rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Economic context and data**

The study of poverty in Morocco has seen major developments during the last two decades thanks to the increased availability of micro-data and thanks to the increased capacity on the part of the government to analyze poverty. The High Commission for the Plan (HCP), for example, has published several studies during the last decade covering a range of topics such as pro-poor growth, geographical targeting, poverty maps and poverty dynamics. All these studies relied on expenditure as a measure of welfare, took a unidimensional approach to the study of poverty and found consistent results in that poverty has declined in both urban and rural areas between 2001 and 2007.

This positive result is consistent with the general macroeconomic framework and key reforms implemented by the government in recent years. Starting from the 1990s, Morocco has embarked in a process of liberalization of the economy that led to increased foreign investments and trade. The country has also implemented a large program of public investments throughout the decade and has been able to improve significantly on other fronts such as health and education. Average GDP growth has increased to 4-5% a year with peaks of 7%. During the second half of the last decade, the government has also launched a large human development program (Initiative for National Human Development – INDH) that covers all poor areas of the country and finances local development activities from private entrepreneurship to public recreational centers. The INDH program has now completed its first phase and is currently launching a second phase. The evaluation of the first phase is extremely complex given the nature of the program but stakeholders agree that the program had a visible impact on local communities.

A multi-dimensional approach to the study of poverty can complement uni-dimensional studies and also offer a better sense of whether recent reforms and improvements in welfare have concerned most dimensions of welfare or only a few dimensions. We therefore rely on the same data used by other studies including the National Survey on Consumption and Expenditure carried out between 2000 and 2001 and including 14,
To capture the cyclical component of economic activity, we use annual growth in GDP at constant prices. The data are from the IMF’s World Economic Outlook, April 2018 update (IMF 2018). The data are available for 193 nations over the period 1980-2018. The identification strategy consists of instrumenting economic growth with commodity terms of trade shocks. Data on commodity prices are obtained from the IMF’s database for a basket of 45 commodities (see Gruss and Kebhaj, 2019).

To measure the level of democracy, we use the Polity IV database from the Center of Systemic Peace (Marshall et al., 2018). We use the following three measures: political competition, constraints to the executive and a composite measure of democracy. The first measure (POLCOMP) combines measures of the regulation of participation and the competitiveness of participation in the political arena. This variable takes values between 1 (suppressed) and 10 (institutionalized electoral) political competition. The second measure (XCONST) captures the extent to which there are executive constraints in a polity. The measure refers to the presence or absence of formal checks on the power of the executive branch. It takes values between 1 (unlimited authority) and 7 (executive parity or subordination). The third measure (POLITY2) captures the level of democracy. The composite index combines data on the (i) competitiveness and openness of executive recruitment, (ii) constraints on executive power, and (iii) political competition. This variable takes values between -10 (strongly autocratic) and +10 (strongly democratic).

We consider a country as having: i) strong (weak) political competition if the country’s average political competition index is greater or equal to (strictly lower than) 7; ii) strong (weak) executive constraints if its average executive constraint index is greater or equal to (strictly lower than) 5; and iii) a democratic (autocratic) political system, if the average Polity2 index is greater or equal
VET development is not rural-specific, the lower fees made possible by financial aid and tuition reductions is particularly attractive to rural students. One study, based on a survey of 132 VET schools in Henan, a designated pilot province for VET expansion, finds that 87% of the students came from rural areas (Yi et al. 2018). Despite the improvement in education attendance of rural youths, as reflected in their decreased migration rate, it is important to note that the rural-urban education divide in both quantity and quality remains a key feature in China’s education sector, posing a challenge for China’s future growth (Chen et al. 2019; Rozelle and Hell 2022).

Another evident change in long-term migration trends is that the median age of migrants has been increasing over time (Figure 4), and the share of migrants over 45 has increased sharply. The migrant shares of men and women over 45 were 16.8% and 4.5%, respectively, in 2003, and increased to 19% and 7% by 2013. As the migrant share declines among younger cohorts and increases for older cohorts, the share of the registered population working as migrants is getting older: the median age has increased from 26 to 32 for women during the 2003 and 2013 period, and from 31 to 35 for men.<sup>1112</sup> This is consistent with the prediction by Li et al. (2012) that as fewer young rural laborers are available, the marginal migrant worker will become older.

