rafmacalaba/datause-encoder-data huggingface dataset:
the coefficient on the interaction term is not only insignificant, but is negative, not positive as implied by an argument for reverse causation.

In our second variant of this identification strategy, we distinguish donors by selectivity in aid allocations. Specifically, we posit that donors that are more sensitive to quality of country policies and systems when allocating aid will also be more sensitive to the quality of systems when deciding whether to rely on them for managing their aid. In contrast, there is no particular reason to expect the strength of any reverse causation from use of country systems to vary with donors’ sensitivity to policy in allocating aid. An empirical finding of heterogeneous impacts in this instance can be explained better by the causal argument than by reverse causality (from use of systems to system quality). Equation 5.2 includes an interaction term, to test the hypothesis that PFM Quality will affect use of country systems more for donors that are more policy selective in allocating aid among recipients.<sup>18</sup> For this purpose, we use the donor Policy Selectivity scores from Knack, Rogers and Eubank (2011), based on aid disbursements in 2007. The interaction coefficient is positively signed, as hypothesized, and significant at the .01 level. These heterogeneous impacts are more consistent with a causal explanation from PFM Quality to use of PFM systems than with an argument for reverse causation from use to quality.

Endogeneity bias cannot be entirely ruled out by the results in Tables 4 and 5, or of course by the use of lagged values of PFM quality in all of the regressions. However, these results are all consistent with a direction of causality running predominantly from PFM Quality to use of country PFM systems, and much harder to square with an argument of reverse causality.

> 18 The interaction term is equal to the product of the deviations of PFM Quality and Policy Selectivity from their respective means.  The coefficient on PFM Quality thus indicates its marginal effect conditional on the mean value of Policy Selectivity.  Because Policy Selectivity is donor-invariant, its coefficient is subsumed by the donor-year fixed effects.

25
this information upfront when refugees are first registered. Vice-versa, if poverty estimates imputed with the PG data are not statistically different from the true rates (i.e., those produced directly from the HV data), this would suggest that existing PG variables are already suitable to produce reliable poverty estimates.

We also use two alternative models to estimate regression errors: one where we assume a standard normal distribution for the error term, and another where we remove this assumption and use the (non-parametric) empirical distribution of the error term instead. If the error term is not distributed normally, our poverty estimates would be biased, and a non-parametric model based on the empirical distribution would likely perform better.

Table 1 present the summary results and Table 2.1 in Appendix 2 provides the full regression results. Table 1 shows that all the estimates using the normal linear regression model fall within the 95 percent confidence interval (CI) of the true poverty rate, for both Sample 1 and Sample 2. In other words, these estimates are not statistically significantly different from the true poverty rates reported at the bottom of the table. Estimates using Specification 2 with more variables on household assets and house characteristics are somewhat better and closer to the true poverty rate than those using Specification 1 for both samples. For example, the poverty estimate using Specification 1 (Table 1, first column) is 52.6 percent, which is 1.1 percentage points larger than the true poverty estimate of 51.5 percent. The poverty estimate using Specification 3 (Table 1, third column) is 52.3 percent, which is 0.8 percentage points less than the true poverty estimate. This is likely because imputation models that include household assets are usually found to perform better than those that do not (Christiaensen _et al._ , 2012; Dang _et al._ , 2019).<sup>10</sup>

> 10 On the other hand, adding more control variables does not necessarily lead to a better model fit. While this result may appear counter-intuitive, one possible reason is that doing so may overfit the data and thus does not offer more accuracy, which is shown with empirical evidence from India and Jordan (Dang _et al._ , 2017; Dang and Lanjouw, 2018). A recent theoretical study also suggests that for misspecified regressions, adding more variables may result in

12
_Lemma 3 As carrying capacity declines the optimum steady state allocation of land to wildlife increases and as the relative payoffs to hunting increase the optimum steady state allocation of land to wildlife_ ∂Lwss ∂Lwss _declines . That is_ < 0 and < 0 _._ ∂q ∂πN In policy terms Lemma 3 seems especially instructive. Activities that lower carrying capacity ( _q_ ) call for an increase in land allocated to wildlife – often the reverse of what is observed. Intuitively as _q_ increases (decreases), wildlands become more (less) productive, so any given payoff from _W_ can be obtained with less (more) land devoted to wildlife.

