rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 8: Productivity Differential between Rental Partners: Pair Fixed Effects with Owned Plots Only</u>**

|||||**With s**|**electivity cor**|**rection**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Cultivated by tenant|0.174***|0.178***|0.262***|0.475***|0.490***|0.598***|
||(3.894)|(2.842)|(3.808)|(3.085)|(2.839)|(3.298)|
|Tenant with female partner||-0.007|0.067||-0.025|0.051|
|||(0.091)|(0.782)||(0.240)|(0.428)|
|Holding size in hectares (log)|||-0.106***<br>(2.896)|||-0.109*<br>(1.914)|
|Number of years possessed|-0.006**|-0.006**|-0.005**|-0.005|-0.005|-0.004|
||(2.380)|(2.372)|(1.982)|(1.524)|(1.520)|(1.134)|
|Good soil quality|0.265***|0.264***|0.253***|0.281***|0.281***|0.269***|
||(3.884)|(3.878)|(3.706)|(3.770)|(3.758)|(3.570)|
|Medium soil quality|0.160**|0.160**|0.160**|0.159**|0.159**|0.159**|
||(2.383)|(2.380)|(2.383)|(2.115)|(2.109)|(2.098)|
|Flat land|<br>0.131|<br>0.131|<br>0.113|<br>0.141|<br>0.141|<br>0.123|
||(1.314)|(1.314)|(1.132)|(1.327)|(1.334)|(1.151)|
|Gently sloped land|0.105|0.105|0.091|0.120|0.121|0.106|
||(0.995)|(0.996)|(0.863)|(1.065)|(1.071)|(0.928)|
|Irrigated land|0.349***|0.349***|0.351***|0.354***|0.355***|0.355***|
||(3.733)|(3.732)|(3.757)|(2.837)|(2.839)|(2.857)|
|Teff|<br>0.521***|<br>0.521***|<br>0.540***|<br>0.518***|<br>0.518***|<br>0.536***|
||(9.222)|(9.217)|(9.502)|(7.765)|(7.781)|(7.860)|
|Wheat|<br>0.435***|<br>0.435***|<br>0.449***|<br>0.431***|<br>0.431***|<br>0.445***|
||(7.288)|(7.286)|(7.509)|(7.042)|(7.045)|(7.136)|
|Sorghum|-0.164***|-0.164***|-0.161***|-0.156***|-0.156***|-0.153**|
||(2.926)|(2.927)|(2.874)|(2.613)|(2.622)|(2.563)|
|Pulses|-0.100|-0.100|-0.081|-0.099|-0.098|-0.078|
||(1.458)|(1.454)|(1.175)|(1.111)|(1.097)|(0.892)|
|Constant|7.080***|7.080***|7.028***|6.945***|6.943***|6.864***|
||(54.679)|(54.664)|(53.876)|(35.611)|(35.595)|(34.478)|
|**Selection variables**|||||||
|<br>Landlord cultivators||||-0.076|-0.077|-0.098|
|||||(0.687)|(0.692)|(0.886)|
|Tenants||||0.325***|0.327***|0.322***|
|||||(2.764)|(2.755)|(2.737)|
|Number of observations|2224|2224|2224|2224|2224|2224|
|Number of households<br>|366|366|366|366|366|366|
|_R_<sup>2</sup>|0.144|0.144|0.148|0.150|0.150|0.154|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Household level variables are for cultivator households.

a In order to describe selection into rental market participation regimes, we use a multinomial selection method proposed by Lee (1983). The reported t statistics are robust to any general form of heteroscedasticity. The method suggested by Dubin and McFadden (1984), and generalized by Bourguignon _et al._ (2007), is used alternatively, but the results are not reported as they are similar to those obtained using the Lee 91983) approach.

