rafmacalaba/datause-encoder-data huggingface dataset:
44 Table 15 Regression Estimates for the Public Sector Dependent Variable: PCM; t-values in parentheses

|Independent<br>Variables|Mode|l 1|Mode|l 2|Mode|l 3|Mode|l 4|
|---|---|---|---|---|---|---|---|---|
|Intercept|0.178|(1.9)|0.183|(2.0)|0.257|(5.6)|<br>0.281|(5.7)|
|KOR|-0.008|(1.1)|0.002|(0.3)|-0.020|(2.5)|<br>-0.010|(1.8)|
|IMP|-0.002|(0.6)|0.002|(0.6)|-0.030|(3.2)|<br>-0.003|(2.1)|
|KOR.IMP|||-0.005|(2.9)|||-0.001|(0.8)|
|Year Dummny Var|iables||||||||
|1977|-0.101|(2.2)|-0.131|(2.8)|-0.102|(1.6)|<br>-0.108|(1.7)|
|1978|-0.098|(2.0)|-0.132|(2.7)|-0.101|(1.6)|<br>-0.109|(1.7)|
|1979|-0.143|(2.9)|-0.171|(3.5)|-0.151|(2.3)|<br>-0.157|(2.5)|
|1980|-0.203|(4.3)|-0.218|(4.6)|-0.207|(3.2)|<br>-0.210|(3.2)|
|1981|-0.086|(1.7)|-0.109|(2.3)|-0.089|(1.4)|<br>-0.094|(1.5)|
|1982|-0.027|(0.6)|-0.045|(0.9)|-0.028|(0.4)|<br>-0.032|(0.5)|
|1983|-0.022|(0.5)|-0.041|(0.9)|-0.024|(0.4)|<br>-0.028|(0.4)|
|1984|-0.069|(1.4)|-0.083|(1.8)|-0.079|(1.2)|<br>-0.081|(1.2)|
|1985|-0.055|(1.2)|-0.081|(1.8)|-0.051|(0.8)|-0.056(|10.8|
|Industry Dummy|Variables||||||||
|311|-0.062|(0.7)|-0.055|(0.6)|||||
|313|0.535|(5.5)|0.549|(5.7)|||||
|321|0.018|(0.2)|0.033|(0.3)|||||
|324|-0.087|(0.9)|-0.076|(0.8)|||||
|331|-0.055|(0.6)|-0.043|(0.5)|||||
|332|0.363|(3.8)|0.367|(4.0)|||||
|341|0.003|(0.0)|0.026|(0.3)|||||
|342|0.053|(0.6)|0.065|(0.7)|||||
|351|0.186|(2.1)|0.203|(2.4)|||||
|352|0.011|(0.1)|0.066|(0.8)|||||
|353|0.178|(2.2)|0.173|(2.2)|||||
|354|-0.046|(0.5)|-0.022|(0.2)|||||
|356|0.061|(0.4)|0.094|(0.7)|||||
|361|0.130|(1.4)|0.169|(1.9)|||||
|369|0.091|(1.0)|0.108|(1.2)|||||
|371|0.141|(2.0)|0.263|(2.9)|||||
|372|-0.002|(0.0)|0.205|(2.0)|||||
|381|0.081|(1.1)|0.170|(2.2)|||||
|382|-0.033|(0.3)|-0.108|(1.2)|||||
|383|-0.109|(1.6)|-0.131|(1.9)|||||
|A<sup>2</sup>|0.5207||0.5402||0.0822||0.0806||
|F-Test|8.253||8.599||2.686||2.513||
|KOR=Capital-|output rati|o; IMP|<br>Import|penetrat|ion.||||
households. This is unlikely in our study area, but since we do not have the infrastructure data, we cannot control for these village / conservancy level characteristics.

It can be argued that even within a program such as a conservancy, the economic benefit households derive may differ (Jalan and Ravallion, 2002). For example, better educated households may reap more economic profits relative to less educated households. We test for differential conservancy benefits by using a set of interaction dummy variables in equation **(2)** where X includes all the covariates specified in equation **(1)** , _C_ is the established conservancy dummy, _nC_ is the dummy for comparator conservancies, yZ is a social characteristics dummy such as high education, female-headed households, asset-rich, and livestock-rich. _nZ_ represents either low education, male-headed households, assetpoor, or livestock-poor. Thus, the coefficient β1 where _yZ_ is high education, for example, tells us the additional income gain accruing to higher educated households within established conservancies, compared to less educated households within comparator conservancies. Households with education attainment of grade 10 and above are defined as high education households. Households in the third quintile or above with respect to asset index are defined as asset-rich. Livestock-rich households are similarly defined in terms of livestock index. The reference group is the _nC*nZ_ (for example, comparator conservancy low education).

