rafmacalaba/datause-encoder-data huggingface dataset:
households. The first round of the Local Level Institutions study documented<sup>that, while</sup> little recognized by officialdom, local activities and spontaneous local<sup>organizationshave</sup> flourished at the local level alongside the externally imposed _desa_ structures (Chandrakirana 2000, Grootaert 1999). In addition, analysis of the household data from the first round found significant positive coefficient of a social capital index<sup>(formed as a</sup> function of number of household group activities and their characteristics)<sup>in a</sup> multivariate regression on per capita consumption (Grootaert 2000). This<sup>analysis also</sup> provided some evidence of contributions of social capital to reported<sup>collective action</sup> and evidence of differential effects of different types of groups (Grootaert<sup>1999,2000).</sup> In the second round of the LLI study a multi-module household questionnaire collected information from 1200 households (30 households in each of<sup>40</sup><sup>_desas)'0 ._The</sup> questionnaire included standard modules on: (a) demographic information,<sup>(b) the</sup> SUSENAS "short-form" consumption expenditures, (c) household assets,<sup>(d) household</sup> shocks and coping strategies. In addition the survey collected information<sup>on two more</sup> unique aspects: household social activities and households participation<sup>in, and</sup> perceptions of, _desa_ government.

_IIIA) Measures of social engagement._ The survey elicited information on all hotusehold social activities--from<sup>pure</sup> sociability to membership in formal organizations. To capture "sociability",<sup>households</sup> were asked about the frequency with which they visited and were visited<sup>by other</sup> households. In addition, each household made a complete list of all its group<sup>activities in</sup> the past month and their purpose. For each group activity the household<sup>was askedif this</sup> 10
12 with our view that excessive seigniorage led to an acceleration of inflation. In Bolivia, for instance, the increase.in seigniorage occurred in 1982 while the hyperinflation became apparent only 1984. The picture is less clear in Germany, because the lag was much longer. A protracted period of very high seigniorage eventually led to the hyperinflation. Annual data indicates, however, that inflation entered into an accelerating trend around 1917, but became unstoppable only in the second half of 1922.

Finally, it is important to keep in mind that the CH took place in countries where high inflation was the exception rather than the rule. The hyperinflations of the twenties occurred when the world was by and large operating under the gold standard, and in an environment where price deflation was not unusual. The norm was definitely low inflation. Likewise, inflation in Bolivia during the sixties and seventies was moderate by Latin American standards. The worse inflationary episodes occurred in the midfifties when the annual inflation rate remained above 100 percent for a couple of years. Since then inflation remained fairly low; evidence of this was the fact that the country was on a fixed exchange rate regime since 1959 (with only two devaluations till 1982).

# StoDDina the Classical Hyperinflations

The classical hyperinflations were stopped always and everywhere abruptly through a comprehensive program that stabilized the exchange rate, reduced the budget deficit sharply, and send a clear signal that the Central Bank would end domestic credit to the government. In Germany, the exchange rate was stabilized on November 20th, and prices stabilized the following week.<sup>6</sup> Likewise, the hyperinflation in Bolivia was stopped in its tracks, 6 See data in Webb (1986) p.788.
Figure 5 plots the trends in informal shares of nonfarm employment by region between 2005/06 and 2016/17. We define informal employment as self-employment (including as a contributing family worker) or wage-employment without a written contract. Given this definition, the resulting informal shares should be treated as upper-bound estimates, as some (presumably small) percentage of the self-employed are likely to be formal and misclassified under our definition.

As the figure shows, most regions have observed a decline in the informal share of nonfarm employment. Whether a region shows a decline in informal share and to what extent does not appear to be related to whether the region is comparatively poor or rich.

# **III. Regional intersectoral productivity gaps**

# A. _Data, sample, and key variables_

Our main empirical analysis is based on data from Ghana’s Integrated Business Establishment Survey (IBES) 2014, administered by the GSS. The IBES 2014 is the latest census of nonhousehold establishments across all economic sectors.<sup>6</sup> In the survey, the GSS defines an establishment as “a unit of production or service engaged in a single economic activity under a single ownership at a single fixed location,” and defines nonhousehold establishment as one that “has a fixed location and operates from a structure not predominantly used for domestic activities.” The IBES was conducted in two phases, phase I and phase II. Fielded beginning in September 2014, phase I surveyed 638,480 business establishments. Data collected under phase I included location, form of organization, type of ownership, year of establishment, registration status, sector, principal and secondary activities, workforce size, skill distribution of the workforce, and type of financial accounting records maintained. The reference date for the data was August 31, 2014.

