rafmacalaba/data-use-mentions-extended huggingface dataset:
education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

As a final exercise, Table 9 and Table 10 investigate the factors that underlie farmers’ expectation building. Neither a set of respondent characteristics nor past shock exposure or other factors potentially associated with the process of forming an expected harvest estimate are significant predictors of the expectation gap across crops and countries.<sup>47</sup> We gain some explanatory power by constraining the sample to those plots where expectations exceeded realized harvest, i.e. those 46 In Ethiopia, we also have data from crop cut harvests for a sub-sample of plots. This allows us to verify whether expectations are also strong predictors of what is commonly taken as the “gold standard” measure of realized harvest. We find that expectations do not seem to be a reliable predictor for crop cut harvest, and in particular crop cut yields. Furthermore, this does not seem to be due to bias only inherent in self-reported expectations as there is only a slightly stronger association even between self-reported harvest _realizations_ and the crop cut measure. We thus conclude that expectations only provide a fair proxy of the attainable harvest when also using self-reported harvest realizations. The reason for this is likely that both share a similar underlying mental model and thus similar biases that cancel out when comparing them.

47 The fact that we do not find a robust effect of past shock exposure, measured either by community reports for the past two years, on expectation building is somewhat surprising as there is strong evidence in the literature that past shock exposure affects risk attitudes and expectations about the likelihood of future shocks. This may be because the shock proxies used are not specific enough, i.e. they do not necessarily measure adverse experiences with past _agricultural shocks_ and may not capture individual exposure.

30
3 of these measures are sensitive to the choice of consumption rather than income as the measure of a family’s means. Second, we review the findings of multi-country and global studies of out-of-pocket health expenditures. These studies are relatively few and have typically used just one or two of the abovementioned measures. Third, in part to help guide future research but also to set the stage for our new empirical results presented below, we highlight the challenges faced by researchers in this area. These include identifying and accessing relevant household surveys, choosing between different adaptations of these surveys, computing out-of-pocket expenditures, and computing consumption and income. Finally, we present new estimates of out-of-pocket health expenditures for 146 countries. Our estimates cover more countries than previous global studies (133 countries), and in contrast to previous multi-country and global studies, where typically just one or two measures are used, we report estimates for all measures used in previous studies. We also explore the implications for 48 countries of choosing consumption rather than income as the measure of a family’s means, and present multiple regression results showing the macroeconomic and health system (partial) correlates of the various measures.

# **Previous studies of out-of-pocket health expenditures**

A large literature exists on out-of-pocket health expenditures. This section summarizes this literature, beginning with an overview of the indicators used, and then moving on to the results of the literature to date, focusing on multi-country and global studies.<sup>4</sup>

## <u>Indicators and caveats</u>

Table 1 lists the different indicators used in the studies to date. The first simply asks how much households spend per capita (in international dollars) on out-ofpocket health expenses. This number is sometimes reported in single-country studies (see e.g. Banthin et al. 2008), usually based on data from international databases like the OECD’s Systems of Health Accounts (SHA) and the World Health Organization’s (WHO) Global Health Expenditure Database
lands should be encroached upon for settlement or cultivation. The reality, however, is very different.

Land encroachment is the result of:

- **_loopholes_** _in_ **_land reform_** _legislation:_ The Orissa Estate Abolition Act, 1952 was biased in favour of estate owners as it allowed them to keep 33 acres for personal cultivation. All tenants on the 'resumable' area of each estate were evicted for fear that they would otherwise acquire ownership rights to the land. The Orissa Tenant Protection Act, 1948 and the Tenant Relief Act, 1955, while seeking to protect tenants from unlawful eviction, could not be enforced adequately owing to the absence or manipulation of land records. Evicted tenants were thus compelled to eke out an existence by encroaching on government or common lands;