The differential welfare benefit from established conservancies to households with high education is given by (β1 _-_ β2 _-_ β3). That is, if (β1 _-_ β2 _-_ β3) = 0, households with high education do not enjoy any extra benefit from established conservancies. If (β1 _-_ β2 _-_ β3) > 0, then high education households gain more from conservancies than low education households. Conversely if (β1 _-_ β2 _-_ β3) < 0, the conservancy benefits may be accruing more to low education households. The interpretation of (β1 _-_ β2 _-_ β3) for female headed households, asset-rich households, and livestock-rich households are similar.

# **_Evaluation of economic impact of participation in conservancies_**

The 2002 survey (table 2) shows that among the 7 conservancies, the proportion of household participation in conservancy programs ranges from 16 percent in Mayuni to 75 percent in ≠Khoadi //Hoâs. To evaluate the impact of participation, we first analyze the determination of participation.

9
_Appendix Table 8: Log food price impacts of program saturation at the province level, 2006-2014_

||Egg|Fish|Chicken|Rice|Snacks|Sugar|
|---|---|---|---|---|---|---|
|_Panel A: Covariation with Saturation_|||||||
|Program saturation|0.192<br>(0.124)|0.152<br>(0.112)|0.126<br>(0.103)|0.022<br>(0.060)|0.083<br>(0.096)|0.023<br>(0.077)|
|Province Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number ofObservations|640|608|608|648|640|648|
|_Panel B: Covariation with First Differenc_|_ed Saturation _||||||
|Change in Saturation > 0|0.020**|0.001|0.004|0.009|-0.007|-0.001|
||(0.008)|(0.007)|(0.008)|(0.008)|(0.009)|(0.014)|
|Change in Saturation > 0.05|0.015*|0.011|0.012*|0.010|-0.002|-0.016|
||(0.009)|(0.010)|(0.006)|(0.010)|(0.006)|(0.010)|
|Change in Saturation> 0.10|0.018|0.012|0.014|0.007|0.002|0.003|
||(0.013)|(0.010)|(0.008)|(0.009)|(0.008)|(0.016)|
|Province Fixed Effects|No|No|No|No|No|No|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Observations|640|608|608|648|640|648|
|Provinces|79|75|78|80|79|80|
|Mean Price (Philippine_pesos_, 2006)|5.388|118.949|133.175|24.911|8.161|38.092|

note: *** p<0.01, ** p<0.05, * p<0.1 1Egg: log price of one medium chicken egg

> 2Fish: log price of one kilogram of milkfish ( _bangus_ )

3Chicken: log price of one kilogram of mixed chicken parts 4Rice: log price of one kilogram of standard white rice

> 5Snacks: log price of one 60-gram foil pack of _Pancit Canton_

6Sugar: log price of one kilogram of unbranded refined sugar Source: Provincial Consumer Price Index Data 2006-2014 57
Demirguc-Kunt, Asli, and Vojislav Maksimovic, 2002. Firms as Financial Intermediaries: Evidence from Trade Credit Data, World Bank Working Paper.

Diamond D., 1991. Debt Maturity Structure and Liquidity Risk. _Quarterly Journal of Economics_ , 56. 709-738.

Fabbri Daniela and Leora Klapper, 2008. The Choice to Collateralize. Working Paper.

Fabbri, Daniela and Annamaria Menichini, 2007. Trade Credit, Collateral Liquidation and Borrowing Constraints. Swiss Finance Institute Working Paper.

Ferris, J. Stephen, 1981. A Transaction Theory of Trade Credit Use. _The Quarterly Journal of Economics_ 96, 247-270.

Fisman, Raymond and Inessa Love, 2003. Trade Credit, Financial Intermediary Development, and Industry Growth, _Journal of Finance_ 58, 353-374.

Fisman, R., Raturi, M., 2004. Does Competition Encourage Credit Provision? Evidence from African Trade Credit Relationships. _Review of Economics and Statistics_ 86, 34552.

Frank, Murray and Vojislav Maksimovic, 2005. Trade credit, Collateral, and Adverse Selection. University of Maryland Working Paper.