Fielded between November 2015 and April 2016, phase II surveyed 24,400 establishments (of an original intended sample of 31,152 establishments drawn from the set of establishments surveyed under phase I).<sup>7</sup> Data collected under phase II included workforce, wages and salaries, stocks, value of fixed assets, quantity and cost of inputs purchased, other

> 6 Ghana’s statistical authorities have administered four previous business establishment censuses focused exclusively on industrial establishments.

> 7 With establishments surveyed under phase I serving as the frame, the intended phase-II sample is comprised of all establishments with workforces of 50 or more workers and a representative sample of establishments with workforces of fewer than 50 workers.

8
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# **<u>Table 4:</u>** <u>Scenarios and Assumptions</u>

|**Scenarios**|**Realization of**<br>**hypothetical**<br>**productivity**<br>**gains (%)**|**Assumptions**|
|---|---|---|
|Scenario 1<br>(S1)<br>(moderate<br>reforms)|10%|•<br>The baseline growth projections for 2011–16 are<br>obtained from the Eleventh Malaysia Plan (11MP).<br>While the projections may have already included<br>some<br>assumptions<br>about<br>raising<br>domestic<br>competitiveness,<sup>a</sup><br>we<br>assumed<br>that<br>further|
|Scenario 2<br>(S2)<br>(more<br>purposeful<br>reforms)|20%|improvements in allocative efficiency come from an<br>increased effort and purposeful implementation of<br>structural reforms. We also assume a quicker<br>removal of non-performance–based incentives. We<br>also simulated growth scenarios using a more<br>realistic GDP baseline.<br>•<br>Capital and labor contributions to GDP growth<br>remain the same as in the baseline.|
|Scenario 3<br>(S3)|30%|•<br>Productivity gains from better allocation of resources<br>mainly flow through the productivity channel, and<br>have limited implications on capital and labor.|
|(aggressive<br>reforms)||•<br>The share of the manufacturing sector to the overall<br>economy remains at 23.4%, like its share in 2010.|

a. Refer to Chapter 2 of the Eleventh Malaysia Plan (11MP) (Malaysia, Economic Planning Unit 2015).

The first scenario assumes a realization of 10% productivity gains, which requires moderate reforms, particularly removing non-performance– based incentives (Eleventh Malaysia Plan, 2016–2020). The 10% realization of productivity gains is assumed to occur in five years, equivalent to an average productivity gain of 1.9% per year, a rate that is close to the U.S. pace of productivity gains between 1997 and 1987.<sup>26</sup> The second and the third scenarios assume an additional 10% linear increase each, amounting to 20% for scenario 2 and 30% for scenario 3. The optimistic scenario of a 30% realization of productivity gains is close to China’s 4.7ppt CAGR during the 2001–05 period.<sup>27</sup> For the three scenarios, we also consider two baseline GDP growth trajectories. The first baseline adopts the official growth projections published in the Eleventh Malaysia Plan, 2016– 2020 (11MP), which projects economic growth to increase an average of 5.8% during the 2016–20 period. The choice of this baseline raises concerns on two fronts. First, the growth forecast appears to be slightly optimistic. Second, the baseline in the 11MP already implicitly assumes some improvements in domestic competitiveness.

> 26 Hsieh and Klenow (2009) estimate TFP gains from equalizing TFPR within industries in the United States to be 36.1% in 1977 and 30.7% in 1987.  The decrease in the percentage of TFP gains represents a compounded average growth rate (CAGR) of 1.6ppt improvement in allocative efficiency during the 10-year period.

> 27 Hsieh and Klenow (2009) estimate China’s hypothetical TFP gains when moving toward the United States level of efficiency in 1997 to improve from 37% in 2001 to 30.5% in 2005.
<!-- Start of picture text --> OECD  Product  Market  Regula5on  Sub-­‐Indexes  and  Produc5vity<br>3.00<br>2.50<br>discriminatory  procedures<br>(against  foreign  firms)<br>domes5c  price  controls<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>50.00  70.00  90.00  110.00  130.00  150.00<br>Absolute  Produc5vity  US  1995=100<br>PMR  indexes  0  to  7<br><!-- End of picture text --> Figure 1: **OECD regulatory indexes and market service productivity** Francois (2009) have labeled this the ”proximity burden.” The proximity burden is recognized implicitly in the policy community, where the cross-border and local presence (or commercial establishment) components of international service transactions are referred to as modes of supply. Indeed, an important paper by Sampson and Snape (1985) that developed a typology of four modes of exchanging services that includes the establishment aspect of services trade was influential in leading policymakers to define trade in services in the GATS as spanning the four modes of supply.

