rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Transforming informal institutions**

|**Source of change**|**Mechanism of Change**|**Pace of Change**|
|---|---|---|
|Formal institutional change|Change in the effectiveness of<br>formal institutions|Variable|
|Formal institutional change|Change in the design of formal<br>institutions|Often relatively rapid|
|Cultural evolution|Change in societal values|Very slow|
|Changes in distribution of<br>power,resources|Bargaining|Often slow, incremental|
|Coordinated change approach|Shift to new set of norms|Rapid|

_Source_ **:** Adapted from Helmke and Levitsky 2004, p 733 Religion plays a key role in the generation and transmission of values. Often, religion is a source of prejudiced attitudes about certain groups. Norms and behavior originating from religious beliefs may be seen as difficult to break, as religion is often characterized as being impervious in nature. However, scholars and activists have illustrated that religions evolve, refining and even revolutionizing previous beliefs and norms. While all religions make claims to truth, this truth is mediated by practical, cultural and even legal considerations. As such, development actors should not shy away from addressing religion as a source of discriminatory values. In fact, this point applies not only to religion, but to the range of entrenched culturally-based informal institutions.

Even when changes occur within informal institutions due to endogenous or exogenous factors (e.g. changes in formal institutions that result in changes in informal institutions), the effects of past discrimination can persist over time for well-identified groups. Hoff and Pandey (2008) illustrate the persistence of the caste system despite legal reform and how affirmative action policies try to redress caste discrimination. Similarly, the widespread presence of discriminatory attitudes—as found in the survey data presented above—persists even in countries where formal institutions are free from discrimination. How can such informal discrimination be tackled? A first step may be to draw people’s attention to their own prejudiced attitudes and discriminatory behavior of which they may often be unaware.<sup>23</sup> We discuss some ways in which stigma around HIV/AIDS in health care can be addressed.

# **_CASE: Improving health-service delivery for people living with HIV_**<sup>**_24_**</sup>

> 23 A simple test has been developed for this. Bertrand, Chugh and Mullainatan (2005) propose the Implicit Association Test, a way of exposing through a series of quick-fire written and visual tests peoples’ attitudes to members of excluded groups. They contend that the test can draw peoples’ attentions to the biased attitudes that they unknowingly hold. Further, they argue, it is a cheap, easy to administrate and direct way of addressing deepseated and widely-occurring prejudices

> 24 Sources: Nyblade, Stangl, Weiss and Ashburn (2009) and Feiss, Abebe, Girma and Woldie (2012)

33
|**Table 13. Estimation Results of Logit Model of the Det**|**erminants of On-Lending**|
|---|---|
||Coefficient/s.e.|
|Fund capital||
|Initial VF capital|0.318***<br>|
||_(0.102)_|
|VF allows for saving|0.170<br>_(0.147)_|
|VF requires members to save|0.138<br>_(0.137)_|
|Characteristics of VF committee||
|Proportion who have some higher education|0.060<br>_(0257)_|
|Proportion who have accounting experience|_._<br>-0.689<br>_(1280)_|
|Need training in loan appraisal (yes=1)|_._<br>-0.119<br>_(0.121)_|
|Accounts are computerized (yes=1)|<br>0.101<br>_(0128)_|
|VF officers are chosen, not elected (yes=1)|_._<br>0.062<br>_(0.162)_|
|VF would cut loans equally if funds were short (yes=1)|-0.149<br>_(0.130)_|
|Village size and organization||
|<br>Number of households in village (thousands)|-0.209<br>_(0247)_|
|Village has a market (yes=1)|_._<br>0.102<br>_(0.131)_|
|Rural (yes=1)|-0.143|
||_(0.157)_|
|Household characteristics||
|Proportion of households who are self-employed farmers|0.363<br>_(0358)_|
|Proportion of income generated by wages|_._<br>-0.518<br>_(0.413)_|
|Proportion of population with some secondary education|<br>0.568<br>_(0.459)_|
|Household assets (excluding land)|<br>0.065<br>_(0150)_|
|Proportion who have a loan from a formal bank|_._<br>0.772**<br>_(0.304)_|
|Proportion of borrowers who are women|-0.288<br>_(0.420)_|
|Expenditure per capita|<br>-0.219***<br>_(0.049)_|
|Loan terms and conditions||
|VF would borrow more if funds were short|2.076***<br>_(0.170)_|
|VF could lend out another 1m baht safely|<br>0.937***<br>_(0.182)_|
|VF wants to borrow more from BAAC, GSB, etc.|<br>0.853***<br>|
||_(0.130)_|
|Memo items:||
|Number of observations|2,203|
|p-value for equation|0.00|