- **_displacement_** _as_ **_a result_** _of_ **_development activities:_** Inadequate attention tends to be paid to resettlement and rehabilitation of people displaced by large development projects (dams, irrigation schemes, mining operations, etc.) (see Box 11). To the extent that displaced communities are resettled at all, they are frequently allocated remote land of poor quality. Consequently, displaced people tend to encroach, illegally, on nearby government land;

- _lax_ **_implementation_** _of_ **_existing land_** _laws by_ **_revenue officials:_** For instance, those who encroach on government land are required to be 'booked' by the revenue inspector (RI) and have proceedings initiated to evict them. However, encroachment represents an opportunity for rent-seeking by revenue officials. Instances of encroachment on public and common land are overlooked, in return for a suitable consideration, usually payable annually. This practice clearly encourages encroachment in the long-run, and favours larger landowners who are most able to pay the requisite bribes;

49
where they are, owing to the unequal bargaining power between agents, attempts to 'fix' relations in one sphere can lead to compensatory shifts in other contracts to leave tenants net worse off (Mearns 1998).

This section examines the extent and nature of land-lease (tenancy) markets and the terms of the contracts across Orissa to understand how formal restrictions on tenancy along with loopholes in the law, its lax implementation, lack of updated land records, and the manipulation of revenue administration by the relatively rich and powerful, combine to restrict access to land by the rural poor.

# _4.4.1 Extent and nature of tenancy in Orissa_

There is general agreement that available data on both the number of tenants and the acreage under tenancy are underestimates (Singh, 1988). Both the Census of Land Holdings carried out by the National Sample Survey Organisation, and the Agricultural Census, tend to underestimate the proportion of land under tenancy. However, the former is considered more reliable as it is based on independent household surveys while the agricultural census is based on a retabulation of the land records of owner-cultivators. The under-estimation of tenancy stems from reporting bias as lessors tend to understate the leased-out area owing to (i) the fear that tenants will stake claims in favour of continued right of cultivation; (ii) desire to escape the ceiling on land holdings; or (iii) a combination of (i) and (ii). The share of operated area leased-in is therefore considered to be a more reliable measure of the magnitude of agricultural tenancy than the share of owned area leased-out as lessees have fewer incentives to under-report the extent of area leased-in (Narain and Joshi, 1969). Nonethless, land reform legislation designed to prohibit sub-letting, regulate rents, and confer security of tenure has given many landlords and tenants a common interest in concealing agreements which may be deemed illegal (Singh, 1988). Only intensive field surveys and careful village studies can provide more precise estimates of tenancy since '[Tihe longer the
household with access to the Internet, number of household members, age of the household head, sex of the household head and years of education of the household head.<sup>13</sup> Initially, we assume all countries have the same four random slope variables. These variables are: 1) the household’s principal income from agriculture, 2) the presence of toilet facilities in the dwelling that are linked to the sewer, 3) access to electricity, and 4) access to water. We select these variables based on the assumption that natural hazards can particularly affect these dimensions. However, after performing LASSO and multilevel estimations, not all four variables are always included in each country's case. Depending on LASSO results and the significance of the varying-slope coefficients, all of them or a subset of them ended up being included in each country's model. Finally, all the community-level variables from household survey ( ) and climate change ( ) are included in each country’s income equation. On the other hand, the natural hazard and disaster variables ( ) selected ii ii ZZ II for the expected variance equations vary depending on the variability of the country's index. ii NN Table B2 in the Appendix details the variables used for each country estimation. Column (2), named Income Community-Level Covariates, refers to the eight community covariates variables and of the income equation of the model. Column (3), named Individual and HH characteristics at the ii ii ZZ II household level, refers to the household-specific variables characteristics i of the model chosen after LASSO and column (4) named Slope vars includes the variables that we assume have varying XX coefficients. Column (5), named Risk Community-Level Covariates, shows the community covariates variables of the expected variance equations of the model. Finally, the assessment is conducted at the household level using the most recent household survey NNii year available in each country, even though the estimations are based on the five rounds. The poverty
import tariffs between 1995 and 2002. The MIDP is considered by some economists a major success of export and industrial policy, although it has also been challenged in world trade circles. The program was initially scheduled to run for five years but it has been extended three times and is now scheduled to end in 2020.The aim of this paper is to assess the effectiveness of the MIDP in improving South Africa‟s automotive (vehicles and components) export performance. To achieve this aim, we provide a first attempt at a quantitative analysis of the MIDP using the difference-indifference methodology and taking a two-tier approach. In a _first analysis_ , we perform a comparative analysis between different manufacturing sectors within South Africa before and after the MIDP came into effect. In a _second analysis_ , we apply the same methodology to another analysis between South Africa and a number of comparator countries that are automotive producers and exporters before and after the MIDP were implemented.

