rafmacalaba/datause-encoder-data huggingface dataset:
|X_ijt/Xjt_<br>(7)<br>(8)|0.407***<br>0.393***<br>(0.137)<br>(0.077)<br>0.228***<br>0.179***<br>(0.052)<br>(0.045)<br>0.185***<br>0.238***<br>(0.066)<br>(0.040)<br>0.186*<br>0.201***<br>(0.108)<br>(0.048)<br>-1.171***<br>-1.815***<br>(0.096)<br>(0.061)<br>-0.605***<br>-1.069***<br>(0.058)<br>(0.043)<br>-0.168***<br>-0.279***<br>(0.053)<br>(0.029)<br>1982-2017<br>1982-2017<br>Col (4) plus<br>Col (4) plus<br>Extrapolation<br>Extrapolation<br>153<br>153<br>153<br>153<br>156,789<br>156,789<br>it, jt, ij<br>it, jt, ij<br>to column 6, standard errors in parentheses are<br> (2019) procedure, implemented in Stata with<br>he remaining missing values using the evolution|
|---|---|
|(6)|0.364***<br>(0.105)<br>0.200***<br>(0.043)<br>0.170***<br>(0.056)<br>0.171**<br>(0.077)<br>-1.154***<br>(0.054)<br>-0.600***<br>(0.039)<br>-0.159***<br>(0.028)<br>1982-2017<br>Col (4) plus<br><br>Extrapolation<br>153<br>153<br>156,789<br>it, jt, ij<br>. From column 1 <br> Weidner & Zylkin<br> 8 we extrapolate t|
|(5)|0.242***<br>(0.054)<br>0.109*<br>(0.058)<br>-1.166***<br>(0.052)<br>-0.600***<br>(0.039)<br>-0.156***<br>(0.027)<br>1982-2017<br>Col (4) plus<br>Extrapolation<br>153<br>153<br>156,789<br>it, jt, ij<br> always included<br>rected using the<br>mn 5 to column|
|(4)|0.279***<br>(0.050)<br>0.130**<br>(0.057)<br>-1.115***<br>(0.051)<br>-0.572***<br>(0.039)<br>-0.141***<br>(0.029)<br>1982-2017<br>Linear<br>Interpolation<br>131<br>91<br>111,632<br>it, jt, ij<br>fxed efects are <br>timates are cor<br>rpolated; in colu|
|(3)|0.271***<br>(0.050)<br>0.109*<br>(0.056)<br>-1.103***<br>(0.051)<br>-0.569***<br>(0.040)<br>-0.142***<br>(0.029)<br>1982-2017<br>UNIDO<br>114<br>87<br>99,363<br>it, jt, ij<br>porter (_ij_) <br> and point es<br> linearly inte|
|(2)|0.620***<br>(0.070)<br>0.044<br>(0.072)<br>1982-2017<br>UNIDO<br>114<br>87<br>99,363<br>it, jt, ij<br>d Exporter-Im<br>ndard errors <br>estic sales are|
|(1)|0.610***<br>(0.057)<br>1982-2017<br>UNIDO<br>114<br>87<br>99,363<br>it, jt, ij<br>-time (_jt_) an<br>n 7 both sta<br>alues in dom|
|Dep Var:<br>X_ijt_|PTA_ij,t_<br>PTA_K_#1<br>_ij,t_<br>PTA_K_#2<br>_ij,t_<br>PTA_K_#3<br>_ij,t_<br>Transitory PTAs_ij,t_<br>INTL BRDR_∗_1980<br>INTL BRDR_∗_1990<br>INTL BRDR_∗_2000<br>Period<br>Treatment for missing<br>Intra-National fows<br>N. Countries (max)<br>N. Countries (end year)<br>Observations<br>FEs<br>_Notes_: Exporter-time (_it_), Importer<br>clustered by country-pair.<br>In colum<br>_ppml_<br>_fe_<br>_bias_. In column 4 missing v<br>of a country total exports.|

