a 2011 survey, Sandefur and Siddiqi (2013) found that only four percent of crimes and disputes in rural Liberia were taken to the formal justice system. In a 2008-2010 panel study in rural areas Blair, Blattman and Hartman (2012) found that while overt collective violence was rare, property disputes were pervasive, indicators of intra-communal cohesion were deteriorating, and inter-tribal biases and perceptions of inter-ethnic inequity remained endemic. Blair et al. found that key correlates of violence included factors related to ethnic diversity, a legacy of the ethnically charged war (Boas and Utas, 2014). Much of the country remained accessible only via unpaved pathways, and nearly the entirety of the country outside the capital of Monrovia lacked access to a reliable electricity grid or piped water. A 2007 survey conducted by the authors found that in rural areas, two-thirds of respondents lacked regular access to radio, the primary form of media access in the country. Wordof-mouth was the primary channel of political information in rural areas. These logistical and communication challenges greatly hampered voter registration efforts (Look, 2011). Liberians and international onlookers worried about regional crises in neighboring Cote d’Ivoire and Guinea spilling over into Liberia (International Crisis Group, 2011).

(2011), Lyons (1999), and Moran (2006).

15
knowledge from foreign partners and competitors or through reacting to the demands of foreign markets (De Loecker, 2013; Bernard et al, 2007).

Firms that trade in international markets tend to innovate more. Among non-exporters, the share of innovative firms is about 11%, while it increases to close to 21% for importers. Innovation is particularly prevalent among exporters and participants in GVCs, where 33% of firms introduce new products and processes (Figure A3).<sup>10</sup> Unsurprisingly, foreign ownership is also strongly associated with participation in GVCs (Figure A4). When the right conditions are in place, attracting FDI fosters investment in new or improved products and processes, and participation in GVCs tends to increase the quality of exports and stimulate product upgrading (Javorcik et al, 2017; Harding and Javorcik, 2012).

Firms trading in international markets tend to invest more in R&D and to renew machinery and equipment. Compared with non-traders (firms that do not trade or are only importers), they tend more often to introduce new or improved products that are new to their main market, suggesting that they develop more innovation (Table 3). Similarly, they are more likely to invest in R&D and to upgrade machinery and equipment. The adoption and adaption of technologies developed elsewhere is the fastest way to catch up with more advanced economies (Hausmann and Rodrik, 2003) as it is often accompanied by investment in skills and further process innovation which may increase the firm’s productivity (Halpern et al., 2015).<sup>11</sup> Table 3 Investment in new or improved products and processes, R&D, and machinery and equipment upgrades (% of firms)

||Innovati|on type|R&D a<br>and equi|nd machinery<br>pment upgrades|
|---|---|---|---|---|
||New to the<br>company|New to the<br>market|R&D<br>investment|Machinery and<br>equipment upgrades|
|Trader|10.9|18.5|22.6|44.5|
|Non-trader|6|11.4|10.6|36.5|

_Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey.

Confirming the idea of self-selection into trade, firms engaging in international trade tend to be more productive. Table 4 shows that there are significant productivity and firm size premia associated with trade participation. We also find that firms that trade and innovate at the same time tend to be much more productive than other firms. Traders are on average 25% more productive and 30% larger than non-traders. Innovators are around 40% more productive and larger. However, the premia are even higher for firms that trade and innovate at the same time. In addition, innovative traders tend to have higher sales growth.

> 10 Table A2 shows that this positive association between trade and innovation also holds in regression analysis in most MENA countries (with the exceptions of Lebanon and the West Bank and Gaza).

> 11 In this analysis, controlling for firm size is important, as larger firms are also more likely to use foreign licensed technology, use a website, have recently upgraded their machinery or use international quality certification.

11
> - 81 -

the total vehicle flow had decreased - suggesting that cross-town through traffic had been curtailed - concomitant with an increase in traffic on the rig roads. Three, both the drastic cut in daily license fee for cars and taxis plus the introduction of charging of carpools resulted in a net increase in inbound automobile tffic during the morning peak of thre-tenths, relative to May 1989 (in which a high daily license fee of **_S$5_** previously applied). Four, a significant reduction in inbound automobile traffic of seven-tenths naturally occurs during the afternoon - peak relative to May, 1989 (when no charges were applied at all in the afternoon peak). Five, by contrast, since goods vehicles (and motorcycles) are now charged when entering the Restricted Zone, there has been a sgnificant reduction in these vehicles of six tenths, during both the moming and afternoon peak periods, as intuition would lead us to expect. Six, as a result of introducing the evening ALS, average traffic speed within the Restricted Zone increased to 30 kph in 1990, as indicated in the Report of the Select Committee on Land Transportation Policy (see Seventh Parliament of Singapore, 1990; Olszewski and Tan, 1991). Seven, despite - a growing economy, the recent lowenng of the daily license fee possibly the outcome of a - trial-and-eror pricing process suggests that the Singapore Government is currently using ALS as a traffic management tool to curtail congestion rather than as a revenue-generating device.

# D. Financial and Economic Evaluation

86. Even though the purpose of the ALS is not to raise revenue, it fulfills the government's multipronged objective of seeking a traffic restraint measure that does not involve subsidization Croh, 1977). From a purely financial point of view, this policy measure is appealing to local authorities, especially in an age of declining municipal funds.

87. The capital cost of the ALS is SS6.6 million [US$2.8 million] in 1975 and the recurrent cost for the period 1975-89 is S$l.0 million tUS$0.4 million), including about 40 entry point atendants opating 22 gantries for enforcement purposes, in 1975.62 The monthly revenue Nobt that the US dollar equivalent here is eprssed in 1975 dolla, using di exch an t of S$2.40 _-_ **_US$1._** T1euays that immed8iy follows i based an fir from Waton and Hlland's (1978) sudy for tie first year only, nd the rest is computed from the d tho author cocd firm Sao's Public
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- Winter-Nelson, A., and A. Temu (2005): “Impacts of Prices and Transactions Costs on Input Usage in a Liberalizing Economy: Evidence from Tanzanian Coffee Growers.” Agricultural Economics 33(3): 243-53.

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- World Bank (2006): National Fertilizer Sector Project: Implementation Completion Report. Africa Region. Washington: World Bank.

World Bank (2008): World Development Indicators. Washington: World Bank.

- Wubeneh, N. G., and J. H. Sanders (2006): "Farm-Level Adoption of Sorghum Technologies in Tigray, Ethiopia," Agricultural Systems 91(102): 122-134.

- Xu, Z., Z. Guan, T. S Jayne, and R. Black (2009): “Factors Influencing the Profitability of Fertilizer Use on Maize in Zambia,” Agricultural Economics 40(4): 437-46.

- Yesuf, Mahmud and Gunnar Köhlin (2009): “Market Imperfections and Farm Technology Adoption Decisions – A Case Study from the Highlands of Ethiopia,” Working Papers in Economics 403, Gothenburg: University of Gothenburg.

- Zabel, J. E. (1992): "Estimating Fixed and Random Effects Models with Selectivity," Economics Letters 40(3): 269-272.

28
# **Figure 13(a): China: Return to skill by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4 Agriculture/Mining<br>0.3 Manufacturing<br>Trade/Services<br>0.2<br>0.1<br>0<br>1999 2005<br>Return<br><!-- End of picture text -->

**Year**

**Figure 13(b): China: Proportion of skilled labor by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 80<br>70<br>60<br>Agriculture/Mining<br>50<br>40 Manufacturing<br>30 Trade/Services<br>20<br>10<br>0<br>1999 2005<br>% skilled<br><!-- End of picture text -->

**Year**

55