The third feature observable in long-term migration trends is that conditional on out-migration, the average labor supply of migrants has increased over time.<sup>13</sup> As shown in Figure 5, the average days per year that an individual from a rural area spends working as a migrant increased during the period from 2003 to 2013. Interestingly, rural women, once engaging in rural-to-urban migration, spend more days working as migrants than men. The gender difference seems to
data is available, there has been a decline in the measured value of services output per employee. These countries also have not increased their overall labor productivity performance in the last decade. Noteworthy is the performance of the Baltic countries, where labor productivity in services outpaced the productivity increase in other sectors of the economy. Convergence with respect to high-income OECD countries in terms of productivity levels is still far from being achieved, however.

**Figure 2: Labor productivity**

<!-- Start of picture text --> Labor Productivity of Service Sector by Country Labor Productivity of All Sectors by Country<br>Group, 1991 vs. 2001 Group, 1991 vs. 2003<br>60000 70000<br>50000 60000<br>50000<br>40000<br>1991 40000 1991<br>30000 2001 30000 2003<br>20000 20000<br>10000 10000<br>0 0<br>OECD CEE SEE FSU1 FSU2 FSU3 FSU4 OECD CEE SEE FSU1 FSU2 FSU3 FSU4<br>1995 US$ 1995 US$<br><!-- End of picture text --> Note: For definition of country groups, see Figure 1.

Input-output tables for the year 2001, the latest available year for many ECA countries, provide information on differences in economic structure and the extent to which ECA countries have converged to comparators in the rest of world as regards both intermediate services use and final demand, as well as on the service intensity of exports. Table 1 reports information on the sectoral intensity of exports: the direct contribution of agriculture, mining, manufactures and services to total exports, expressed as a share of total exports of goods and services. Albania, Croatia and the Baltic States are the most services-intensive in exports. The first column in Table 2 reports the sum of the direct and indirect linkage effects generated by a unit of export revenue—the total activity generated by (going into) one unit of foreign exchange (exports). The average “
poorer countries for each capital category.<sup>7</sup> The share of intangible capital in total wealth is also positively correlated with income, as shown in Figure 2b. For the OECD countries, this share is on average 78 percent and always larger than 60 percent. Panel B in Table 2 also shows IC increasing over time, in absolute value and as a percentage of total wealth. The _z_ -statistic for a test of a difference between the means in the IC shares in years 2005 and 1995 is 1.12.

# _2.2 Human capital_

The analysis in World Bank (2006) suggests that the value of the stock of human capital is a large component of a country's wealth. This fits well with the intuition that an important asset of a country is its people. Not surprisingly, the statistical agencies in some developed countries are starting to systematically compile human capital accounts to monitor the evolution of the stock of human capital. A method typically employed to value the stock of human capital in dollars is the lifetime income approach of Jorgenson and Fraumeni (1989, 1992a, 1992b). Under this approach, human capital is valued as the net present value of the income flow it produces over an assumed lifetime. In practice, the application of the method requires information on survival, enrollment and employment probabilities as well as earnings, by sex, age and educational attainment. Although these are relatively modest informational requirements, applying this method to a cross section of countries is impracticable.

Most of the empirical studies studying the role of human capital on growth and development accounting have focused instead on a cost-based approach to measure human capital. In order to increase future labor productivity and future income, people forgo consumption and invest in education. The human capital embodied in the labor force can then be seen as a function of education. Even accepting education as a valid proxy for investment in human capital, the relationship between education and human capital needs to be correctly specified.<sup>
more mixed. Several studies examining the impact of the release of stratospheric aerosols from volcanic eruptions have found improvements in photosynthesis and carbon sequestration in natural ecosystems.<sup>17,19</sup> Work specifically examining the impact of these eruptions on crop yields, however, suggests that both C3 (e.g. soybean) and C4 (e.g. maize) crops suffered yield declines as the reduction in total sunlight dominated the increase in diffuse light.<sup>7</sup> While the role of wildfire smoke in changing crop yields has received less attention than clouds or aerosols broadly, available evidence suggests that the effect is similar to other sources of aerosols. The 2018 wildfire season, a particularly intense season, led to large increases in the fraction of light that was diffuse in California’s Central Valley, a location that typically has few clouds and receives mostly direct sunlight.<sup>23</sup> This led to increases in daily integrated gross ecosystem productivity of between 1.2 and 4.2%. These effects were concentrated in alfalfa and corn and were substantially larger than reductions in productivity due to associated increases in surface level ozone.<sup>23</sup> These estimates come from a single research plot and one especially intense fire season in an area with relatively high levels of direct radiation. It remains unquantified how wildfire plumes impact yields at a landscape level, in areas with higher average cloud cover, and across many years.

