rafmacalaba/datause-encoder-data huggingface dataset:
import parity price. While most farmers sell their produce at the farm gate, others sell at nearby market centers and depending on the month in which sales are made, face either the harvest season (May to October) price or the lean season price (November to April). Apart from representing a price that farmers can potentially face in the maize market, the lean season maize price also represents the opportunity cost to farmers of purchasing maize if they are not able to produce enough maize to avoid household-level, seasonal maize deficits. The import parity price of maize is also considered in order to account for the government’s opportunity cost to home production of having to import maize. Accounting for all of these maize output prices will help to provide a broader picture of the expected profitability of fertilizer use.

The present paper also extends the scope of previous work on fertilizer profitability by using the NUE and profitability estimates that are likely more internally and externally valid than in previous studies from Malawi. For, example, the NUE estimates used in the official evaluation of Malawi’s FISP is assumed to be 16.8 on average (Dorward et al. 2008). These estimates are based on work from the late 1990s from the Malawi maize productivity task force (MPTF) and use data on master farmer fields to estimate NUE (Benson 1999). Arndt et al. (2015) use the estimates from the MPTF to employ a CGE model of benefits from FISP and find that with an NUE of 16.8, FISP generates an economywide benefit/cost ratio of 1.62 when spillover benefits are considered in addition to direct benefits from increased maize production. They find that the benefit-cost ratio falls below 1.0 (break-even level) at an NUE of 13. Harou et al. (2017) also use the MTPF data from the late 1990s to estimate fertilizer profitability in Malawi. The authors find that bundles of fertilizer distributed under FISP (100 kilograms) are almost always profitable on average in Malawi, and profitability of fertilizer is robust to tripling of fertilizer prices, and to a 50% decrease in maize price and to drought. The objective of our paper is to build on the previous literature using the most recent data in Malawi from a nationally representative sample of panel households, along 5
Coordination and monitoring within government was another major weakness of the old industrial policy. It continues to be a weak point today in many governments. However, a number of countries have made serious efforts to break the status quo with new ambitious schemes that attempt to improve coordination. Several illustrations are presented here.

In Brazil, Figure 4 outlines the coordination that was set up for the PDP strategy<sup>35</sup> . One virtue is that it was a first attempt to break out of the disconnected “silos” that traditionally plagued program implementation and replace it with a more whole-of-government approach. As can be seen, the Ministry of Development, Industry and Trade was the general coordinator of the programs in the PDP. It also was a member of a managing council in which the Casa Civil of the Presidency is represented, the organ that linked the PDP to other initiatives such as the one in science and technology (this link apparently was quite effective)<sup>36</sup> .

Figure 4 COORDINATION OF THE PRODUCTIVE DEVELOPMENT POLICY (PDP) <!-- Start of picture text --> Coordination and Management<br>CNDI<br>MDIC Managing Council: Casa Civil,<br>General coordination MF, MPOG, MCT, MDIC<br>ABDI, BNDES, MF<br>Executive-Office<br>SistematicActions HighlightsStrategic Programs forMobilizing Programs toStrengthen Consolidate andPrograms to<br>Strategic Areas Competitiveness Expand Leadership<br>Coord.: ABDI Coord.: MCT Coord.: MDIC Coord.: BNDES<br>Coord.: MF<br>5 programs 6 programs 12 programs 7 programs<br><!-- End of picture text --> MF= Ministry of Finance, MPOG=Ministry of Planning, MCT= Ministry of Science and Technology MDIC=Ministry of Development, Industry and Trade, BNDES= National Development Bank, ABDI=Agency for Industrial Development, CNDI= National Industrial Development Council Source: Government of Brazil

> 35 Interestingly, the cascading of responsibilities is somewhat similar to how Singapore managed its most recent flagship development strategy (Devlin and Moguillansky, 2011).

> 36 Personal rapport also matters in this issue and that apparently existed between the teams of the BNDES and the Ministry of Science, Technology and Innovation.

26
for taking a relativist approach.<sup>5</sup> It may well be that the average Colombian or Pole has a higher economic standard of living than the average Western European or American in 1930; however, they still aspire be as well off as the average Western European or American today.

