rafmacalaba/datause-encoder-data huggingface dataset:
reason, and since temperature was not significant in the more robust model specification when the WASP variables were included, we do not conclude that there is a meaningful temperature result.

The regression analysis using industrial value added and poverty headcount as the dependent variables did not yield any statistically significant results (Not reported here). In the case of industrial value added this represents a marked difference with the findings of the Sub Saharan Africa – only analysis. In that case, industrial value added was sensitive to drought. The large percentage of electricity generated through hydroelectricity, and the portion of industrial activity that is related to agriculture are the likely causative links. However, using the global dataset the wide heterogeneity of industrial activity and energy sources reduces the effects of climate on industry in most developed countries.

**Table 5: Agricultural Value Added with Country FE’s**

Dependent variable: Agricultural Value Added

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip|0.01<br>(0.00)**||0.01<br>(0.00)|||||||
|Temp||-2.32|-2.31||-2.10||-2.35||-1.98|
|||(0.17)*|**(0.17)*|**|(0.22)*|**|(0.22)***||(0.28)***|
|WASP_minus1||||||-1.18|-0.75|-0.48|-0.16|
|||||||(0.51)*|* (0.50)|(1.11)|(1.09)|
|WASP_plus1||||0.31|0.23|||0.41|0.50|
|||||(0.58)|(0.56)|||(1.26)|(1.23)|
|Observations|4286|4286|4286|2360|2360|2523|2523|1418|1418|
|Countries|171|171|171|166|166|167|167|120|120|
|R-squared|0.001|0.045|0.046|0.000|0.040|0.002|0.048|0.000|0.038|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1).

The analysis of poverty headcount was not informative due to the small sample size (Not reported here).

For most countries only a small number of observations were available.

Lagged regression analyses were conducted using the data for dependent variables for the year that follows the values of the independent variables. These analyses are an attempt to identify impacts that might be delayed in their effect on economic activities. For example, when a drought hits, a household 17
To explore more this angle, we use GDELT, a data set that contains a snapshot of global news published and distributed every 15 minutes and translated from 65 languages. The GDELT database covers 98.4 percent of the global news (GDELT 2020). This data set records the main themes of each news piece, and in that way, allows us to estimate the volume of news about a given topic in a given country over time. Since news events compete for limited space in media attention, increased coverage of certain events happens at the expense of coverage of other events (Eisensee and Stromberg, 2007). Increased coverage of news related to the COVID-19 pandemic may imply a displacement of news covering other topics, and therefore indicate how much public debate is focused on the pandemic. To the extent more media attention is focused on the pandemic, it may be harder for citizens to “return to normal” slowing the economic recovery associated with the reopening process. At the same time, media coverage is a reflection of the pandemic and economic situation itself. Hence, while causality is difficult to ascertain, press coverage is likely to both shape and be influenced by the evolution of the pandemic and the economic activity.

Table 5 presents the result of this exploratory analysis based on Specification (5). Using data from GDELT, we include a daily indicator of relative frequency of different themes in the news for every country as an additional regressor. There is a strong and significant correlation between the increasing coverage by the press of themes related to “quarantine” and “pandemic” and the electricity consumption. At the same time, a higher presence of the theme “corruption” in the news might indicate a trend to the “return to normal,” which is reflected in the positive and significant coefficient on this indicator. These correlations indicate that the news media can play an important role in shaping public perception and response to the current global health emergency and might affect as well as reflect the reopening trajectory.

# **7. Addressing endogeneity of government policies**

Two main concerns about the validity of our result are the potential endogeneity of the wait period and the time-varying effects of the initial conditions on energy consumption. A government might time the start of reopening based on the economic situation in the country and select the reopening date to maximize the speed of recovery. For example, a country with a large tourism industry might want to open earlier to attract tourists. The influx of tourists stimulates the economy and attracts more tourists, accelerating the recovery. Also, as we argue in Section 5, trust in government is an important determinant of economic recovery. Governments might be sensitive to public 20
Baron, J. (2000). _Thinking and Deciding_ . New York: Cambridge University Press.

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Benartzi, S., R. H. Thaler. (1995). Myopic loss aversion and the equity premium puzzle. _Quarterly Journal of Economics, 110_ **,** 73-92.

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Boyd, R. and Richerson, P. J. (1995). Why Does Culture Increase Human Adaptibility? _Ethology and Sociobiology. 16_ : 125–143.

Bowles, S. (2004). _Microeconomics: Behavior, Institutions, and Evolution_ . Princeton University Press, pp. 27–29.

