rafmacalaba/datause-encoder-data huggingface dataset:
28 Joyce, J. and L. Kamas (1997) "The relative importance of foreign and domestic shocks to output and prices in Mexico and Colombia", Weltwirtschaftliches Archiv, Vol. 133, 3.

Kamin, S. and 0. Babson (1999) "The contributions of domestic and external factors to Latin American devaluation crisis: An early warning system approach" International Finance Discussion Paper, 645, September. FRBG.

Kamin, S. and M. Klau (1998) "Some multi-country evidence on the effects of real exchange rates on output." International Finance Discussion Paper, 611. FRBG.

Koop, G., H. Pesaran and S. Potter (1996) "Impulse response in non-linear multivariate models", Journal of Econometrics, 74, pp.<sup>1</sup> 19-147 Pesaran, H. and B. Pesaran (1997) Working with Microfit 4.0 Camfit Data ltd.

Pesaran, H. and Y. Shin (1998) "Generalized impulse response analysis in linear multivariate models", Economics Letters, pp.<sup>1</sup> 7-29 Pesaran H. and R. Smith (1995) "Estimating long-run relationships from dynamic heterogeneous panels", Journal of Econometrics, vol 68 pp. 79-113 Pesaran, H. and M. Weeks (1999) Non-nested hypothesis testing: An overview. University of Cambridge. Mimeo. September.
**Urbanization rate.** Bairoch (1985: Table 13/1, p. 279) gives the year 1700 range (based on cities greater than 5,000) to be 13 to 16 percent. For 1688, we have used the lower bound of the range (13 percent).

**Mean income in $PPP** . Obtained by interpolation from Maddison’s (2001, p. 247) estimates of  English and Welsh GDI per capita in 1600 and 1700.  An alternative calculation based  directly on King’s estimates yield almost the same result. If we take the ratio between the mean income from King’s social table (9.6 pounds per capita per annum) and the subsistence minimum assumed to be one-third above the vagrants’ income (2.7 pounds), we get a mean income which is 3.5 times the subsistence which, combined with the subsistence minimum of $PPP 400, yields  an average income of $PPP 1400. The interpolation based on Maddison’s data is $PPP 1418.

# **REFERENCES**

Bairoch, Paul (1985), _De Jéricho à Mexico_ , NRF, Paris: Gallimard.

Maddison, Angus (2001), _The World Economy: A Millennial Perspectives_ , Paris: OECD Development Centre.

51
bias (positive RRA). Imposing this restriction is a more efficient way of estimating the effects that relative price distortions have on economic growth, under the assumption that the restriction holds. It is an empirical question as to whether the restriction holds, and we come back to testing this restriction in Section 5.1.

We use the change in the log of GDP per capita because the Maddala and Wu (1999) panel unit root test does not reject the null hypothesis of the level of the log of GDP per capita containing a unit root (see Appendix Table 1). Yet this test comfortably rejects the null hypothesis of the change in the log of GDP per capita containing a unit root at the 1 percent significance level. The panel unit root test also rejects the null hypothesis of unit root in the relative rate of assistance. Thus, we relate the change in GDP per capita to the change in the relative rate of assistance, noting that equation (1) has a level form representation (2) ln( _GDPi,t_ ) = _ai_ + _bt_ + _ci t_ + _η_ abs( _rrai,t_ ) + _BXi,t_ + _ei,t_ Unless there exists a cointegration relationship, the error term _e_ will be non-stationary. However, a cointegration relationship between GDP and the RRA is not possible: the RRA is a stationary variable, as indicated by the unit root test results in Table 2. Hence, our main estimating equation relates the change in the log of GDP per capita to the change in the RRA.

As a baseline, we estimate the average marginal effect η that within-country changes in relative agricultural price distortions have on economic growth. We then examine lagged effects of these price distortions on economic growth by means of a distributed lag model where we include further lags of the relative rate of assistance on the right-hand side of the estimating equation. By doing so, we can examine both short-run and medium/long-run growth effects. For example, the short-run growth effects could differ from the longer-run growth effects if there are adjustment costs to capital so that it takes time for the capital stock in the sectors to fully adjust to the relative agricultural price distortions.

