rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

One of the most enduring challenges in the field of economic growth and development is to understand the sources of the large variation in economic well-being across countries. The current consensus in the literature is that even though cross-country differences in physical and human capital stocks are important factors explaining development gaps, aggregate productivity remains the predominant source. In this paper, we provide a systematic study of one of the most promising avenues for accounting for total factor productivity (TFP) gaps, those stemming from an inefficient allocation of resources across firms in one of the poorest yet least explored regions in the world: Sub-Saharan Africa (SSA). Combining novel census-based firm-level manufacturing databases with a structural theory of misallocation, the paper provides a characterization of the degree and nature of resource misallocation as well as a quantification of the productivity losses associated with these inefficiencies in Cˆote d’Ivoire, Ethiopia, Ghana, and Kenya.

Following the work of Hsieh and Klenow (2009), we measure allocative distortions in the data as deviations from the output-maximizing prescription of equalizing marginal returns across comparable production units. We summarize the information about the degree of misallocation by reporting statistics about the joint distribution of productivity and distortions that we back-out from the data. In particular, we report measures of dispersion in the deviation from the efficient allocation and investigate the correlation between these deviations with idiosyncratic characteristics of the firms, such us their physical productivity and their age. We then undertake a counterfactual exercise to quantify the aggregate manufacturing TFP gains that would result from a reversal of these distortions and a reallocation of resources in accordance with the output maximizing rule.

We find evidence of large within-industry misallocation of resources in the four countries that we study, with Kenya exhibiting the largest idiosyncratic distortions, followed by Ghana, Ethiopia, and Cˆote d’Ivoire. As points of reference, the degree of misallocation measured by the dispersion in revenue total factor productivity (TFPR) is on average larger than in India and China, and is similar to those of the
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. As mentioned earlier, this is also associated with the issues of consumers not being willing to pay for a service. The direct implication for policy making is that scaling-up improvement in water service systems might require subsidies, which might be difficult in countries where the Non Revenue Water – the volume of water the facilities do not get paid for, usually made of leaks, illegal connections and marginal use (e.g. firefighting) – represents a large share of the facilities’ revenues.

Using existing WTP estimates, and data on water outages in 123 countries from the World Bank’s Enterprise Survey<sup>10</sup> , water interruptions are estimated to cost between 0.11-0.19% of GDP, ppp each year, what corresponds to USD 88 and 153 billion globally. The method of estimation comprises some limitations that increase uncertainty. First, the WTP used to assess the global cost are issued in two

> 10 Data available here: http://www.enterprisesurveys.org/

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also excluded a number of other countries with idiosyncratic features that would make optimal simple rules difficult to calculate, such as large numbers of migrant workers or political instability.<sup>17</sup> Table 1. Exogenous-Income Model Calibration to Algeria and Trinidad & Tobago

|**Param.**|**Value**|**Description**|**Algeria**|**TTO**|**Target/Source**|
|---|---|---|---|---|---|
|_β_|0.96|Discount Factor|-|-|s.s. 4% annual real interest rate|
|_σ_|2|Coefcient of risk aversion|-|-|Common value in literature|
|_ω_|0.5|HtM HH share|-|-|Galí _et al_ (2007)|
|_Yss_|1|SS non-resource GDP.|-|-|Normalization|
|_PssQ_|0.33|SS resource GDP|0.33|0.33|resource GDP / non-resource GDP (BJS2013)|
|_Ass_|0.3|S.s. SWF|0.33|0.28|SWF / non-resource GDP (SWF institute)|
|_Bss_|0|S.s. private assets|-|-|Symmetric s.s. consumption|
|_ψ_|0.01|Debt-elasticity of interest spread|-|-|Schmitt-Grohe & Uribe (2003,2016) (see text)|
|_τ_|0.15|Income tax rate|0.16|0.15|Tax revenue / non-resource GDP (IMF)|
|_ρy_|0|Persistence of non-resource income shocks|Insig|0.3*|Estimate of _ρ_ based on HP Filtered data.**|
|_ρp_|0.93|Persistence of commodity export prices|0.94|0
# **Online Appendices**

# **Appendix 1: Timeline**

April 2008 Round 1 - Screening Survey and Baseline I October 2008: Round 2- Booster Sample and Baseline II April 2009: Round 3 August 2009: Wage Subsidies Begin October 2009: Round 4 (During Intervention) April 2010: Round 5 (During Intervention) May 2010: Wage Subsidies End October 2010: Round 6 April 2011: Round 7 October 2011: Round 8 April 2012: Round 9 October 2012: Round 10 April 2013: Round 11 April 2014: Round 12 Supplementary Treatments: Savings Treatment began November 2008, ended August 2009 Business Training Treatment: June-July 2009