This study examines how North American smoke plumes have affected corn and soybean yields in the Midwestern and eastern United States over the period 2008–2020. This region experiences frequent exposure to smoke plumes over our sample period (Figure 1). We obtain data on smoke plumes from the National Oceanographic and Atmospheric Agency’s (NOAA) Hazard Mapping System (HMS) and data on the location and yield of corn and soybean crops from the United States Department of Agriculture’s (USDA) National Agricultural Statistics Service
, while third-generation (3G) technologies enable more rapid Internet browsing and data downloading.

> 2 The number of active mobile broadband subscriptions per 100 inhabitants in Africa in 2017 was 34.0, while the corresponding rate for fixed broadband subscriptions was 0.4 (ITU 2019).

> 3 Data on backbone networks have been taken from Africa Bandwidth Maps (database), Hamilton Research, Bath, UK,

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# **4. Empirical Evidence**

In this section, we present some preliminary empirical evidence that broadly supports the theory developed above. This evidence is not intended as a formal test of the theory, but rather as suggestive that we are capturing some aspects of the behaviour of the current account in the real world. We begin by assuming that Xj+p3 and itj are unobservable. Hence, the threshold level of debt above which output shocks lead to current account deficits cannot be observed. Under this assumption, the content of our theory can be understood as a probabilistic statement that the higher is the level of debt of the country, the more likely favourable output shocks lead to current account deficits.<sup>22</sup> We take per capita GNP growth as an imperfect measure of the shocks emphasized by the theory. This measure is imperfect since it does not distinguish between output and productivity shocks. Yet to the extent that output shocks are present in the data, we would expect to find that the correlation of the current account with per capita GNP growth is smaller in countries with higher levels of debt. Accordingly, we study how this correlation varies with the level of debt of a country.

## Data

For our empirical work, we require appropriate measures of debt and the current account. We construct a measure of debt using data on the international investment positions (liPs) of OECD economies as reported in the International Monetary Fund's Balance of Payments Statistics Yearbook. The IIP is a compilation 22 The unconditional probability of an output shock leading to a current account deficit is Pr(kj+3j>-r-fj)=Pr(aj>i)=1/2. However, conditional on the level of debt of the country and for any distribution of kj+pj, this probability is Pr(kj+Pj>-r-fjlfj) which is increasing in fj.

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countervailing duties (CVD), quality monitoring (QUAL) and specific tariffs (SPECT). Separate analyses of these NTMs are undertaken and reported.

Hoekman, Ng and Olarreaga (2003) have compiled data on the ad valorem equivalent of ad valorem and specific tariffs applied by the US on agricultural imports from the European Union on a set of agricultural commodities that also enjoy export subsidies in the US. Whether these measures of protection are also associated with lobbying spending is also empirically investigated.

Since NTM data are at the HS 6-digit level, they are readily merged with the trade and production data. The sample includes commodities that accounted for about 30% of total agricultural imports and 45% of total agricultural output in 1998.

# _B.      Results_

Table 2 presents OLS estimates from a simple linear model with three explanatory variables, plus a constant term. They are (i) the import-output ratio (M/Y), (ii) the export-output ratio (X/Y), and (iii) a measure of lobbying. Two measures of lobbying, termed %PACEXP and RANKPACEXP, were constructed for each cross-sectional sample. %PACEXP is PAC spending as a fraction of total PAC spending in the sample under consideration.

RANKPACEXP is the rank of PAC spending (a high rank implies high PAC spending) scaled by sample size _N_ . So RANKPACEXP varies between 1/ _N_ and 1, and monotonically increases with PAC spending. RANKPACEXP is less influenced by large values of PAC spending than %PACEXP, and so their estimated coefficients indicate robustness to these lobbying measures. Six regression models are estimated here, two each for 1993, 1996 and 1999. For the year 1993, the two models have M/Y and X/Y in common, but include either %PACEXP or RANKPACEXP. The idea behind estimating the simple model is basically to discover whether lobby matters to non-tariff barrier protection of agriculture.

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cointegration) is interpretable as a sign of efficient, competitive markets. In contrast, the absence of comovement indicates the presence of market failures. Impediments to trade such as lack of infrastructure, high transportation costs, imperfect information as well as regulatory barriers are expected to result in a lower degree of price transmission.

