rafmacalaba/datause-encoder-data huggingface dataset:
the deseasonalized consumption measures computed in such manner from De Magalh ̃aes et al. (2015), who find that the amount of consumption in the pre-harvest month of March is 0.4 log points below the annual average in Malawi.<sup>11</sup>

# **2.2 Unsold Agricultural Production and Extended Household Income**

The extraordinarily detailed information on agricultural production is one of the most novel aspects of the ISAs. The new agricultural modules substantially extend previous LSMS surveys. The correct account of agricultural production (and income) has proven difficult in the past, as in such settings most rural households represent small-scale farms that sell only a small fraction of what they produce in the open market. ISAs provide detailed collection of agricultural production (separately for each main harvest season, including permanent crops) and their costs (including rentals of capital equipment and structures), fishery production and their costs, as well as business income, wages, and informal sources of revenue (e.g., “ganyu” in Malawi). Agricultural information is available mostly at the plot level (household may farm non-contiguous plots), and fishery and business income are available at the household level. Information on wages for each type of formal and informal activity are available for each and all members in the household (not only husbands and wives). This set of information allows us to construct precise extended-household income measures.

# **2.2.1 Unsold Agricultural Production**

In very poor agricultural economies such as the ones we study, assigning a monetary value to unsold agricultural production is essential to the measurement of household income. The reason is that unsold production represents the majority of total household production. We use maize in Malawi to illustrate this issue. First, we convert maize production into the same units to find that maize represents 69% of the total agricultural production in kilograms. Most households produce maize as their main source of food and calorie intake,<sup>12</sup> but few sell it. Of the 9,280 households in the Malawi survey who report producing maize, only 1,618 (17%) report selling any maize. Among the top 20% of the income distribution in rural areas, 30% of households report selling maize; among the bottom 20%, only 6% sell maize. It is noteworthy that even the rich keep their own production for consumption. Moreover, sales among the poor may indicate

> 11There, we further show that consumption measures that are not deseasonalized tend to underestimate inequality. We use the Integrated Household Survey of 2004-05 (also rolled over the entire year) as the second year-month observation to deseasonalize consumption for Malawi. For Tanzania, we use the ISA 2008 and for Uganda, the ISA 2005.

> 12See the Malawi Bioenergy and Food Security country brief by the United Nations Food and Agriculture Organization.

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Survey (IFLS) 2014.<sup>4</sup> The estimates from the full sample (1950-1989 birth cohorts) suggest that the conditional variance in children's schooling declines with father's education in all three countries, thus conrming the conjecture that the children born to more educated fathers enjoy double advantages in the form of a lower variance in addition to a higher expected (mean) schooling attainment.

We nd evidence of substantial heterogeneity across countries, geographic location (rural vs. urban), gender, and birth cohorts. Conditional variance in children's schooling is the highest in India (18.76) and the lowest in Indonesia (13.58), with China in between (16.83). The inuence of father's education on conditional variance of children's schooling follows a reverse cross-country pattern: Indonesia (-0.51), China (-0.48), and India (-0.38). Conditional variance is higher in the rural areas in a country, but the inuence of father's education on conditional variance is smaller in magnitude. The rural-urban dierence is specially striking in India where the estimate is negative and large (-0.77) in the urban sample but small and statistically not signicant (10 percent level) in the rural sample (-0.022). In contrast, the rural-urban dierence is small in China: -0.55 (urban) and -0.52 (rural). We also nd substantial gender dierences with a larger negative eect on sons. The gender dierences in India are the starkest: the estimated eect is negative in the sons sample, but positive in the daughters sample.<sup>5</sup> The results from cohort-based analysis suggest that the negative eect of father's education on conditional variance has become stronger over time in all three countries. In the rural and daughter's samples in India and Indonesia, the estimate turned from positive in the 1950s cohort to a strong negative eect in the 1980s cohort.<sup>6</sup> We check some alternative explanations for the observed relations between conditional variance in children's education and father's education. We provide evidence that functional

> 4These surveys are chosen to ensure that the estimates are not biased because of sample truncation due to coresidency restrictions. It is well known that truncations biases the estimated variance downward (Cohen (1991)). Recent evidence suggests that coresidency causes substantial downward bias in the estimate of relative educational mobility as measured by IGRC; see Emran et al. (2018).

> 5Government policies and social norms can make the relation between father's education and conditional variance of children's schooling positive. For example. gender based social norms such as son preference and Purdah may results in low conditional variance in low educated households as parents target a reference level of schooling for the daughters, and the girls' schooling attainment bunches around that reference point. This can also give rise to a positive eect in the conditional variance regression. Please see section 2 below.