Giannetti, Mariassunta, Burkart, Mike and Tore Ellingsen, 2006. What You Sell is What You Lend? Explaining Trade Credit Contracts. _Review of Financial Studies_ , forthcoming Guedes, Jose and Tim Opler, 1996. The determinants of the Maturity of Corporate Debt Issues. _The Journal of Finance_ 51, 1809-1833 Hart, Oliver and John Moore, 1991. A Theory of Debt Based on the Inalienability of Human Capital. _The Quarterly Journal of Economics_ 109, 841-879.

Johnson, Simon, John McMillan and Christopher Woodruff, 2002. Courts and Relational Contracts. _Journal of Law, Economics and Organization_ 18, 221-277.

Lee, Yul W. and John D. Stowe (1993), “Product Risk, Asymmetric Information, and Trade Credit,” _Journal of Financial and Quantitative Analysis_ 28, 285–300.

Lee, Hau L. and Chung-Yee Lee Springer, 2007, Building Supply Chain Excellence in Emerging Economies, Science+Business Media: United Kingdom.

Long, Michael S., Ileen B. Malitz and S. Abraham Ravid, 1993. Trade Credit, Quality Guarantees, and Product Marketability. _Financial Management_ 22, 117-127.

34
Moreover, to account for the possibility of opportunistic behavior by governments, we introduce the possibility that, when a new government is elected, there is a small chance that it expropriates the rents secured by the firm in the initial contract. If the probability of this outcome is not too large, no ratchet effect occurs and we have a model displaying both Pareto improving and rent shifting government-led renegotiations. If it is large, there is ratchet effect or no investment at all, so no renegotiation occurs.

As a result, we obtain an equation for the probability of government-led renegotiation that allows us to derive theoretical predictions for the main variables in our analysis. While the direction of the effects are unchanged for a number of variables, like shocks or the existence of a regulator, theory leads to expect opposite effects in the case of the variables affecting the status quo of the parties in an eventual renegotiation, namely the financing variables (investment requirements and private financing) and institutional quality.

We then present empirical results based on the same sample as in GLS, using as dependent variable the occurrence of a renegotiation at the initiative of the government. From the empirical viewpoint, a number of differences with firm-led renegotiations are also to be expected. One open question is the influence of price cap regulation. Firms’ calls for renegotiation were the intuitive outcome when the cap appeared to be too restrictive. Here, we could expect a reverse effect, in the sense that governments would like to renegotiate caps that proved too generous and leave excessive profits to the firms (the opportunistic calls). However, it could also be the case that governments concerned about the continuity of service or the realization of planned investments would step in when firms are making losses, resulting in a similar effect of price cap on firm-led and government-led renegotiations. Another aspect of interest is the behavior of variables affecting the status quo of the parties in a renegotiation, in particular the financing variables, which effect should be the opposite as with firm-led renegotiation. Finally, it is not clear how institutional quality variables should behave, as a number of channels involving the quality of the bureaucracy and the possibility of capture can be envisioned. Indeed, corruption can be expected to give rise to hidden and non-transparent renegotiations (e.g. the Mexican Northeast Railway concession or the generic case of highway franchising in Latin America, discussed in Engel, Fischer and Galetovic, 2003), or on the contrary to produce ex ante biased awards through direct adjudication or the manipulation of insider information, resulting in a situation in which concessionaire and government representatives share excessive rents and have little incentive to 6
**- –** **<u>Table 10. Comparative statistics, Intra regional trade (2010 2014)</u>**

|||<br>Region<br>Leader|<br>World excl.<br>SSA (10th<br>percentile)|Industrial<br>countries<br>(median)|East Asia &<br>the Pacific<br>(median)|
|---|---|---|---|---|---|
|Panel A.<br>Growth|SSA|1.54%|1.18%|0.34%|0.59%|
|0.019|Resource Rich|1.58%|1.22%|0.38%|0.63%|
||Non-Resource Rich|1.52%|1.16%|0.32%|0.57%|
|Panel B.||||||
|Capital|SSA|0.81%|0.62%|0.18%|0.31%|
|0.01|Resource Rich|0.83%|0.64%|0.20%|0.33%|
||Non-Resource Rich|0.80%|0.61%|0.17%|0.30%|
|Panel C.||||||
|TFP|SSA|2.19%|1.67%|0.49%|0.84%|
|0.027|Resource Rich|2.24%|1.73%|0.54%|0.89%|
||Non-Resource Rich|2.16%|1.65%|0.46%|0.81%|