**Figure 2 (a) and (b):  Two Views on a “Trap”—Relative and Absolute MIC Thresholds**

<!-- Start of picture text --> (a) LAC and Colombia: Ratio of GDP per capita relative (b) G7 and Modern MIC comparators: Absolute levels<br>to the United States of income per capita (constant, PPP)<br>16000<br>0.4 US<br>14000<br>0.35<br>Canada<br>12000<br>0.3 Poland (2008)<br>United<br>10000 Kingdom<br>0.25<br>France<br>8000<br>0.2 Colombia (2008)<br>6000 Germany<br>0.15<br>LAC Aggregate<br>4000 Italy<br>0.1<br>0.05 Colombia 2000 Japan<br>0<br>0 1900 1904 1908 1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968<br>1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Source: Authors’ elaboration based on data from Maddison (2010).

The relative or absolute approach also has an impact on the type of analysis that one might undertake to understand the dynamics of a presumed MIC trap. The relative approach immediately takes one down the path of two relevant threads of the economics literature: (a) absolute convergence (or the lack thereof); and (b) inter-country distribution of income. On the former, there is a vast theoretical literature on neo-classical growth models,<sup>6</sup> and a vast empirical literature on testing convergence or lack thereof (Pritchett, 1997). On the second thread, the literature on the world distribution of income has noted that, in recent decades, widening global gaps between rich and poor individuals have been driven more by increases in the difference between countries’ incomes per capita than by differences in income across households within countries (Bourguignon and Morrison, 2002; Milanovic, 2005).

The absolute definition might lead one to focus more on why some countries enter actual stagnation rather than failed “catching up.” Here issues of macroeconomic stability may have played a leading role—as noted, in particular, in the literature on the Latin American experience with periodic debt crises of the last quarter century (Calvo, 1998; Calvo and Reinhart, 2002; Reinhart et al, 2003). The stagnation in per capita GDP levels also relates to the literature on

> 5 For a new “weakly relative” measure that makes an explicit accounting for social cohesion, see Ravallion and Chen (2011). For the implications for global poverty measurement, see Chen and Ravallion (2012).

> 6 Parente and Prescott (2005), and Banerjee and Duflo (2005) present surveys from different perspectives.

5
Table 9: Effect of electricity access and national identity

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||All||Urban|Rural|
|Electricity grid in the community|0.121***|0.253***|0.139***|0.107|0.064|
||(0.036)|(0.047)|(0.038)|(0.085)|(0.045)|
|Electricity G X President’s Ethnicity|-0.188***|-0.298***|-0.181***|-0.093|-0.133|
||(0.063)|(0.089)|(0.062)|(0.162)|(0.084)|
|Share of President’s Ethnic Group|-0.003|0.107|0.021|-0.110|-0.006|
||(0.058)|(0.081)|(0.057)|(0.161)|(0.063)|
|President’s Ethnicity X Polity IV||-0.076***<br>(0.014)||||
|Polity IV Project Index 2013||0.017**<br>(0.008)||||
|ElectricityCommunity X Polity IV||-0.031***<br>(0.008)||||
|Electricity X Pres. Ethn. X Polity IV||0.027*<br>(0.015)||||
|Opposition|||-0.071***<br>(0.027)|||
|ElectricityCommunity X Opposition|||-0.046<br>(0.033)|||
|Observations|38,170|38,170|38,170|14,669|22,922|

Robust standard errors in parenthesis and clustered at the PSU level. Estimations are done using OLS. The survey sampled across 7,137 communities/towns (4,435 in rural areas, 2,702 in urban areas). The dependent variable is the response to the question whether respondents identify more with their ethnic group or their country. Responses are ordered in the following way: 1. identify only with their ethnic group; 2. identify more with their ethnic group than their country; 3. Identify with both equally; 4. identify more with the country than the ethnic group; 5. identify only with the country. All regressions in the table control for individual/household-, community-, and country-level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score 31
certain about the future, as evidenced by the considerable wide dispersion of their expectation. In particular, the expected change in sales in SAR deviates from the average by 36%, which is higher than that in SSA (22%) and EAP (18%) (Figure 4, panel b). Given that SAR is among the worst-hit regions, pessimistic expectation and high uncertainty are not surprising.

# **3. How are South Asian firms adjusting to the COVID-19 related economic disruptions?**

In this section, we discuss the margins of adjustment on employment and changes in operations models, including digitization, in response to COVID-19.