5. Our regression results cover the period 1996-2006 and suggest that the impact of the MIDP on automotive exports in South Africa is positive and significant. We find that the largest response to the MIDP in terms of improved manufacturing exports occurs with a delay after the adoption of the law as investments take time to materialize and become productive. Also, the results suggest that the effectiveness of the tax incentives fade in time. This finding supports the common belief that tax incentives may affect some business decisions particularly in the short run, but they may not be a primary consideration for investors in the long-run. In the particular case of South Africa, a number of investment climate surveys note that major challenges remain for foreign investors to invest in the country, including a volatile exchange rate, crime, shortage of skilled labor, inadequate infrastructure and a rising regulatory burden.

6. The paper is organized as follows. In _section two_ of the paper we review two streams of literature which form the basis for the study. _Section three_ provides background information about South Africa‟s economic performance, details on the
Christopher Hoy<sup>1</sup> , Laban Simbeye<sup>2</sup> , Muhammad Abdullah Ali Malik<sup>3</sup> , Aliisa Koivisto<sup>4</sup> and Mashekwa Maboshe<sup>5</sup>

# **JEL-Classification:** D22, J21, J23, O12, O55.

# **Keywords:** Firms, COVID-19, Tax administrative data, Zambia, Economy recovery.

> * This study is reproduced here with full acknowledgement of UNU-WIDER, Helsinki. The study was originally published as a WIDER Working Paper under the UNU-WIDER project Building up efficient and fair tax systems – lessons based on administrative tax data, which is which is part of the Domestic Revenue Mobilization programme. The programme is financed through specific contributions by the Norwegian Agency for Development Cooperation (Norad). The authors wish to thank the Zambia Revenue Authority through the Research and Corporate Strategy department for facilitating the study of the tax administrative data and to acknowledge that the enterprise survey data was collected through a partnership between the Poverty and Equity Global Practise and Enterprise Analysis Unit of the World Bank. Particularly they would like to thank Arden Finn and Gemechu Aga for the pivotal role they played in facilitating the study of the enterprise survey data. The authors are grateful for comments made on an earlier draft by Pierella Paci, Arden Finn, Gemechu Aga, Filip Jolevski, Pierre Bachas and Rob Swinkels. The views expressed in this paper are those of the authors and do not necessarily reflect those of the organizations that they work for.<sup>1</sup> World Bank and corresponding author (choy@worldbank.org),<sup>2</sup> Zambia Revenue Authority,<sup>3</sup> World Bank,<sup>4</sup> UNU-WIDER,<sup>5</sup> University of Zambia and ZIPAR.
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 2. Kenya’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-income<br>countries|**Kenya**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km2 of arable land|86.6|**152**|507.4|
|Unpaved road density|km/1000 km2 of arable land|504.7|**930**|1,038.3|
|GIS rural accessibility|% of rural population within 2 km of all-<br>season road|21.7|**32**|59.9|
|Paved road traffic|Average annual daily traffic|1,049.6|**1,108**|2,786.0|
|Unpaved road traffic|Average annual daily traffic|62.6|**38**|12.0|
|Paved network condition|% in good or fair condition|80.0|**84**|79.0|
|Unpaved network condition|% in good or fair condition|57.6|**63**|58.3|
|Perceived transport quality|% firms identifying roads as major<br>business constraint|23.0|**37**|10.7|

Source: Gwillliam and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data.