The GATS modes translate into logical channels for modeling services trade and the impact of policy. Modes 1 and 2 will both manifest themselves as more or less standard cross-border trade. What is less obvious is that mode 3 (FDI and FATS) can also involve cross-border trade, because FDI can facilitate trade where imported services are ultimately delivered through foreign affiliates. As such, restrictions on FDI can manifest themselves, econometrically, as effective determinants of cross-border trade (Francois and Hoekman, 2010). In our view, the apparent complementarity between FDI policy and cross-border flows implies that the CGE literature should move toward more explicit linkages between affiliate sales and cross border sales. Even in models where FDI-based delivery is not modeled explicitly (meaning the bulk of the literature), a focus on cross-border trade implies an implicit role for FDI policy, and for joint modeling of modes 1, 2, and 3 trade. As discussed below, the approach in the CGE literature has so far involved rather crude modeling of nontariff barriers to trade in services as a mix 14
be most resistant to a change in norms are those that would fare worst under the new norms. This, as we will see in the next section, is an important insight for a successful development process. These strategic attempts to influence what norms others accept is often conscious for some agents, but there is no reason to think that everyone has a strategic attitude towards the norms in question. It might not even be the case that any of the agents are deliberately trying to manipulate the norms. When we are uncertain about which norm that applies in a situation, we often rely on what was done in previous situations similar to the current one. If a particular norm was applied before, we take it to be appropriate in the current situation too. But since people have different experiences, they can differ on what they take the appropriate norm to be, and all feel entirely justified in their insistence on their preferred norm. For example, in a study of competitive decision‐making groups, different norms emerged in different groups, depending on the experiences in previous rounds (Bettenhausen and Murnigan 1985). In other cases, obviously, the manipulation is conscious. For example, when a company wants to cut wages, management generally prefers to ascribe the need for cost cuts to a difficult market situation, rather than to management failures. They try to trigger the norm of sharing burdens equally rather than the norm of rewards and punishment according to merit, and they can do so by choosing to ascribe the need for cuts to an external cause rather than an internal management one (see for example Bicchieri 2006, p 78).

# **How can we influence social norms?**

The success of development efforts often depends on whether changes fit with or goes against pre‐ existing social norms. Therefore, development economists must consider how to influence social norms themselves. Social policy makers are becoming more and more aware of the importance of influencing social norms in order to achieve their goals. Our understanding of how to effectively influence social norms is, however, still very incomplete. This section will outline relevant strategies; some are already used by policy programs that attempt to change social norms, others have not yet been much explored in the literature and by policy makers but seem plausible given the nature of social norms.

Social norms influence our behavior both through our reflective, conscious mind, and through our more automatic, habitual system. Similarly, we can influence social norms by affecting both what people think, when in a reflective mode, and their more automatic, non‐conscious responses to choice situations. Most strategies for influencing social norms rely on both, to differing degrees.

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**Table 3: Financial literacy by retirement planning**

||(1)<br>Planners:<br>private<br>pension<br>funds|(2)<br>Planners:<br>public<br>pension<br>funds<br>only|(3)<br>Non-<br>planners|(1)<br>vs.<br>(2)|(1)<br>vs.<br>(3)|(2)<br>vs.<br>(3)|
|---|---|---|---|---|---|---|
|_Number of Observations_|259|918|189||||
|<br>|<br>_I_|<br>_nterest rate q_|<br>_uestion_||||
|Correct|46.7%|33.12%|37.57%|4.05 ***|1.94 *|-1.18|
|Do not know|2124%|3682%|2963%|-474 ***|-204 **|188 *|
|<br>|.|.<br>_Inflation que_|.<br>_stion_|.|.|.|
|Correct|57.53%|49.02%|50.26%|2.42 **|1.53|-0.31|
|Do not know|14.67%|29.19%|26.98%|-4.75 ***|-3.26 ***|0.61|
|||<br>_Risk quest_|<br>_ion_||||
|Correct|26.25%|9.48%|10.58%|7.2 ***|4.19 ***|-0.47|
|Do not know|27.03%|36.71%|40.74%|-2.9 ***|-3.08 ***|-1.04|
|||_Overall_|||||
|Interest & inflation correct|29.34%|20.04%|20.11%|3.2 ***|2.22 **|-0.02|
|All correct|7.72%|1.85%|2.65%|4.82 ***|2.32 **|-0.71|
|Number of correct answers|1.305|0.9161|0.9841|6.7 ***|3.99 ***|-1.05|

# <u>Notes:</u>

* p<0.10, ** p<0.05, *** p<0.01: From a t-test of mean differences. _<u>Planners: private pension funds</u>_ are defined as those who chose at least one of the following responses:  ̳Pension that you will receive from a privately owned retirement fund‘,  ̳Income from leasing and selling property‘,  ̳Additional pension or financial aid from an enterprise where you have been working‘, or  ̳Your own savings‘. _<u>Planners: public pension funds</u>_ are identified as those that responded,  ̳Pension that you will receive from a publicly owned retirement fund‘. _<u>Non-planners,</u>_ incorporate responses to the categories:  ̳Your own earnings (I will continue work after retirement)‘;  ̳Support from children, relatives, acquaintances‘;  ̳Support from church and charitable organizations‘; and  ̳Don‘t know‘.