It is possible that the growth effects of relative agricultural price distortions may be country10
With respect to government control variables, we find that closer control of the government might be related to lower performance of the SOE in our sample. Directly owned SOEs seem to have a lower performance-based productivity. Likewise, the higher the share-holding percentage, the lower an SOEs’ productivity. This suggests that government participation could be associated with a relatively lower performance of SOEs, supporting a large strand of literature that has documented that SOEs underperform in markets.<sup>50</sup> _Table 2. Performance of SOEs explained by SOE characteristics_

|VARIABLES|(1)<br>Labor productivity (Operating<br>revenues per worker)|(2)<br>Labor productivity (Operating<br>revenues per worker)|
|---|---|---|
|Directly owned|-0.371***|-0.371***|
||-0.029|-0.089|
|State participation (continuous variable)|-0.008***|-0.008***|
||0.0000|-0.002|
|Region: Europe & Central Asia|-0.088*|-0.088|
||-0.048|-0.123|
|Region: Latin America & Caribbean|0.982***|0.982***|
||-0.133|-0.311|
|Region: South Asia|-0.277*|-0.277|
||-0.156|-0.205|
|Region: Sub-Saharan Africa|-0.021|-0.021|
||-0.104|-0.187|
|Sector contestability: Natural Monopoly|-0.131***|-0.131|
||-0.024|-0.151|
|Sector contestability: Partially Contestable|-0.031|-0.031|
||-0.031|-0.261|
|Size :100-250|0.494***|0.494***|
||-0.032|-0.121|
|Size :20-100|-0.046*|-0.046|
||-0.025|-0.111|
|Size :250+|0.432***|0.432***|
||-0.033|-0.145|
|Age :16-25 years|0.081**|0.081|
||-0.035|-0.119|
|Age :26-50 years|0.03|0.03|
||-0.036|-0.112|
|Age :50+ years|-0.298***|-0.298|
||-0.047|-0.272|
|Age :6-15 years|-0.043|-0.043|
||-0.035|-0.121|

50 Forthcoming analytical analysis is exploring these relationships in more detail including a comparison in performance between private owned companies and SOEs using ORBIS data.

32
undergone international validation, including developing country settings, include the International Physical Activity Questionnaire (IPAQ) in its short- and long-form versions (Craig et al., 2003), the WHO-led Global Physical Activity Questionnaire (GPAQ), and a Sub-Saharan Africa Activity Questionnaire (Sobngwi et al., 2001). The instruments are designed to measure activities in different domains (work, travel to and from places, and recreational activities) and the intensity of these activities (e.g. low, moderate or high physical activity) rather than precisely measuring energy expenditures. While validation studies for these survey instruments exist (e.g. Craig et al., 2003), the instruments remain prone to the types of biases that may be encountered in any subjective self-report based on survey recall, and their accuracy has been called into question as it appears to often produce results that are in contradiction with objective measures (Corder and Van Slujis, 2010).

In the last decade, activity trackers have been developed as instruments that have the potential to provide sufficiently accurate estimates of physical activity and energy consumption at the individual level. Accelerometers are possibly the most commonly used type of device, but other types of sensors are also being used or tested (Baranowski et al., 2012; Storm et al., 2015).<sup>6</sup> Applied work making use of accelerometers has investigated the relationship between physical activity and the built environment (Sallis et al., 2016) or contrasted patterns of physical activity across countries or populations (Salvo et al., 2015). One particular challenge with this method is study subject adherence to the protocols necessary for accurate measurement – the accelerometers must be worn in a consistent fashion during waking hours for several consecutive days. Given these challenges, certain field studies have seen up to 40% of individual-day data to be invalid (Sherar et al., 2011). Despite these adherence challenges that can lead to data censoring, wearable technologies including accelerometers hold a great deal of promise due to their accuracy in realtime measurement.

> 6 Most recently, a review of accelerometer studies with sample sizes greater than 400 identified accelerometry data for more than 275,000 individuals, from 76 studies and 36 countries (Wijndaele et al, 2015). Efforts have also started to develop internationally agreed protocols for accelerometer data collection, sharing, and dissemination (Cain and Geremia, 2012; Wijndaele et al, 2015).