# **4. Partial Equilibrium Simulations**

This section numerically simulates the impacts of key pressures, individually and in combination to assess the response of agents in the model. As noted in Section 1 the model is parameterized for the Serengeti given the availability of data for this biome and changes in the surrounding areas. We consider the consequences of changes in carrying capacity.<sup>16</sup> This section seeks to assess the possible bio-economic implications of such activities. In terms of the model parameters, policies to boost agricultural production would increase payoffs and incentives to expand the agricultural frontier into former wildlands, with implications for wildebeest numbers, tourism and hunting. Other proposals, which typically involve use of the ecosystem for other purposes are expected to lower the carrying capacity of the ecosystem, especially if they impede the wildebeest

> 16    Sources include examples such as building proposals, illegal artisanal mining within the protected area as well as commercial mining in the peripheries. For reports of illegal mining within the Park see <u>http://allafrica.com/stories/201203270109.html</u> . Further N Leader Williams ,J A Kayera and  G Overton (2006) _Mining in Protected Areas in Tanzania_ Institute of Wildlife and Development 2006, list the precious metals being mined. See also Hance (2011) (http://news.mongabay.com/2011/0414-hance_tanzania_gov.html.)

17
Table 1 The Potential Coverage of Regulations

||Labor force|Formal secto|r employment|
|---|---|---|---|
||(thousand|(thousand|persons)|
|Country|persons)|_||
|||Private|Public|
|Burkina Faso|4167.1|35.6|34.2|
|(1990, 1993, 1991)|[100.0]|[0.9]|[0.8]|
|Central African R.|1384.5|13.0||
|(1990)|[100.0]|[0.9]||
|C6te d'Ivoire|4598.6|131.5|116.0|
|(1990)|[100.0]|[2.81|[2.5]|
|Mali|2958.7||40.2|
|(1990, 1991)|[100.0]||[1.4]|
|Niger|3619.0|15.8|39.7|
|(1990, 1991, 1991)|[100.0]|[0.4]|[1.1]|
|Senegal|2347.6|79.6|66.3|
|(1988)|[100.0]|[3.4]|[2.8]|
|Togo|1396.3|22.4|33.7|
|(1990, 1991, 1991)|[100.0]|[1.6]|[2.4]|

Notes: Figures in parenthesis indicate the years the data refer to, while figures in brackets are percentages of the labor force. The fonnal private sector may include state-owned commercial firms in some countries. Data on the labor force for all countries, except Senegal, are from the International Labour Office. Data on private sector employment in C6te d'Ivoire are from the _Banque de Donnees Financieres._ In Senegal, they are from _Banque de Donnees Economiques et Financieres._ Other data for Senegal are from the Ministry of Finance. For other countries, data on formal sector employment are from the _Statistiques Economiques_ of BCEAO.

4
the 59 countries that collect SME lending data on a regular basis, 86 percent are low and middleincome countries. Table 1 provides detailed information from the survey.

In a number of countries where data are not collected through regular reporting, they are collected through surveys of financial institutions or by estimating lending volume in credit registries. Credit registries contain loan level data and allow estimation of a variety of SME statistics using loan size as a proxy for an SME definition. In 13 countries, including Argentina, Tunisia and Brazil, regulators use public credit registries to estimate the volume of SME lending. In 23 countries regulators conduct periodic surveys of financial institutions to monitor SME lending. The frequency of surveys varies across countries. For example, Estonia, Singapore and Armenia conduct such surveys annually, Algeria and Tunisia monthly, while Uganda does it on an _ad hoc_ basis. Financial institution surveys can be an important tool for a regulator to collect information not only on SME lending volumes, but on other aspects of SME finance such as fees and number of applications received and rejected, all of which are essential for the implementation of SME finance reforms and programs. In 10 countries, 8 of which are in the Western African Monetary Union, regulators combine information from the credit registries and periodic surveys from financial institutions.