## **3.1 Employment adjustment**

Even with the damaging effect on sales and financial stability, a majority of South Asian firms, surprisingly, have not _yet_ resorted to massive layoffs (extensive margin). More than half of firms in SAR offered reduced hours and wages or furlough (intensive margin) to cope with the crisis, compared to 19% who resorted to worker layoffs (Figure 5, left panel).<sup>7</sup> The larger adjustment on the intensive margins across firm size categories is apparent in Figure 5 (right panel). This pattern holds across all firm dimensions - sectors, gender of owner and exporter status - as illustrated in the Appendix Figure A.6. While seemingly laudable, the small scale of lay-offs does not imply that jobs in SAR are not insecure. In fact this could perhaps be due to rigid labor regulations or the low share of permanent employees. Moreover, it is unclear how long can South Asian firms sustain this type of adjustment. Without appropriate policy support, the increasing insolvency issues and massive decline in demand may result in related jobs being permanently lost.

<!-- Start of picture text --> Figure 5: Adjustment in employment at extensive and intensive margin<br>Regional/by income group comparison Comparison by firm-size<br>60 58 80<br>55<br>53<br>43 44 60<br>40<br>47.7 48.6<br>42.9<br>40<br>20 19 20 21 21<br>11 20 1 5. 8 18 19.4 20.9<br>8.8<br>0<br>Extensive (fired) Intensive (hours, wages, absence) 0<br>Extensive (fired) Intensive (hours, wages, absence)<br>SAR EAP SSA<br>Non-high income (excl. SAR) High income Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>Proportion of firms<br>Probability of adjustment (%)<br><!-- End of picture text -->

## **3.2 Adjustments in operations model**

Digital technology offers an unprecedented opportunity to weather the damage caused by the COVID-19 disaster. Efforts to develop, deploy, and sustain IT systems during “normal” times are paying off in the pandemic (Konstenbaum and Dener, 2020), which can enable some businesses

> 7To furlough means to “lay off or suspend temporarily”, usually without pay.

10
HONG KONG

- 2. Projections NRR-1 by 2030

|_YTo_|Pqputatfin|DW.<br>**ratio**|p*ood|caAth<br>**rate**|**W**|CDR|**TFR**|**Nalot**|**Fmets**|it|S<br>x10O0|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|5779|43.3|1990-94<br>|0.96<br>|13.0<br>|5.6<br>|1.55<br>|75.6<br>|80.9<br>|6<br>|8<br>|
|1995|6069|43.3<br>|1995-99<br>|0.79<br>|11.6<br>|6.0<br>|1.55<br>|76.7<br>|82.0<br>|5<br>|7<br>|
|2000|6313<br>|43.2<br>|2000-04<br>|0.65<br>|11.1<br>|6.4<br>|1.64<br>|77.7<br>|83.0<br>|5<br>|6<br>|
|2005|6523<br>|42.3<br>|200S -09<br>|0.53<br>|11.0<br>|7.1<br>|1.73<br>|78.2<br>|83.6<br>|4<br>|6<br>|
|2010|6696<br>|41.1<br>|2010-14<br>|0.44<br>|11.0<br>|7.6<br>|1.81<br>|78.7<br>|84.2<br>|4<br>|5<br>|
|2015<br>|6846<br>|45.3<br>|2014-19<br>|0.35<br>|10.9<br>|8.1<br>|1.89<br>|79.2<br>|84.5<br>|4<br>|5<br>|
|2020<br>|6967<br>|52.3<br>|2020-24<br>|0.27<br>|10.8<br>|8.4<br>|1.96<br>|79.7<br>|85.4<br>|3<br>|5<br>|
|2025|7062|63.1|202S-49<br>|-0.15<br>|10.7<br>|12.2<br>|2.06<br>|80.7<br>|86.8<br>|3<br>|4<br>|
|2050|6800<br>|80.6<br>|2050-74<br>|-0.21<br>|11.4<br>|13.5<br>|2.06<br>|81.7<br>|88.2<br>|2<br>|3<br>|
|2075|6453<br>|74.4<br>|2075-99<br>|-0.01<br>|11.7<br>|11.8<br>|2.06<br>|82.2<br>|88.9<br>|2<br>|3<br>|
|2100|6437<br>|74.0<br>|2100-24<br>|0.02<br>|11.7<br>|11.5<br>|2.06<br>|82.6<br>|89.5<br>|2<br>|3<br>|
|2125<br>21S0|6466<br>6483|74.5<br>74.8|2125-49|0.G1|11.6|11.5|2.06|82.8|89.7|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>86-89<br>80-84<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. comparison of Country with Region<sup>and Income Group, 1985-90</sup>