7
Table A.2.2 Studies on EMDEs (some including advanced economies) (continued)

|Authors|Questions|Economies<br>(sample period<br>and frequency)|<br>Methodology|Results|
|---|---|---|---|---|
|Lei, Lu, and<br>Zhang (2015|<br>)<br>Do consumers and<br>professional<br>forecasters update<br>their inflation forecasts<br>with information from<br>the news media?|<br> <br>China<br>(2001-12,<br>quarterly)|Regression of inflation expectations on<br>news about prices|The news media can have a strong influence on inflation<br>expectations.|
|Mehrotra an<br>Yetman<br>(forthcoming|d<br>)<br>Are long-term inflation<br>expectations well<br>anchored?|<br>23 advanced<br>economies and<br>21 EMDEs<br>(2005-12,<br>monthly)|<br>Estimation of the decay function such that<br>inflation forecasts monotonically diverge<br>from a long-run anchor toward actual<br>inflation as the forecast horizon shortens|<br>Inflation expectations have become more tightly anchored over<br>time in both inflation targeting economies and in those following<br>other monetary policy regimes.|
|Minella et al<br>(2003)|.<br>Does inflation<br>targeting anchor<br>inflation expectations?|<br>Brazil<br>(2000-03,<br>monthly)|Regression of inflation expectations on<br>inflation target, lagged inflation rate,<br>exchange rate, and other control variables|In Brazil, actual inflation was above the upper limit of the<br>tolerance interval in 2001 and 2002 after the introduction of<br>inflation targeting in 1999. Inflation expectations react<br>significantly to the inflation target and past inflation. The<br>inflation targeting regime has been successful in helping to anchor<br>expectations.|
|Miyajima an<br>Yetman<br>(2018)|d<br>Are long-term inflation<br>expectations of<br>individual agents well<br>anchored?|<br>South Africa<br>(2001-17,<br>quarterly;<br>1993-2017,<br>monthly)|Estimation of the decay function such that<br>inflation forecasts monotonically diverge<br>from a long-run anchor toward actual<br>inflation as the forecast horizon shortens|<br>Inflation expectations of businesses and trade unions are anchored<br>to levels above the official target range although overall, inflation<br>expectations have become more strongly anchored in South Africa<br>in recent years.|
|Patra and<br>Ray (2010)|What are the<br>determinants of<br>inflation expectations?|<br>India<br>(1997-2008,<br>monthly)|Regression of inflation expectations on<br>lagged inflation, changes in prices of fuel<br>and primary articles, the output gap, and<br>real interest rate|Lagged inflation and movements in fuel and food prices affect<br>inflation expectations.|
|Reid (2009)|<sup>Are long-term inflation</sup><br>expectations well<br>anchored?|<br>South Africa<br>(2001-07,<br>quarterly)|Test for anchoring by regression of changes<br>in inflation expectations on macroeconomic<br>news|<br> <br>Inflation expectations are well anchored in South Africa due to the<br>inflation targeting framework.|

33
which is reimbursed by the buyer. Bangladeshi farmers own their own means of transport (whereas those from India and Nepal predominantly do not).

**Figure 3: Ownership of Means of Transport**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>No<br>40%<br>Yes<br>20%<br>0%<br>Bangladesh India Nepal<br>Source: Field Survey<br>Transport<br>with Own Means of<br>Proportion of Farmers<br><!-- End of picture text --> In India, farmers typically pay for delivering produce to market (DAP terms) whereas in Bangladesh and Nepal it tends to be the buyer or processor (C&F). In the latter case there are two methods of payment for the transport: 1) one farmer may own transport and the others pay per kg; 2) negotiate with the manufacturer for transport prices. Generally, the transporters own their own trucks unlike those in India who interact through brokers.

One of the major differences between the three markets is in the timing of payments to farmers, where 90% of Bangladeshi and three quarters of Indian farmers are paid within 2-3 weeks of delivering product to market. However, in Nepal almost 90% of farmers in the sample were found to be paid after more than three weeks of having delivering their product (Table 16).

**Table 16: Length of Time for Payment (%)**

||**Bangladesh**|**India**|**Nepal**|
|---|---|---|---|
|Less than 2 weeks|-|29|-|
|2-3 weeks|90|44|11|
|3-4 weeks|9|18|83|
|More than 4 weeks|1|6|6|
|_Total number surveyed_|_30_|_61_|_59_|
|_Source: Field Survey_||||

Overall, in Darjeeling middlemen like brokers play a great facilitating role at each stage of tea trade. In Nepal, however, middlemen do not play any significant role. In Darjeeling, the markets (auctions) are the primary node of the supply chain, while in Nepal it is the processors.

# <u>Degree of Tea Market Integration</u>

In integrated and competitive agricultural markets, price differences between the regions should equal transfer costs while price differences between regions that do not trade with each other will be less than or equal to transfer costs. The relationship between spatial market integration and price differences was established in the Takayama and Judge formulation of 1971 and can be tested statistically for a perfect economic system, often a theoretical one as in Faminow and Benson (1990). The model can be used to explore the extent to which two or more sites trade with each other especially if they produce similar products whose only differentiation is location.

24
search assistance and training (140). Conversely, public works reforms were the least common among all ALMP reforms, but they were the second-most popular reform in low middle- and low-income economies (Figure 8a).