_Source:_ Village Fund Survey 2010 and Thailand SES 2009. 2,203 observations. Robust standard errors in parentheses. * p<0.05, ** p<0.01, *** p<0.001. Intercept not shown. Dependent variable is 1 if VF borrows to on-lend, 0 otherwise.

23
(IMF 2009). Other estimates suggest that it boosted real GDP growth from an annualized low of 6.2 percent in the first quarter of 2009 to 11.9 percent in the first quarter of 2010 (Deng and others 2011). An Inter-American Development Bank study using a multicountry dynamic general equilibrium model simulation suggests that absent the fiscal stimulus, China’s GDP would have been 2.6 percentage points lower in 2009 and 0.6 percentage points lower in 2010 (Cova, Pisani, and Rebucci 2010). An input-output analysis shows that with a multiplier of 0.84, the stimulus generated 18–20 million new nonfarming jobs in the first year after implementation of the stimulus (He, Zhang, and Zhang 2009). The same study shows that the multiplier is about 1.1 in the medium run, as fiscal spending leads to higher household consumption and corporate investment.

Calculating the net fiscal multiplier is difficult, because there is no simple way to control the “fiscal experiment” (Barro and Redlick 2009; Christiano, Eichenbaum, and Rebelo 2009). The consensus in the literature is that fiscal expansion through higher spending has larger multiplier effects than expansion via a tax cut. In the short term, an increase in government capital spending has larger multiplier effects than untargeted increases in government expenditure (Ducanes and others 2006).

Estimates of the fiscal multipliers depend on the methodology, period, initial conditions, and controls applied. Models that assume full employment, rational expectations, and some version of Ricardian equivalence are more likely to have near zero or negative fiscal multipliers.<sup>2</sup> The size of the multiplier tends to be larger when leakages are minimized, monetary conditions are accommodative, and the country’s fiscal position is favorable. In the United States in the past 20 years, multipliers on government spending are estimated to have been between 1.5 to 2.0. Tax cut multipliers, estimated to be between 0.5 to 1 0 (Nallari 2010), are much smaller, because a large share of the tax reduction is often saved rather than spent. In the United States, the marginal propensity to consume out of disposable income in the past three decades averaged

> 2 Ricardian Equivalence (R.E.) is a theoretical economic concept. It suggests that when a government makes an attempt in stimulating the economy by increased government spending financed by debt, the overall demand in the economy remains unchanged. This, according to R.E. concept, is because the public will attempt to save its excess income obtained through the additional government spending in order to pay for future tax increases that will be initiated by the government to pay off the debt. http://en.wikipedia.org/wiki/Ricardian_equivalence

8
# **3. Determinants of Poverty**<sup>**21**</sup>

Many individual characteristics such as education, experience and labor market association are important correlates of poverty and the dynamics thereof. In this section, We investigate the marginal impact of each individual attribute on the likelihood that a household fall below the poverty line of absolute poverty. The analysis is undertaken applying probit regression techniques. Ferreira, Lanjouw and Neri (1999) use PPV data in a similar study, and these authors perform the study for Brazil as a whole and for one year, which gives a good but static picture of the situation in Brazil. Here the analysis is more dynamic in nature as it is based on nine PNAD data sets from the years 1981, 1985, 1988, 1990, 1993, 1995, 1997, 1998 and 1999. Hence, nine poverty analyses are performed for the individual states in the Northeast region and also for the all Northeast. This allows for an evaluation of the evolution of poverty over time and the most important variables determining poverty. This analysis reveals: (i) conditional correlation between poverty and characteristics of household heads; (ii) information about the volatility of the impact of the attributes on the likelihood that a household experiences poverty during the 1980s and 1990s; and (iii) information about groups that are particularly vulnerable and changes thereof over the past decades. The findings are presented in appendix D.

