rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: Manufacturing output shares (** % **GDP) regressions with post-1990 indicator - Southern SSA**

||Coefcients|
|---|---|
|Population (logarithm)|4.384***<br>(1.409)|
|Population squared (logarithm)|-0.184***<br>(0.044)|
|GDP per capita (logarithm)|5.689*<br>(3.011)|
|GDP per capita squared (logarithm)|-0.333*<br>(0.196)|
|GDP per capita (logarithm) _×_ post1990=1|-2.185<br>(2.399)|
|GDP per capita squared (logarithm) _×_ post1990=1|0.132<br>(0.152)|
|Constant|-53.967**<br>(21.526)|
|Observations|394|

Standard errors in parentheses. Results based on fixed effects fractional logistic regressions in equation (2) with a post-1990 dummy variable for Southern SSA. See Table A.3 for additional details on subregional grouping. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 010

# **6 Robustness and other considerations**

Our main finding so far is that we do not find robust evidence for deindustrialization in SSA. While manufacturing employment shares do not appear to follow an inverted U-shaped relationship, manufacturing output shares tends to be inversely U-shaped. Our results suggest that the RMVA result is driven by Southern SSA countries who appear to have deindustrilized when examining manufacturing output shares. This section investigates two specification issues in an attempt to understand the nature of SSA’s manufacturing experience. First, we augment the set of covariates beyond the population size and per capita income in equation (2). In particular, we include other potential correlates of deindustrialization such as the levels of remittances and foreign direct investments (FDI) (Lartey, Mandelman & Acosta 2012). Second, we also conduct our analysis by limiting the sample to countries for which we have non-oil GDP data. Recall that we have a subsample of SSA countries for which we have non-oil GDP data spanning 2004-2016 (see Section 2 for details). Our objective is to examine whether there other channels 21
35 (a) Completing the picture Except for Indonesia and Malaysia, for which we have detailed household surveys that cover the period of adjustment, we have no systematic evidence of the likely trends in poverty and income distribution during the adjustment period. A main advantage of the simulation models is that they provide a comprehensive period-by-period account of the evolution nf household incomes that allows for inferences about the likely evolution of income distribution and poverty indicators during adjustment.3 3 In addition, the simulations are useful for examining the importance of shocks and of the timing of adjustment measures. We highlight a few results.

In C6te d'Ivoire, the 1983 drought occurred in the middle of adjustment. How important was the resulting shortfall in agricultural supply, and by how much did it increase poverty? Lambert, Schneider, and Suwa simulated the economywide effects of the drought and found that it expanded the number of people in poverty by more than 15 percent. They also examined the benefits of migration back to rural areas during the crisis for those in the informal sector.

Governments tend to postpone adjustment until it can no longer be avoided. What are the effects of such delays? For Malaysia - the country with the most successful adjustment program - Demery and Demery show that an earlier adjustment would have smeothed the intertemporal distribution of income, which would have increased welfare if households were risk-averse. They also find that earlier adjustment would have led to a slight decrease in the number of people in poverty, although the intensity of poverty would have been slightly higher. By contrast, Morrisson finds that in Morocco, substantial gains would have occurred from a policy of early adjustment, which would have reduced the size of the transfers needed to stabilize the income of the poor.<sup>3</sup>
32 deteriorated, allowing different government departments to pull in different directions and resulting in piecemeal change (FIAS 2005f).

# **IMPROVING MONITORING**

Measuring and monitoring results, though an essential part of the reform process, is often overlooked or not systematically followed up. This function involves several tasks: evaluating the potential costs and benefits of the reform, translating reform objectives into specific performance targets or indicators (as discussed above) that can then be monitored, and reviewing compliance and outcomes once implementation begins.

In only a few cases was monitoring effective. Australia was the most positive example. The Australian program included monitoring agreements from the start, setting out quantitative targets and requiring state governments to submit annual reports to the National Competition Commission detailing progress made and difficulties encountered. Latvia introduced “report card”-type surveys to monitor inspections reform (box 5). Measuring initial conditions made it possible to later benchmark the progress of reform against target indicators.
# V. **The Model**