**Figure 7. Number of UB policies during the Covid-19 pandemic**

<!-- Start of picture text --> a. By policy and income b. By region<br>44 Increase in the amount of UB 58<br>45 Increase in the coverage of UB 60<br>40 Increase in the duration of UB<br>50<br>35 Change in the application process of UB<br>40<br>30 25 30 29<br>25 22 30 24<br>20 17 20<br>9 9<br>15 10 9 10 10<br>10<br>3 4 4 3 4 3 0<br>5 1 0<br>0<br>HIC UMIC LMIC<br><!-- End of picture text --> Source: World Bank's Social Protection and Jobs Policy Inventory (SPJPI). Data cover March 2020 – January 2022. Note: LIC did not have any UB reforms captured by the SPJPI.

**Figure 8. Number of ALMP programs during the Covid-19 pandemic**

<!-- Start of picture text --> a. By program and income group b. By region<br>Adjustments in labor regulations<br>177<br>180 Wage subsidies 250 233<br>160 Job search assistance and training<br>141<br>Public works 200<br>140 125 168<br>148<br>120 150 132<br>110<br>100<br>78 100<br>71<br>80<br>61 49<br>60 42 50 25<br>34 33<br>40 27<br>17 20 19 0<br>20 8 4 8<br>0<br>HIC UMIC LMIC LIC<br><!-- End of picture text --> Source: World Bank's Social Protection and Jobs Policy Inventory (SPJPI). Data cover March 2020 – January 2022.

In terms of regions, OECD-HIC exhibited the highest number of ALMP reforms, totaling 233 (Figure 8b). Unlike UB reforms, where ECA had the second highest and SSA had the lowest number of reforms, in ALMP, ECA carried out the fourth and SSA had the third highest number of reforms, with 132 and 148, respectively. LAC accounted for the second highest count of ALMP reforms (168), whereas MENA and SAR procedures during the pandemic; (v) adapting working conditions to promote telecommuting, flexible hours, locations and rest periods; (vi) modifying paid vacation, sick leave, or other leave entitlements to grant employers greater flexibility in mandating leave; (vii) changing labor inspection policies to enhance guidelines on sanitation, social distancing, personal protective equipment use, and reducing transmission risks; and (viii) reducing administrative burdens on employers through changes in bureaucratic processes, reporting requirements related to labor regulations, and other relevant regulatory modifications.

17
<!-- Start of picture text --> Non OECD Countries<br>35<br>7<br>25<br>£<br>[3<br>8 20<br>ESin<br>E15<br>g<br>bd<br>10<br>5<br>0<br>1999 2000 2001 2002 2003 2004 #2005 2006 2007 2008<br>——Non OECD Countries ©—Linear Trend<br><!-- End of picture text --> <!-- Start of picture text --> OECD Countries<br>220<br>son SS<br>g 180<br>=<br>S<br>a2 160<br>£<br>&<br>140<br>120<br>100<br>1999 2000 2001 2002 2003 2004 #2005 2006 #2007 2008<br>— OECD —Linear Trend<br><!-- End of picture text -->
no significant effect, (b) the marginal effect of the anticipated price shock is increasing as the land size becomes larger, and (c) the marginal effect of the anticipated price shock is decreasing as the initial wealth increases. Less wealthier farmers have a greater incentive to invest when they anticipate an increase in price (the interaction term is significant at 5%). The result indicates that households’ general wealth level (i.e. ownership of non-liquid wealth) is an important determinant of productive asset investments.

For the anticipated shock, the marginal effect is peaked at ∆P<sup>P</sup> =0.15 for poor farmers and at ∆P<sup>P</sup> =0.13 for rich farmers. In both groups, the effect diminishes as the anticipated shock increases beyond the above peaked price levels. As shown in the lower panel, for large land owners, the positive price effect becomes larger linearly with the anticipated price shock. The impact of the unanticipated shock on farm investments is always negative, regardless of the initial wealth and land sizes).<sup>12</sup> Finally, in column (3), we examine whether households’ risk aversion on future price volatility reduced their incentives to increase farm investments. For this purpose, we adopt the method used in Kazianga and Udry (2006) and investigate whether higher moments of future price shocks affected investment decisions by controlling for a price volatility measure in Eq. (10). We assume that farmers rationally expected the volatility of the post-crisis food prices based on the difference between realized prices Pt and the mean price level (Et−1(Pt)), which they (we) can predict based on the pre-crisis food price data from 2004 to 2007. We include the

> 12 We note that the concavity of anticipated price shocks is partly driven by some outliers (i.e., those with anticipated price shocks above one), and therefore the concave relationship is not quite robust.  However, even without the concave relationship, the impact of anticipated price shocks on farm investments will be peaked around the similar points even after trimming these outliers.