<!-- Start of picture text --> TFR eO Growth rate<br>S~~~~~~~~ a<br>Country Region Income group Country Region Incose group Country Region<br><!-- End of picture text --> <!-- Start of picture text --> Country Region Income group<br><!-- End of picture text --> 5S
In the revisited original‐sample villages in 2007, 20 households were resampled per village from the 1994/95 sample and the split households (defined below) were included. In the new villages, 24 households were sampled in each village. The 2010 survey followed the sample households in all 98 villages.

Between the surveys, some household members separated from the original 2007 households to start their own new households or to join other households. In the survey, a split household is defined as a new household in which (i) a member of the original household became the head of a new household, and (ii) the household resides in the same village. The above two conditions define split households in this study; household members who moved out of the original village or simply joined other households within the village are not called splits in the survey. From 2007 to 2010, 9.25% of the 2010 households were split households under the above definition. 12.97% of the sample individuals recorded as household members in 2007 migrated out, i.e., either moved out of their village or joined other households. The proportion is 11.82% once the group is restricted to those aged 20‐55 in 2007.

Anthropometry data were collected in both 2007 and 2010. Child’s height, weight, and his/her mother’s weight were measured in the field. In addition, birthweight was recorded from official sources. However, the age coverage differs between the two rounds. In the 2007 round, children aged 0 to 6 were covered, and the 2010 round extended the age range to cover children aged up to 12. Figures 3a and 3b plot heights and weights over the two rounds, respectively, to show their dynamics. Following the standard procedure, we calculated the height‐for‐age z scores for children aged 0 to 12 and the weight‐for‐age z scores for children aged 0 to 10. In the panel analysis, we use the sample of children aged 0‐6 in 2007 (thus, they are aged 3‐9 in 2010).

The surveys collected consumption and expenditure data using the following formats. For food consumption, which is of our primary interest, respondents were asked to do a one‐week recall to provide information, for each commodity, on quantity consumed and, if purchased, the price and how much of the total consumption is from own production or as gift. The 2007 round used different units of quantity but they were converted into kilogram when the data were processed. The 2010 round recorded quantities all in kilograms when households were interviewed (if necessary, converted in the field). The analysis uses price per kilogram. From the above data, we computed the proportion of calories produced by own production. Two measures were constructed to represent main foods and staple food (see Figures 4a and 4b, respectively). The main foods include (i) staple, (ii) fish, meat, tofu and tempe, (iii) beans (pulses), (iv) vegetables and fruits, and (v) milk and eggs.

Price indices were constructed from the food consumption data to understand the general trend of food price changes. We computed the Laspeyres and Paasche indices using the 2007 and 11
17 econometric or statistical issue that crops up only in or disproportionately in the health field. Like ̳Economic evaluation‘ ̳Health statistics and econometrics‘ is a relatively mature development within health economics. ̳Health and its value‘, although not a major focus of work for most health economists, contains the most cited article of all (Acemoglu et al., 2001) as well as many articles relating to the concept of health, its measurement and its valuation. (Valuation questions specifically in the context of economic appraisals were placed in ̳Economic evaluation‘.) ̳Human resources‘ focuses mainly on physicians and their professional behavior patterns. ̳Demand for health care‘, despite its centrality for economists, was a relatively small group but dominated by the extremely heavily cited classic by Grossman (1972). ̳Markets in health care‘ is the smallest group – perhaps surprisingly. There is, of course, a sense in which the idea of markets, exchange and the interaction of demand and supply underlie nearly all health economics and this may help to explain the puzzle. Beyond Shleifer‘s (1985) classic, which far transcends health, most of the items in this group were much less frequently cited than those in the other groups.

Table 3 shows the topical breakdown of the 50 most-cited publications in each decade. (The numbers do not add to 50 necessarily because some publications were not allocated to a topic.) Some exhibit no clear trend: the 1990s saw classic papers appear in the areas of ̳Health and its value‘ and ̳Economic evaluation‘. By contrast, the topics ̳Medical insurance‘ and ̳Supply of health services‘ show a clear downward trend, while the topics ̳Determinants of health and ill-health‘ and ̳Health statistics and econometrics‘ show a clear upward trend. These diverging trends may also reflect of course the prior discovery effect: the early years saw a large volume of publications on insurance and supply issues, and the big breakthroughs had already occurred by the end of the 1980s. But it seems likely that the changes also reflect a broadening of interest in health economics.