Let us assume that enterprise productivity is determined by a functional relationship where Xi is a K dimensional set of observed characteristics (technology, competition, ownership, industry type, etc.) affecting productivity and c, is the error term which includes characteristics unobserved by the researcher. Assuming further a linear functional relationship for F(-) produces: _yi_ = **_Xi8 +_** _Ei_ (2) If elements of { **Xi, c,** } are not correlated, the OLS will produce consistent estimates of vector a. However, if they are not independent OLS estimates of coefficients will not be consistent. It is important to see in this regard where a possible correlation between elements of X and E could be coming from. Assume that something that affects enterprise performance also affected, say, the privatization outcome and thus private share ownership at privatization - a variable included in X. Since the omitted or missing variable in question is not a part of X, it will have to be a part of 6, thus creating correlation between X and F. In reality it is not unreasonable to assume that ownership mix as well as ownership concentration are likely to be endogenous to, or jointly determined with, enterprise performance. It might be argued that "better" firms are more likely to have large foreign (as well as insider) ownership (see Smith _et al._ (1997) for discussion). Similarly, the state might choose to privatize better companies earlier and undertake restructuring in the remaining ones (which are perhaps being viewed as noncompetitive in the new environment). In any of these cases, even though the change in ownership per se does not lead to a change in behavior, a positive correlation between ownership and performance variables will be observed<sup>21</sup> . Moreover, if any of the 21 The selection bias would perhaps be harder to detect in insider, particularly in managerially, owned enterprises. The reason is because knowing that the enterprise will be subjected to a MEBO (management-employee buyout), managers of a "good" enterprise have every incentive to understate the true value of the company (by 17
**Risks and Opportunities of Participation in Global Value Chains**<sup>***1**</sup>

# **Gary Gereffi**

Duke University

# **Xubei Luo**

The World Bank

**JEL classification:** F16; F23; L25.

**Key words:** Global Value Chains; Risk; Opportunity; Economic Upgrading; Social Upgrading.

> * This paper was prepared as a background paper of the World Development Report 2014: Risk and Opportunity. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of their affiliated organizations.

> 1 The authors would like to thank Anirudh Krishna for valuable discussions and comments.
permanent and rather intensive cropping – these benefits are helpful but possibly as yet not a crucial issue. In the case of eucalyptus, the environmental benefits and also costs need to be looked at more carefully, not least since it can be grown in most pats of the country, including on land of relatively low fertility.

The planting of eucalyptus trees used to be largely confined to State owned plantations and community woodlots, but increasingly it is also grown on household farms (Jagger and Pender (2001)) It is considered a better performing species than many indigenous sources of wood, since it grows fast and is rather resilient, providing a helpful source of woody biomass, and contributes to limiting erosion and land degradation. Nevertheless, it also has proven negative externalities on crop production on nearby plots, and part for this reason the regional government of Tigray has even banned eucalyptus on land suitable for crop production, even though there is little or no evidence of enforcement of this ban. Some researchers, e.g. Jagger and Pender (2001) have questioned the magnitude of these ecological risks, arguing that the potential ecological and income benefits far outweigh these costs. In any case, in many areas were eucalyptus is not banned, it can provide a ready source of cash income, and tenure security may well influence the decision to plant trees, allowing us to use it as an example for assessing the impact of transferability and security on investment decisions.

Descriptive statistics on the plot level data are given in table 1. A plot is defined as a clearly identifiable piece of land, as the farmer himself or herself decides to demarcate it.<sup>5</sup> To identify land tenure security and transferability, a number of variables are available. First, we have plot level data for each of the four rounds on the mode of acquisition (i.e. whether the plot was bought before land reform, acquired from the state during land redistribution, rented or sharecropped in, or inherited). Table 1 also gives data on this for our sample. About 72 percent of plots are inherited. Land purchases largely refer to pre-land reform of 1975 purchases (after which

> 5 One particular feature of the available data should be highlighted here. The data were collected as a household level panel, not a plot level panel. Matching of plots has proved difficult and is not attempted for the purposes of this paper. In each round, households were asked to give us details about their plots. Questions were asked without specific reference to past information gathered. Since this part of the questionnaire always proved one of the most difficult parts to complete, the respondents’ own concept of the plots it cultivates at present dominates. Furthermore, the analysis is conducted using an unbalanced panel. Attrition (although only about 7 percent between 1994 and 1999) will have reduced the number of plots in the sample while newly added plots increased the sample.