_NOTE: The estimations are calculated based on the results from regressions in Table 7 Columns [4] [5] and [6]._

**Table 11** reports the actual gains from improved international trade integration over a 10-year span (2010-14 vs. 2000-04) and a 20-year span (2010-14 vs. 1990-94) in terms of growth of output per worker, the accumulation of capital per worker and TFP growth. The increase of manufacturing trade in SSA over the past 10 years (2010-14 vs. 2000-04) contributed to higher growth of output per worker by 0.33 percentage point whereas it contributed to an accelerated of the accumulation of capital per worker and TFP growth by 0.30 percentage point and 0.18 percentage point, respectively. An improvement in SSA export productive diversification over the past decade led to a growth per worker benefits of 0.14 percentage point. TFP did not account for more than 0.15 percentage point in the past 10 years. The impact on growth per worker of higher export product diversification in SSA is transmitted higher by greater accumulation of capital than by higher TFP growth, 0.12 and 0.06 percentage points per year, respectively. What are the effects of increasing intra-regional trade? Table 11 depicts that the progress has been greater in the past 20 years. Improved intra-regional trade has increased growth per worker in the region by 0.08 percentage point per year over the past 20 years, and the impact is mainly transmitted through faster TFP growth (0.11 percentage point per year).

28
implications of the analysis for the design of strategies to promote growth and reduce poverty in low-income countries.

# **II.  Conventional Channels**

Macroeconomists typically emphasize three “conventional” channels through which public infrastructure may affect growth: a direct productivity effect on private production inputs, a complementarity effect on private investment, and a crowdingout effect on private spending through the financial system.

# **1.  Productivity of Private Inputs**

The direct productivity effect of infrastructure is the argument that is most commonly proposed to account for a growth effect of public capital. If, as it is normally the case, production factors are gross complements, a higher stock of public capital in infrastructure would tend to raise the productivity of other inputs, such as labor and the stock of private capital, thereby reducing unit production costs<sup>3</sup> . Given decreasing returns, the magnitude of this effect would depend, of course, on the initial stock of public capital. In mature economies, productivity effects are likely to be limited; but in low-income countries, they could be substantial. In turn, the increase in the productivity of private capital may raise the rate of private investment (given that the return to capital is higher) and spur growth.

To illustrate this effect, suppose for instance that the production function of the private sector takes the Cobb-Douglas form

> 3Several country-specific studies based on the estimation of cost functions have found indeed that public infrastructure typically entails cost reductions in private production. See for instance Cohen and Paul (2004), and Teruel and Kuroda (2005).

6
35 slightly over 10 percent of **revenue,**<sup>but since that time collection</sup> costs have fallen to an average of<sup>1 to 3 percent, even inlese</sup> developed countries.

Aside from the direct administrative<sup>costs, taxpayers often</sup> reduce tax payments by evading or<sup>misrepresenting the value on which</sup> their tax is assessed. During<sup>the 13th century, movable property</sup> taxes in England were occasionally<sup>assessed for to pay ransoms for</sup> captured kings, provide a dowry<sup>for princesses, or to finance holy</sup> crusades. During this time, English<sup>taxpayers were not slow to</sup> discover the benefits of under assessing<sup>property:</sup> We have lately heard that the four jurors<sup>...</sup> are violating their oaths by false valuations, namely,<sup>they generally value</sup> an ox at five shillings, when it is worth<sup>ten shillings or</sup> even more; a pig which is worth two are<sup>three shillings, they</sup> value at 6d.; ... we shall not receive half of the thirtieth granted to us so generously by the magnates and others of our realm. [Close rolls, 1234-1237, p. 569;<sup>from Mitchell (1970)]</sup> What are the costs involved here?<sup>There are clearly</sup> administrative problems, but if every<sup>juror persists in undervaluing</sup> the property, then the target revenue<sup>could just as well be raised</sup> by doubling the tax rate. Yet<sup>assessors could compensate for the</sup> doubling by undervaluing to an even<sup>greater extent. However,</sup> variations in assessment practices<sup>will lead to inequities in tax</sup> collection as well.