15
# **Table 4: Effects of the movie on learning about BREGS**

||Heard|Days|Gender|BPL|Work lag|Unempl.|Wage rate|Wage lag|Contractor|
|---|---|---|---|---|---|---|---|---|---|
|_PANEL A: “STILL_|_RIGHT”_|||||||||
|**Full sample**||||||||||
|Film in village|0.028***|0.256***|-0.061|0.032|-0.038|0.132**|0.207***|0.109|0.127|
|Constant|0.946***|0.384***|0.676***|0.662***|0.313***|0.468***|0.407***|0.130***|0.274***|
|N|3100|717|1583|1346|149|605|937|252|459|
|**Men**||||||||||
|Film in village|-0.002|0.246***|-0.075|0.071|-0.056|0.205***|0.207***|0.020|0.181*|
|Constant|0.990***|0.449***|0.689***|0.681***|0.389***|0.510***|0.460***|0.198***|0.361***|
|N|1511|548|838|819|106|449|620|157|307|
|**Women**||||||||||
|Film in village|0.052***|0.285**|-0.046|-0.019|-0.013|-0.072|0.196**|0.192|0.011|
|Constant|0.907***|0.162***|0.663***|0.636***|0.102*|0.369***|0.309***|0.078**|0.108***|
|N|1589|169|745|527|43|156|317|95|152|
|_PANEL B: “LEARN_|_ING”_|||||||||
|**Full sample**||||||||||
|Film in village|0.022|0.115***|-0.009|0.069*|0.068***|0.106***|0.129***|0.082***|0.058*|
|Constant|0.789***|0.161***|0.644***|0.594***|0.084***|0.263***|0.302***|0.087***|0.167***|
|N|649|2167|1346|1577|2770|2318|1975|2650|2459|
|**Men**||||||||||
|Film in village|-0.155|0.110**|0.030|-0.001|0.090***|0.100*|0.109**|0.098***|0.149***|
|Constant|0.941***|0.264***|0.662***|0.692***|0.105***|0.349***|0.398***|0.107***|0.198***|
|N|75|915|647|664|1373|1035|856|1315|1172|
|**Women**||||||||||
|Film in village|0.049|0.120***|-0.044|0.117**|0.047|0.113**|0.140***|0.066**|-0.025|
|Constant|0.764***|0.091***|0.628***|0.525***|0.064***|0.195***|0.236***|0.068***|0.141***|
|N|574|1252|699|913|1397|1283|1119|1335|1287|

_Note_ : ‘Still right’ is defined as being aware in R1 and staying aware in R2. ‘Learning’ is defined as not being aware in R1 but answering correctly in R2. If an individual has not heard about BREGS, answers to other questions are coded as missing. *** p<0.01, ** p<0.05, * p<0.1 based on robust standard errors. Definitions of each knowledge question are given in Table 1.

24
vulnerable population (along with other social groups, as in Figure 1), we use the surveys covering the period 1992-2012. To assess the coverage of public transfers on the same population, we employ the Module of Social Programs data commissioned by the Mexican Ministry of Social Development (SEDESOL) as part of the ENIGH for 2002, 2004, 2006 and 2010.

# **3. Why vulnerability to poverty matters in Mexico**

Mexico, as many other countries in LAC, has made laudable progress in the last decade in reducing poverty and widening the middle class. Poverty has declined by 18 percentage points (from 40.1 to 22.2 percent) during 2000-12 (Figure 2). And since the early 2000s Mexico has had more people in the middle class ($10 to $50 a day) than in poverty (less than $4 a day) (World Bank, 2013; World Bank, 2014).

**Figure 2: Size of the socioeconomic groups in Mexico** _(Percentages)_

<!-- Start of picture text --> Percentage of households<br>50.0 45.2<br>45.0 39.2 38.0 41.1 38.5 39.7 41.1 39.7 40.6 38.6 40.9 39.7<br>40.0<br>35.0 37.4 36.5 35.4 36.8 30.1 30.0 36.0 38.1 36.2 38.5<br>30.0 33.0 33.6<br>25.0 25.9 27.5 26.6<br>20.0 23.3 23.8<br>15.0 21.7 18.1 20.5 20.0 19.9 20.0 17.7<br>10.0<br>5.0<br>0.0<br>1992 1994 1996 1998 2000 2002 2004 2005 2006 2008 2010 2012<br>< $4.0 $4 - 10 $10 - 50<br>Percentage of population<br>60.0 54.2<br>50.0 47.3 46.1 48.8<br>40.1 39.8 42.0 40.9 42.9 40.6 43.5 43.0<br>40.0 35.4 35.0 34.6<br>32.1 32.3 32.6 32.5<br>37.6 36.4 29.8 29.8 30.0<br>30.0<br>27.7<br>20.0 24.3 25.1 24.7 24.9<br>20.7 22.3 22.2<br>10.0 16.3 17.5 13.0 15.6<br>0.0<br>1992 1994 1996 1998 2000 2002 2004 2005 2006 2008 2010 2012<br>< $4.0 $4 - 10 $10 - 50<br><!-- End of picture text --> _Source_ : Author’s estimations based on ENIGH 1992-2012. Estimations are based on the net per capita income definition, used by Coneval for income poverty measurement in Mexico.

