rafmacalaba/datause-encoder-data huggingface dataset:
over time, across destinations, and products, are typically swept up in these studies into an economywide trade cost or a product-specific fixed effect.

Furthermore, there have also been substantial developments in empirical research into firmpricing behavior (domestically and internationally), the importance of the relationship between the parties in international trade transactions, and the extent to which exchange rates pass through into transaction-level prices. This literature has deepened our understanding of the substantial differences in the prices of tradeable goods across borders. However, very little research on productspecific trade policy changes - many of which have magnitudes that dwarf the annual changes to exchange rates - has been undertaken in a way that would inform our understanding of the remaining pricing puzzles.

The fact that fewer than 20 percent of manufacturing firms in major economies export anything to anyone suggests that policy barriers to trade continue to matter. Similarly, increasingly disaggregated, high-frequency data on the prices of traded goods indicates that cross border price differences remain sizeable. Is there more that can be learned by incorporating similarly detailed trade policy data into microeconomic studies of firms and international pricing? Our hope is that this chapter sparks research ideas about how to take advantage of the rich variation in policy data to learn more about these other important questions facing the global economy.

To wrap up our analysis, we make a final, more practical, point regarding the increasing availability of trade policy data. Although a wealth of policy data is now almost continuously becoming available, because these data are still relatively new, in many instances, the data are not yet ‘clean’. Thus working with such data will require researchers to make some human capital investment into the details of the policies themselves so that they can check and verify the accuracy of newer datasets. A basic knowledge of trade agreements and the relevant domestic institutions is extremely helpful to understand the structure of data reporting and the potential substitutability of policy tools. In our view, the opportunities provided by this newfound data availability far outweigh the costs of this one-time investment.

To assist economists embarking on research in trade policy, we include two additional resources. First, we provide a Data Appendix with an in-depth description of the underlying sources for the data that we utilize in our empirical exercises. Second, and as our discussion of the landscape of trade policy has revealed, a large amount of policy data is collected and referred to by distinct GATT legal provisions that are known as ‘Articles.’ For ease of reference, in Table Appendix A, we provide a summary which links each of the key GATT Articles that we have utilized in the chapter to the main economic policies, exceptions, or concepts that they address.

58
32 failures of the experimenting firms do not necessarily imply the failures of their experiments.

The reality of the patterns studied in the dynamic development perspective is not open to serious dispute; there are too many well-studied examples. But since those studies have typically focused on the first few decades in the development of a particular product markets or industry, they do not themselves provide a direct approach to the general question of how patterns in firm demographics relate to the general functioning of the economy. It is possible that the aggregate picture is dominated quantitatively by what is happening in mature markets; alternatively, it is possible that the dynamic phenomena are important enough to distort any analysis of sectoral or aggregate data that does not attend to their existence.' The issue here is quantitative, but an adequate resolution would have to deal with large number of conceptual and measurement issues, including the operational definition of a "mature" market and the problem presented by the belated adjustment of industrial classification schemes to new forms of activity. In any case, the literature does not seem to provide much insight into this question.

9 Although not directly concerned with firm demographics, the job creation study of Davis, Haltiwanger and Schuh, discussed above, is subject to similar distortion. It gives no attention to the possibility that the aggregate patterns of job creation and destruction in small and large firms might be bound up with historical patterns of development -- other than referring to the point that the limitation of their data to the manufacturing sector might be a significant one.
7 government, (iv) commercial, and (v) industrial.<sup>5</sup> Initially, the rate of increase for social and residential tariffs was somewhat higher than for other uses, which reduced cross subsidies slightly, but this has been reversed somewhat since 1993 (Figure 1). For example, in 1989 industrial rates were 170 percent higher than residential; this fell to 130 percent in 1993, then rose to 160 percent in 1996.6 Although rates are highest for industrial users, these fall mostly on smaller industries, since large industries can afford to drill their own boreholes.

_Figure 1. Average Water Tariff_ <!-- Start of picture text --> 0.90<br>>7 0 .7 5 - -- - -- - -- -- -- - -- - -- - - -- - -- - -- - -- -- -- - - -- - -- -- - - -- - -- - -- - -<br>0.60 - - -- - - - E<br>0 .4 5 - -- - - - -- -- --- -- - -- - -- -----.<br>0 .3 0 -- - ------ --- -----<br>F--|<br>- 0.15 E<br>1989 1990 1991 1992 1993 1994 1995 1996<br>E| flSocial (1996 US) E Domestic (1996 US) r Goverrnment<br>Commercial Industrial - Weighted Avg<br>Source: Author's Calculations based on data from SEDAPAL Annual Reports<br><!-- End of picture text -->

Only 39 percent of connections were metered by 1996, and only 15 percent of connections had meters that were read (see statistical appendix table 3); an unknown but even smaller number were actually charged according to their meters.<sup>7</sup> Metering varied by type of user, with a greater proportion of industrial users charged according to a

> 4 Webb and Associates estimated the medical cost income costs of water borne diseases at US$13 per capita a year in 1992. Average family size is five persons and household income is US$240.