# **Appendix 2: Further Details on Sampling**

About half of our sample for this project comes from a larger panel survey which is representative of all urban areas in Sri Lanka outside the northern province. From this panel survey, we selected 717 male self-employed workers with 2 or fewer paid employees in urban areas in Sri Lanka: Colombo, Kandy and the Galle-Matara area. This part of the sample was constructed through a listing exercise conducted in early 2008. We selected a total of 18 Division Secretariat (D.S.) Divisions in the three urban areas. Within each D.S. Division we then selected 10 (in Colombo and Kandy) or 5 (in Galle/ Matara) Grama Niladhara (GN) divisions and listed 50 households 45
online appendix

# **J**

As described in Section 3.3, we estimate large decreases in COVID-19 infections caused by domestic mask manufacturing in Rwanda. In this section, we compare these impacts to available estimates of the fiscal costs of promoting domestic mask manufacturing in order; this provides the cost-effectiveness of promotion of domestic mask manufacturing as a policy to reduce COVID-19 infections.

First, to calculate the decrease in COVID-19 infections caused by domestic mask manufacturing in Rwanda, we multiply, in May through August, monthly national new COVID-19 infections (from the Rwanda Biomedical Center) by our estimates of the monthly impacts of domestic mask manufacturing on COVID-19 infections from Section 3.3. As noted in Section 3.3.1, these estimates provide a lower bound on the impacts of domestic mask manufacturing on COVID-19 infections. Using this approach, we calculate domestic mask manufacturing averted 4,300 COVID-19 infections from May through August.

Second, the fiscal costs of promoting domestic mask manufacturing are the sum of the costs of two policies described in Section 2.2: the VAT exemption for domestically manufactured masks, and administrative licensing costs. We calculate an upper bound on a fiscal costs of the VAT exemption as the value-added tax rate (18%) times the total turnover of exempted masks to final consumers from May through August. While we do not have data on licensing costs, we note that the small number of mask manufacturers suggests that the associated licensing and audit costs are likely to be small relative to the costs of the VAT exemption. Using this approach, we calculate a total cost of RwF 41 million (approximately $40,000) of the promotion of domestic mask manufacturing.

Third, we take the ratio of these two estimates, and find the cost of averting a A41
weakest assumptions needed to address measurement error in empirical work.

Our research design yields a second important finding: the presumption that diaries should be the benchmark for measuring household well-being is an illusion. Although a consumer demand model implies that acquisition is an unbiased estimate of usual consumption, we demonstrate empirically that the difference between these two quantities can be substantial. For example, the likelihood of measuring less than half of the actual value of usual consumption with a diary is 15.1% and 14.1% for households in the first and fourth consumption quartiles, respectively. The likelihood of attributing a value for usual consumption at least twice the size of the actual one is 4.1% and 3.7% for these households, respectively. Importantly, we find that diary errors remain large for perishable components of food, which implies that our conclusions are not mechanically driven by consumption from stocks or large cash outlays. We conclude that, if enumerator visits are effective to reduce misreporting in diary entries, mistaken conclusions about welfare measurement from diary data must primarily depend on how households decide to smooth their consumption rather than on the frequency of acquisitions. This implies that even when error-free measurements of consumption in the interview week are attained, misleading conclusions about actual well-being could still ensue, as well-being is a longer-run average of weekly intakes.

Our investigation does not find evidence of zero-mean or classical errors in recall data, which is a conclusion often conjectured in the literature. Our research design allows us to prove this conjecture, and also provides guidance on how to sign the bias from recall errors in empirical work. Specifically, we show that – in our case study – overreporting of usual consumption is between three and four times more likely than underreporting. This finding is not only consistent with telescoping effects but also with more general cognitive errors by respondents when they compute the market value of their consumption. Moreover, we find that the quality of recall data improves with the actual value of usual consumption, which suggests that errors may be
is, surprisingly and uniquely, also observed among those employed in the public sector in these countries<sup>16</sup> . Conversely, in New EU Member States and Turkey, in particular, public workers have a less negative perception of their economic mobility. Central Asia is the only sub-region in which private employment is associated with a more positive perception on mobility, and in which the opposite happens for working in the agricultural sector<sup>17</sup> .

# **5. Conclusions**

**A substantial share of households in the region perceives that their standards of living in 2006 have fallen compared to 1989.** Using as a definition of movement a change in at least two steps in any direction, 47% of the households in the region declared to have moved down in the income distribution, regardless of their starting step. Around 40% of households perceive they have stayed in the same step, or moved at most one step in any direction. Less than 13% of households in the region perceive an improvement in their relative position.

