rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The national statistical system (NSS) is the ensemble of statistical organizations and units in a country that jointly collect, process, and disseminate official statistics on behalf of the government (OECD 2002). It plays a vital role in modern economies, providing a range of stakeholders with statistical information on the country’s socioeconomic evolution at the national and subnational levels.

NSS performance is constrained by its statistical capacity, defined as “the ability of a country’s national statistical system, its organizations, and individuals, to collect, produce, analyze, and disseminate high quality and reliable statistics and data to meet users’ needs” (Paris21 2017a). Weak statistical capacity restricts development. Planning and monitoring progress are hampered when statistics are inaccurate, irrelevant, slow to be disseminated, or missing entirely (Devarajan 2013). Lack of data limits stakeholders’ ability to hold governments accountable for their actions and prevents civil society from drawing attention to gaps in government services (UNSC 2017).

Weak statistical capacity restricts innovation. Some high-capacity NSSs have developed new methods, tapped new data sources, and quickly formed strategic partnerships to produce the information necessary to understand how best to respond to the COVID-19 crisis (UNECE 2020a). But many others have struggled to adapt their data-collection processes in the face of quarantines and other social distancing measures (UNDESA 2020).

Statistical systems in many developing countries rely on international support to build capacity. These efforts focus on improving the supply of data, often neglecting strengthening data demands by the country’s state and nonstate actors. State demand for data reflects the technical capacity of the bureaucracy to use data to develop programs and policies and deliver public services. This technical capacity is higher in countries where the bureaucracy can work without political interference (Fukuyama 2013) and with autonomy from pressures that might jeopardize objective policy implementation (Bersch, Praça, and Taylor 2017). Nonstate actors—think tanks, academia, civil society, private sector actors, and the public—use data to hold governments accountable, advocate for policy priorities, make business decisions, and track socioeconomic conditions.

The share of official development assistance for data and statistics has stagnated over the last decade while data demands soared (PARIS21 2017a). In this environment, better-targeted, smarter 2
3 The income loss from the decline in the terms of trade for the region (excluding Nigeria) was around 10 percent of GDP between 1965-73 and 1987-90. Though large, the loss was spread over 20 years. Adding Nigeria to the picture reduces the 20 year loss to just 3.6 percent of GDP or 0.2 percent a year. The reason for such an insignificant contribution of terms of trade losses can be found in the composition of exports.

In terms of the composition of Africa's exports, there are three major commodity groups that account for bulk of the export-oil and gas (40 45%), metals and minerals (2025%) and agriculture (20-25%). At this disaggregated level, the impact of the terms of trade has been uneven and not necessarily in the same direction for oil exporters, mineral exporters and agriculture exporters. Between 1970 and 1990, oil exporters enjoyed improvements of more than 100 percent while mineral exporters were hardest ht, their terms of trade fell by around 50 percent. Agriculture exports suffered a decline of 34 percent while the more diversified exports 30 percent. Thus, at an aggregate level, the picture for the whole of .SubSaharan Africa does not look as dismal as at the individual country level because the positive, movement in one commodity group neutralizes the negative movements of the other groups.

At a country level, the situation is more differentiated. Several countries that derive their export revenues from coffee, cocoa and copper, whose prices were severly depressed over the decade of the 1980s and real prices had, in fact, dropped to the historically low levels in the second half of the 1980s, were indeed badly hurt.

The AAS, therefore, concluded that poor economic policies played a bigger role in explaining economic decline except a few cases. The task of adjustment was, however, made more difficult by larger terms of trade decline recorded in the period 1986-91. Tle outcornes would have been much more positive if the commodity prices had not fallen so precipitously, debt burden had been reduced, and the Southern Africa had not faced a terrible drought

# **The** Extent of Policy Reforms

The major contribution of the AAS was a careful measurement of the extent to which six sets of policy reforms-macroeconomic, trade, agriculture, public enterprise, financial sector and public sector management-were implemented in the 29 countries in the period 1986-91. The study<sup>7</sup> found:

> 7The first part of this section is drawn from Jones, C. and Kiguel M., 1994, AMica's Quest for Prosperity: Has Adjustment Helped'. and the second pat is based on Husain, 1., 1994 'Results of Adjustment in Africa: Selected Cases", in Finance and Dc"elcpmcnL
2002? We use educational attainment and occupational choices as two salient indicators of economic status in rural China.