24
9 term. Furthermore the estimates involving the hyperbolic form will be sensitive to how the experience variable is scaled. A functional form that is not subject to these criticisms is the two parameter Pade' form: The above form has the virtue that as X increases s/8* smoothly converges from its initial value (l+a) to the asymptotic value unity.8 (The behavior of these functional forms based on our estimates is presented later.) C. DA.t

# 1. Sources and Discussion of Variables

Models similar to (4) and (5) are estimated, employing loan level data for the period of 1968 to mid-1981. The data incorporates information about the loans as well as borrowers' characteristics. Information on the loans consists of the identity of the borrower, the time of the loan, its terms, and some qualitative aspects such as whether the loan is public. Loans data for the 1973-81 period is obtained from the various issues of the World Bank's Borrowing in International Capital Markets. The data for the prior period, however, has been obtained through an exhaustive search of the financial press as well as the central bank reports of the borrower countries. We have included only $U.S. denominated loans that have variable interest rates and that have LIBOR (London Interbank Offer Rate) as the base rate. These restrictions are incorporated to avoid complications that may arise from comparisons across different types of financial instruments. Furthermore, the data are constrained to include borrowers that have taken at least three loans. Finally, since this paper is concerned with expansion of the market and we want to avoid problems surrounding the debt crisis, we limit the data to the period ending in mid-1981. After these restrictions, the
28 Consider next the debt substitution experiment. Under this experiment, the government retires foreign debt equal to 10 percent of GNP and raises the revenue by issuing an equivalent amount of interest bearing debt domestically. This clearly deteriorates its fiscal position, since the real interest cost of domestic debt is 6 percentage points higher ( 12 instead of 6) than the cost of foreign debt. Such a policy would increase the equilibrium inflation rate to over 150% . Clearly, at current interest rate differentials, a policy of retiring foreign public debt through issue of domestic debt will in fact deteriorate the fiscal position of the government.

# 5 Conclusions

We have designed a simple framework for the assessment of the consistency between fiscal policy and the requirements imposed by financial sector reform, external debt and medium run inflation targets. The framework draws on the public finance approach to inflation pioneered by Phelps (1973) and is designed with practical application in mind. The approach has relatively modest data requirements because of its focus on consistency rather than on intertemporal optimality. The design of an intertemporally optimal fiscal policy requires information about parameters such as the intertemporal elasticity of substitution, about which we do not even have ball park estimates. Data requirements for application of the framework set up here do not exceed routinely available financial sector and national accounts data. Extra effort is required, however, in obtaining detailed information about the regulatory framework for the financial sector and about the size of th'e actual deficit of the
# Contents

|1<br>Intr|oduction<br>4|
|---|---|
|2<br>Stru|cture of the Model<br>8|
|2.1|Production<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>8|
||2.1.1<br>Rural Sector . . . . . . . . . . . . . . . . . . . . . . . .<br>9|
||2.1.2<br>Urban Informal Sector . . . . . . . . . . . . . . . . . .<br>10|
||2.1.3<br>Urban Formal Private Sector . . . . . . . . . . . . . . .<br>10|
||2.1.4<br>Public Production<br>. . . . . . . . . . . . . . . . . . . .<br>12|
|2.2|The Labor Market<br>. . . . . . . . . . . . . . . . . . . . . . . .<br>12|
||2.2.1<br>Rural Wages, Employment, and Migration . . . . . . .<br>14|
||2.2.2<br>The Urban Labor Market<br>. . . . . . . . . . . . . . . .<br>16|
||2.2.3<br>International Labor Migration . . . . . . . . . . . . . .<br>22|
|2.3|Export Supply and Import Demand . . . . . . . . . . . . . . .<br>22|
|2.4|Aggregate Supply and Demand<br>. . . . . . . . . . . . . . . . .<br>23|
|2.5|Profts and Income . . . . . . . . . . . . . . . . . . . . . . . .<br>25|
|2.6|Savings and Wealth Accumulation . . . . . . . . . . . . . . . .<br>27|
|2.7|Private Investment . . . . . . . . . . . . . . . . . . . . . . . .<br>28|
|2.8|Asset Allocation and the Credit Market . . . . . . . . . . . . .<br>31|
||2.8.1<br>Households<br>. . . . . . . . . . . . . . . . . . . . . . . .<br>31|
||2.8.2<br>Firms<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .<br>35|
||2.8.3<br>Commercial Banks . . . . . . . . . . . . . . . . . . . .<br>35|
||2.8.4<br>Interest Rates and Risk Premia . . . . . . . . . . . . .<br>38|
|2.9|Public Sector<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .<br>42|
||2.9.1<br>Central Bank . . . . . . . . . . . . . . . . . . . . . . .<br>42|
||2.9.2<br>Government Budget<br>. . . . . . . . . . . . . . . . . . .<br>43|
|2.10|The Balance of Payments and the Exchange Rate . . . . . . .<br>45|
|2.11|Currency and Bond Market Equilibrium<br>. . . . . . . . . . . .<br>46|
|2.12|Price Determination<br>. . . . . . . . . . . . . . . . . . . . . . .<br>47|
|2.13|Default Risk, Credibility, and Expectations . . . . . . . . . . .<br>49|
|3<br>Cali|bration and Solution<br>53|
|4<br>Poli|cy Experiments<br>53|
|4.1|Increase in Ofcial Interest Rates . . . . . . . . . . . . . . . .<br>54|
|4.2|Fiscal Adjustment . . . . . . . . . . . . . . . . . . . . . . . . .<br>59|
||4.2.1<br>Increase in the VAT Rate<br>. . . . . . . . . . . . . . . .<br>60|

