rafmacalaba/data-use-mentions-extended huggingface dataset:
)|
|_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|0.91|TTO: weighted ave of _ρoil_ = 0_._94 and _ρgas_ = 0_._89<br>Algeria: _ρoil_ = 0_._94. (from BJS2013)|
|_σy_|4%|Std. deviation of non-resource income|4%|2%|SD of error from AR(1) reg on fltered data**|
|_σp_|24%|Std. deviation of resource prices|23%|26%|Average of SD export prices (BJS2013)|

Notes: * In part due to oil prices, see Section 6 on “correlated shocks” **Regression of _lnXt_ = _α_ + _ρlnXt−_ 1 + _et_ Despite their other differences, in most cases Algeria and TTO have very similar characteristics relevant for the model (in the cases where parameters differ, we usually take the average). We calibrate the tax rate _τ_ = 0 _._ 15 as non-resource taxes are 15% of GDP in Trinidad and Tobago (IMF 2014 Article IV) and 16% of GDP for Algeria (IMF 2016 Article IV). Algeria and Trinidad & Tobago also have very similar sized SWFs. The Algerian SWF represents a share of 33% of the

> 16Note that Algeria also exports natural gas, though these are not included in the calibration. In general, the results of the paper are fairly insensitive to the exact size of resource exports around a reasonable baseline.

> 17For example, Gulf Co-operation Countries (GCC) have a high share of migrant workers. This could mean that in times of low oil prices, governments reduce migration — a channel of adjustment not available to other countries — which might affect the optimal fiscal rule.
of sectoral employment based on the DHS data. We divide the Lake Chad region into 24 regions, including 8 first-order administrative divisions in Cameroon, 6 in Nigeria, 8 in Chad. Figure A23 shows the share of agricultural employment and population for each of these subnational regions.

## **4.3.1 Productivity shocks from rural electrification**

The counterfactual consists in providing access to electricity for all in each region based on the current coverage measured by Night Time Lights as described in the Data section. We calibrate the increase in productivity using the estimated productivity increase of 28.4 percent for Ethiopia from Moneke (2020) and the current coverage of electricity per location. The lower the existing coverage, the higher the productivity increase in the counterfactual. The new productivity levels _A_<sup>∗</sup> are given by : _A_<sup>_i_</sup> _n_<sup>∗=</sup><sup>_Ai_</sup> _n_<sup>+</sup><sup>_Ai_</sup> _n_<sup>×(1−</sup><sup>_Elecn_)×0</sup><sup>_._284</sup><sup>_,_</sup> _n_ = _M,S_ with _Elecn_ the current ratio of electrified population. Map 5 shows the percent of productivity increase per location.

24
fth of the within-country dispersion of export shares. While not a rst order determinant of export composition, the mechanism is quantitatively meaningful. Furthermore, as discussed above, measurement error in the use of annual correlations and variances to proxy for their within-year counterparts, as well as the evidence from BLS price data that the within-year measures have less dispersion than their annual counterparts, suggest that the true coecient for the correlation in equation (7) may be larger than the ones reported in Table 3. Therefore, it is likely that these quantitative impacts are a conservative estimate of the true economic relevance of this mechanism.

Bilateral regressions show similar results. The coecient is also signicantly negative across specications. In this case, an increase in the volatility of a country's exchange rate against the US dollar from the 10th to the 90th percentile (75 percentage points increase) raises by 20 percent the ratio of the export share of a good at the 10th percentile level of correlation (-0.65) relative to a good at the 90th percentile (0.29) (measured at the average level of price volatility). This corresponds to 11 percent of the average within country-pair standard deviation of log export shares. Comparing hypothetical commodities with correlations of -0.7 and 0.7 yields a 30 percent increase in the relative export share of the former, corresponding to 16 percent of the within countrypair variation of relative shares. This smaller explanatory power is not surprising since the term included in the regression is only part of the actual covariance between bilateral exchange rates and importer's prices, the other term being the covariance between the importer and exporter exchange rates against the dollar, which is absorbed non-parametrically.

Overall, the results for the baseline specication indicate that dierences in natural hedge across commodities have a small but non-negligible impact of natural hedge on trade patterns. This is encouraging considering the typically poor t of Heckscher-Ohlin regressions.