A central issue in the literature on intergenerational persistence in economic status has been the possibility that the estimated partial correlation from regression analysis may be driven primarily by unobserved heterogeneity such as genetic transmissions from parents to children. Since our focus is on the changes in the intergenerational linkages over a period of a decade and a half, the estimated link between parents and children in education and occupations from multivariate regressions are likely to identify the direction of change in the intergenerational linkages reasonably well. To investigate the possible role played by positive selection on unobserved characteristics in determining the strength of intergenerational persistence in a given year, we take advantage of the sensitivity analysis developed by Altonji, Elder and Taber (2005). To estimate the causal effect of parental education on children’s years of schooling, we use Klein and Vella’s (2009) approach, which exploits heteroskedasticity for identification. The results from the empirical analysis show that intergenerational occupational mobility has increased dramatically in rural China from 1988 to 2002. Although in 1988, parents seem to exert considerable influence on children’s occupation choices, the effect of parental non‐farm participation on children’s non‐farm choice is effectively zero in 2002, for both daughters and sons. In contrast, the intergenerational educational mobility has remained largely static for daughters, while it has become significantly less mobile over time for sons. We provide possible explanations for such divergence in occupational and education mobility in rural China in the post‐ reform period.

39
29 A '*' over a variable indicates the level it would take in a hypothetical unified system. Thus, E is the exchange rate that would be consistent with equilibrium in the balance of payments in a unified system. D denotes the non-financial public sector deficit; x and m denote central bank purchases and sales of foreign exchange from the private sector.

The first term in brackets in equation (1) shows the effect of the multiple system on the nonfinancial public sector's budget. The second term shows the implicit gains or losses incurred by the central bank in its foreign exchange transactions with the private sector under the multiple system.

Table 6 presents the evidence from five countries in our project, with the information classified in terms of the two components of (1)."' While it was possible to obtain specific estimates for the effect on central bank profits, the effect on the domestic currency budget could be assessed only in qualitative terms, with the exception of the case of Tanzania.

Table 6 shows that the fiscal effect of parallel rates is not uniform across countries. There are countries with central banks profits and others with central bank losses; some in which the domestic currency budget improves and others in which it worsens. Furthermore, the two components of (1) do not necessarily work in the same direction.

The qualitative effect on central bank profits depends primarily on whether the central bank is a net buyer or a net seller of foreign exchange to the private sector." This is paricularly the case when the central bank's buying and selling exchange rates for its transactions with the private sector are equal. Since this exchange rate is usually appreciated with respect to the shadow exchange rate (equal to the parallel exchange rate in all the estimations in table 6), net sales to the private sector generate losses while net purchases generate profits. In Zambia, for examnple, the central bank was a '' Far Venezuela and Zanbia the estinates of the effects on central bank accounts reflect our calculations based on data provided by the authors.

19 Since all foreign exchange transactions of the public sector are usually conducted through the central bank. "central bank" net foreign exchange transactions with the private sector are equivalent to "overall public sector" net foreign exchange transuctions<sup>with</sup> the private sector.
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there is still an unidentified “African” component to inequality. The hypothesis will be empirically tested in Sections 3 and 4 of the paper. The previous two sections present some evidence on Africa’s current level of inequality and income by contrasting it to Asia and Latin America. They also look at whether similar levels of inequality obtained at the time of independence. In the last Section, we present some reflections on the role of ethnicity and suggestions for further research.

# <u>2. Eyeballing Africa</u> 8

Figure 1 compares African with Asian and Latin American countries across two dimensions: inequality (measured by the Gini coefficient) and level of income (measured by the GDP per capita in 1995 $PPP). The Ginis come from the most recent household surveys conducted (in all three regions) around 1998. In almost all cases, calculations are done from the primary (individual-level) data. All surveys are nationally representative and in all of them inequality statistics are calculated across individuals ranked by their household income or expenditure per capita.<sup>9</sup> (The full list of countries, and surveys used is given in the Annex 1.) A comparison of Africa with Asia and Latin America allows us to make several interesting conclusions. First, not surprisingly, Africa is poor and unequal. Average population-weighted GDP per capita (in 1995 $PPP terms) of African countries included in the sample is around 1,700 which is less than a half of the Asian level, and about a quarter of the Latin American level. The unweighted average GDP per capita ratios are similar. But in addition, Africa is unequal. At Africa’s level of income (around $PPP1,000) there are only a few countries in Asia. But they are uniformly more equal. The difference, easily observable if we compare Panels a and b in Figure 1, amounts to about 10 Gini points. This is a substantial difference: it is equal to the difference in

> 8 Africa is defined here to include only sub-Saharan Africa.