An additional cost of tax administration<sup>is tax compliance.</sup> Even if taxpayers are perfectly<sup>honest, they must still spend time</sup> and money on keeping records for<sup>tax collection, and on preparing</sup>
# **Appendix 3: Specifications using the database adjusted with a simple average and provincial population and per capita GDP to proxy market capacity**

|Sector|Ln(ACCESSr<br>)|Ln(NPs,r,t<br>)|Ln(Nr,t<br>)|Ln(GDPPCr,t<br>)|Ln(POPr,t<br>)|R<br>2<br>|P-value|Obs.|
|---|---|---|---|---|---|---|---|---|
|All<br>*<br>|-0.018<br>c<br>|0.078<br>a<br>|0.027|0.370<br>a<br>||0.427|0.000|1428|
||-0.039<br>a<br>|0.100<br>a<br>|0.076<br>a<br>||0.00|0.409|0.000|1428|
|Food & beverages<br>*<br>|0.049<br>a<br>|0.109|0.115<br>c<br>|0.090||0.491|0.000|303|
||0.045<br>a<br>|0.139<br>c<br>|0.156<br>a<br>||-0.136<br>a<br>|0.504|0.000|303|
|Textile|-0.076<br>b<br>|0.178|-0.124|0.726<br>a<br>||0.370|0.000|201|
||-0.072<br>c<br>|0.161<br>a<br>|0.041||-0.125|0.312|0.000|201|
|Wood products|-0.032|0.236<br>a<br>|0.055|0.090||0.355|0.000|145|
||-0.038|0.251<br>a<br>|0.029||-0.184<br>a<br>|0.364|0.000|145|
|Paper & printing|-0.051|0.114|-0.110|0.418<br>a<br>||0.312|0.000|86|
||-0.065|0.224<br>b<br>|-0.00||-0.281|0.288|0.000|86|
|Chemicals<br>*<br>|-0.076<br>b<br>|0.161|-0.117|1.089<br>a<br>||0.451|0.000|152|
||-0.131<br>a<br>|0.269<br>c<br>|-0.206||0.452<br>a<br>|0.402|0.000|152|
|Minerals|-0.059<br>b<br>|-0.426<br>a<br>|0.263<br>a<br>|0.215||0.349|0.000|233|
||-0.032|-0.377<br>a<br>|0.190<br>a<br>||0.307<br>a<br>|0.379|0.000|233|
|Basic metals<br>*<br>|-0.131<br>a<br>|0.066|0.064|0.050||0.568|0.000|114|
||-0.137<br>a<br>|0.072|0.191<br>b<br>||-0.286<br>b<br>|0.586|0.000|114|
|Fabricated metals<br>*<br>|0.019|0.118|0.022|0.760<br>a<br>||0.737|0.000|194|
||-0.027<br>c<br>|0.256<br>a<br>|0.188<br>b<br>||-0.404<br>a<br>|0.728|0.000|194|

> <sup>a</sup> significant at 1%;<sup>b</sup> significant at 5%;<sup>c</sup> significant at 10%,<sup>*</sup> specifications using distance to nearest major seaport.

30
Building prosperity for a majority of Nepalis will entail not just safeguarding and cementing the gains already made, but also boosting productivity of the economy in a manner that will help them realize higher income levels. Work related migration, primarily to overseas destinations, has been a critical driver of mobility experienced by Nepalis over their lifetimes and across generations. But taking it to the next level and building a robust middle class will require the creation of good jobs in a large enough scale for those that remain in Nepal.

# **References**

Alesina, A. and N. Fuchs-Schuendeln (2007), "Good Bye Lenin (or not?) The Effect of Communism on People’s Preferences," _American Economic Review_ , 97:1507-1528.

- Banerjee, A. and E. Duflo (2008). “What is middle class about the Middle Classes around the World”. _Journal of Economic Perspectives_ 22(2):3-28

- Benabou, R. and J. Tirole (2006), "Beliefs in a Just World and Redistributive Politics," _Quarterly Journal of Economics_ , 121(2), 699-746.