There is a clear regional pattern in reporting SME data. More than half of the countries in Asia and Africa report that they collect information on SME finance on a regular basis. These are also regions where regulators tend to identify access to finance as a priority. Financial regulators in high-income countries and in Latin America and the Caribbean are the least likely to collect data on SME finance, as some other agency is usually assigned this task. Most importantly, the survey was able to collect data on the volume of SME financing from 50 countries, which enables us to assess cross-country variation in the levels of SME financing.

# **2.1 SME definitions**

One of the main challenges in performing a cross-country analysis of SME data is the absence of a universal definition of what constitutes an SME. A number of efforts aim to streamline and harmonize SME definitions (OECD, 2004), although the heterogeneity of SMEs themselves and the nature of the economy they operate in might mean that establishing a global definition is not 7
36 of financial depth. We focus on two measures of credit, but also use a variety of other measures to ensure that our statistical generalizations are robust. Given that our theoretical analysis applies to private credit flows (and that publicly provided credit contains a strong subsidy element) the measure of greatest interest is private domestic credit. Combining the claims on the private sector by the monetary authorities and the deposit money banks (IFS line 32d); we usually refer to this variable as "credit". We also concentrate on "quasi-money" (IFS line 35), the difference between M2 and Mi. This variable may be viewed as an indicator of the depth of financial intermediation, since it reflects the component of intermediation that is more likely to be driven by lending and borrowing and borrowing considerations, than by the demand for a transactions medium. To check our concXusions, we often use variables such as total domestic credit (including claims on central and local governments as well as other banking institutions, IFS line 32) and 142 (the sum of IFS lines 34 and 35) for credit and quasi-money respectively. We also experiment with broader measures of money and credit, but do not report them here since the results are largely unchanged. Finally, we obtained measures of external borrowing from the world debt tables.

We usually convert our variables to real per capita data measured in American dollars. To do this, we use the country-specific period average (nominal bilateral) market exchange rate (IFS series "rf") to convert data into dollars, and subsequently divide the data by the product of the domestic population and the American GNP price deflator. We usually transform all variables by taking natural logarithms.

# _Vb. Results_

We attempt to establish a broad empirical characterization of relationship between financial depth and real per capita income. To do so we examine correlations between the (log of the) level of (real per capita dollar income), and the (log of the) ratio of credit to GDP. We find strong evidence of a positive correlation: countries with higher income have deeper financial systems. Again, we stress that our work is descriptive, so that the causal
**Table 12 Firm Size Growth and Investment Climate Variables**

|IC Variable Used|Smallt‐3|Mediumt‐3|Larget‐3|Aget‐3|IC Vart|Const|Obs|R<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|
|External Fin|‐0.13|‐0.152|‐0.113|‐0.003|0.003|0.316|668|0.21|
||(0.040)***|(0.032)***|(0.060)*|(0.001)**|(0.001)**|(0.090)***|||
|Collateral|‐0.10|‐0.156|‐0.153|‐0.005|‐0.001|0.475|219|0.35|
||0.102|(0.092)*|0.127|0.004|0.001|(0.129)***|||
|Sales on Credit|‐0.14|‐0.157|‐0.114|‐0.003|0.000|0.333|660|0.20|
||(0.039)***|(0.033)***|(0.060)*|(0.001)**|0.002|(0.150)**|||
|Overdraft (y/n)|‐0.137|‐0.154|‐0.11|‐0.003|‐0.144|0.348|656|0.21|
||(0.040)***|(0.033)***|(0.062)*|(0.001)**|0.122|(0.076)***|||
|Line of credit (y/n)|‐0.145<br>(0.039)***|‐0.163<br>(0.034)***|‐0.125<br>(0.064)*|‐0.003<br>(0.001)**|0.334<br>(0.079)***|0.216<br>(0.087)**|665|0.22|
|Loss PowOut|‐0.218|‐0.266|‐0.22|0.000|0.003|0.272|223|0.33|
||(0.094)**|(0.073)***|(0.081)***|0.002|0.006|0.204|||
|Infor Pay|‐0.157|‐0.167|‐0.096|‐0.003|0.029|0.281|551|0.23|
||(0.044)***|(0.041)***|(0.053)*|(0.001)**|0.024|(0.084)***|||
|Tax Inspect|‐0.195|‐0.199|‐0.155|‐0.004|0.000|0.525|324|0.30|
||(0.084)**|(0.057)***|(0.084)*|(0.001)***|‐0.001|(0.133)***|||
|Time Tax|‐0.116|‐0.123|‐0.086|‐0.003|0.002|0.207|617|0.18|
||(0.037)***|(0.032)***|0.063|(0.001)**|‐0.002|(0.104)*|||
|Days to Export|‐0.171|‐0.247|‐0.229|‐0.004|0.010|0.316|336|0.38|
||0.13|(0.114)**|0.138|(0.002)**|(0.004)**|(0.112)***|||
|Days to Import|‐0.379|‐0.489|‐0.501|‐0.005|0.002|0.414|208|0.52|
||(0.142)**|(0.125)***|(0.122)***|(0.002)**|0.003|(0.139)***|||
|Inspections|‐0.13|‐0.159|‐0.121|‐0.003|‐0.005|0.253|636|0.19|
||(0.043)***|(0.037)***|(0.063)*|(0.001)**|0.003|(0.065)***|||