We regress the status of the household—poor or nonpoor—on relevant individual and household characteristics. The income concept used for the dependent variable is average per-capita income and the poverty line applied is the indigence. The dependent binary variable takes the value of one when income is below the indigence line and zero otherwise. The vector of independent variables includes: (1) attributes of household head: gender; ethnic origin; education; experience and labor market connection (whether the household head works, the type of relation with the labor market, sector of employment, and tenure in the job); (2) family variables: size and age of its members; (3) housing

> 21 Appendix D contains a significance analysis for poverty probits for Brazil, the Northeast and individual states in the Northeast based on PNAD 1999 data.

> As the results of the individual state-probit regressions are similar to the results of appendix D, they are only reported in graphical form in Figures 10 to 15 for the Northeast and Rio Grande do Norte in order to preserve space.

23
<!-- Start of picture text --> Figure 7: Position of national deciles in global distribution (1988 & 2008)<br>8 4China: rural & urban 8 +Indonesia: rural & urban<br>i= i=<br>2 2<br>38 38<br>2 2 IDN-u 08<br>2 CHN-u 0 2 IDN34,88<br>=go CHN-u 88 _ / ; =go Y ”<br>D a / iS) oA. 4<br>ra) 7 CHN-r08 Lv a“ re) -F 08<br>& J 2 & “<br>= ¥ = FAN<br>oDoorat] J. ~aCHN-r 88 oDoow /_ TDN-r 1988<br>2<br>_<br>-4— =~ _ 2 - Za<br>1 2 3 4 5 6 7 8 9g 10 1 2 3 4 5 6 7 8 9 10<br>decile of country's distribution decile of country's distribution<br>8 4Cdte d'lvoire & Nigeria 8 4Germany and Brazil<br>e e DEU 8 —<br>Ss Ss 7<br>2 3 . 2 8 DEU 08 BRA 0: = wa<br>zB= a7civ 88. — ~/% zBi A BRA88<br>8 ° Uo clvo 8 ° —<br>ro) / ro) 7<br>S / A 88 S /<br>2/2<br>=<br>2 / a = i<br>So 2 o /<br>oR TA oR<br>o o /<br>Qa 7 GA 08 Qa ;<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>decile of country's distribution decile of country's distribution<br><!-- End of picture text --> In this context, China-rural, China-urban, Indonesia-rural and Indonesia-rural are treated as “national” income distributions. 2008 values are always drawn as a solid line, 1988 values as a dashed line.
estimate the production function parameters. Once I have my measure of productivity, I then use a difference-in-differences matching estimator as proposed by Heckman et al. (1997) to examine whether firms improve productivity when they begin to export and the relative importance of the two channels (imports and investment in R & D).

The paper contributes to the literature in the following ways. _First_ , I use a measure of productivity that captures physical productivity, which does not suffer from the drawbacks of productivity measures that are generally used in the trade-productivity literature. I do so by measuring output from firm revenues and firm prices rather than industry prices, as is typically done. _Second_ , I explicitly model both imports of intermediate inputs and R & D into the production function. This allows me to examine two mechanisms for productivity improvement and to decompose gains to productivity from export entry into these two channels. _Third_ , I use data on firms from India. This country provides an interesting setting to study this question. While theoretical models emphasize the market size - investment in technology route to productivity improvements through export market entry, most of the world R & D takes place in developed nations. In 2011, seven countries accounted for 72 percent of global R & D spending.<sup>6</sup> There is little known on the R & D-productivity link in developing countries. The evidence on the R &D productivity link in India is mixed. Sharma (2014) finds the role of R & D in overall manufacturing productivity growth to be insignificant using a production function framework, but finds some evidence of it using a growth accounting framework.