The impression of a broadening scope of the sub-discipline is indicated in Figures 4 and 5, which are based on the commonest 150 words in the titles of all articles, not just the most cited ones. Judging from the title words, the topical focus of health economics articles has indeed broadened over the four decades, away from hospitals, insurance and the demand for health and health care, to a more diverse set of topics, with a greater degree of attention to specific
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The recent experience of the Morupule coal-fired generation project, half of which has been funded with private resources, illustrates Botswana’s considerable to raise private finance for other areas of infrastructure; such as power.

**Figure 17. Botswana’s pattern of capital investment in infrastructure differs from that of comparator countries**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>Botswana<br>1.8<br>1.6<br>1.4 MIC<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Public ODA Non-OECD Private<br>Source : Derived from Briceño-Garmendia, Smits, and Foster (2009).<br>Note: Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic<br>Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and<br>sanitation; MIC = middle-income country.<br>Figure 18. Many African countries capture more private investment than Botswana<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% of GDP<br>% GDP<br><!-- End of picture text --> _Source:_ Private Participation in Infrastructure Database (2009). _Note:_ GDP = gross domestic product; ICT = information and communication technology.

# Efficiency gap

As of 2008, around $68 million of additional resources were lost each year by inefficiencies of different kinds (table 16). Budget underexecution was a significant issue in the transport sector, leading to 34
<!-- Start of picture text --> 100<br>KGZ<br>BES R yee<br>75 MMR_ aewanare fhBeaeeSBe<br>ware ae MAR ATI BS"<br>SBEAT RED MMR SEB<br>G ONG TAS LVA aver 3, ial<br>ao ne Rd o ay<br>2 oe CO} D Wee =<br>we IRQ aii “<br>95 SORIER! syR °<br>MHL at<br>0<br>0 25 50 75 100 i) 25 50 75 100<br>2016 value<br><!-- End of picture text -->
**Table S1:** Baseline means and balance

||Baseline|Dropout||Baselin|e Schoolgirl||
|---|---|---|---|---|---|---|
||Mean|(s.d.)||Mean(s.d|.)|p-value|
||Control group|Conditional<br>group|Control<br>group|Conditional<br>group|Unconditional<br>Group|(CCT-<br>UCT)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Urban Household|0.181|0.129|0.346|0.478|0.418|0.726|
||(0.385)|(0.335)|(0.476)|(0.500)|(0.494)||
|Mother Alive|0.783|0.749|0.839|0.800|0.828|0.431|
||(0.413)|(0.434)|(0.368)|(0.401)|(0.378)||
|Father Alive|0.656|0.649|0.709|0.718|0.76|0.341|
||(0.476)|(0.478)|(0.454)|(0.451)|(0.428)||
|Household Size|6.120|6.104|6.375|6.341|6.659|0.156|
||(2.388)|(2.617)|(2.262)|(2.134)|(2.063)||
|Asset Index|-0.831|-0.743|0.632|1.100|1.373*|0.572|
||(2.233)|(2.484)|(2.575)|(2.721)|(2.444)||
|Age|17.579|17.162|15.228|14.919|15.466|0.002|
||(2.397)|(2.478)|(1.904)|(1.828)|(1.926)||
|Highest Grade Attended|6.105|5.940|7.506|7.262|7.928**|0.004|
||(2.856)|(2.864)|(1.651)|(1.601)|(1.587)||
|Never Had Sex|0.315|0.294|0.800|0.807|0.790|0.682|
||(0.465)|(0.456)|(0.400)|(0.395)|(0.408)||
|Ever Pregnant|0.445|0.420|0.021|0.029|0.029|0.964|
||(0.498)|(0.494)|(0.144)|(0.169)|(0.168)||
|Chi-squared joint test of<br>orthogonality (p-value)||0.168||0.122|0.121|0.032|

_Notes:_ Mean differences statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence. Stars on the coefficients in columns (2) indicate significantly different than the control group for baseline dropouts.Stars on the coefficients in column (4) and (5) indicate significantly different than the control group for baseline schoolgirls. Means are weighted to make them representative of the target population in the study EAs.

81