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for foreign and government ownership, export status, 2‐digit industry, and region fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

20
The mode1 (onsists of 45 equations,<sup>28 of which are behavioral</sup> and 17 of' which are identities. The complete model specification is showrn it' Table 9. The lists of endogenous and exogenous variatbles in the model are shown<sup>Tables 14 and 15 in Appendix A.</sup> The simultaneous relationships among<sup>variables in the real sector</sup> are demonstrated throughl the national income identity, where income level comprises of private<sup>and public spending.</sup> In turn, under the hypothesis that<sup>private aggregate spending depends on</sup> pprmanent. income, thus income level<sup>also affects private spending</sup> level simultaneously. The interlink between the<sup>real sector and</sup> the monetary sector is made<sup>via the credit availability effect in</sup> the investment function<sup>and via the income effect on the demand</sup> for financial assets. For the price level, it is determined<sup>by</sup> external influences and the excess demand conditions in the domestic market.

The real sector of the model was<sup>estimated by the instrumental</sup> variable method. For the private portfolio and the benk portfolio blocks, the Seemingly<sup>Unrelated Regression method was</sup> employed to improve the efficiency<sup>of parameter estimates.</sup> The Cochran-Orcutt iterative technique was utilized whenever an autocorrelation was detected. Annual data were employed from 1'70-1987. Sources of data were the National<sup>Economic and Social</sup> Development Board, the Bank of Thailand, and the IMF International Financial Statistics. Results of Estimation are provided in Appendix B.

# The Trade Sector

Thailnd is considered as a price taker<sup>in the world market. On</sup> both imports and exports, the small country assumption was adopted.<sup>1</sup> / Thus, only the demand equations<sup>for imports and the</sup> export supply equations are included in the model. Export commodities are separated into agricultural and industrial products. The prices of agricultural exports and industrial products are determined by<sup>the world price levels.</sup> The demand for merchandise imports are classified into three categories: agricultural products, industrial products, and petroleum products.

The quantity of agricultural exports supplied depends on its previous year's net export<sup>price reflecting the lag adjustment</sup> pattern of agricultural production. Nevertheless, the current domestic price of agricultural products is also included, indicating the possibility for exporters to choose between supplying to foreign or domestic<sup>ma-kets.</sup> The substitution is made, not between supplying<sup>to export markets and non-tradable</sup> sector, hut between supplying to export markets and domestic ______________ 1/ Although Thailand affects the world export prices of rice and cassava, their share in Thailand's total exports reduced continuous>' from 25 percent<sup>in 1970 to only 13 percent in 1989.</sup> 11
# **Appendix B. Detailed scenario results**