The results from the paper suggest that productivity improvements from selling in international markets have largely been understated in the export-productivity empirical literature. After accounting for firm pricing heterogeneity, I find that over a six-year period, the difference in productivity growth between export entrants and their non exporter counterparts is about 11 percentage points. The estimates indicate that about 15 percent of the differences in productivity growth is explained by imports of intermediate inputs, while investment in R & D explains about 85 percent of this difference in productivity growth. Thus, investment in R & D is an important driver of productivity growth even in developing countries like India.

The rest of the paper is organized as follows. Section 2 describes the environment the firm operates in and the production technology of the firm. Section 3 presents the empirical production function where I discuss the measure of productivity that is used. Section 4 is on the estimation strategy employed. Section 5 describes the data. I discuss the findings in Section 6 and Section 7 concludes.

> 6National Science Board. 2014. Science and Engineering Indicators 2014. Arlington VA: National Science Foundation (NSB 14-01)

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17

# **4. QUANTIFICATION OF SERVICES POLICY INFORMATION**

It is notoriously difficult to measure policies affecting services trade because of their variety and complexity (see the overview by Deardorff and Stern, 2008). We consider alternative approaches (see Section 3 in Borchert, Gootiiz and Mattoo, 2012); the database presents one approach to quantification which we call the Services Trade Restrictions Index (STRI) because it is simple, transparent and fairly robust, and captures the essence of policy restrictiveness consistent with more sophisticated methods.

The STRI is a measure of the restrictiveness of a country‟s policy regime based on the policy information alone. This measure helps depict the overall patterns in policy across countries and sectors. It builds on a relatively long tradition of restrictiveness indices, ranging from simple counts of policy barriers (Hoekman, 1996) to more complex weighted averages, where weights reflect prior (usually subjective) assessments of the relative restrictiveness of specific policy barriers (OECD, 2009; 2011). Methods that assign fixed weights to all types of restrictions (entry, operational, regulatory) and that treat the restrictions as additive are flawed in certain respects. For instance, if foreign suppliers are not allowed to enter in the first place, then that restriction is binding and other restrictions on operations and regulatory environment simply do not matter. Similarly, a foreign equity limit of 49 percent already precludes foreign corporate control and so adding to it a further (frequently encountered) requirement that the majority of board of directors be nationals would amount to double counting.

We construct a single measure of overall openness for any subsector-mode combination, e.g. one for the cross-border supply of bank loans and another for accepting bank deposits by establishing commercial presence abroad. This measure of openness remains subjective but is relatively simple and transparent, and avoids the pitfalls of the approaches mentioned above. Essentially, we assess policy regimes for each subsector-mode combination in their entirety and assign them into five broad categories: completely open, i.e. no restrictions at all; completely closed, i.e. no entry allowed at all; virtually open but with minor restrictions; virtually closed but with very limited opportunities to enter and operate; and a final residual “middle” category of regimes which allows entry and operations but imposes restrictions that are neither trivial nor stringent. Table 4 presents
model 2 explains 94 percent of the GDP per capita dispersion. If one moves residence from a BB country, the base group, to an AA country, she or he would enjoy a 193 percent income rise, on average. A person moving to a BA country could expect income improvement of 88 percent, and someone moving to an AB economy could expect 91 percent improvement. Wald tests strongly reject the notion that country groups’ coefficients are zero or equal.

Model 4 presents the impacts of country groups on growth, after controlling for country fixed-effects. Only the coefficient of AAs is not significant, suggesting that they tend to grow at the same pace as BBs. As expected, Wald test rejects the hypothesis that groups’ coefficients are all zero or equal. These results suggest that country groups are relevant in predicting income and growth.