Brewer, M. B. (1979). In-group bias in the minimal intergroup situation: A cognitivemotivational analysis. _Psychological Bulletin, 86,_ 307-324.

Camerer, C. (2000). Prospect Theory in the wild. In: D. Kahneman & A. Tversky (Eds), _Choices, values and frames_ . New York: Russell Sage.

Cialdini, R.B., (2007). Descriptive social norms as underappreciated sources of social control. _Psychometrika, 72_ , 263-268.

Dake, K. (1991). Orienting dispositions in the perception of risk: An analysis of contemporary worldviews and cultural biases. _Journal of Cross-Cultural Psychology, 22_ , 61-82.

Damasio, A. (1994). _Descartes' Error: Emotion, Reason and the Human Brain_ . Penguin Books.

38
- 24 -

|T2013|0.537|(0.209) ***|0.507|(0.211) **|0.502|(0.211) **|0.503|(0.211) **|
|---|---|---|---|---|---|---|---|---|
|T2014|1.018|(0.198) ***|0.904|(0.212) ***|0.909|(0.213) ***|0.927|(0.217) ***|
|T2015|0.134|(0.216)<br>|<sup>-0.053</sup>|<sup>(0.240)</sup><br>|<sup>-0.035</sup>|<sup>(0.242)</sup><br>|<sup>-0.020</sup>|<sup>(0.244)</sup>|
|T2016|0.379|(0.214) *|0.176|(0.235)|0.218|(0.237)|0.246|(0.241)|
|Constant|1.928|(1.440)|1.703|(1.428)|1.702|(1.423)|1.710|(1.424)|
|Obs.|4639||4639||4639||4639||
|Of which, truncated|3595||3595||3595||3595||
|No. of groups|444||444||444||444||
|Wald chi2|.||.||.||.||

The dependent variable is the log of crop sales at market. Cluster standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Turning to other types of household income, no impact is found on livestock ( **Table 9** ). This may be able to be understood because pastoralism has a certain mobile aspect and is less transport-intensive. In Ethiopia, many pastoralists are scattered in remote areas. Thus, the quality of transport infrastructure may not matter much, though livestock can be reared using intensive methods in non-pastoral areas and obtaining feed and forage for the stock and delivering the stock to slaughter and urban markets is potentially more dependent on good trunk roads.

On the other hand, non-agricultural income seems be affected positively by improved RSDP roads and negatively by URRAP roads. This can be interpreted to mean that trunk road improvements generated some indirect impacts in the project areas. Given the increase in crop production and market sales, local economies may be boosted by increased activity in non-agricultural sectors. Interestingly, this impact seems to have intensified over time: the time-specific fixed-effects are significant and positive. The coefficients of _T_ become larger the more time passes after the road improvement. The current household survey data do not allow the possible reasons behind this non-agricultural growth to be identified. Some additional data will now be examined.
However, we also find that recall is not much different over 12 months compared to the sum of monthly data collected four times during the year. In the fifth round of the SLMS, in April 2006, we asked firms for their sales over the previous 12 month period (from 1 April 2005 through 31 March 2006). We then compare this to the total 12 month sales computed from the quarterly surveys. Each quarterly survey asks sales for each of the previous three months (recall of one, two and three months). Figure 2 shows the two measures are very close to one another, with a Pearson (Spearman) correlation of 0.763 (0.841). Mean sales are only 3 percent lower with annual recall than with the quarterly reports. This suggests that while some precision is lost in going from monthly to longer recall periods, firm owners can give quite consistent recall of annual sales.

# **7. Bookkeeping Experiments**

One obvious solution to recall problems, fungibility issues, and mismatching of expenses and revenues is to try and get microenterprise owners to keep better records. This approach has long been a staple of household consumption surveys, with some surveys asking households to keep diaries of all expenditures during a set period. Deaton and Grosh (2000) summarize several studies of this nature, including experiments comparing diaries to recall. The use of diaries was found to increase food expenditure by 46 percent in Latvia and 33 percent in Armenia compared to recall, with more mixed results for nonfood items. However, Deaton and Grosh note that there is evidence that the rate of reporting declines over time, with more reported in the first week of a two-week diary than in the second week.