> 9 We had no alternative but to use both income and expenditure as the welfare aggregate, for concentration on either one would have drastically reduced the sample of countries (see the Annex).

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-42 -

Suppose we now turn to a somewhat more interesting example that reflects our initial concern with ways of reducing the size of the public sector. Accordingly, we carry out a simulation which fixes the government's demand for reserves, as in the example reported in Table 5.6, but reduces real government spending on goods and services, both current and capital, by 25 percent in each period. Presumably this should reduce the rate of growth of infrastructure, but should also have antiinflationary effects. The results, however, are rather different.

Table 5.8. Simulation with Fixed Central Bank Reserves and Reduced Government Spending a/

||19|83|19|84|19|85|
|---|---|---|---|---|---|---|
|A GNP|||3.0.<br>|<br>(2.5)|-0.6<br>|<br>(0.3)<br>|
|D|-10.6|(-8.5)|-6.5|(-5.4)|-3.2|(-2.0)|
|I|10.0|(10.2)|9.0|(9.7)|12.3|(12.2)|
|X|13.6|(13.5)|10.3|(10.3)|9.3|<br>(9.3)|
|H|6.0|<br>(5.9)|6.5|(6.4)|6.6|<br>(6.6)|
|TB<br>|7.6|<br>(7.6)|3.8|(3.9)|2.7|<br>(2.7)|
|if<br>|||68.8<br>|(62.3)<br>|37.7<br>|(35.6)<br>|
|i|77.1|(70.0)|74.9|(72.1)|72.4|(72.7)|
|&|||41.1|(36.7)|25.1|(22.7)|
|R|3.43|(3.43)|3.43|<br>(3.43)|3.43|(3.43|
|E|||(19.6)|(18.7)|10.1|(10.5)|

a/ The numbers in parenthesis are those of Table (5.6) while all footnote of Table (5.6) apply here also.
more staggering and widespread across countries and products, both in terms of values and quantities. Figure 1 presents the distribution of trade discrepancies for a sample of more than 40 importing countries.<sup>2</sup> At the sample mean, the trade discrepancy is about 85 percent –suggesting that most import records are much larger than the export records, for both quantity and value.

What could cause such discrepancies in the o¢ cial trade statistics? There are several contributing factors. The discrepancies could be at least in part due to the di¤erent valuation methods applied by customs agencies, which normally include freight and insurance costs in import values but not in export values.<sup>3</sup> Another contributing factor is that the transshipment and re-export of products going through third countries cause a mismatch in the country of origin.<sup>4</sup> The discrepancies could also result from trade fraud in the form of undervaluation in imports in order to evade import tari¤s,<sup>5</sup> or the undervaluation in exports in order to evade export taxes or VAT.<sup>6</sup> The analysis of trade discrepancies has also been increasingly used to detect tari¤ evasion, especially by the customs authorities of developing countries.<sup>7</sup> However, even taken together, these contributing factors cannot fully explain the large discrepancies in the data, which tend to vary across products and trading partners, particularly with the recorded import quantity larger than the export quantity.

The objective of this paper is to assess whether part of the large discrepancies in the o¢ cial statistics is actually trade fraud due to the avoidance of costs associated with the non-tari¤ measures (NTMs) of the importing countries.<sup>8</sup> Such trade fraud may not only

> 2In this paper, trade discrepancies are de...ned according to Equation (8) ; which measures the percentage di¤erence between the importer’s and exporter’s statistics. Figure 1 plots the distributions of the value discrepancies (gapvsh) and quantity discrepancies (gapsh) at the HS 6-digit product level across countries. Please refer to Table 1 for the list of importing countries.

> 3Hummels and Lugovskyy (2006).

> 4Ferrantino and Wang (2008).

> 5Bhagwati (1964), Fisman and Wei, (2004), and Javorcik and Narciso (2008).

> 6Ferrantino, Liu and Wang (2012).

> 7Cantens (2015); Grigoriou, Kalizinje and Raballand (2019); Grigoriou(2019); Chalendard, Fernandes, Raballand and Rijkers (2020).