The results presented in columns (2), (3)
This scenario suggests a deceleration in the period 2017-2022, with a lower (annualized) proportionate rate of poverty reduction than seen over the period 2008-17. This stickiness is coming mainly from Africa’s weaker progress against poverty, coming from the lower growth rates in this region—a full 1 percentage point lower than for the developing world as a whole.

Arguably a better characterization of the ambitious target is to assume instead that Africa enjoys the same rate of growth as the developing world as a whole. Consider a growth process in which all income levels grow at the same rate—maintaining overall inequality (between and within countries). To quantify such an ambitious target one can use the baseline distribution and project this forward with a higher mean until any given poverty rate is met, and then see what growth rate is required. This automatically takes account of the nonlinearity in how the cumulative distribution function of consumption varies with the poverty line relative to the mean.

The results of this exercise are found in Table 3, Column (1), which gives the growth rate in household consumption per capita needed to bring the $1.25 a day poverty rate down to various levels assuming no increase (or decrease) in inequality within the developing world. The calculations in column (1) are anchored to the 2008 distribution—the last year for which the whole distribution can reliably be constructed from survey data.<sup>27</sup> (The discussion will return shortly to column (2).) A 5% growth rate in mean household consumption per capita without any increase in inequality would get us close to a 4% poverty rate in 2022. However, the required growth rates more than double to get the target down from 4% to 1%. Without lower inequality in 2008, the developing world as a whole would need to achieve China’s growth rate over the last 20 years or so (but without the rise in inequality that China has seen) to get the poverty rate
Bank Services Trade Restrictiveness Index (STRI),<sup>12</sup> with the most restrictive scores in financial and professional services, which are critical inputs across all industries for productivity growth and competitiveness (Figure 7).<sup>13</sup> In addition to restrictive policies and regulations, there is also a distortive tax system for trading services in Brazil: the tax burden on service imports in Brazil compares unfavorably to other countries, which contributes to hamper the absorption of imported technology (Figure 8).<sup>14</sup> <!-- Start of picture text --> Figure 8. Tax burden on service imports: Figure 9. Logistic Performance Index (2016)<br>Brazil vs selected peer countries (%) [very low” (1) to very high” (5)]<br>60.0 5.0<br>50.0<br>4.0<br>40.0<br>3.0<br>30.0<br>2.0<br>20.0<br>10.0 1.0<br>0.0<br>0.0<br>Source: CNI (2017) Source: W orld Bank staff elaboration using World Bank LPI<br>dataset<br>Saudi Arabia South Africa China Germany UK Netherlands South Korea Italia Japan EUA India Mexico Brazil Spain Argentina France<br><!-- End of picture text --> Brazil also faces challenges in trade logistics and trade facilitation. To compete in the global economy, traders require seamless supply chains, including efficient border management and clearance processes, as well as the development of efficient logistics services and improvements in both the hard and soft international logistics infrastructure. The score of overall logistics competence (and quality) in Brazil – as captured by the Logistics Performance Index (LPI) in 2016 – is below Mexico, Turkey, India,
# _Matching SIMPLES_

Firms registering for SIMPLES provided (i) their street name and number, (ii) their fantasy name, and (iii) their activity, as well as a phone number (which could be that of their accountant or a personal phone number instead of the business phone number). We therefore matched on (i) to (iii), using the phone number where available in both data sets as an additional check. We consider it to be a **definite match** if the listing data match the administrative data on all three criteria, or if they match on street and number plus activity and there is no fantasy name, or if (ii) and (iii) match and the street is the same and street number is only different by one digit. We consider it a **definite or probable match** if it is a definite match, or if it matches on street and number and the listing omits the fantasy name and activity, or if it matches on two criteria but the third is different. In this case, we only consider a match on fantasy name if it is unique and not generally descriptive (e.g. we would not consider the fantasy name “bar” as grounds for a match) and there is only one occurrence of that fantasy name in the entire administrative data.