However, poverty reduction after 2006 has been stagnant. Particularly, the long-run trend of declining poverty rates using the $4 a day line in Mexico stopped between 2006 and 2008 when poverty rates moved from 25.1 to 24.7 percent and then to 24.9 percent in 2010, although there may 7
<!-- Start of picture text --> S<br>N<br>csofthninkt |ii | TT. PIPE Paar<br>CHL<br>ge<br>2g<br>3<br>2<br>co<br>3 ><br>P}<br>@<br>eg<br>©<br>oO<br>2<br>1 2 3 4 5<br>[} e=-0.1 [ _) e=-0.2<br>[} e=0.3 [| e=0.4<br><!-- End of picture text -->
attempts to incorporate some quality measures.<sup>18</sup> When some of the above regressions are run with this index instead for developing countries only, the results are similar as shown in Tables 12a-b.

**Table 12a. An Alternative World Bank Index: Developing Countries Only**

||_DATASET:_|_KAUFMANN,KR_|_AAYANDZO_|_IDO-LOBA_|_TON_||
|---|---|---|---|---|---|---|
||Howauthoritie|s are selectedand<br>laced|Capacityoft<br>implementsol|hestateto<br>ndpolicies|Respectofthes<br>citizensfor|tate andthe<br>therules|
||Voiceand<br>Accountability|Political Instability<br>andViolence|Government<br>Effectiveness|Regulatory<br>Burden|RuleofLaw|Graft|
|SocialistLegalCode|-.23|-.06|-.31*'|-.70***|-.42*"|-.52#"|
||(-1.23)|(-.32)|(-2.24)|(4.15)|(2.82)|(-3.76)|
|FrenchLegalCode|-.16<br>|-.23<br>|.01<br>|-.15<br>|-.33**<br>|-.31**<br>|
||(-1.12)|(-1.38)|(.10)|(-1.30)|(-2.63)|(-2.76)|
|GermanLegalCode|.20|-.59**|-.0005|-.97***|.13|-.35**|
||(1.06)|.<br>+(2.56)|(-0.00)|(-6.00)|(.73)|(-2.16)|
|Income|.30***|.31s**|.24***|.32***|.36*"<br>|.32"*|
||(2.96)|(2.87)|(2.81)|(4.55)|(4.35)<br>|(3.32)<br>|
|WorldBankDataIndex|-.18**|-.<br>25***|-.15''|-.<br>23***<br>|-.16*<br>|-.06<br>|
||(-2.27)|(-2.93)|(-2.63)|(4.14)|(-2.55)|(-.87)|
|Constant|-1.87*|-1.87*|-1.67**|-.23"*|-2.38***|-2.45***|
||(-1.98)|(-1.81)|(-2.22)|(4.14)||(-2.76)|
|#obs|96|90|91|95|95|90|
|R'|1<br>.30|.32|.27|.42|.39|.33|