> 5 The connection charge in 1998 for water was US$224 and for sewerage, US$296.

> **6** The residential rates were US$0.18 per M<sup>3</sup> compared to industrial tariffs of USS0.49 in 1989; by 1996 the rates were US$0.33 and US$0.85 respectively.

> **'** In 1992 only 8 percent were charged according to a meter (LMG Consultants). These data are not strictly comparable, however, because SEDAPAL has contracted out metering and meter reading.
<!-- Start of picture text --> 4,000<br>2,000<br>0 s=22%= 22%<br>s=0%<br>-2,000<br>-4,000<br>S=-22%<br>-6,000<br>-8,000<br>-10,000<br>IQ1 1Q2 1Q3 IQ4 IQ5<br><!-- End of picture text --> <!-- Start of picture text --> 100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>-20,000<br>-40,000<br>-60,000<br>-80,000<br>-100,000<br>aSotamtasceeee° a ooeeeeta — ©eoeevo} 3 aoanwrvownrerenwtronnaeseeeeconti i i i i tzi azaazei ioA<br><!-- End of picture text -->
fertility decline in a municipality in Brazil began after the first year that the municipality had gained access to the TV soap operas. The decline was especially great for respondents close in age to the leading female character in at least one of the soap operas aired in a given year, which is consistent with a role model effect. The decline was comparable to the effect of two additional years in women’s education. For women ages 35–44, the decrease was 11 percent of mean fertility.<sup>40</sup> When individuals in South Africa were randomly invited to watch a soap opera that featured a character whose excessive borrowing and gambling led to her financial distress, the viewers’ understanding of financial management increased (Berg and Zia 2013). The viewers became less likely to engage in risky financial behavior themselves. Focus group findings suggest that their emotional connections with the leading character helped change their behavior.

When a radio soap opera with messages of intergroup tolerance was presented to randomly chosen groups in communities in Rwanda, the viewers’ perceptions of social norms and desirable behavior shifted. The viewers also became more willing to express dissent with authority. This is important, since blind deference to authority is believed to be a factor in mass violence, such as genocide (Paluck 2009, Paluck and Green 2009).

In Nigeria, soap operas are being designed to change behavior in ways that will slow the spread of HIV-AIDS. Experimental evaluation is in progress (Banerjee, La Ferrara, and Orozco 2015).

We have presented three examples in which actions or media influenced perception, cognition, and behavior. The examples were exposure to women leaders, organization of SHGs, and soap operas. They show that exposure to the new contexts has clearly shaped individuals. The idea that preferences and the mental models that underlie them are fixed can unambiguously be rejected. Of course, a die-hard devotee of fixed preferences could argue, for example: the soap opera has changed individuals’ posterior distributions concerning the consequences of different behaviors; that is, beliefs about the net payoffs from different conduct have changed.

> 40 <mark>The causal impact is based on the arguably random timing when different parts of Brazil obtained access to the TV emissions.  There was, however, no direct test available of a change in mental models.  Similarly, a study in the US by Kearney and Levine (2015) used historical MTV ratings to show that MTV’s</mark> _<mark>16 and Pregnant</mark>_ <mark>, a US reality TV show, caused a 4.3 percent reduction in teen births.</mark>

36
The shift from agriculture to manufacturing can in theory raise labor productivity in several ways: growth in labor productivity in the agricultural sector, growth in labor productivity in the manufacturing sector, and growth in labor productivity from the structural change between lowerproductivity agriculture and higher-productivity manufacturing. In the context of growth in African nations in the last two decades, it turns out that labor productivity growth outside agriculture is almost entirely from structural change, not from within the manufacturing or service sectors. In this section, we provide evidence for this claim; in the next section, we discuss how the patterns of Africa’s manufacturing growth between smaller informal firms and larger formal firms helps to explain what has been happening.

To describe these patterns, we use the recently released Economic Transformation Database (ETD) (de Vries et al. 2021). There are 18 Sub-Saharan African countries included in the ETD; in 2018 these countries accounted for about 74% of Sub-Saharan Africa’s GDP and 64% of its population. It includes the two most populous countries, Ethiopia and Nigeria, the two richest countries, Botswana and Mauritius (measured using GDP per capita in 2018) and two of the poorest countries in Africa, Malawi and Mozambique (author’s calculations using the World Development Indicators). For the most part, economywide value-added per worker in these countries is extremely low. For example, in 2018 employment-weighted value added per worker was only 4,689 current U.S. dollars; however, the value for 10 of the 18 countries fell well below this average. Following Diao et al. (2019), we examine the period 2001-2018—which corresponds most closely to the beginning of the “growth boom” in many African countries.