# _Matching MEIs_

Firms which register as a MEI do not provide a fantasy name in their registration, so the matching could only be on street name and number, and activity (and sometimes phone number). A **definite match** matched on both these. A **definite or probable match** is a definite match, or any one of the following: a match on street name and number, with a very similar activity (e.g. “bar” and “comercio varejista de bebidas”); an exact match on street name and number, with no name or activity data in the listing; or a
.28%|24.31%|29.03%|
|Air Conditioner|2.29%|1.66%|2.62%|1.68%|
|Washing Machine|0.57%|0.44%|1.52%|1.96%|
|Cars and other vehicles|10.43%|9.48%|11.90%|9.99%|
|Generator|22.82%|25.74%|31.76%|25.49%|
|Microwave|2.23%|2.33%|2.94%|1.74%|
|TV Set|42.86%|44.89%|50.15%|47.73%|
|Computer|4.61%|4.48%|4.78%|4.17%|
|Imported rice|49.11%|52.12%|53.87%|47.13%|
|Beef|47.27%|45.36%|44.55%|43.91%|
|Fish-fresh|15.32%|12.65%|12.98%|15.71%|
|Recharge cards|56.90%|64.54%|79.68%|85.07%|

Note: the table shows the average value of non-monetary indicators used in the consumption model for survey-to-survey imputation purposes for each wave of the GHS. For the 2018/19 GHS we use a zone-weight adjustment using population weights from the official 2018/19 NLSS to ensure comparability.

Table 9 Imputed poverty headcount rates at the US$1.90 and US$3.20 poverty line, by GHS wave

||Poverty rate US$1.90|95% Confide|nce Interval|
|---|---|---|--
<br>Kenya Philippines Kenya<br>Philippines*<br>40% 40%<br>Tajikistan Tajikistan<br>20% Vietnam 20% Vietnam<br>0%<br>0%<br>50% 70% 90% 110%<br>-50% 0% 50% 100%<br>Full-Cost Recovery Change in Full-Cost Recovery<br>Regulation Index Regulation Index<br>Regulatory Autonomy Regulatory Autonomy<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

58. The relationship between the de facto tariff regulation index (capturing what tariff regulations are enforced in practice) and absolute levels of cost recovery or improvement in cost recovery is stronger (Figure 14a and b) This suggests that the structure of the sector—i.e., if an independent regulator exists and whether or not it is autonomous—is less important than the tariff regulation implemented in practice. For example, Colombia, which has reached full cost recovery, scores lower than the sample average on the regulation index (35 percent) and regulatory autonomy index (60 percent), but scores high on the de facto tariff regulation index (92 percent). The country has been able to achieve cost reflective tariffs with regular adjustments that are promptly implemented, despite having a regulator that is exposed to 30
, given our focus on intergenerational mobility in rural areas, and use the family roster to obtain a complete list of father-son pairs that includes all sons of the household head irrespective of their residency status at the time of the survey.

The main samples for our analysis consist of children aged 18 - 54 in the 1999 REDS survey, and 29 - 65 in the 2010 CFPS survey. This ensures that we focus on the same age cohorts of children who went to school mostly during the 1980s and 1990s. It is important to recognize that such an analysis for the overlapping age cohorts is meaningful for education, as most of the children under focus (29-65 years old in 2010) in China have completed their schooling by 1999, even though the information was gathered later in 2010. The

> 28The exceptions are Fan et al. (2019) and Azam and Bhatt (2015).

> 29Two of the authors of this paper, M. Shahe Emran and Yan Sun, used CHIP 2002 data to analyze the effects of farm and nonfarm occupations on intergenerational educational mobility in rural China (see Emran and Sun (2015b)). We decided not to publish that paper because of the worry about the biases due to sample truncation arising from coresidency. This paper replaces Emran and Sun (2015b) and the conclusions on rural China here supersede those in Emran and Sun (2015b).

15
paid their contribution or have insufficient allowances to surrender.