However, to our knowledge, the diary approach has not been pursued experimentally with firms. We therefore set out in both the KMS and SLMS surveys to see how the use of simple ledgers can improve the collection of data from small enterprises. We designed simple ledgers for firms, with five columns to be filled out:

- (a) Expenses on goods and raw materials

- (b) Other expenses

- (c) Goods taken from the enterprise for household use

16
# **6 Conclusion**

Structural gravity is applied to model barriers to services trade across many sectors, countries and time based on development of an integrated data set for services production and trade. Border barriers are flexibly inferred relative to internal costs. An important regularity is that relative border barriers are declining in the size of sectoral activity. The cause of this external scale economy merits further investigation. We also find that border barriers have generally fallen over time but also identify differences across services sectors and countries; in particular, border effects for the smallest economies have remained stable, giving rise to a divergent pattern across countries.

The good fit and intuitive interpretation of the results encouraged development of a projection model whereby services production and trade data can be generated believably. A crucial step in this procedure decomposes border barriers according to their structural components, and the empirical estimation of the resultant model sheds light on the role of institutions, geography, size and digital infrastructure as determinants of border barriers. The success of the projection method suggests that it could be usefully applied to analyze developing countries’ services trade. More generally, beyond services trade, for which the missing data problem is especially severe, our projection method may be useful when other trade or production data quality is suspect.

The full general equilibrium effect of border barriers in services trade includes their effect on multilateral resistances (see Agnosteva _et al._ , 2014). We leave this extension for future work. Such general equilibrium analyses may also combine goods and services trade, for which the methods and results developed in this paper would be useful.

32
**<u>Table 10.  Probit estimation results of correlates of assistance from a skilled birth attendant</u>**

||Model 1|Model 2|Model 3|
|---|---|---|---|
||(Program areas)|(Poorest 20%)|(Richest 20%)|
||Coef(t-ratio)|Coef(t-ratio)|Coef(t-ratio)|
|Wealth|0.000***<br>(5.16)|-|-|
|Year 2005|0.129*|-0.084|0.170|
||(1.90)|(-0.53)|(1.52)|
|Year 2011|0.349***|0.227|0.449***|
||(5.12)|(1.57)|(3.99)|
|Y 2014|0.640***|0.656***|0.804***|
|ear|(8.45)<br>|(4.60)|(6.35 )|
|Interaction: Time tend X wealth_index|-0.000<br>(-0.73)|-|-|
|Rl|-0.866***|-0.329*|-0.954***|
|ura|(-11.25)|(-1.65)|(-10.54)|
|Ae|-0.063***|-0.043|-0.078***|
|g|(-4.48)|(-1.27)|(-2.92)|
|Education|0.452***|0.151|0.695***|
||(9.63)|(1.25)|(8.18)|
|Region dummies (historically<br>||||
|disadvantaged)||||
|_Afar_|-0.178**|-0.662***|0.016|
||(-1.96)|(-4.22)|(0.11)|
|_Sli_|-0.147|-0.098|0.040|
|_oma_|(-1.24)|(-0.69)|(0.19)|
|_Bhl_|0.228***|0.178|0.269**|
|_ensangu Gumuz_|(3.28)|(1.07)|(2.31)|
|_Gbll_|0.654***|0.994***|0.585***|
|_amea_|(8.29)|(7.35)|(3.90)|
|Ctt|-0.901***|-1.907***|-0.620***|
|onsan|(-10.07)|(-6.98)|(-4.84)|
|Number of observations|36,387|9,935|6,011|

Note: *** p<0.01, ** p<0.05, * p<0.1 37
**Table 13. Percentage Change in Output From Full Uruguay Round With and Without Projections**