# **Table 12b. An Alternative World Bank Index: Developing Countries Only**

<!-- Start of picture text --> DATA SET. ICRG, Averaged over the period 1984-1997<br>ICRG Corruption Rule of Bureaucracy Contract Expropriation<br>.____________ __Law ________ Quality Repudiation Risk<br>Socialist Legal Code .55*** 1.03*** 1.34*** .14 .42 .63"'<br>- (2.84) (3.76) (4.88) (.58) (1.42) (2.91)<br>French Legal Code -.48" -.26 -.37 -.83** -.33* -.52"'<br>(-2.68) (-.94) (-1.59) . (-3.21) (-1.79) (-3.50)<br>Gernan Legal Code .46** 02 -.06 .51 1.05*** .28<br>(2.13) (.06) (-.20) (1.52) (4.67) (1.59)<br>Income .34*** .29 .44** .4 5 ** .37*** .28"<br>(3.34) (1.57) (3.7) (3.01) (3.46) (3.12)<br>World Bank Data Index -.24** -.15 -.22** -.23** -.26*'' -.30*"<br>(-3.11) (4.57) (-2.27) (-2.23) (-2.99) (4.04)<br>Constant 1.77" 1.30 .46 .81 1.83* 3.12**<br>(2.08) (.90) (.48) (.69) (2.06) (3.99)<br>#obs 62 62 62 62 62 62<br>R 2 .43 .15 - .32 .84 .41 .51<br>*indicates significance at .l level, ** at .05 level and *** at .01 level.<br><!-- End of picture text --> _Note: The index of Scandinavian. Legal Code is dropped out of this regressions, because this World Bank index only has' data for developing countries, and no developing countries have the legal origin as Scandinavia._ 18 See website http://www.worldbank.org/data/tas/scbpaper.pdf 31
_-_ _<u>Figure 7. Evolution in the Share of HH living in Sub standard Housing</u>_ <!-- Start of picture text --> Upon arrival Currently<br>LEBANON KURDISTAN JORDAN<br>Notes : Authors’ own tabulation using data from SRHCS.<br>.<br>Figure 8. Rental Markets for Syrian Refugees outside Camps (2015)<br>Pays rent on land or dwelling Difficulty paying rent Written rental contract Rent/HH income<br>LE B ANON KUR DISTAN JOR DAN<br>Notes : Authors’ own tabulation using data from SRHCS. To minimize the influence of<br>outliers and measurement error, ratio between rent and household income does not take into<br>account households with ratio values greater than 2.<br>- -<br>Figure 9. Labor Market Outcomes, Working Age Syrian Refugees (20 60 years old)<br>Kurdistan Lebanon Jordan, camp Jordan, out of camp<br>LAB OR FOR C E JOB LE SSNE SS E M PLOYM E NT WAGE C ONTR AC T<br>PAR TIC IP AT IO N R ATE E M PLOYM E NT WOR KE R<br>R ATE (C ONDITION A L (C ONDITION A L (C ONDITION A L<br>ON ON ON WAGE<br>PAR TIC IP AT IO N) E M PLOYM E NT) E M PLOYM E NT)<br>Notes : Authors’ own tabulation using data from SRHCS.<br>98.3% 98.7%<br>75.3% 78.3% 78.3%<br>56.5%<br>SHARE<br>99.5% 96.8% 97.3%<br>90.6%<br>76.3%<br>71.4% 70.8% 69.7%<br>63.9%<br>46.9%<br>38.6%<br>14.6%<br>SHARE AMONG RENTERS<br>0.93 0.94 0.97<br>0.88<br>0.79 0.78 0.81<br>0.66 0.71 0.69<br>0.56<br>0.48 0.50 0.47 0.45<br>0.32 0.29<br>0.08 0.05<br>0.01<br><!-- End of picture text --> 15
6 sharp reduction in the fiscal deficit. However, they do not make any distinction about what the main source of the fiscal consolidation was, namely, tax increases or expenditures cuts.

Table 1 and figure I show the behavior of main economic indicators during the 1978-81 Argentine stabilization. As we can see, it fits the pattern described by Kiguel and Liviatan (1992).

**Tabl. 1.** Main Economic Indicators During Argentine 1978-81 Stabilization

|Indicator|1950-70||<br>1978|j<br>1979|1980||<br>1981|
|---|---|---|---|---|---|
|GDP Growth|3.6|-3.4|7.6|1.1|-6.3|
|CA Surplus/Exports||24.5|-5.8|-48.3|-43.8|
|Real Effective Ex. Rate|94.9|102.9|77.2|69.2|87.0|
|Real Wage Index||72.3|83.0|92.8|83.0|
|IIrflation Rate|23.8|175.5|_159.5_|100.8|_104.5_|
|PublicSec.Surplus/GNP||-3.2|-2.7|-3.6|-8.1|
|Growth of Private Consumption|3.22|-3.07|14.39|_5.59_|-3.64|

_Source:_ Ramos (1986) The main conclusion of Kiguel and Liviatan (1992) is that the expansionary phase of ERBS can be explained as a speculative boom that builds on the expectation of future reversals of policy. The role that credibility plays is that of being absent. In their view, it is _because_ agents expect the stabilization to be only temporary that they shift future consumption and investment<sup>6</sup> expenditures to the present. This behavior soon leads to a real exchange rate appreciation, current account deficits and the collapse of the plan.