23
reveals net counterfactual mean outcomes for those treated; _TT_ gives the mean gain net of losses amongst participants. Instead of focusing solely on the net gains to the poor (say) we may ask how many losers there are amongst the poor, and how many gainers. We already discussed an example in section 7, namely the use of panel data in studying impacts of an anti-poverty program on poverty dynamics. Some interventions may yield losers even though mean impact is positive and policy makers will understandably want to know about those losers, as well as the gainers. (This can be true at any given poverty line.) Thus one can relax the “anonymity” or “veil of ignorance” assumption of traditional welfare analysis, whereby outcomes are judged solely by changes in the marginal distribution (Carneiro et al., 2001).

Heterogeneity in the impacts of anti-poverty programs can be expected. Eligibility criteria impose differential costs on participants. For example, the foregone labor earnings incurred by participants in workfare or conditional cash transfer schemes (via the loss of earnings from child labor) will vary according to skills and local labor-market conditions. Knowing more about this heterogeneity is relevant to the political economy of anti-poverty policies, and may also point to the need for supplementary policies for better protecting the losers.

Heterogeneity of impacts in terms of observables is readily allowed for by adding interaction effects with the treatment dummy variable, as in equation (5.1), though this is still surprisingly far from universal practice. One can also allow for latent heterogeneity, using a random coefficients estimator in which the impact estimate (the coefficient on the treatment _T C_ dummy variable) contains a stochastic component (i.e., μ _i_ ≠ μ _i_ in the error term of equation 4). Applying this type of estimator to the evaluation data for _PROGRESA_ , Djebbari and Smith (2005) find that they can convincingly reject the common effects assumption in past evaluations. When there is such heterogeneity, one will often want to distinguish marginal impacts from average impacts. Following Björklund and Moffitt (1987), the marginal treatment effect can be defined as the mean gain to units that are indifferent between participating or not. This requires that we model explicitly the choice problem facing participants (Björklund and Moffitt, 1987; Heckman and Navarro-Lozano, 2004). We may also want to estimate the joint distribution of _Y T_ and _Y C_ , and a method for doing so is outlined in Heckman et al. (1997a).

However, it is questionable how relevant the choice models found in this literature are to the present setting. The models have stemmed mainly from the literature on evaluating training and other programs in developed countries, in which selection is seen as a largely a matter of 57
|**Table 4: Inter**|**action term re**|**sults for social a**|**ttitudes toward**|**s women and la**|**w & order situ**|**ation**|
|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Exports (%, cell<br>average)*Men have<br>more right to scarce<br>job (WVS)|0.624***<br>(0.215)|0.610***<br>(0.209)|||||
|Exports (%, cell|||0.345***|0.278***|||
|<br>average)*Women &<br>men have same rights<br>(WVS, 20 yr.<br>average)|||(0.099)|(0.099)|||
|Exports (%, cell|||||-0.098**|-0.087*|
|average)*Courts<br>Obstacle (cell<br>average)|||||(0.046)|(0.046)|
|Exports (%, cell|-1.177***|-1.238*|-2.822***|-3.212***|0.253***|-0.239|
|<br>average)|(0.445)|(0.739)|(0.826)|(0.939)|(0.068)|(0.528)|
|Courts Obstacle (cell|-1.080|-1.081|-0.599|-0.494|0.353|0.501|
|<br>average)|(1.471)|(1.466)|(1.034)|(1.040)|(0.833)|(1.413)|
|Exports (% cell||0.009||0.102||0.053|
|,<br>average)*GDP per<br>capita (logs)||(0.071)||(0.076)||(0.056)|
|No. of Workers||||0.175|||
|(logs)*Women & men||||(0.459)|||
|<br>have same rights<br>(WVS, 20 yr.|||||||
|average)|||||||
|No. of Workers||-1.949**|||||
|(logs)*Men have||(0.858)|||||
|<br>more right to scarce<br>jobs (WVS)|||||||
|No. of Workers||||||-0.056|
|(logs)*Courts<br>Obstacle (cell<br>average)||||||(0.359)|
|No. of Workers (logs)|1.151***|5.168***|0.671**|-0.781|0.926***|0.992**|
||(0.398)|(1.826)|(0.329)|(3.744)|(0.294)|(0.475)|

47