- Birdsall, N. (2010). “The (Indispensable) Middle Clsas in Developing Countries; or, the Rich and the Rest, Not the Poor and the Rest”, _Working Paper #207 Center for Global Development_ , Washington DC

- Birdsall, N., F. Ferreira, L.F. Lopez-Calva and J. Rigolini (2011). “The Middle Class in Developing Countries: Concept, Measurement and Recent Trends”. _Mimeo_ . The World Bank, Washington DC

- Chetty, R., J. Friedman, N. Hilger, E. Saez, D. Schanzenbach, and D. Yagan. 2010. “How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project STAR.” _NBER Working Paper 16381_ , National Bureau of Economic Research, Cambridge, MA

- Dang, H-Anh and Lanjouw, P. (2013). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”, Policy Research Working Paper Series 6504. The World Bank, Washington DC

- Dang, H-Anh, P. Lanjouw, J. Luoto and D. McKenzie (2014). “Using Repeated Cross-Sections to Explore Movements In and Out of Poverty”. _Journal of Development Economics_ . 107(6) 112-128

- Easterly, William (2001). “The Middle Class Consensu and Economic Development”, _Journal of Economic Growth_ . 6(4): 317-35

- Ferreira, Francisco H. G., Julian Messina, Jamele Rigolini, Luis-Felipe Lopez-Calva, Maria Ana Lugo and Renos Vakis. (2013). “ _Economic Mobility and the Rise of the Latin American Middle Class_ ” World Bank, Washington DC

20
may render faster growth per worker (although at a slower pace than increasing inter-regional trade linkages); however, the impact of intra-regional trade would work through boosting TFP growth.

|**Table 7. Trade, diver**<br>_Dependent variable norma_<br>_Estimation Method: GMM_|**sification, an**<br>_lized by per wor_<br>_-IV System Estim_<br>|**d sources of**<br>_ker (5-year non-_<br>_ator (Arellano a_<br>|**growth: Inte**<br>_overlapping ave_<br>_nd Bover, 1995_<br>|**r- and intra-**<br>_rages)_<br>_; Blundell and B_<br>|**regional trad**<br>_ond,1998)_<br>|**e**<br>|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
||Output per|Capital per|TFP|Output per|Capital per|TFP|
||worker|worker||worker|worker||
|Initial GDP per worker|-0.023***|-0.011***|-0.014***|-0.019***|-0.006***|-0.012***|
|_(in logs)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exports, inter-regional|0.087***|0.069***|0.052***||||
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)||||
|Exports, intra-regional|0.014|0.021**|0.044***||||
|_(% of GDP)_|(0.059)|(0.005)|(0.000)||||
|Trade, inter-regional||||0.048***|0.056***|0.016***|
|_(% of GDP)_||||(0.000)|(0.000)|(0.000)|
|Trade, intra-regional||||0.019**|0.010|0.027***|
|_(% of GDP)_||||(0.003)|(0.177)|(0.000)|
|Product Concentration|-0.021***|-0.020***|-0.010***|-0.023***|-0.020***|-0.010***|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Secondary enrollment rate|0.010***|-0.000|0.002***|0.007***|-0.002|0.003**|
|<br>_(initial level, logs)_|(0.000)|(0.798)|(0.001)|(0.000)|(0.224)|(0.003)|
|ICRG Political Risk Index|0.006***|-0.000|0.005***|0.007***|0.001**|0.005***|
|_(principal components)_|(0.000)|(0.774)|(0.000)|(0.000)|(0.002)|(0.000)|
|Credit to private sector|-0.011***|-0.005***|-0.008***|-0.013***|-0.006***|-0.010***|
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Government consumption|0.007***|0.011***|-0.003*|0.005**|0.008***|-0.004**|
|_(% of GDP)_|(0.000)|(0.000)|(0.018)|(0.003)|(0.001)|(0.004)|
|Growth volatility|0.194***|-0.010|-0.029**|0.215***|0.040|-0.014|
|_(S.D. growth per worker)_|(0.000)|(0.460)|(0.009)|(0.000)|(0.062)|(0.280)|
|Constant|0.151***|0.059***|0.139***|0.130***|0.017**|0.117***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.010)|(0.000)|
|Observations|<br>606|<br>606|<br>606|<br>602|<br>602|<br>602|
|_AR(1)_|0.000|0.177|0.000|0.000|0.250|0.000|
|_AR(2)_|0.478|0.016|0.780|0.503|0.046|0.882|
|<br>_Hansen_|0.435|0.438|0.516|0.361|0.622|0.486|
|_p_-values in parentheses|||||||
|* _p_ <0.05,** _p_ <0.01,*** _p_ <0.00|1||||||

To economically interpret our findings, we use the regression estimates in columns [5] and [6] of Table 7. Doubling the amount of inter-regional trade in Sub-Saharan Africa would accelerate physical capital accumulation and TFP growth by 2 percentage points and 0.6 percentage point per year, respectively. Additionally, doubling the amount of intra-regional trade in Sub-Saharan Africa would