## **Table B1. All scenarios: Macro indicators (% of GDP)**

||**Fi**|**nalyear**|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicator**|**2012**|**base**|**agrpweff in**|**dupweff**|**transeff**|**fuelpwm **|**remitdecr tr**|**nsfrpoor**|**allgrw**|**allgrw-tr**|
|**Absorption**|140.3|136.9|124.1|120.2|132.8|139.0|133.8|137.2|119.6|119.8|
|**Consumption - private**|91.7|88.6|79.2|77.0|85.5|91.0|87.6|89.3|76.1|76.5|
|**Consumption - government**|18.7|18.3|17.1|17.0|17.3|18.7|18.6|18.1|16.8|16.7|
|**Investment - private**|27.1|27.3|25.3|23.8|27.3|26.6|25.0|27.1|24.4|24.3|
|**Investment - government**|2.8|2.7|2.5|2.4|2.6|2.7|2.6|2.7|2.4|2.4|
|**Stock change**|Eps|Eps|Eps|Eps|Eps|Eps|Eps|Eps|Eps|Eps|
|**Exports**|49.7|50.4|49.1|54.3|49.8|54.0|52.6|49.5|53.4|53.0|
|**Imports**|-90.0|-87.3|-73.1|-74.5|-82.6|-92.9|-86.4|-86.7|-73.0|-72.8|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|16.5|17.6|15.0|14.4|16.3|19.2|18.0|19.6|14.0|15.2|
|**GDP at factor cost**|83.5|82.4|85.0|85.6|83.7|80.8|82.0|80.4|86.0|84.8|
|**Foreign savings**|2.2|3.0|2.0|1.7|2.7|3.2|3.1|3.1|1.7|1.7|
|**Gross national savings**|27.7|26.9|25.8|24.4|27.2|26.1|24.5|26.8|25.1|24.9|
|**Gross domestic savings**|-10.4|-6.9|3.7|5.9|-2.8|-9.6|-6.1|-7.3|7.2|6.9|
|**Foreign government debt**|17.5|20.8|14.1|12.0|18.7|22.0|21.5|21.0|11.6|11.7|
|**Domesticgovernment debt**|0.6|2.3|1.8|1.6|2.1|2.6|2.4|2.4|1.5|1.5|

Note: Columns other than 2012 show results for 2020.

42
Household Budget Surveys estimate returns to education in order to explain demand for education and report OLS estimates between 0.3% to 16.3%, depending on the sub‐group, and not controlling for endogeneity. Using nationally representative cross‐section data from of the Integrated Labour Force Surveys (IFLS) for 2001 and 2006, Kerr (2011) estimate returns between 8% and 13% when using OLS. When allowing for nonlinearities the results suggest that returns are strongly convex, but when also addressing endogeneity exploiting a change in the education system in the mid 1960s, returns are concave and higher at the lower levels of education, which the authors argue reflects an ability bias.<sup>37</sup> These results also shed light on earlier findings by Söderbom, Teal, Wambugu, Kahyarara (2006), who, using data for employees in the manufacturing sector in Tanzania for 1993, 1994, 1999 and 2001, also find a convex earnings function when taking endogeneity into account. Their estimates exceed the OLS estimates, which may be a consequence of self‐selection on ability into the manufacturing sector. Our estimates also aligh well with those from other African countries. Schultz 2004 reports wage gains of 5‐20% for each year of schooling for five African countries.<sup>38</sup> Our results underline that survey methods matter for the estimation of structural parameters, such as Mincerian returns to education, and indicate that care is needed in the comparison of these returns across data sources, as is typically the case with worldwide comparisons, as well as comparisons over time

# **7. References**

Angrist, J. and A. Krueger. 1991. “Does Compulsory School Attendance Affect Schooling and Earnings?” _The Quarterly Journal of Economics_ 106(4): 979‐1014.

Anker, R. 1983. “Female Labour Force Participation in Developing Countries: A Critique of Current Definitions and Data Collection Methods.” _International Labour Review_ 122(6):709‐724.

Bardasi, Elena, Kathleen Beegle, Andrew Dillon, and Pieter Serneels. 2011. “Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania.” _World Bank Economic Review_ 25(3): 418‐447.

> 37 While the instrument reflects an exogenous policy change that extended primary school from 4 to 7 years in the mid 1960s, it is unclear to what extent it is applicable to the entire sample of analysis.  As the change in policy took place 40 years ago, it had an immediate impact on those who were 10‐13 years old in the mid 60s, or 50‐53 in the mid 2000s. With average age of 38, this is likely to be a minority in the IFLS sample.  Because the change in policy took place so long ago, we do not consider this as identifying variable for our control function estimates. 38 Kenya, Nigeria, Ghana, Burkina Faso, and Cote d’Ivoire

21
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