# **Challenges**

Nevertheless, the country faces a huge rehabilitation backlog that must be addressed before the trunk network can be considered to be in a maintainable condition (figure 4). As of 2006, levels of capital spending for the roads
# 4. THE PRODUCTION AND EXPORTS LANDSCAPE

To understand why Egypt is not well integrated into global markets, one must look first into the production range as the basis of any trade potential. Product space is a term used to describe the extent to which the goods produced within an economy are related. This rests on the argument that the ability of a country to produce (and export) a product will depend on whether it already produces _related_ products, and that its development path is determined by its capacity to accumulate the capabilities required to produce different and progressively more sophisticated goods. In this context, the concept of _relatedness_ between products is defined by the similarity in the inputs required for their production, including skills, institutional and infrastructure requirements, and technological similarity (Hausmann and Klinger, 2007; Hidalgo et al., 2007; and Hidalgo and Hausmann, 2009) _._ Exports data are usually used to appreciate countries’ product space and their productive structure. This is based on a general acknowledgment that countries can produce many goods but will only export those they are good at producing. Additionally, using trade data allows for an accurate comparability between countries, as well as seeing the evolution of production structure and capabilities at a disaggregated level and over time.<sup>3</sup> We analyze Egypt’s exports along three dimensions that are key for exports performance and future growth: (i) composition and relatedness of exported products; (ii) geographic and products concentration; (iii) relatedness to globally‐traded products.

## _4.1 Composition and Relatedness of Egyptian exports_

Egypt’s exports have historically been dominated by oil exports, but their weight has gradually declined from half to around a third of total exports, in favor of non‐oil exports. In FY2017, non‐oil exports constitute close to 70 percent of the total. Yet, exports are chiefly concentrated in products that are either traditional or have a low value‐added. A long tradition of subsidizing energy
# **Abstract**

The Latvian economy made great strides in recovering from the economic shock of the early transition and the adverse after-effects of the 1998 Russian financial crisis. Nevertheless, Latvia faces serious challenges to its future growth and prosperity despite these impressive achievements and the outward appearance of macroeconomic stability and economic progress.

However, a wide variety of recent studies suggest that the Latvian economy is not particularly competitive and, even more worrisome, they indicate that Latvia is not well positioned to gain ground in the race for global competitiveness, prosperity, and rising standards of living. Most of Latvia’s growth to date has come from one-off gains generated by structural reforms, privatization, and reallocating resources, not inexhaustible reservoirs of growth. Latvian enterprises will be able to sustain economic growth and create high wage jobs only by becoming internationally competitive, innovating, accumulating new knowledge and technology, and finding a high value added niche in the European and global division of labor.

This paper is designed to help Latvian leaders (i) develop and clear diagnosis of the innovation and competitiveness challenges facing Latvia as it prepares to enter the EU and, more importantly (ii) design and implement policies and programs to ensure that Latvia reaps the maximum possible benefits from EU structural funds. Section II analyzes the current structure of Latvia’s production, imports, and exports. Section III utilizes data from a number of competitiveness reports to benchmark Latvia’s current progress against a number of comparator countries and to pinpoint Latvia’s strengths and weaknesses as an innovative economy. Section IV offers a detailed list of potential policies and programs that could improve the competitiveness of Latvian enterprises and the efficiency of the Latvian NIS. The recommendations include specific policies and programs to improve (i) the production of knowledge in Latvia, (ii) the commercialization of technology produced by Latvian scientists, small companies, and research institutes, and perhaps most importantly, (iii) local firms’ capacity to absorb, adapt, and adopt existing knowledge produced outside Latvia for
# Table 3: **The Effect of Blackouts on Owner Labor and Wages (Profit/Hour)**