**6** Investment and durables consumption are the components of expenditures that are most affected by speculative activity. They are also both greatly affected by falls in real interest rates, as the ones observed in the "tablita" stabilizations during the late 1970s.
fections may lead to the emergence of multiple equilibria, one of them corresponding to a middle-income trap. Yet, at the same time, public policy is not powerless–forceful measures aimed at mitigating the impact of these imperfections (or, more specifically, those aimed at reducing the unit cost of monitoring R&D projects) may allow a country to escape from that trap. As we define it here, the middle-income growth trap is consistent with the evidence on the high cost of financial intermediation in developing countries in general, and middle-income countries in particular (see for instance World Bank (2008)). It is also consistent with the fact that those among these countries that have experienced a significant and sustained slowdown in growth are often characterized by insufficient labor quality and limited R&D activity. But to a significant extent, the characteristics of the middle-income trap, as highlighted here, could equally apply to some high-income OCED countries where growth has remained anemic for significant periods of time, and promoting innovation remains a key policy challenge.

The broader policy lessons of our analysis are indeed consistent with the recent evidence (reviewed in Section 2) for both industrial and developing countries suggesting that financial constraints may be a key factor behind low innovation performance. If new entrants and startup firms are the most affected by the lack of finance, it may be important for governments to provide some form of direct assistance to these firms. Such measures include direct R&D funding, tax incentives for R&D, as well as support for innovation-related collaboration across firms and between universities/labs and firms (see OECD (2010), Lilischkis (2011), and LSE Growth Commission (2013)). Ensuring access to more funding sources may lead to more intensive innovation, more successful R&D projects which, in turn, may result in lower unit monitoring costs, thereby helping to further promote financial intermediation, innovation, and economic growth.<sup>23</sup> However, our analysis also suggests that, rather than providing direct subsidies, an alternative policy would be to implement measures that are aimed directly at mitigating asymmetric information problems and reducing unit monitoring costs, such

> 23The fact that in practice the importance of financial constraints may vary with firm characteristics points also to another conclusion: for any innovation policy program to be effective it is vital not to rely on uniform R&D and innovation support measures, but to provide programs that support different firms in different ways. Policies aimed especially at the most constrained firms are likely to yield the strongest benefits.

28
l

# IABLE It: IOIAL CREDITORS

|||1989|1990|1991|1992|1993|1994|1995|
|---|---|---|---|---|---|---|---|---|
|dRES|0) On Reserves:<br>Change in Reserves|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|dPR<br>dFI|C) On Foreign Investment:<br>Incre2sed foreign Profits<br>Increase in OFI|0.00<br>0.00|0.00<br>0.00|0.00<br>0.00|0.00<br>0.00|0.00<br>0.00|0.00<br>0.00|0.00<br>0.00|
|dNlb|0) On Ioney Creation<br>Increase in Sase Haney to<br>finance Debt-Equity Stups|0.00|0.00|0.00|0.00|O.00|0.00|0.00|
# Annex 2: Robustness: Results of Rigorous De-Duplication

|Country|R<br>Basic<br>House<br>Price<br>_Apartment_<br>_(Current_<br>_USD)_|andom Fore<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|st-based Pre<br>Local<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|diction<br>MAPE|Local<br>MAPE|Basic<br>House<br>Price<br>_Apartment_<br>_(Current_<br>_USD)_|OLS-bas<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|ed Prediction<br>Local<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|<br>MAPE|Local<br>MAPE|
|---|---|---|---|---|---|---|---|---|---|---|
|Albania|64,747.53|1.05|0.99|0.30|0.21|57,899.49|1.05|0.99|0.29|0.27|
|Costa Rica|111,008.21|1.00|1.02|0.32|0.26|117,282.53|1.03|1.12|0.38|0.31|
|Morocco|57,186.95|0.99|1.05|0.31|0.33|47,841.95|1.01|0.88|0.43|0.42|
|Pakistan|22,990.29|0.99|1.04|0.35|0.29|25,225.80|1.02|1.17|0.44|0.40|
|South<br>Africa|63,728.53|1.01|1.05|0.30|0.30|85,469.41|1.12|1.28|0.54|0.55|

_Note_ : MAPE refers to mean absolute percentage error; local MAPE refers to the mean percentage error based on predictions accuracy of apartments sized between 50 and 60 square meters; local ratio refers to the ration between observed and predicted values for apartments sized between 50 and 60 square meters.