10
Table 6: Heterogeneity by income level of non-OECD countries

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)<br>(1)|_log_(1 +_exp_)<br>(2)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(3)|_log_(1 +_imp_)<br>(4)|_log_(1 +_exp_)<br>(5)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(6)|
|**A. High-income non-OECD countries**|||||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.3187***|0.4851***|-0.1664**|1.0488***|1.1087***|-0.0599|
||(0.0800)|(0.1202)|(0.0626)|(0.2927)|(0.2944)|(0.1987)|
|First stage :|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0027***|0.0027***|0.0027***|
|||||(0.0003)|(0.0003)|(0.0003)|
|Observations|80,080|80,080|80,080|80,080|80,080|80,080|
|_R_<sup>2</sup>|0.6674|0.8266|0.3889|0.6593|0.8217|0.3886|
|Kleibergen-Paap rk Wald-F-stat||||110.960|110.960|110.960|
|**B. Low and middle-income non-OECD countries**<br>_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1370*|0.4044***|-0.2674***|0.5716**|1.1913***|-0.6197***|
||(0.0688)|(0.1247)|(0.0702)|(0.2094)|(0.3193)|(0.1593)|
|First stage :|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0027***|0.0027***|0.0027***|
|||||(0.0003)|(0.0003)|(0.0003)|
|Observations|808,733|808,733|808,733|808,733|808,733|808,733|
|_R_<sup>2</sup>|0.6366|0.7677|0.3939|0.6333|0.7587|0.3908|
|Kleibergen-Paap rk Wald-F-stat||||111.830|111.830|111.830|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the sub-samples for non-OECD countries depending on their level of income per capita. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
It is important to note that while the growth of Vietnamese exports to the United States jumped in 2002 and 2003 after the BTA went into effect, the growth of Vietnamese exports to other key trade partners remained stable (Figure 2.2.2). This is an important support to our argument that the estimated effects on local job markets are not driven by correlation of exports to other countries.

**Figure 2.2.2: Exports to the U.S. relative to other key trade partners** Source: UN Comtrade

<!-- Start of picture text --> China France US Japan<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>BILLIONS DOLLARS<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**3. Data**

We use two waves of the Vietnam Household Living Standards Survey (VHLSS) in 2002 and 2004. VHLSS provides information on household expenditures, occupation, employment, and various other household and individual characteristics. The 2002 VHLSS was conducted between January 2002 and December 2002. The 2004 VHLSS interviewed households only from May 2004 through November 2004, with the majority of households being interviewed in June and September. For both surveys, the recall period for expenditures and employment is the past 12 months. The two surveys are nationally representative.

8
- Faber, B. and T. Fally (2017): “Firm Heterogeneity in Consumption Baskets: Evidence from Home and Store Scanner Data,” _NBER Working Paper_ , No. 23101.

- Feldman, N. E. and J. Slemrod (2007): “Estimating Tax Noncompliance with Evidence from Unaudited Tax Returns,” _The Economic Journal_ , 117, 327–352.

- Gadenne, L. (2020): “Can Rationing Increase Welfare? Theory and an Application to .

- India’s Ration Shop System,” _Forthcoming, American Economic Journal: Economic Policy_

- Gemmell, N. and O. Morrissey (2005): “Distribution and Poverty Impacts of Tax Structure Reform in Developing Countries: How Little We Know,” _Development Policy Review_ , 23, 131–144.

- Gerard, F. and G. Gonzaga (2016): “Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program,” _NBER Working Paper_ , No. 22608.

- Gordon, R. and W. Li (2009): “Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation,” _Journal of Public Economics_ , 93, 855 – 866.

- Hanna, R. and B. Olken (2018): “Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries,” _Journal of Economic Perspectives_ , 32, 201–226.

- Harris, T., D. Phillips, R. Warwick, M. Goldman, J. Jellema, K. Goraus, and G. Inchauste (2018): “Redistribution via VAT and Cash Transfers: An Assessment in Four Low and Middle Income Countries,” _IFS Working Paper_ , W18/11.

- Hsieh, C.-T. and B. A. Olken (2014): “The Missing ’Missing Middle’,” _Journal of Economic Perspectives_ , 28, 89–108.

- Huang, J. and J. Rios (2016): “Optimal Tax Mix with Income Tax Non-Compliance,” _Journal of Public Economics_ , 144, 52–63.

- Jacobs, B. and R. Boadway (2014): “Optimal Linear Commodity Taxation under Optimal Non-Linear Income Taxation,” _Journal of Public Economics_ , 117.