All specifications include time fixed effects, firm fixed effects, and a vector of dummies indicating the number of days (of 5) for which a response about blackout status was reported by the firm owner. Standard errors are clustered at the neighborhood level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)<br># Days<br>Worked|(2)<br>Total Hours<br>Worked|(3)<br>Profits/Hours<br>(GhC)|
|---|---|---|---|
|**Panel A: One-Person Firms**||||
|# Blackout days reported (out of 5 days)|-0.10<br>(0.06)|-1.33**<br>(0.60)|-0.17**<br>(0.06)|
|Outcome Variable Average|3.20|27.12|1.05|
|Observations|1,265|1,265|1,010|
|**Panel B: Firms With Workers**||||
|# Blackout days reported (out of 5 days)|0.02<br>(0.05)|0.11<br>(0.47)|0.08<br>(0.06)|
|Outcome Variable Average|4.01|36.84|1.33|
|Observations|1,097|1,097|1,010|

Source: Author calculations from The Hohoe Garment Makers Project Data 18
ethnic groups in Afghanistan are less likely to exclusively breastfeed, whereas some lowcaste groups in India are more likely to do so (Benedict et al. 2018a), suggesting that sociocultural practices may influence exclusive breastfeeding. In India, girls are less likely to be exclusively breastfed than boys (Benedict et al. 2018a).

# _Continued breastfeeding_

Continued breastfeeding from six months to two years and beyond is an important source of key nutrients for healthy growth (Victora et al. 2016) and is particularly important in settings where households have inadequate access to nutritious complementary food. Of all regions, South Asia has the highest rate of continued breastfeeding, at two years, but it has seen little progress between 2000 (68 percent) and 2016 (71 percent) (UNICEF 2016; UNICEF 2018a). Rates are lowest for Pakistan (56 percent) and Afghanistan (59 percent), and highest for Nepal (89 percent) and Sri Lanka (87 percent).

Comparing the continued breastfeeding rates at one year and two years of age indicates a large decline in continued breastfeeding (exceeding 15 percentage points) in Afghanistan, Bhutan, and Pakistan during the second year of life (figures 1c and 1d). A shorter duration of breastfeeding has previously been observed among girls in India (Jayachandran and Kuziemko 2011); however, it is encouraging that 2015–16 statistics from India do not show a significant difference between girls and boys in continued breastfeeding at two years (Benedict et al. 2018a). Continued breastfeeding at two years of age is more likely among women with low body mass index in India, and among less-wealthy households in both India and Pakistan (Benedict et al. 2018a). Children whose mothers have low decision-making autonomy are less likely to be breastfed at two years in both Nepal and Pakistan (Benedict et al. 2018a), in contrast to findings from a multi-country study showing an association between higher autonomy and shorter duration of breastfeeding (Smith et al. 2003).

# _Timing, frequency, diversity
- 16 -

Figure 6: Changes in Contractual Savings and Market Capitalization, 1990 - 1996 <!-- Start of picture text --> D(MC/GDP)<br>1-<br>AUS<br>SGP<br>.5 j<br>0<br>KOR<br>JPN<br>-.2 0 .2 .4 .6<br>D(CSIGDP)<br>The fitted line is given by i, = 0.161 + 0.849x, with a t statistics of 2.593 for the slope. See Table<br>12 in Appendix 2 for the list of countries.<br>1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.<br>Figure 7: Changes in Contractual Savings and Value Traded,<br>1990- 1996<br>D(VT/GDP)<br>NLD<br>USA<br>.5SAAU<br>.6 ~~~~~~~~ESP CAN g -B<br>O~~~~~~~TJ DNK CHIN ZA<br>0<br>JPN<br>-.2 ~~~~0 .2.4 .<br>D(CSAGDP)<br><!-- End of picture text --> Notes: The fitted line is given by **i,** = 0.161 + 0.849x, with a t statistics of 2.593 for the slope. See Table 12 in Appendix 2 for the list of countries.

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Notes: The fitted line is given by y, = 0.044 + 0.968x, with a **t** statistics of 3.289 for the slope. See Table 12 in Appendix 2 for the list of countries. **Source:*