We next examine whether the positive association between financial literacy and retirement planning persists in regression analyses. Table 4 presents marginal effects and robust standard errors from probit regressions, with _Planners: private pension funds_ as the dependent variable equal to 1 (and taking the value 0 if the individual plans to rely on public pension funds only or is a non-planner).

The first two columns present the baseline private retirement planning estimates, including two financial literacy measures as dependent variables, one at a time: (i) the dummy variable for correct response to all three questions, and (ii) the number of correct responses, respectively. Both variables show a significantly large and positive relationship 12
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Table 5

# Estimated Coefficients for ,I1 and k Under Altemative Measures of Speculative Assets

|Coefficient:|1984|1985|1986|1987|1988|
|---|---|---|---|---|---|
|Subsidizd<br>Loans<br>(P)<br>(i): all land and buildings<br>assumed to be speculative<br>(h=0)|0.257<br>(4.29)|0.185<br>(5.87)|0.066<br>(5.13)|0.295<br>(9.25)|0.196<br>(3.07)|
|(ii): all land and buildings<br>assumed to be productive<br>(h=l)|0.106<br>(4.50)|0.006<br>(0.96)|0.009<br>(1.39)|0.026<br>(1.89)|-0.013<br>(0.79)|
|Propoion<br>of_Filnmsi_<br>||||||
|(i): all land and buildings<br>assumed to be speculative<br>(h=O)|0.530<br>(30.46)|0.491<br>(28.71)|0.444<br>(27.41)|0.428<br>(26.10)|0.366<br>(22.45)|
|(ii): all land and buildings<br>assumed to be productive<br>(h=l)|0.459<br>(29.05)|0.436<br>(28.31)|0.390<br>(27.74)|0.397<br>(27.57)|0.330<br>(23.40)|

# Absolute values of t- statistics are in parentheses.
per million (ppm) by the end of the 21<sup>st</sup> century, and global mean temperature warming by between 2 and 4 ̊C (Meehl et al., 2007). For our analyses, we select “run 1” from each of the 22 climate models that archived surface temperature and precipitation data for the A1B 21<sup>st</sup> century scenario.

Given the importance of adequate precipitation for maize production in Tanzania and the world (e.g., Ahmed et al., 2011; Lobell et al., 2008), we focus our analyses on the occurrence of dry years in Tanzania and key (or potential) trading partners during the 20<sup>th</sup> and 21<sup>st</sup> century CMIP3 simulations. For the purposes of this study, we take a dry year to be any year in which the annual precipitation is equal to or less than the historical 1-in-10-year dry event. We take the historical period to be 1951-2000, meaning that for each trading partner, the 1-in-10-year dry event is the 5<sup>th</sup> driest year in that partner during the 1951-2000 period, and a dry year is any year in which the annual precipitation is less than or equal to the precipitation value of the 5<sup>th</sup> driest year of the 1951-2000 period<sup>17</sup> .

We use the CMIP3 climate model experiments to quantify the likelihood of different trading partners experiencing dry years, including the likelihood that these dry years co-occur in Tanzania and the trading partners. To do so, we first calculate the annual precipitation in each partner-country in each year from 1951 through 2000 in each of the CMIP3 historical simulations. We then calculate the magnitude of the 1-in-10-year dry event for each trading partner for the 1951-2000 period, which we may call the “dry year threshold”.

Having obtained the dry year threshold for each partner for the 1951-2000 period, we calculate three metrics of dry year occurrence in each decade of the CMIP3 A1B 21<sup>st</sup> century projections:

- We calculate the number of dry years in Tanzania and its key trading partners. This metric indicates the likelihood that each trading partner will experience adverse climate conditions for grains production. For each trading partner, we report the number of dry years in each decade of the 21<sup>st</sup> century.

- We calculate how often Tanzania experiences a dry year in the same year that her key trading partners do not experience dry years. This metric indicates the likelihood that Tanzania’s trading partners will experience non-adverse climate conditions in the same year that Tanzania

- experiences adverse conditions, therefore offering the potential for Tanzania to ameliorate

> 17 We note that although sub-annual variability is certainly important for agriculture, use of annual-scale aggregations allows us to compare countries from different hemispheres using a single metric.

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