- Jaravel, X. (2018): “The Unequal Gains from Product Innovations: Evidence from the U.S. Retail Sector,” _The Quarterly Journal of Economics_ , 134, 715–783.

- Jenkins, G. P., H. P. Jenkins, and C. Y. Kuo (2006): “Is the Value Added Tax Naturally Progressive?” _Working Paper, Economics Department, Queen’s University_ , No. 1059.

- Jensen, A. (2019): “Employment Structure and the Rise of the Modern Tax system,” _NBER Working Paper_ , No. 25502.

- Keen, M. and J. Mintz (2004): “The Optimal Threshold for a Value-Added Tax,” _Journal of Public Economics_ , 88, 559 – 576.

- Kleven, H. J., C. T. Kreiner, and E. Saez (2016): “Why Can Modern Governments Tax so Much? An Agency Model of Firms as Fiscal Intermediaries,” _Economica_ , 51.

35
22 condemning their economies to stagnation. Elections were to be held in 1989 in both Argentina and Brazil. The front-runner in Argentina, Peronist Carlos Menem, spoke of a five-year moratorium on debt payments (sometimes using the qualification "negotiated"). Two leading presidential candidates in Brazil, leftists Leonel Brizcla and Luis Inacio da Silva (the labor leader "Lula"), called for a moratorium on debt payments. Even in Mexico, the new president Carlos Salinas de Gortari had faced unprecedented domestic opposition in considerable part over the debt question, and his statements on taking office emphasized the need for debt reduction (but not through moratorium or confrontation). And the new president of Venezuela, Carlos Andres Perez, had similarly seemed to take a tough line on debt (and the country found itself compelled to suspend a large portion of principal payments at the end of 1988 as its non-gold reserves fell precariously low).

Political debt fatigue is easy to understand. In Mexico, real wages in 1987 stood almost 30 percent below their 1980 level. Inflation reached unprecedented dimensions in Latin America in 1987-88. Per capita income for the region in 1988 fstood 6.6 percent below its 1981 level.<sup>13</sup> The understandable but simplistic reaction was to blame external debt for all of these economic ills. An equally simplistic and dangerous tendency was to infer that some form of radical debt relief would mean renewed high growth and price stability.

> 13 ECLA 1988, table 3.
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34
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# **1. Introduction**

Household survey data commonly exhibit considerable nonclassical measurement error (NCME) (Bound et al., 2001). This is true even for key assets and factors of production, like agricultural land, that are readily measurable and observable to the survey respondent, and that heavily affect farmers' incomes and livelihood. The measurement error literature largely treats NCME as an econometric challenge to overcome. This would be appropriate if NCME arises purely due to respondents misreporting their true, accurate beliefs about land holdings and other key variables. In that case, NCME carries no implications for behavior and respondents’ decision-making processes; it is merely a statistical nuisance. Abay et al. (2021) show, however, that a large share of NCME in plot sizes reported by farmers in four African countries appears to reflect farmers' reporting mistaken beliefs, not misreporting of accurate beliefs.<sup>1</sup> Such findings suggest that NCME in household survey data not only matters for statistical inference but can also shed light on respondents’ decision-making processes and behaviors in ways that may reveal actionable information, consistent with a longstanding behavioral economics literature that routinely finds that people often act upon mistaken beliefs or misperceptions (Tversky and Kahneman, 1973; Kahneman and Tversky, 1984; Angner and Loewenstein, 2012).

If at least some NCME reflects mistaken beliefs, then uncovering the nature and sources of NCME and their prospective implications for measurement and policy design becomes important for multiple reasons. First, mistaken beliefs might reveal smallholder farmer behavioral phenomena that drive these errors, such as inattention, self-esteem, or confirmation bias. As a burgeoning literature on “choice architecture” underscores, organizations worldwide increasingly design policies around behavioral anomalies (Thaler and Sunstein, 2009; Ariely, 2016), but the agricultural development community has been slow to do so.

Second, we know little about whether and how farmers incorporate objective information to correct mistaken beliefs, although this surely matters for information-based interventions, such as agricultural extension programming, cadastral surveys, and market information services. If there exists heterogeneity or asymmetries in learning – i.e., the updating of reported beliefs – or learning

> 1 Berazneva et al. (2018), Burke et al. (2020), Wineman et al. (2020), Michelson et al. (2021) and Wossen et al. (2021) provide similar evidence of misperception in soil quality, plot size, crop variety, chemical fertilizer quality, and crop variety, respectively.

2