**This pattern is consistent across sub-regions.** With the exception of New EU Member States and Turkey – the ones with higher economic growth during the period - in all sub-regions the most prevalent perception is of downward mobility. And even in these countries, almost 40% of the households declared to have moved down, behind around 50% of the households that consider doing only as well as in 1989. Western Balkans and South Caucasus are the regions with the most negative perceptions, where households that feel they have moved down reach 60% and 65% respectively. In Russia, Moldova, Ukraine and Belarus, and Central Asia countries, this share of households is close to 50% as well.

**Less affluent, older, rural and unemployed people show a more negative perception of economic mobility.** Taking advantage of the information available in the survey for 2006, we obtained that these characteristics were
In at least 17 formal WTO disputes during the period, the complaining country alleged that the respondent violated some element of the GATT/WTO provisions on customs valuation.

There is a small but growing empirical literature examining how these administrative channels affect trade; indeed, governments have recently put a priority on them through the WTO’s newly negotiated Trade Facilitation Agreement. The World Bank’s _Doing Business_ reports are the best known source of comprehensive data about time delays and related problems associated with moving goods across a border. Djankov, Freund and Pham (2010), for example, use these data to estimate a gravity model of trade and find that each additional day of delay before shipment reduces trade by more than 1 percent. Furthermore, Volpe Martincus, Carballo and Graziano (2015) utilize detailed export transaction data from Uruguay to estimate the impact of customs delays on firm exports. Finally, research using data on customs valuation to examine bureaucratic corruption and tariff evasion includes Javorcik and Narcisco (2008) for Eastern Europe and Mishra, Subramanian, and Topalova (2008) for India.

# **4 The Historical Evolution of Border Barriers Under the GATT**

Focussing thus far on trade barriers imposed at the border, we have characterized the contemporary landscape of trade policy as both having significant heterogeneity in applied tariffs, but also as being littered with numerous other border barriers. Collectively and cumulatively, the status quo is of trade policy marked by variation across countries, products, and trading partners. But how did the international trading system arrive at this point? With only a few exceptions, our discussion of history thus far has been limited to the evolution of trade policy since the 1995 inception of the WTO. Here we briefly appeal to a longer view of the history of the multilateral trading system by focusing on major trade policy developments taking place over the period spanning 1947-1994, covering the full GATT era.

We begin with the level of tariffs for the major economies at the outset of the GATT negotiations in 1947 that established the
Table A.1: Overlap of Monetary Poverty (USD 1.90$ a day) and Deprivations in the NE in GHS Wave 3

|Monetary|Individual Lev<br>Health D|el Child Depriv<br>eprived|ations<br>Food De|prived|Education|Deprived|Not in Scho|ol Deprived|
|---|---|---|---|---|---|---|---|---|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|34%|15%|24%|8%|31%|4%|27%|8%|
|Poor|39%|12%|52%|16%|44%|21%|40%|25%|
||Household Le<br>|vel Child Depri<br>|vations<br>||||||
||Sanitation|Deprived|Shelter D|eprived|Water D|eprived|Informatio|n Deprived|
|Monetary|||||||||
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|33%|2%|27%|8%|20%|16%|33%|2%|
|Poor|50%|15%|31%|34%|28%|37%|56%|9%|

Source: Authors’ estimates based on GHS Waves 3 for the North East.

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Liberalization programs and incentive schemes jointly produced significant synergy in export performance. The country saw a steep rise in its export earnings that surged to US$31 billion in 2015 from US$0.75 billion in the 1980s. This was mainly due to the phenomenal performance of the export-oriented RMG industry, which could successfully utilize the facilities under the MFA quotas, GSP, limiting competition and providing ‘protected’ or ‘reserved’ markets for Bangladesh, along with other RMG producing LDCs. Nevertheless, the MFA also provided a sound base for Bangladesh’s RMG industry that helped sustain the magnificent performance of this industry even after the elimination of the MFA in 2005.

To stimulate export-led industrialization, Bangladesh allows the duty-free import of raw materials to manufacture export products. The government imposed a concessionary duty rate of 7.5 percent on imports of capital equipment for several years. Recently, special bonded warehousing licenses have improved the concessionary duty arrangement by allowing the exporters to have their local banks stand as guarantor with one-third liquidated during installation of the equipment and another to be liquidated at a later stage. The industry also received exchange rate benefits before 1992 to bridge the gap between the official and market exchange rates (Younus and Yamagata, 2012).