89. **Rules would be needed, for instance, for the adjustment of rates or quantities** and to address disputes as to their application. On the tax side, the closest parallel is with excise and VAT arrangements in the EU. These, as noted, are specified mainly in terms of minimum rates. Experience is that the requirement of unanimity has made these very difficult to change, and that the unwillingness of Member States to rely on implementation by others has indeed hindered the development of the VAT. Experience with the EU-ETS is, perhaps, more encouraging. Under a global regime for international shipping, many of these governance issues may be easier to overcome to the extent that countries are content to have decisions made in appropriate bodies established under the new treaty instrument, taking into account rules of procedures and the provisions of the treaty. Countries may also wish to have reasonable assurance that others are themselves applying the charge properly, not least because they may need to give credit against charges they themselves would apply for charges levied by others.

90. **Further issues arise with the ceding of revenues** . These relate not only in the use made of the proceeds but in relation, for instance, to the amounts ceded and whether there will safeguard clauses enabling countries in dire revenue need to maintain a larger amount. Here the closest parallel is perhaps with arrangements for the collection of customs duties and some other charges in the EU: this is delegated to Member States but the proceeds (net of an allowance for collection costs) are a source of finance for the union itself. That this appears to work well doubtless reflecting a sense that these revenues properly belong to all Member States, not just those at which entry happens to occur. A similar sense may come to be held in relation to taxes in international transport, reflecting as they do the exploitation of the common property—belonging to no nation in particular but to all in general—
Table 7 shows results of OLS models with the Y-variable expressed in rupees of the gross value of output (column 1) and also as the natural log of the rupees of the gross value of the output (column 2) in the Rabi season. We estimate the value of Rabi output from plot-level data of the total production of all crops grown by the farmers in the season and the median price realized for the crop. We value the entire production, including the portion saved for home consumption or use as seeds, at the price farmers received for the portion they sold in the market. 200 of the 757 farmers in our sample did not cultivate any Rabi crop in 2020-21. The gross value of crop output in the Rabi season for these farmers is zero. This significant clustering of the values of the Y variable around 0 is the reason we also estimate a Tobit model (column 3). Tobit is our preferred model here because the OLS results are likely to be biased and inconsistent.

**Table 7. OLS and Tobit Regression Estimates on the effects of JCLIS on Gross Value of Output in Rabi 2021**

|**Gross Value of Output in Rabi 2021**|**(1)**<br>**Log GVO**|**(2)**<br>**GVO (Rs.)**|**(3)**<br>**Tobit-GVO (Rs.)**|
|---|---|---|---|
|**JOHAR Farmer (vs non-beneficiary)**|1.275***|4,864*|9,627***|
||(0.430)|(2,747)|(3,507)|
|**Months of Operation of JOHAR System (**Base: <=18||||
|months**)**|||
106|
|Observations|14119|7019|7127|14119|7019|7127|
||||**Panel B: 10-**|**point wind**|**ow**||
|Above threshold|-0.151|-0.324|-0.025|-0.072*|-0.070|-0.062|
||(0.160)|(0.336)|(0.059)|(0.039)|(0.060)|(0.049)|
|Control Mean|0.634|0.934|0.329|0.060|0.084|0.036|
|Control SD|3.507|4.158|2.654|0.238|0.277|0.186|
|MDE|0.448|0.940|0.164|0.109|0.168|0.136|
|Observations|11030|5856|5195|11030|5856|5195|

1 Covariates include: point score, threshold x point score, ejido indicator, ln(polygon ha) percent forest in 2000, cohort indicators, ecosystem indicators, and state dummy variables. Standard errors are clustered at the ejido level for ejidos, and at the level of the municipality for private properties. Sample is limited to polygons greater than 5 ha and with more than 50% forest in 2000. *** p _<_ .01; ** p _<_ .05; * p _<_ .10 Table 6: Heterogeneity by level of deforestation risk (% Forest Cover Loss)