A primary concern for policymakers and researchers after the 2007-08 food price spike was to understand the degree to which world prices transmit to domestic grain markets in African countries, many of whom were dependent on food imports. One of the early studies examining this question used agricultural monthly market price data from 62 markets in 11 African countries and applied an error correction model to estimate the price transmission between world prices to these markets (Minot, 2011). The market price data included a mix of retail and wholesale prices, and the results suggested that rice markets in Africa were more closely linked to world prices, while only 10 percent of the maize markets were found to show co-integration with world prices. In particular, for Malawi, the author finds that the price increases for rice and maize during 2007-08 was the highest amongst the sample of southern African countries in the study. In terms of links to world prices, three out of the eight markets considered in Malawi exhibited a significant long-run integration with world maize prices. These markets were: Chitipa (located in the north, close to maize regions in Tanzania), Lilongwe (the capital city) and Nkhata, a principal port on the coast of Lake Malawi (Minot, 2011); all of which are likely to be the leading trade markets for maize in Malawi.

> 1 Several studies have also employed computable general equilibrium (CGE) models and their variants to estimate the effect of trade policies changes on welfare outcomes in various contexts (Sadoulet and de Janvry (1992), Minot and Goletti (1998), Nouve and Wodon (2008), Arndt et al. (2008), Dorosh, Dradri and Haggblade (2009), Xinshen and Kennedy (
estimates make sense, we use data from World Bank (2010) to calculate net income, and then apply expression (2) in order to derive the implicit rate of return on comprehensive wealth in each country. The distribution of rates of return is plotted in Figure 1, which shows that 80% of the rates lie between 4% and 6%.

Produced capital stocks are derived from historical investment data using a perpetual inventory model. Most natural resources are valued by taking the present value of resource rents—the economic profit on exploitation—over their assumed lifetime. Resources considered include energy, mineral, timber and non-timber forest resources, cropland, pastureland and protected areas.

Finally, the wealth estimates in World Bank (forthcoming) account for ownership of capital. The interest payments derived from foreign financial assets or obligations will affect future levels of consumption of the country's residents and, by construction, total wealth. The adjustment to the wealth estimates to account for ownership is particularly important in the light of the acceleration in cross border asset trade observed in the last decades.<sup>5</sup> By definition intangible capital includes any asset other than physical capital, natural resources and net foreign assets. It thus includes human capital—the sum of knowledge, skills, and knowhow possessed by the population. It includes the institutional and social infrastructure of the country. It also includes resources omitted in the natural capital calculations such as subsoil water, diamonds, and fisheries.<sup>6</sup> Table 2 presents descriptive statistics of total wealth and its components. Our sample includes an unbalanced panel of 115 developed and developing countries for years 1995, 2000 and 2005 (total of 315 observations). All the numbers in Table 2 are expressed in per capita terms in constant 2005 US$. For most countries in our sample, intangible capital is the largest component of total wealth; on average it constitutes 64 percent of total wealth, although it varies from a minimum of 5 percent
income changes drastically.; Accordingly, it may be misleading to focus solely on key indicators of monetary policy for the purpose of detecting "a _credit crunch"._ Analyses of monetary and credit aggregates need to be complemented with a more detailed investigation of the channels through which firms, banks, and households are affected by changes in monetary policy. In fact, as Bernanke and Gertler (1995) argue, when the economy is hit by a negative shock, it is often impossible to distinguish whether the usual deceleration in bank lending stems from a shift in demand or supply. On the one hand, the corporate sector may be demanding less credit because fewer investments are undertaken; on the other hand, it could be that banks are less willing to lend and, therefore, charge higher interest rates or decline more credit applications.

In order to address this problem of identification, we will rely on literature that examining the transmission of monetary policy restrictions through the credit channel. In particular, we will focus on the evolution of the spread between bank lending rates and rates on risk-free assets (see Bernanke and Blinder, 1988). To Return to the example above, once we recognize that lending has shrunk while the spread has widened, it becomes clear that demand for loans could not have declined more than loan supply.

# **2.2 The Propagation of the Credit Crunch**

According to the Council of Economic Advisors (1991), credit crunch is _"a situation in which the supply of credit is restricted below the range usually identified with prevailing market interest rates and the profitability of investment projects"._ When a credit crunch occurs, it alters the relationship between credit availability and interest rates. A credit crunch mostly occurs in two forms: (i) a leftward shift of the credit supply curve at a given interest rate level (price mechanism), and (ii) rationing of the credit supply, irrespective of interest rates (non-price mechanism; Box 1 and Box 2 provide more details