> 6This suggests that the positive eect in the full sample (1950-1989) found earlier for rural India and the daughters in India is driven by the earlier cohorts.

3
Table A2. 9 Administrative burden on corporations

|**Adminsi**<br>|**trative burde**<br>**_Weight on_**<br>**_compliance_**|**ns for co**|**rporations**<br>**S**|**cale 0-6**|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>**_type (c k )_**|**0**|**1**<br>**2**|**3**|**4**<br>**5**|**6**|**Bulgaria**|**Romania**|**Croatia**|**Albania**|**OECD**|**EU15**|**Poland**<br>|**Slovak Republic**|
|Number of mandatory procedures required to register|||||||||||||||
|a public limited company (pre-|1/4|<=3|<=5<br><=8|<=12|<=16<br><=20|>20|||||||||
|registration+registration)|||||||11|5|21|15|14.9|14.5|28|15|
|Number of public and private bodies to contact to|||||||||||||||
|register a public limited company (pre-|1/4|0|1<br>2|3|4<br>5|6|||||||||
|registration+registration)|||||||-|-|2|2|5.0|5.1|6|8|
|Number of working days required to complete all|||||||||||||||
|mandatory procedures for registering a public limited|1/4|<=16.4|<=32.8<br><=49.2<br>|<=65.6|<=82<br><=98.4|>98.4|||||||||
|company (pre-registration+registration)|||||||32|11|6|12|23.8|22.3|90|15|
|Total cost (euros) of registering a public limited<br>company (pre-registration+registration)|1/4|<=500|<=1000 <=1500<br>|<=2500<br>|<=5000 <=7500|>7500|180.33|107.54|455.5|247.82|1108.14|283.40|n.a|721.97|
|**Country scores(0-6)**|||kckansw|erk|||1.37|0.80|1.25|1.00|1.90|1.83|4.33|2.00|

NOTE: Values for Bulgaria and Romania were obtained based on Doing Business 2005 data. Since Doing Business information on number of procedures, number of days and cost connected with starting a company are not directly comparable to the same information in the OECD International Regulation Database, a normalization process was necessary to homogenize the scores obtained. The normalization process proceeded as follows.

1) A standard score (also called z-score or normal score) was obtained as Z = (raw score - meanOECD-DB)/standard deviationOECD-DB based on the Doing Business sample, where the values for OECD countries refer to Doing Business 2003. The z-score reveals how many units of the OECD standard deviation Croatia and Romania are above or below the OECD mean.

2) A transformed score, comparable to OECD scores obtained from the OECD International Regulation Database, is calculated for Croatia and Romania as T = Z*(standard deviation OECD-PMR) + mean OECD-PMR 48
Figure 21: Probability of accessing public support by firm attributes.

<!-- Start of picture text --> (a) By firm size (b) By firm sector<br>50 50<br>40 36 39 40 39<br>33 33 31<br>30 30 30 26 29 28<br>23 24<br>20 17 21 20 19 17 19 17 17<br>13<br>10 10<br>0 0<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+) Hospitality Knowledgeintensive servicesOther Manufacturing Retail Agriculture<br>Wave 1 Wave 2 services<br>Wave 1 Wave 2<br>(c) By firm productivity<br>40<br>31 31<br>30 29<br>20 15 17 17<br>10<br>0<br>Low Middle High<br>Labor productivity tercile (within country)<br>Wave 1 Wave 2<br>Probability of accessing public support Probability of accessing public support<br>Probability of accessing public support<br><!-- End of picture text -->

# **5. Conclusions**

More than a year into the COVID-19 pandemic, policy makers’ attention has shifted from immediate crisis mitigation to policies that support medium-term adaptation and a longer-term, productivitydriven recovery. Utilizing novel panel data from 38, mostly developing countries, this paper provides new evidence to guide policy makers in this process. This evidence has been grouped into a set of six stylized facts on the shape of the recovery, its heterogeneity across indicators, countries, sectors, and types of firms; its relationship with uncertainty; digital technology adoption; financial development; and the evolution of policy support.