Despite attractive policy measures, the volume of FDI from India in the T&C sector has failed to increase due to poor infrastructure and weak support services. Bangladesh has so far allocated two SEZs for India for setting up RMG units or supporting industries. Apart from this, the lack of a clear timeline to improve infrastructure, sharing of utilities (especially gas and power), and inadequate port facilities in Bangladesh have also proved to be detrimental in attracting Indian investments into Bangladesh.

# **6. Evidence from a Firm-Level Survey**

In order to support our preliminary findings, we conducted a directed survey and generated primary data from informed parties. These included firms involved in the value chain; government officials from the Ministry of Commerce and Industry, Customs, Directorate of Textiles;
are different. MFGSERV countries have the lowest shares of NTR2 across all employment city sizes (Figure 4(b)). By contrast, DEINDUSTR countries have the highest employment shares of NTR2 across _all_ city sizes. In NRXGDP countries, the employment share of . NTR2 increases with city size and is largest in the case of the _largest cities_ This analysis suggests that the whole urban system in the deindustrializing countries has experienced a shift from jobs in manufacturing to jobs in nontradable services, while in resource-rich countries only larger cities have substantially higher shares of nontradable services. In both cases, larger cities have much higher shares of urban nontradables than smaller cities, but for all city sizes these shares are much larger in the deindustrializing countries than in the resource-rich countries.

**Robustness: Panel.** We obtain the employment shares for 184 country-years and 62 countries from IPUMS and run panel regressions with country and year fixed effects (FEs). We regress the selected sectoral labor share in urban areas in time period _t_ on MFGSERV in _t_ , NRXGDP in _t_ -10, DEINDU between 1980 and _t_ (= 0 before 1980), the urban share in _t_ , and log population in _t_ and its square. We include countries with at least three years of data and cluster the standard errors at the country level. Since the sample size reduces to 124 observations, we cannot include more lags.

Most panel results are consistent with the long-difference results. We find significantly lower shares of employment in MFG and MFG+FIRE in the urban areas of DEINDU countries relative to MFGSERV countries (Web Appx. Table D.4). While these shares
clear that the recipe of stronger fiscal positions, more flexible exchange rates, deeper financial markets and less foreign currency mismatch has not insulated emerging markets from sudden stops; the frequency of the event has not declined over time. Any benefit from stronger country fundamentals has been offset by larger external shocks emanating from the rest of the world. Nor has progress on the policy front limited the negative output effects. As we show below, the drop in output in the first four quarters is no smaller in the second subperiod than the first; if anything it is slightly larger.<sup>5</sup> It would appear with the continued growth of international financial markets and transactions, countries are now exposed to larger capital flow reversals when foreign lending stops, and those larger reversals have more disruptive output effects. It is troubling that neither national officials, with the increased policy space, nor the international financial institutions, with their proliferation of new financing facilities, have succeeded in cushioning emerging markets from these effects.

# **2. Basics**

Our country sample is all emerging markets with their own currencies for which capital flow data are available for at least 24 consecutive quarters between 1991 and 2014. Our primary source of quarterly gross capital flow data is the International Monetary Fund’s International

> 5 Although the difference is not statistically significant at standard confidence levels.

3
Where we instrument the follow-up survey report of whether the firm had received a municipal inspection in the past year with assignment to the inspector treatment group. We estimate this equation using the follow-up survey data for the control group and inspector group only. We consider both ALF registration, which is the registration form most closely tied to municipal inspection, as well as our overall measure of formalizing.

Table 8 displays the results. We see the point estimates range from 0.214 to 0.265, so that receiving an inspection results in a 21 to 27 percentage point increase in the likelihood of formalizing. The statistical significance is greatest for ALF registration, where the p-value is 0.108 for definite registration, and 0.051 for definite or probable registration. This is the impact on the group of firms who answer the follow-up survey. It therefore removes firms which had closed or which cannot be found easily, but still includes some firms which were actually already formally registered. Our estimate is thus a lower bound for the impact that inspections would have on formalization of informal firms.

The estimated cost of an inspection is R$64.34, which is based on an estimated inspection taking 56 minutes per visit plus 17 minutes of travel time (estimates provided by PBH). The inspectors visited 387 firms (the rest being closed or not found), so the total cost of inspections is estimated at R$24,900.Taking our estimated impact of 11 to 22 more firms formalizing, the cost per firm formalized is R$1132-2264. Our IV estimates suggest that since many of these inspections would have occurred anyway, approximately four additional inspections are required to get one firm to register for an ALF license, so the estimated cost of formalizing one firm is approximately R$256.

Annual tax revenue is R$620 for a MEI, and based on 4 percent revenue tax on the average revenue of R$57,000 for newly formalized firms with an ALF, is R$2280. Firms report