The ETD data includes value added, employment and price deflators for 51 countries for the period 1990-2018. Notably, reliable data on capital inputs per sector are not available, precluding calculations of total factor productivity growth. In any case, our preferred outcome measure here is value added per worker since it corresponds most closely to GDP per capita and because it is more straightforward to interpret. Using these data for the 18 African countries, we compute value added per worker and employment shares by sector and use these values to decompose labor productivity growth into its within and between components. The within component is computed as an employment weighted average (using initial period employment weights) of the change in value added per worker across sectors. The between component – or structural change component of productivity growth – is the sum across sectors of changes in employment shares multiplied by end of period productivity; it is positive when workers move from lower productivity sectors to higher productivity sectors. Because the 4
<!-- Start of picture text --> 35<br>30<br>25<br>2<br>&5 20<br>“3Ba°<br>vo<br>26<br>©o2<br>a<br>10<br>5<br>fe)<br>Lack of study Economic Not smart enough Low grades Lack of parental Time management Lack of Frequent illness for Do not know yet<br>habits or constraints support connections _ oneself and family<br>determination<br><!-- End of picture text -->
|**_Theme_ / Layer**|**Description**|**approximate grid**<br>**size**|**Source**|
|---|---|---|---|
|_Initial conditions_||||
|dist2cap|Distance to capital|N/A|NSO and Global|
||||Insights v6.1|
|dist2border|Distance to border|N/A|NSO|
|dist2major<br>river|Distance to major river<br>(centerline)|N/A|NSO and Nat-<br>ural Earth 3.0<br>1:10 million|
||||(2017)|
|dist2rail|Distance to railway|N/A|NSO and Global<br>Insights v6.1|
|elev30m|Elevation from SRTM|90m and 1km2|Jarvis et al.<br>(2008)|
|srtm<br>~~p~~opmask|Elevation in populated ar-|1km2|Jarvis et al.|
||eas in 2012 from Landscan||(2008); Bright<br>et al. (2013)|
|Slope|Slope (degrees of slope<br>(x100))|30-arc-seconds|Verdin et al.<br>(2007)|
|Ruggedness|Ruggedness Index|3 arc-seconds|Nunn and Puga<br>(2012)|
|rugged<br>~~p~~opmask|Ruggedness Index in popu-<br>lated areas|1km2|Nunn and Puga<br>(2012); Bright<br>et al. (2013)|
|_Agriculture_||||
|ag<br>~~h~~ybrid|Agricultural Hybrid|1km2|Fritz et al.<br>(2015)|
|lgp50<br>~~v~~6990|Length of Growing Period|10km2|FAO-GAEZ ver-<br>sion 3 (Fischer<br>et al. 2012)|
|rain<br>~~c~~rop<br>~~s~~h|Share of area with rainfed|1km2|GFSAD1000|
||crop||V1.0: (Telu-|
||||guntla et al.<br>2014)|

17
debt. Argentina is a case of legislated forced lending to the government at very low nominal interest; it was originally intended to pass on to the central government the benefits of the zero cost financing through money base issuance; but lending to the government has many times exceeded the monetary base, originating therefore not only a marginal but a total loss to the Central Bank. In other cases. for example Mexico, the interest the government pays to the Central Bank is determined ex-post, once the actual average cost of Central Bank funding is knownl ; in this case the benefits of cheap financing will be fully passed to the government but the Central Bank will incur no losses. Both methodologies deviate from the guidelines provided by the IMF Manual of Government Statistics which recommends charging the government a market rate for the entire debt and later transferring the Central Bank's profits to the government. This recommendation would in principle not change the central government deficit as resulting from the Mexican approach but would improve the account classification: Central Government interest service would fully reflect market rates and Central Bank's profits would be accounted as non-tax revenue.<sup>2</sup>

- l.Lending to the government is assumed to be financed first by the (noninterest bearing) monetary base, and the excess that cannot be funded in this way, is assumed to be funded by remunerated reserve requirements. Therefore the government is charged with a market rate at the margin.