The results show that, some 7-12 months since the onset of the pandemic, business activity is recovering. Three in four firms that had temporarily closed during the early months of the crisis are operating again. Although firm sales are still 28% below pre-pandemic levels, it is a significant improvement from a 41% decline in the months immediately following the onset of the crisis. Firms in countries with more severe mobility restrictions early in the pandemic — that generally experienced larger declines in sales — are recovering faster than businesses in countries with less severe initial mobility limits. And within countries, more productive firms have been more resilient than less productive businesses, consistent with other evidence of Schumpeterean “cleansing” effects of COVID-19. Last but not least, the pandemic has significantly accelerated the adoption of digital 28
# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of infrastructure in Africa, including the countries of the Southern African Development Community (SADC). The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

The purpose of this regional report is to present the key AICD findings for the SADC community. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag in the data. The period covered by AICD runs from 2001 to 2006. Most of the technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today’s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis is based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the regional situation. Nevertheless, some specifically regional issues—particularly of the regulatory and institutional variety—may not have been explicitly addressed in the national data collection effort.

Fourth, while water resource management is an important aspect of regional integration in Africa, this report does not explore water resource issues. The reason is that the AICD project did not cover water resources per se, but rather the specific water resource needs associated with the power, irrigation, and water supply sectors.

## Why infrastructure matters

The regional economic community has 15 member states: Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe. For the period 1995–2005, all countries for which data are available registered economic growth, albeit at varying rates. The average annual growth, in purchasing
the number of high-skilled workers in the city, reflecting the number who use motorized transport, and decreases with the number of kilometers of lanes of highway.

# **_Parameters_**

Where possible, estimates of parameters for the model use data sources from Dhaka. Household consumption shares are estimated from the 2010 Bangladesh Household Income and Expenditure Survey (BBS 2010). Input-Output matrices are constructed for each sector for the Asian Development Bank’s 2006 calculations (ADB 2016), aggregated into our sectors. Other parameters are derived using sources from the economics literature, with details provided in the appendix.

# **2.3  Calibration**

Given technology, preferences, geography (the connectivity structure) and endowments (labor of each type and land in each place) the model computes the equilibrium, determining land use, location of residence and employment, and consumption and production. The model is calibrated by finding productivity and amenity values, for each productive sector and household type (by skill and housing type) in each place, such that the model exactly fits the base data for 2011. Maps documenting the employment in each union, the population in each union, as well as the distribution of rents, wages, and the amenity and productivity parameters, are provided in the appendix.

# **3. Projection to 2035: Scenario A**

The model is calibrated to 2011, but the policy experiment – the dike and associated transport improvements – will not be completed until the early 2030s. The base-line for the experiment has therefore to be constructed, and this is done by using the Dhaka Structure Plan 2016-2035 (RAJUK 2016) in combination with our model, to project out to 2035. The Dhaka Structure Plan 2016-2035 (RAJUK 2016) provides the following as ‘data’. Total population of the Greater Dhaka area reaches 24.6 million in 2035, so the average annual population growth rate between 2010 and 2035 is 2.3% percent. Of the population in 2035, 40% are high-skilled (up from 37% in the 2011 data). At a sectoral level, total factor productivity is assumed to grow at 1% per year in each sector. We impose the assumption that the value of land outside the city grows at the same rate as the increase in productivity.

We revise connectivity to include pre-planned transport investments, including 42.3 km of Bus Rapid Transit and 60.1 km of Mass Rapid Transit, both of which are used by low and highskilled workers if it shortens their journey times. Approximately 1,200 km of lanes of road are also built, a combination of the widening of existing routes by adding new lanes, and the construction of entirely new highways. These are shown in maps in the appendix. The increase in high-skilled population, who have access to motorized transport, increases the number of cars on the road, whereas at the same time there is an increase in the number of kilometers of road lanes. We combine these two variables into a number of vehicles per kilometer of road, 10
and 20 percent in 1999, and to 24 and 15 percent by the year 2000 (Bautista, Power and Associates 1979; Manasan and Pineda 1999; Habito 2002).

Between 1994 and 2000, the overall weighted nominal tariff declined by 66.9 percent, the declines ranging from 88.9% in mining through 64.0% for non-food manufacturing, 57.6 for livestock, 45.6 for crops to 19.9 for “other agriculture.” In 2000, the average sectoral tariff rate was highest in food manufacturing (16.6 percent), and lowest in “other agriculture” (0.2 percent).

Revenue from import tariffs remains one of the major sources of government funds. In 1990, the share of revenue from import duties and taxes in total revenue was 26.4 percent. It increased marginally to 27.7 percent in 1995, but then dropped sharply to 19.3 percent in 2000, largely due to the tariff reduction program. The reduction in tariff revenue was largely replaced by an increase in income and profit taxes from 27.3 percent in 1990 to 30.7 percent in 1995 and 38.6 percent in 2000, with the excise and sales tax share fluctuating between 23 and 28 percent.