**Table 10: Impact of country** **<u>groups on GDP per capita level and growth - pooled OLS regressions</u>**

||**Dep. variable: G**|**DP per capita**|**Dep. variable: GD**|**P per capita growth**|
|---|---|---|---|---|
|**Explanatory variables**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|AA|1.237 (41.1)|1.929 (31.12)|.603 (1.63)|-.612 (-.30)|
|BA|.619 (8.29)|.879 (14.76)|4.761 (5.17)|9.107 (4.53)|
|AB|.362 (5.31)|.914 (15.35)|-2.979 (-3.55)|-5.49 (-2.98)|
|R2|.57|.94|.05|.13|
|F test|342 (.00)|402 (.00)|13.09 (.00)|3.81 (.00)|
|N|1,268|1,268|1,224|1,224|
|Country dummies included|no|yes|no|yes|
|Wald test that groups'<br>coefficients are equal|108.01 [.00]|253.9 [.00]|21.21 [.00]|34.06 [.00]|
|Wald test that groups'<br>coefficients are zero|569.7 [.00]|333.8 [.00]|15.05 [.00]|22.71 [.00]|

Notes: t-test in parentheses. p-value in brackets. Time and time square included in all models.

If country groups indeed capture common country features, they need to fit not only income and growth data, but also other relevant economic and social variables. To assess this hypothesis, we ran models similar to the ones above, but with other dependent variables. Table 11 shows that most country-group dummy coefficients are statistically significant at the 5 percent level and have the expected signs. Model 1, for example, shows that agriculture value added as a share of GDP in AA countries is, on average, about 17 percent below that of BB countries, the base group; and that, as a share of GDP in AB countries, it is 5 percent higher. Accordingly, BB countries are more heavily dependent on agriculture. Country groups plus time and time squared can explain about 33 percent of the variance of agriculture value added.

Model 2 shows that AA and BA countries have a greater share of industry in GDP than do BB countries. Model 3 shows that AA countries have a larger service sector.

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# **Table A.7**

# **What are the Correlates of Participation in the New Rural Pension Program? Dependent Variable: Participation (Yes=1, No=0)**

Probit Marginal Effects

|Controls|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Age|-0.002<br>(0.002)|-0.002<br>(0.002)|-0.003<br>(0.002)|-0.003<br>(0.002)|-0.003<br>(0.002)|-0.003<br>(0.002)|
|Age-Squared|0.000|0.000|0.000|0.000*|0.000|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Schooling|-0.002*|-0.000|-0.002*|-0.000|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Post-Secondary Education|-0.030***|-0.018*|-0.030***|-0.018*|-0.016*|-0.012|
||(0.008)|(0.009)|(0.008)|(0.009)|(0.009)|(0.010)|
|Married|0.009|0.005|0.011|0.007|0.004|0.005|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Female|-0.006|-0.004|-0.003|-0.002|-0.000|0.000|
||(0.006)|(0.005)|(0.006)|(0.005)|(0.006)|(0.005)|
|Rural Hukou Migrant||0.046***<br>(0.005)||0.046***<br>(0.005)|0.044***<br>(0.005)|0.038***<br>(0.005)|
|Currently Has a Primary Job|||0.019**|0.015*|-0.164|-0.139|
||||(0.008)|(0.008)|(0.359)|(0.482)|
|Participates in Employer-Based||||||-0.036***|
|Pension Scheme||||||(0.005)|
|Self-Employed|||||0.015|0.004|
||||||(0.120)|(0.166)|
|_Alternate Controls_|||||||
|Industry|no|no|no|no|yes|yes|
|Work-Unit Ownership|no|no|no|no|yes|yes|
|Observations|5,116|5,116|5,116|5,116|5,052|5,052|
|Average Participation|0.0645|0.0645|0.0645|0.0645|0.0653|0.0653|

Notes (for both panels): Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes 5116 migrant working age adults, aged 18-60. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables. Models (5) and (6) control for 17 industries and 8 work unit ownership types.

46