2
institutional environment, controlling for per capita income, civil conflict, fractionalization within societies, education level and geography.

The rest of the paper is organized as follows. Section 2 describes the data and main variables. Section 3 shows the main regression results, Section 4 provides robustness checks. Section 5 concludes.

# **2. Description of the data**

The sample consists of 133 countries for which information on our main variables is available. The time period covered by the study is 2003-2008. In the analysis we utilize several sources of data, including the World Bank’s Doing Business project and World Development Indicators, Comtrade (United Nations), Polity IV, Freedom House, Armed Conflict Database, Djankov et al. (2007), La Porta et al. (1999) and Sachs and Warner (1997). A definition of all variables and their sources is provided in Table 1. Summary statistics for the main variables are provided in Table 2.

# **2.1** **_Dependent variable_**

The dependent variable is based on data from the World Bank’s Doing Business project. As part of its annual exercise, Doing Business compiles ten sets of indicators covering various aspects of the business climate including starting a business, paying taxes, obtaining licenses, getting credit, protecting investors, employing workers, international trade, property registration, closing a business and enforcement of private contracts. Information is also available on an annual basis on important reforms on each of these indicators. This information is coded as a dummy variable which equals 1 if a country 4
beliefs about how others are behaving– which are slow to change (World Bank, 2016; World Bank, 2017). Political norms are defined as a subset of social norms, pertaining to beliefs about how others are behaving in the sphere of politics and government. Political norms of behavior — what citizens demand from the state, and how they expect others to be acting in the public sector — can explain why well-intentioned reformers, in powerful political positions, find it difficult to institute change. To illustrate the argument<sup>13</sup> , prevalent political norms can be characterized as follows. Rational expectation among “ordinary” (i.e. non-office-bearing) citizens is that other citizens will vote for politicians who share their identity or ideology, and who provide targeted private benefits, even though, in equilibrium, the consequences are harmful for everybody (since voting on the basis of identity and private benefits weakens political incentives to provide public goods). Of these ordinary citizens, among those who have leadership or entrepreneurial qualities, who become contenders for political power (starting from the village level, as local elections spread within countries across the political spectrum), the rational expectation is that other contenders are entering the fray to seek private rents from public resources. Among office-bearing citizens, or state personnel, all the way from high- and mid-level bureaucrats to frontline providers, the rational expectation is that their peers do not care about doing their jobs well because there are few formal or informal (sanctions) rewards for (bad) good performance. The service delivery organizations of the state thus lack both incentives and non-pecuniary sources of motivation (such as peer pressure and professional norms). When a reform leader tries suddenly to strengthen incentives in this low- performance setting, those reforms are resisted and thwarted by well-organized interest groups, such as unions of teachers and doctors, or other political opponents who seek rents from the status quo.

Where even dynamic and motivated leaders find it hard to bring reforms is where success is difficult to deliver using their executive powers alone, and therefore, also harder for them to take credit for and return to office. These difficult areas are human-resource intensive and require sustained change in the day-to-day behavior of a large number of frontline personnel. It is also precisely in these human-personnel-intensive sectors of delivery that leaders

> 13This section draws on Khemani (2019).

24
to alternative measures of income inequality; alternative income inequality data sources; splitting the sample between the pre- and post-1990 periods (end of the Cold War); and restricting the sample to countries located in Latin America and the Caribbean or Asia.