# **1.4  Poverty Profile in the Philippines**

Figure 1 presents the evolution of the poverty headcount index and the Gini coefficient from 1985 to 2000. The poverty headcount index dropped continuously from 49.2 percent in 1985 to 36.9 percent in 1997, but then rebounded to 39.5 percent in 2000 as a result of the 1998 El Nino and the Asian Crisis. El Nino resulted in a 30 percent contraction in agriculture, the greatest drop in more than 30 years. On the other hand, income inequality has steadily increased over this period, as the Gini coefficient climbed from 0.42 in 1985 to 0.51 in 2000.

In 1994, the base year the household survey underlying our analysis, about 41 percent of the population of 67 million was below the poverty threshold. Generally, rural households, which represent roughly half the population, are substantially poorer than urban households. Whether in 10
Second, political risk was exceptionally high in 1996. Boris Yeltsin’s ratings were at single-digit levels as late as March 1996, with elections scheduled for June. He finally staved off a spirited challenge from the communist party leader, Gennady Zyuganov, winning by a small majority in the second round. Third, the big Moscow banks, which served as primary dealers in the GKO market, had considerable monopsony power until external access was liberalized in January 1997 and this might have artificially boosted real interest rates. Note that real interest rates came down after Yeltsin was re-elected in July 1996 and dropped to below 20 percent after April 1997 as the GKO market was liberalized; but by then the nonpayments system had become entrenched, as we shall see in the next sub-section, preventing any beneficial impact on enterprises. Real interest rates then started rising again after the spillover from the East Asian crisis in November 1997.

**Table 1: Macroeconomic Performance Over 1995-1998**

|**Year**|**12-Month Infla**|**tion (%)**|**Fiscal deficit/G**|**DP (%)**<sup>**a/**</sup>|
|---|---|---|---|---|
||Original Program|<br>Actual|Original Program|Actual|
||Target||Target||
|1995 (SBA)|63|131|6.0|5.7|
|1996 (EFF)|25|25|4.2|8.4|
|1997 (EFF)|9|11|3.2|7.0|
|1998 (EFF)|6|84|2.2|5.9<sup>b/</sup>|

Source: Table 2, Kharas, Pinto and Ulatov (2001).

Note: Russia’s quest for single-digit inflation began in July 1995, supported by a three-year Extended Fund Facility (EFF) IMF loan stipulating inflation and fiscal deficit targets for 1996-98. The targets for 1995 had been set earlier in the context of an IMF Stand-By Arrangement (SBA).

a/Deficit of the federal government on a commitments basis. b/ Excluding overdue interest on GKO/OFZ.

The real appreciation of the ruble is easier to explain. As Dornbusch and Werner (1994) note, it is a frequent artifact of exchange rate-based inflation reduction programs: by design, the depreciation of the nominal exchange rate is kept much lower than the prevailing rate of inflation, which converges to the target rate of currency depreciation only slowly over time. This could lead to substantial real appreciation and even overvaluation in the interim if productivity increases do not keep pace. This is in fact what happened in Russia after mid-1995, when the 7
and McIntosh and Vignoles (2001) for the United Kingdom and Cawley, Heckman and Vytlacil (1998), Altonji and Dunn (1996b), Murnane et al. (2000), Murnane et al. (1995), Betts (1995) and Card and Krueger (1992) for the United States.

We use the adult literacy score as a measure of basic skill (adult functional literacy and numeracy)<sup>††</sup> to proxy for unobserved effects. We expect that the inclusion of a direct measure of such skills will reduce the estimated education coefficient, so that the coefficient on education then mostly captures the effect of schooling alone, having controlled for a range skills.

Given a distribution of wages, we assume that workers at different parts of the distribution benefit from different types of skills and abilities, including inherent unobserved ability. Less-skilled workers predominate in the lower quantiles of the distribution, while the highly skilled predominate in the upper quantiles of the distribution. However, it is hypothesized that adult literacy and numeracy (basic skills) are mostly relevant at lower parts of the earnings distribution, compared to the higher end of the distribution, where higher level skills are more relevant.

Consider the Mincerian wage function without and with controlling for basic skills (functional literacy): where Wi, Si, Xi and Li represent the hourly wage, years of schooling, other characteristics and the total literacy score (from the IALS and ALL datasets) of worker _i._ The traditional Mincerian wage

> †† Throughout this paper we will use the terms “adult skills”, “functional literacy” and “adult literacy”  interchangeably.

6