||(1)<br>**Full**|(2)<br>**2011-2012**|(3)<br>**2013-2014**|
|---|---|---
To recap the main points of the paper before we move on to the implications for policy, SSA has an extremely difficult contracting environment. SMEs respond to this poor environment by creating architectures of relation-based governance that substitute for missing market-supporting institutions. In the shock-prone business climate of the region, however, it is hard in some cases to create and sustain cooperative, self-enforcing relationships, because economic instability can undermine repeated game incentives. Tight, ethnically-based networks emerge and thrive in this setting, as there is an added need for the information and collectivist system of enforcement that a network can provide to help make and sustain relational contracts. The RPED surveys show that there is a coordination failure in the indigenous-African manufacturing business community that keeps firms from developing an effective multilateral reputation mechanism analogous to that found in ethnic minority networks. We show that the social capital in minority ethnic networks confer major benefits on member firms, facilitating larger size at entry, higher productivity, and faster growth. But there are features of these networks that can produce a kind of “lock-in:” stable networks and static patterns of business exchange. Lock-in is reinforced by the economic conditions and sparse business environments in SSA. Stable and cohesive, ethnically-based networks can have adverse consequences for income distribution, resource allocation, and competition. “Outsiders” can be excluded from supplier credit and many other business transactions. Adverse network effects fall hardest on indigenous-African firms.

# **III. Implications for Institutions and Policies in SSA**

We are now in a better position to address the questions posed at the beginning of this paper. Do interventions to assist SMEs in developing private institutional support arrangements make sense for countries in Sub-Saharan Africa? And, if interventions are required, what types of policies or programs might be needed?

We have shown that African SMEs lacking access to formal institutions and facing market failures and high transaction costs have been able to engage in a range of productive activities and gain access to needed working capital by creating workable private institutional arrangements to govern these transactions.
* 29 -

57. The results of the estimations indicated that the model's explanatory power was fairly high. The Japan dummy variable was found to be negative in 16 out of 21 equations for 1980; in 9 of the 16, the t-ratio was greater than 2; in 5 others it ranged between 1 and 2. The industries with significant and negative coefficients accounted for 50% of Japanese manufacturing production. Japan's imports were found to be significantly higher than predicted in only one case, nonferrous metals. The results of the exportsshare version yielded similar results, suggesting statistically unusual import behavior in products accounting for 20% of production. When all industry data were pooled into single regressions, each with 273 observations with the coefficients constrained to be similar for all industries, the results indicated that the coefficients of the Japanese dummy took negative values, which were statistically highly significant. Even whein the EC member countries were aggregated into one "country", the results showed that the Japan dummy was negative in 12 out of the total of 14 industries; nine of these negative values were statistically significant (with larger than 2 t- ratios).

# 58. Lawrence concluded:

"These results reject the view that Japanese manufactured imports are not unusually low. They also indicate that the superior quality of Japanese products cannot explain Japanese imports. Japanese export volumes are too small to justify Japan's high share of its home market. But the results leave some issues unresolved because they do not indicate the relative importance of export barriers, import barriers, and unusual buyer preferences....To interpret the coefficient on the dummy variables as a reflection of
EU membership as almost half of the countries in our sample are members of the EU and the agreement is arguably the deepest form of economic integration among states, including specific directives on government procurement. Two policy variables should account for trade liberalization specific to government procurement. One is an indicator for the presence of provisions on government procurement in TAs. We draw from the Deep Trade Agreements database (Mattoo et al., 2020), which includes a section on the presence of around 100 provisions specific to government procurement in each TA. In our baseline specifications, we use a simple dummy, _TAPROC_ for the presence of provisions on government procurement in an agreement. The other variable is a dummy for membership in the WTO GPA. Started in 1996 and revised in 2014, the agreement aims to ensure national treatment to foreign firms in government procurement markets, although each member defines the areas of commitments (e.g. different public entities, goods vs. service) that can thus vary substantially across countries. Importantly and unlike most of the WTO agreement, the GPA is “plurilateral”, meaning that it binds only its signatories having _de facto_ the same structure of a preferential TA.

To take into account how trade barriers around the world create a preference for local purchases, we next estimate an index of home bias, the CHB. Differently from the border effect, the CHB measures how trade frictions shift up the observed internal trade relative to a frictionless benchmark, where international transactions are thus predicted to be much greater. To estimate the index, we manipulate the gravity equation in eq (2) as follows (see Anderson and Yotov, 2010b): where the _φ_<sup>�</sup> term reflects the fact that we observe only an empirical estimates of trade costs (instead of the true ones). The estimated CHB is thus given by the predicted values of the gravity model rescaled by sectoral expenditures and incomes. The predictions are from the gravity specification in eq (10). We first