While the average effect of income inequality on GDP per capita is negative and significantly different from zero, it varies with countries' initial income level. In an econometric model that includes an interaction term between initial GDP per capita and income inequality, the coefficient on the interaction term is negative and significantly different from zero at the 1 percent level. Quantitatively, the size of the coefficient on the interaction term implies that differences in initial income induce a substantial effect on the impact that changes in income inequality have on GDP per capita. For example, at the 25<sup>th</sup> percentile of initial income, the predicted effect of a one percentage point increase in the Gini coefficient on GDP per capita is 2.3 percent (with a corresponding standard error of 0.6 percent); at the 75<sup>th</sup> percentile of initial income, the effect is - 5.3 percent (the corresponding standard error is 0.8 percent). The estimates from the interaction model thus suggest that in poor countries increases in income inequality increase GDP per capita while the opposite is the case in high and middle income countries.

Additional evidence that our empirical results are in line with the Galor and Zeira (1993) model comes from the response of investment and human capital.<sup>1</sup> Our panel estimates show that within-country increases in income inequality significantly increase the investment-to-GDP ratio in poor countries but decrease it in high and medium income countries. Furthermore, within-country increases in income inequality significantly increase the stock of human capital (measured by the average years of schooling and share of the population with secondary and tertiary education) in poor countries; on the other hand, in high and medium income countries increases in income inequality reduce human capital.

> 1 Ideally, in the cross-country time series context, we would like to use data on the distribution of wealth rather than income since wealth inequality is the relevant measure in theoretical models with credit market imperfections. Unfortunately, data on wealth inequality are not available to generate a long time-series for a large number of countries. As noted in previous empirical research (e.g. Perotti, 1996), income inequality and wealth inequality are highly positively correlated.

3
unable to calculate the average treatment effect from ordinary observational data as we only observe either ( _Yi_ (1) _|Di_ = 1) or ( _Yi_ (0) _|Di_ = 0) for participant _i_ .

We can estimate the simple difference-in-means, _E_ ( _Yi_ (1) _|Di_ = 1) _− E_ ( _Yi_ (0) _|Di_ = 0). In observational samples, however, this difference is usually not a consistent estimator of the average treatment effect due to participants self-selecting into treatment. Selection bias occurs when the resulting distributions of participant characteristics differ between the treatment and control groups, and these differences are correlated with outcomes. Here, any difference in outcomes reflects a combination of both the underlying difference of unobserved characteristics and the treatment effect. Conditioning on observed characteristics may not fully control for all relevant sources of differences between treatment and controls.

Perfectly implemented random assignment solves the selection bias problem by inducing independence between the distribution of counter-factual outcomes ( _Yi_ (0) _, Yi_ (1)) and treatment status _Di_ conditional on the variables used in the randomization protocol. In the Jamaica Study, the randomization protocol first stratified the sample by age and sex, denoted by _X_ , and then randomly assigned children within strata to each treatment group.

# **4.1.2 Permutation Tests**

Our aim is to test the null hypothesis of no treatment effect. The small sample size of the Jamaican Study suggests that classical statistical inference methods that rely on large sample asymptotic theory to define the distribution of test statistics may be misleading. We address this problem by using non-parametric permutation tests as implemented in Heckman et al. (2010b). Permutation tests are valid in small samples because they are distribution free and do not rely on assumptions about the parametric sampling distribution.

Permutation tests are based on exchangeability properties generated directly from the randomization protocol. Exchangeability exploits the invariance of the joint distribution of ( _Y, D_ ) under the null hypothesis. Random assignment guarantees that the vector of treatment assignments _D_ is exchangeable within blocks of participants that share the same 10
**<u>Table 2: Descriptive Statistics</u>**

||Obs.|Mean|Std.<br>Dev.|Min.|Max.|
|---|---|---|---|---|---|
|Change in log exports|1,140|-0.007|0.250|-3.969|2.662|
|Change in log imports|1,144|0.003|0.227|-3.248|3.356|
|Relatedness index for imports|1,144|-0.001|0.042|-0.527|0.435|
|Relatedness index for exports|1,144|0.001|0.043|-0.545|0.349|
|Average change in log nominal wage|1,148|0.023|0.025|-0.349|0.433|
|Change in log employment|1,147|-0.013|0.066|-1.114|0.630|

Note: All statistics are calculated using the industries of employment in the first quarter of 2007 as its weight.