> 8This paper focuses only on large discrepancies at the HS 6-digit level, in which the di¤erence between

2
24 Table 1: NL2SLS Estimates of the Skilled **Wage** Equation

||Argentina<br>**Rostricted8**|(1960-86)<br>Unrestrict.db|Colombia (<br>Restrict.dO|1960-85)<br>Unr.strictedb|Chile (<br>Restricted <sup>5</sup>|1960-85)<br>Unrestrictedb|Uruguay<br>Restricteda|(1965-85)<br>Unrestrictedb|
|---|---|---|---|---|---|---|---|---|
|AO (Constant)|2.558<br>(2.653)|0.589<br>(0.834)|-2.344<br>(-2.788)|-3.730<br>(-2.631)|0.007<br>(1.171)|-6.348<br>(-1.600)|0.146<br>(0.207)|1.710<br>(1.000)|
|Al_(6)_|-3.337<br>(-2.423)|-0.804<br>(-0.7S5)|-0.411<br>(-0.578)|1.180<br>(0.778)|1.808<br>(1.771)|1.913<br>(C.761)|0.108<br>(0.363)|-1.100<br>(-0.918)|
|A2(In p)|-4.781<br>(-2.311)|-1.084<br>(-0.618)|3.961<br>(4.038)|(5.60)<br>(3.199)|7.387<br>(3.047)|9.932<br>(1.717)|2.192<br>(2.032)|0.071<br>(0.030)|
|Ag_(0_In p)|--|2.188<br>(1.006)|--|-2.560<br>(-1.218)||-3.064<br>(-O.888)|--|0.424<br>(0.310)|
|A4 (Iniw)|4.759<br>(2.802)|2.401<br>(2.015)|-0.488<br>(-2.518)|-0.724<br>(-2.472)|-0.885<br>(-1.323)|-2.122<br>(-1.328)|0.609<br>(0.645)|1.900<br>(1.132)|
|AS(0Iniw)|--|-2.410<br>(-1.SS7)|--|1.123<br>(2.049)|--|0.8S3<br>(0.972)|--|-0.744<br>(-0.825)|
|AS(N)|-1.226<br>(-2.982)|-0.826<br>(-3.457)|-0.087<br>(-2.461)|-0.160<br>(-2.204)|-0.803<br>(-4.283)|-0.636<br>(-1.703)|-1.934<br>(-3.187)|-2.695<br>(-2.610)|
|A7(ON)|--|0.998<br>(2.571)|--|0.194<br>(1.275)|--|0.329<br>(1.889)|--|1.433<br>(2 OS0)|
|,1|4-1.41<br>(-22.288)|--|-0.077<br>(-0.348)|--|-0.351<br>(-4.222)||-0.408<br>(-8.988)|-|
|**e2**|1.222<br>(5.830)|--|0.610<br>(2.352)|--|0.197<br>(2.629)|--|0.235<br>(1.672)|-|
||0.76|0.95|0.97|0.9s|0.92|0.94|0.95|0.93|
|DV|2.54|1.83|1.86|1.60|1.64|1'.9|2.11|1.4C|
|Chi-squared**valuo**<br>of Weldteat|0.474||1.80||16.739||0.001||
|**Level**<br>ofsignificance|0.491||0.180||0.001||0.97||

a: Equation (3b). b: **Equation (3S)** .

Note: **numbers** in **parentheses are t-statistics.**
Table 7: Multivariate estimations of district traits and specialization/diversity