40
- [18] Maheshwari, S. R. [1977], President's Rule in India, Delhi: Macmillan.

- [19] Manor, J. [1995], "India's Chief Ministers and the Problem of Governability", in P. Oldenburg [ed.], India Briefing: Staying the Course, M.E. Shaxpe, London.

- [20] McCallum, B. [1978], "The Political Business Cycle: An Empirical Test", Southern Economic Journal, 44, 504-515.

- [21] Mundle, S. and Rao, M. G. [1992], "Issues in Fiscal Policy," in B. Jalan [ed.], The Indian Economy: Problems and Prospects, Penguin Books.

- [22] Nordhaus, W. D. [1975], "The Political Business Cycle", Review of Economic Studies, 42, 169-190.

- [23] Ozler, B., Datt, G. and Ravallion, M. [1996], "A Database on Poverty and Growth in India", Mimeo, Policy Research Department, The World Bank.

- [24] Persson, T. and Tabellini, G. [1990], Macroeconomic Policy, Credibility, and Politics, Chur, Switzerland: Harwood Academic Publishers

- [25] Reserve Bank of India Bulletin, Reserve Bank of India, 1960-1995.

- [26] Rogoff, K. [1990], "Equilibrium Political Budget Cycles", The American Economic Review, 80[1], 21-36.

- [27] Rogoff, K. and Sibert, A. [1988], "Elections and Macroeconomic Policy Cycles", Review of Economic Studies, 55, 1-16.

- [28] Tirole, J. [1994], "The Internal Organization of Government," Oxford Economic Papers, 46, 1-29.

- [29] Tufte, E. R. [1978], Political Control of the Economy, Princeton: Princeton University Press.

36
Table 1 presents country averages for the main variables. Summary statistics including standard deviations and the ranges of all variables are available in table 2. Descriptions and sources of both macro and individual level variables are presented in table A1 in the appendix. The level of aggregation – individual or country – is also indicated for all the variables.

# **3.3 Econometric model**

We adopt the econometric model used by Estrin and Mickiewicz (2011). Our dependent variable is a dummy for whether or not an individual has engaged in nascent entrepreneurship with the expectation of creating 10 jobs or more in 5 years. We use a probit model with random country-year effects in all our estimations. This accounts both for unobserved heterogeneity across countries and also measurement error and idiosyncrasies that are country-year sample specific. This estimation model has been found to be a better fit for the GEM data set than country effects as the data set is highly imbalanced with countries appearing once or twice. Furthermore, broad compositional changes in government spending within a country take a long time, and the main variation comes from across countries. Therefore country fixed effects may wash out the more important cross-country variation in government spending. The focus on country-years rather than countries is also appropriate as it fits the logic of the GEM sampling methodology (Estrin and Mickiewicz, 2011).

The base empirical model is as follows: Prob(Entry) = f(Share of social and public goods , Total Government consumptionijt jt jt , Investment jt , GDP/Capita jt , GDP growth rate jt , Individual level controlsijt ) Where i denotes individuals, j denotes country, and t denotes time. Entry is a dummy equal to one if the individual is engaged in nascent entrepreneurial activity and zero otherwise. As stated earlier, we use the random country-year effects probit model. We use this model to present our base estimations. In later 11
**Table 1.  Central Government Operations (in percent of GDP, unless otherwise indicated)**