- 2.However, the results arising from these apparently equivalent approaches would not be identical for several reasons: a) Central Bank's profits could be distributed with a lag of one calendar period; b) Central Bank's statutes may prevent a lOOZ distribution of profits; c) in some circumstances not all 10OZ of profits may belong to the central government.
# Concentration of Wealth

Wealth is even more unequally distributed than income. Saez and Zucman (2016) measure wealth inequality in the United States and find that in 2012 the top 10% of households owned a staggering 87.7% of the total wealth. In LMICs, wealth is also highly concentrated. In South Africa the top 10% owned 85% of total wealth (Chatterjee et al, 2022), while in Latin America, the World Inequality report estimated that the top 10% owned 77% of total household wealth (Chancel et al, 2022). The top 10% owned 67% of wealth in China (Piketty et al., 2019) and 63% in India (Anand and Thampi, 2016). The extreme inequality of wealth in many parts of the world means that the same challenges researchers face when measuring the top of the wealth distribution using household surveys are even more of a concern. For example, unit and item non-response and sparseness are much more severe problems when wealth is concentrated in the hands of fewer people than incomes.

# Solutions to Challenges of Measuring the Top of the Wealth Distribution

The solutions to challenges to measuring the top of the wealth distribution are similar to those described above for income, so we do not discuss the methods themselves again. Instead, we discuss research that has used these solutions to improve estimates of the wealth distribution in LMICs. We noted above that wealth data is much less common than income data. This means that there is much less research on wealth that attempts to improve measurement of the top of the wealth distribution in LMICs.

Alstadsæter et al. (2018) is an example of using non-traditional data on wealth to supplement administrative data estimates of the share held by the top of the wealth distribution. The authors used the data described in Alstadsæter et al. (2019), discussed above, to improve the estimates of the shares of wealth held by the top 0.01% for 10 countries, although the only LMIC included was the Russian Federation, where the share held by the top 0.01% more than doubled once offshore wealth is included. This implies that government-collected administrative data may have important weaknesses when measuring the top tail of the wealth distribution.

Xie and Jin (2015) provide an example of improving the measurement of the top of the wealth distribution using replacement and reweighting. The authors assume that the bottom 99.9% of the Chinese wealth distribution is well represented by the household survey data they use. They then use a Chinese rich list of the top 1,000 wealthiest Chinese and, assuming that the top can be represented by the Pareto distribution, estimate the Pareto parameter from the rich list and then use this to replace the top 0.1% with the values implied by the estimated Pareto parameter. This adjustment means that the estimated share of wealth held by the top 1% rises from 16% to 35%.

24
<!-- Start of picture text --> Mean # of disrupted days per decade<br>(median of all climate models for each urban center separately)<br>low emissions middle of the road emissions high emissions<br>20 - - - ------ 2 rn nnn nnn ee nr ee ere pecs i [\f..<br>BS<br>7 LIRSG<br>0 oo<br>ss FF KC KF KF SC KF KF KF KF S<br>Design year<br><!-- End of picture text -->
did not play any significant role in the intergenerational schooling linkage in rural China, and this is true for both the intercept and the slope of the intergenerational persistence regressions, (iii) both the slopes and intercepts were significantly different across farm and nonfarm households in rural India. The estimates suggest that father’s education and nonfarm occupation were complementary in determining son’s schooling in rural India, but separable in rural China. Structural change in favor of the nonfarm sector contributed to higher educational inequality in rural India during the study period, in part due to the complementarity. In contrast, such structural change was not an important factor in schooling inequality in rural China. Evidence from an approach that combines the biprobit sensitivity analysis of Altonji et al. (2005) with recent evidence on intergenerational correlation in cognitive ability suggests that the observed educational persistence in rural India is unlikely to be due solely to mechanical transmission of ability across generations, while the persistence in rural China could be explained by genetic correlations alone.

We analyze whether the economic forces identified in the extended Becker-Tomes model provide a coherent explanation of the observed pattern of educational mobility across countries (rural China vs. rural India) and over time (older vs. younger cohorts in rural China). A lack of intergenerational persistence in nonfarm occupations in rural China because of the Hukou restrictions seems to have played an important role in making the intercepts similar in rural China, but strong intergenerational occupational persistence in rural India resulted in significant differences between farm and nonfarm households. In rural India, the observed complementarity can be explained by higher returns to education in nonfarm occupations in the parental generation. The separability between father’s education and occupation in rural China was driven by the absence of any significant differences in the household-level returns to education across farm and nonfarm occupations. However, because of economic forces unleashed by the policy reform in China, the returns to education in nonfarm occupations for parents have increased and Hukou restrictions have been relaxed progressively. According to the theory, this should tighten the link between father’s education and son’s schooling in the nonfarm sector. Indeed, we find evidence that the separability between father’s education and occupation broke down for the 18-28 years old sons, implying that structural change in favor of the nonfarm sector is increasingly contributing to educational inequality in rural China.

30