||Spe|cialization in|dex|D|iversity inde|x|
|---|---|---|---|---|---|---|
||Average|Initial|Change|Average|Initial|Change|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log of district population|-0.159|-0.053|0.064|-0.546|-0.101|-0.221|
||(0.457)|(0.455)|(0.155)|(0.376)|(0.444)|(0.175)|
|Log of district population density|-0.058|0.709|0.106|-0.032|-0.216|-0.174|
||(0.506)|(0.508)|(0.195)|(0.492)|(0.550)|(0.205)|
|Educated worker share (% pop graduate)|0.163|-0.255|0.208+|0.275|-0.374|0.146|
||(0.347)|(0.310)|(0.121)|(0.259)|(0.363)|(0.111)|
|Age profile (demographic dividend)|0.183|-0.521|0.170|-0.670|-0.224|-0.012|
||(0.490)|(0.530)|(0.183)|(0.491)|(0.497)|(0.178)|
|Literacy rate|-0.181|0.717+|-0.428*|1.079**|0.888*|0.148|
||(0.417)|(0.389)|(0.165)|(0.372)|(0.441)|(0.138)|
|Infrastructure: electricity access|-0.159|-0.325|0.002|-0.211|-0.470|0.102|
||(0.429)|(0.448)|(0.173)|(0.388)|(0.466)|(0.148)|
|Infrastructure: paved roads|-0.377|0.245|0.158|-0.211|-0.425|-0.160|
||(0.361)|(0.363)|(0.138)|(0.317)|(0.377)|(0.134)|
|Strength of household banking|0.119|-0.359|-0.038|-0.285|0.088|0.116|
||(0.384)|(0.423)|(0.134)|(0.346)|(0.399)|(0.142)|
|Log travel time to closest large city|-0.237|-0.311|-0.055|-0.531**|-0.122|-0.036|
||(0.234)|(0.332)|(0.140)|(0.181)|(0.294)|(0.118)|
|Manufacturing share of local employment|0.124|0.044|0.237*|-0.971**|-0.370|-0.399**|
||(0.347)|(0.320)|(0.106)|(0.302)|(0.290)|(0.127)|
|Urbanization rate (% urban)|0.461|-1.429*|-0.179|-0.801|0.577|0.140|
||(0.563)|(0.576)|(0.224)|(0.575)|(0.621)|(0.218)|
|Log per capita consumption|1.239|1.938+|0.066|-0.089|0.964|-0.577|
||(1.127)|(1.142)|(0.433)|(1.047)|(1.199)|(0.407)|
|Log organized employment|0.559|-0.192|0.032|0.050|0.277|0.119|
||(0.353)|(0.344)|(0.134)|(0.288)|(0.352)|(0.103)|
|Log unorganized employment|-1.006+|0.343|-0.019|-0.205|-0.580|-0.119|
||(0.522)|(0.502)|(0.189)|(0.487)|(0.536)|(0.185)|
|State fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|281|281|281|281|281|281|
|Adjusted R-squared|0.233|0.174|0.161|0.367|0.203|0.181|

Notes: Table documents multivariate correlations between district traits and specialization and diversity index values. Average and initial index values are expressed on a ten-point scale representing deciles. Change values are expressed in unit standard deviations. District traits are from the 2001 Population Census and ASI/NSS. District traits are expressed in log values or unit standard deviations for interpretation. Estimations include state fixed effects, weight districts by log district population, and report robust standard errors. + significant at 10% level; * significant at 5% level; ** significant at 1% level.
# 2.4 Wage Flexibility

> Many of the institutional factors reviewed earlier (most importantly minimum wage regulation, firing regulations, and trade unions with strong bargaining power) may hinder real wage flexibility in Morocco. Comprehensive

> and accurate data on the evolution of real wages across sectors and skill categories over time are not available. Nevertheless, several sectoral surveys

> indicate that real wages are somewhat flexible in the urban labor market. For instance, between 1980 and 1987, data from the Social Security (CNSS)

> are showing that the real average wage in the private formal sector decreased by 10 percent. Using various statistical sources, Morrisson (1991) found that real wages in the industry and the public sector fell substantially between 1980 and 1986. Over the 1985-89 period, due to a rapid growth of temporary contracts with low remunerations, real wages fell by 2.5 percent per year in the manufacturing sector and led to rapid job creation. During the 1990s, real average wages increased in the public and formal private industrial sectors, by around 1.2 percent and 3.1 percent a year, respectively, driven by several revisions in the minimum wage and sweeping wage increases in the civil service.

In Morocco, nominal wages tend to be partially indexed to prices. According to the econometric calculations performed by Mouime (2001), the short-term elasticity of the average nominal wage to the consumer price index is 0.85 in the formal sector.<sup>5</sup> The author also found that in the industrial sector the wage-productivity link is explained by an efficiency wage relationship. Although this analysis is not differentiated by levels of skills (due to data limitations), one would expect the relationship to be particularly relevant for skilled workers, thus introducing a significant degree of downward wage rigidity for that category of labor in the formal private sector.

The evidence presented here does not imply that there is no _relative_ wage rigidity across skill categories and/or sectors of employment. This is important because as discussed below, in the presence of labor market segmentation-an important feature of the Moroccan urban labor marketrelative wage rigidity across segments and skill categories may explain the persistence of widespread unemployment.

> 5The Centre Marocain de Conjoncture (2001) obtains similar results for the industrial sector.

16