48
# **3 Economic Reform, Regulation and Pollution Reduction Since 1987**

# **3.1 The Basic Economics of Industrial Pollution Control**

Figures 2 and 5 reveal great variation in industrial emissions and pollution- **Sulfur Dioxide Concentrations (ug/m3)** related mortality across provinces. As Figure 7 **400** suggests, however, the **300** differences in emissions are **250** not primarily due to **20.** differences in the **scale** of industrial activity. Rather, =m ..... m they reflect differences in s _6r e_ 0 _amd_ =._.. **pollution intensity,** or pollution per unit of output: Industry is 'dirtiest' in Sichuan, 'cleanest' in Shanghai; Liaoning and Guangdong have more **Figure 6: S02 Concentration Levels, 1992** pollution-intensive industry than Beijing. Figure 7 also reveals some remarkable changes in a very short period of time: Pollution intensity has fallen sharply since 1988, with particularly strong and consistent reductions in Guangdong and Sichuan.

<!-- Start of picture text --> CDIrtenief Chirsm PnMrcs 82 Irternie4 Crese PrWrimes<br>40.0 0<br>.|E | I Sg> _ A A 1! ''I<br>210 -~~~~- -<br>co CD<br><!-- End of picture text -->

**Figure 7a: Provincial Differences in Industrial Pollution Intensity**

7
(measured in constant 2000 US$), during the period 2000-2010, workers’ remittances from abroad grew at an annual rate of close to 17 percent. As a result of this rapid growth, Moldova is one of the top countries in the world in terms of the GDP share of private transfers from abroad and the share of its labor force that has emigrated (World Bank 2011).

# **Figure 1. GDP growth 2000-2010 (%)**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>GDP growth<br>Average GDP growth<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-2<br>-4<br>-6<br>-8<br><!-- End of picture text --> Source: Statistics Moldova.

**Figure 2. Balance of payments items, 2000-2010 (% of GDP** )

<!-- Start of picture text --> 30<br>20<br>Net private transfers<br>10<br>0 Direct investment<br>-10<br>Net transfers to<br>-20 government<br>-30 Current account<br>-40<br>Goods and services<br>-50 account<br>-60<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: National Bank of Moldova.

4
theory that reduced opportunities can lead to religious revival (Binzel and Carvalho 2017), unrest (Campante and Chor 2012, Urzúa 2012, Flechtner 2017) and terrorism (Brockmeyer et al. 2020).

Third, aspirations are inherently social and undoubtedly circumstantial. Theory makes a strong case for it, especially for the poor who may come to believe that they can achieve little (Appadurai 2004; Ray 2006; Dalton, Ghosal, and Mani 2016). Others—such as parents, peers, teachers, leaders, etc.— exert tremendous influence on aspirations by acting as role models or promoting social norms. Empirical studies show that role models (Beaman et al. 2012; Macours and Vakis 2014, 2016; Riley 2019; Breda et al. 2020) as well as neighbors or peers (Bernard et al. 2014; Gagete-Miranda 2020; Galiani, Gertler, and Undurraga 2021) influence one’s aspirations and outcomes. The place of residence, living standards and life experiences can weigh heavily on what one gets to know and perceives as possible. For example, empirical evidence shows more directly that adverse circumstantial factors such as conflicts and natural disasters can crush hope and aspirations (Kosec and Mo 2017; Moya and Carter 2019). This is consistent with the nonlinear evolution of educational aspirations of adolescents over time, suggesting that their aspirations evolve with their circumstances (Favara, Chang, and Sánchez 2018).

Fourth, while the bulk of empirical studies points to consistent insights, measurement issues can challenge our understanding of the issue across studies. All empirical measures of aspirations are based on self-reported questions from surveys. However, aspiration measures often differ in the underlying concept they capture but they are rarely the subject of validation or even discussed. Most authors have a common understanding of aspirations as multidimensional and motivating, but the questions used to capture them in surveys differ in their formulation, possibly leading to different results and policy conclusions (see annex 3 for the list of the measures of aspirations used by the empirical studies cited in this paper).

The paper is organized as follows. Section 2 provides a background by defining the concept of aspirations; section 3 discusses empirical measures of aspirations and their challenges. Section 4 presents the empirical evidence of why aspirations matter after referring to the theory. Section 5 addresses the evidence and theory of the drivers of aspirations. Section 6 concludes.

4