||**2004/05**|**2005/06**|**2006/07**|**2007/08**<br>**2**|**008/09**|**2009/10** /1|
|---|---|---|---|---|---|---|
|**Revenue and Grants**|**36.5**|**40.9**|**40.2**|**35.8**|**34.8**|**31.4**|
|Revenue|35.8|40.7|39.5|35.1|34.1|30.4|
|**Tax Revenue**|**20.5**|**22.0**|**23.3**|**21.6**|**23.4**|**20.3**|
|Mineral Income Tax|5.1|5.3|5.6|4.7|3.9|1.4|
|Non-Mineral Income Tax|4.2|5.5|4.5|3.2|5.3|5.1|
|Customs Pool (SACU)|6.6|7.2|9.7|9.8|8.9|9.0|
|Sales Tax/VAT|4.3|3.6|3.3|3.6|5.0|4.5|
|Other taxes, incl. export duties|0.3|0.3|0.3|0.3|0.3|0.3|
|**Non Tax Revenue**|**15.3**|**18.7**|**16.2**|**13.5**|**10.7**|**10.1**|
|Mineral Royalties and Dividends|12.6|15.0|13.6|10.7|7.7|7.6|
|Interest receipts|-0.2|0.2|0.1|0.1|0.1|0.0|
|Other Property Income, of which|0.9|1.7|0.9|1.3|1.6|1.3|
|o/w BOB revenue|0.8|1.4|0.8|1.1|1.5|1.1|
|Fees and charges|2.1|1.9|1.5|1.4|1.3|1.2|
|**Grants**|**0.7**|**0.2**|**0.7**|**0.7**|**0.7**|**1.0**|
|**Expenditures and Net Lending**|**35.3**|**32.4**|**28.9**|**31.0**|**40.2**|**44.6**|
|**Recurrent**|**28.0**|**26.0**|**23.4**|**23.2**|**27.3**|**29.1**|
|Wages and Salaries|10.4|9.6|8.5|8.6|10.0|10.5|
|Interest|0.6|0.6|0.3|0.3|0.3|0.4|
|Domestic Interest|0.6|0.3|0.3|0.2|0.2|0.0|
|External Interest|0.0|0.3|0.1|0.1|0.1|0.0|
|Other|16.9|15.8|14.5|14.3|17.0|18.2|
|**Development Expenditure**|**8.0**|**7.0**|**5.9**|**8.2**|**13.1**|**14.7**|
|**Net lending**|**-0.6**|**-0.6**|**-0.4**|**-0.4**|**-0.2**|**0.8**|
|**Overall Surplus/Deficit**|**1.2**|**8.5**|**11.2**|**4.8**|**-5.4**|**-13.3**|
|Primary surplus/deficit|1.8|9.1|11.6|5.1|-5.0|-12.8|
|**Financing**|**-1.2**|**-8.5**|**-11.2**|**-4.8**|**5.4**|**13.3**|
|External borrowing, net|-0.3|-0.3|-0.3|0.0|-0.1|7.2|
|New borrowing|0.0|0.0|0.0|0.2|0.2|7.6|
|Amortisation (- entry)|-0.3|-0.3|-0.3|-0.3|-0.3|-0.4|
|Domestic borrowing, net|0.0|-1.4|0.0|0.6|1.5|0.7|
|New borrowing|0.0|-1.4|0.0|1.6|2.2|3.0|
|Amortisation (- entry)|0.0|0.0|0.0|-1.1|-0.7|-2.3|
|IMF Transactions|0.0|0.0|-0.1|-0.1|-0.1|-0.1|
|Pension Liability Service Fund|-3.2|-2.1|0.0|0.0|0.0|0.0|
|Other Financing|-1.0|1.4|-0.5|-0.2|-1.2|0.0|
|<br>Change in Cash Balances|3.4|-6.2|-10.4|-5.0|5.3|5.0|
|**_Memorandum items_**|||||||
|Mineral revenue (% of GDP)|17.7|20.3|19.2|15.4|11.6|9.0|
|Mineral corporate tax|5.1|5.3|5.6|4.7|3.9|1.4|
|Royalties, dividends|12.6|15.0|13.6|10.7|7.7|7.6|
|SACU revenue|6.6|7.2|9.7|9.8|8.9|9.0|
|Non-mineral, non-SACU revenue (% of GDP)|10.8|11.8|9.8|8.7|12.1|11.3|
|Non-mineral fiscal balance (% of non-miningGDP)|-28.0|-21.3|-15.4|-19.9|-28.7|-35.5|

Sources: Botswana authorities and author’s calculation. 1/ Estimates.

7
Sufian, F., Mohamad, N., and Muhamed-Zulkhibri , A., 2008. _“_ The Efficiency of Islamic Banks: Empirical Evidence from the MENA and Asian Countries Islamic Banking Sectors” _The Middle East Business and Economic Review_ 20 (1).

Sapienza, P., 2004. “The Effects of Government Ownership on Bank Lending” _Journal of Financial Economics_ 72 (2), 357-384.

Turk-Ariss, R., 2008. “Financial Liberalization and Bank Efficiency: Evidence from Post-war Lebanon” _Applied Financial Economics_ 18, 931–946.

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