|||||||**_Sector_**|**_s_**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Regions_**||**_PAar P_**|**_Food_**|**_NRes_**|**_Text_**|_WApp_**_L_**|**_Mnfc T_**|**_m&ea H_**|**_Mnfc U_**|**_h&cs_**|**_Svces_**|
|USC|1992|2.8|0.7|0.4|-14.3|-45.0|0.1|1.0|0.2|0.2|0.2|
||2005|2.8|1.1|0.2|-9.8|-29.4|-0.2|0.6|-0.1|0.1|0.1|
|EU|1992|-3.0|-0.9|0.4|-5.4|-47.6|0.2|1.0|0.2|0.4|0.4|
||2005|-1.9|-0.7|0.4|-3.0|-22.2|0.3|0.2|0.4|0.3|0.3|
|JPN|1992|-6.9|-2.1|0.7|1.3|-2.5|0.7|-0.2|0.5|0.4|0.2|
||2005|-7.2|-2.1|0.2|1.8|-1.6|0.6|0.3|0.3|0.3|0.2|
|KOR|1992|-8.7|4.4|-0.9|34.8|31.9|4.9|-3.3|1.4|1.2|-0.4|
||2005|-8.8|3.3|-0.7|34.0|34.2|3.6|-2.4|0.6|1.0|-0.3|
|TWN|1992|1.2|4.9|-0.5|7.4|7.6|-1.0|-2.8|-1.1|0.5|-0.2|
||2005|1.4|5.1|-0.3|3.3|2.3|-0.6|-1.3|-1.2|0.4|-0.3|
|HKG|1992|2.5|0.1|10.1|12.2|10.9|2.5|4.1|1.7|-1.5|-2.4|
||2005|2.8|1.0|6.3|10.6|0.7|2.9|5.3|2.1|-1.4|-1.2|
|CHN|1992|1.4|-2.3|-2.2|6.5|35.0|-3.7|-5.3|-3.2|0.2|0.0|
||2005|1.0|-2.1|-2.2|5.9|36.3|-4.1|-5.7|-3.2|0.1|-0.3|
|IDN|1992|-2.2|-1.2|-2.5|46.1|219.0|-5.4|-6.1|-4.1|1.1|-0.2|
||2005|-2.5|-1.6|-3.1|53.3|253.3|-7.0|-7.3|-5.2|1.0|-0.2|
|MYS|1992|-12.2|27.6|-4.3|29.2|51.5|-1.9|-0.1|18.3|1.7|-1.3|
||2005|-13.1|18.2|-3.5|31.0|65.9|-2.1|-0.3|15.0|1.5|-0.6|
|PHL|1992|-16.4|8.9|-1.9|49.9|117.0|-0.6|3.6|-1.8|0.9|-0.9|
||2005|-18.0|6.3|-1.4|51.5|133.6|-0.9|3.0|-2.3|1.1|-0.3|
|THA|1992|-5.5|-2.1|-3.8|17.9|47.5|-1.4|1.3|-0.8|0.9|-1.1|
||2005|-6.3|-3.2|-3.0|17.4|51.5|-3.3|2.1|-2.1|0.7|-0.8|
|LTN|1992|0.6|0.3|0.5|-3.6|-7.5|0.1|0.0|0.0|0.1|0.0|
||2005|0.5|0.3|0.0|0.0|1.0|-0.3|-0.8|-0.5|0.1|0.0|
|SSA|1992|0.1|0.0|1.5|-9.1|-37.3|1.0|1.7|0.5|0.0|0.2|
||2005|0.1|0.1|0.6|-4.4|-17.4|0.1|0.3|-0.1|0.0|0.1|
|SAS|1992|1.1|1.2|-3.4|12.5|59.5|-2.0|-10.3|-6.8|0.1|-0.2|
||2005|0.9|1.3|-3.3|12.4|65.1|-2.3|-10.8|-8.4|0.0|-0.4|
|ROW|1992|1.4|-0.5|0.1|-9.7|-19.8|1.4|1.8|0.5|0.1|-0.1|
||2005|1.4|0.4|0.0|-5.2|-10.3|0.6|0.9|0.0|0.1|-0.1|

31
for staples may offer some buffer for households in Mozambique against the negative welfare effects of price shocks for these commodities.

# **3.4 Recent trends in food production, imports and prices in Mozambique**

Maize and cassava are two of the most important crops grown by farmers in Mozambique while cultivation and production of rice is relatively lower. Production of these staples has traditionally been fairly volatile, and this appears to be the case in recent years as well (Figure 4).<sup>2</sup> In most years Mozambique is a net importer of maize and rice, while most of the cassava production and consumption is done domestically. The predominant source of maize is South Africa (Figure 5), while rice is imported from Thailand as well as some other major countries (Figure 6).

Our analysis focuses on the price spike that occurred in 2016 (Figure 7). The underlying reasons attributed to the food price spike range from unanticipated weather shocks to macroeconomic factors owing to pressure in the currency markets. The overall price of food as measured by the FAOSTAT food and non-alcoholic beverages CPI shows a slight increase at the start of 2015, but climbs down by the mid-2015 (Figure 7, Panel A). The prices start to climb rapidly once again towards the end of 2016 and then continue to rise through most of the year. This overall pattern is reflected in the prices of maize, rice and cassava as well.

Panels B through D of Figure 7 portray the mean of the retail prices for maize, rice and cassava respectively, across provinces.<sup>3</sup> The market-level price data are from Mozambique’s Agricultural Market Information System (Sistema de Informação de Mercados Agrícolas, SIMA). SIMA collects price data on a weekly basis at producer, wholesale and retail levels from main markets across the country. However, consistent reporting is available mostly for retail prices only.<sup>4</sup> There is an upward trend in prices across all the three commodities starting around July 2015, with a higher level of prices all through 2016. Table 3 shows the average increase in the retail price (relative to the increase in CPI Food) for each province between 2014 and 2016. Maize, the key staple of the Mozambican diet, experienced the highest increase in prices, rising by more than 88% in 2016 compared to the average price in 2014. There is substantial spatial variation in price rise

> 2 The main factor for the production shock observed in 2013 is the widespread agricultural damage from flooding during Jan-Feb 2013.

> 3 Cassava prices are reported consistently only for the provinces of Zambezia, Nampula, Niassa and Cabo Delgado. 4 The data used in this study correspond to the monthly reports of prices from SIMA.

16
**Table 4.1:  The Prosperity Gap in 2019 – By Regions, by Income Groups, and for Selected** **<u>Countries</u>**

|||**Prosperity Gap,**<br>**Multiple by Which**<br>**Income Must Increase**|**Share of Population**<br>**below Prosperity**<br>**Threshold, %**|**Millions of People**<br>**below Prosperity**<br>**Threshold**|
|---|---|---|---|---|
|e<br>|Low income<br>|12.4|99.2|663|
|om<br>u|Lower middle income<br>|6.8|98.3|2,864|
|Inc<br>gr|Upper middle income<br>|3.1|85.9|2,463|
||High income|0.9|18.9|234|
||East Asia & Pacific|3.2|90.0|1,893|
||Europe & Central Asia|2.5|75.5|374|
|on|Latin America & Caribbean|3.5|81.1|521|
|egi|Middle East & North Africa|5.0|93.8|370|
|R|South Asia|6.7|99.1|1,820|
||Sub-Saharan Africa|11.4|98.8|1,094|
||Rest of the world|0.8|13.9|153|
||Burkina Faso|9.3|98.1|20|
||Bangladesh|6.5|99.4|162|
||Bolivia|2.5|76.3|9|
||Brazil|3.6|77.7|164<br>|
||China|2.8|88.5|1,246<br>|
||Colombia|4.2|85.8|43<br>|
|ries|Germany|0.5|7.7|6<br>|
|unt|Ethiopia|8.2|99.6|112<br>|
|co|France|0.6|9.8|7<br>|
|ect|Indonesia|5.2|98.1|265|
|Sel|India|6.9|99.1|1,354|
||Japan|0.8|16.5|21|
||Mali|7.3|99.5|20|
||Nigeria|10.1|99.9|201|
||Peru|3.4|88.7|29|
||United States|0.9|11.0|36|
||South Africa|7.8|88.1|52|
||Global|5.0|81.0|6,224|

**Notes:** This table reports the global estimate of W(y, z) using the prosperity threshold of $25 per person per day in 2017 $PPP, the share of the population living below the prosperity threshold, and the number of people living below the prosperity threshold in 2019 for various income groups, regions, and select countries. Income groups are classified according to World Bank FY21 definitions, which uses data from 2019 (see <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups).</u> Regions are classified according to definitions used in the Poverty and Inequality Platform (see <u>https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#regionsandcountries).</u>

30
Table 6: Dependent Variable: ln(number of import varieties)

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.114***|-0.208*|-0.106***|-0.134***|
||(0.016)|(0.124)|(0.018)|(0.013)|
|�2002|-0.110***|0.216|-0.0990***|-0.128***|
||(0.016)|(0.284)|(0.016)|(0.016)|
|�2003|-0.217***|-0.0606|-0.208***|-0.240***|
||(0.029)|(0.419)|(0.026)|(0.016)|
|�2004|-0.274***|0.186|-0.267***|-0.279***|
||(0.039)|(0.352)|(0.035)|(0.015)|
|�2005|-0.342***|0.0535|-0.335***|-0.360***|
||(0.046)|(0.367)|(0.045)|(0.016)|
|�2006|-0.197***|0.122|-0.183***|-0.215***|
||(0.054)|(0.336)|(0.054)|(0.019)|
|�2007|-0.351***|0.131|-0.344***|-0.356***|
||(0.090)|(0.345)|(0.081)|(0.020)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|0.0144|-0.106|0.0171|0.0104|
||(0.025)|(0.332)|(0.019)|(0.020)|
|�wL<br>PY<br>�<br>it|-0.0327|0.899|-0.0374|-0.0608|
||(0.038)|(1.033)|(0.054)|(0.059)|
|N|17871|858|16726|28925|
|R-sq|.0571|.0609|.0589|.0565|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

50