rafmacalaba/datause-encoder-data huggingface dataset:
Likewise, the market-demand curve _D_ for SNAP-eligible products is obtained by the horizontal summation of demand of SNAP recipients _D_<sup>_S_</sup> and non-SNAP recipients _D_<sup>_NS_</sup> . Equilibrium price _P_ and quantity _Q_ are now obtained by the intersection of marginal revenue _MR_ that is obtained from demand _D_ and marginal cost given by supply curve _S_ . We now assume marginal cost is constant based on evidence on retailers by Stroebel and Vavra (2019). If the supply curve is flat under perfect competition, the price response is zero. On the other hand, we illustrate that the price response could be large with market power. The government then disburses SNAP benefits to recipients, shifting the demand curve of SNAP recipients up to _D_<sup>_S′_</sup> . Equilibrium price increases to _P_<sup>_′_</sup> and quantity increases to _Q_<sup>_′_</sup> by the intersection of new marginal-revenue curve _MR_<sup>_′_</sup> and supply _S_ . We now evaluate the changes in welfare as a result of SNAP. The increase in price and quantity leads to increased producer surplus of _P_<sup>_′_</sup> _CDEFP_ . SNAP recipients now act as if they face lower prices of _P_<sup>_S′_</sup> and quantity demanded increases to _Q_<sup>_S′_</sup> due to the disbursement of benefits, and hence their consumer surplus increases by _PMJP_<sup>_S′_</sup> . On the other hand, non-SNAP consumers now face a higher price _P_<sup>_′_</sup> , and their consumer surplus decreases by _P_<sup>_′_</sup> _BGP_ . To consider the deadweight loss of the program, we first observe that the loss in non-SNAP consumer surplus _P_<sup>_′_</sup> _BGP_ directly transfers to a gain in producer surplus. The remaining producer surplus _BCFG_ from SNAP consumers is equal in area to _P_<sup>_′_</sup> _AMP_ . The cost of SNAP benefits to the government is _P_<sup>_′_</sup> _AJP_<sup>_S′_</sup> , which implies part of the deadweight loss of the program is _MAJ_ . However, this deadweight loss is offset by the change in producer surplus _FCDE_ . To better understand the change in total surplus, note that _FCDE_ is identical in area to _MAKL_ minus _BGHI_ . Therefore, the change in total surplus of _FCDE_ minus _MAJ_ is equivalent to _MJKL_ minus _BGHI_ . In other words, the change in deadweight loss of the program is given by the decrease in deadweight loss when the monopolist sells more to SNAP consumers and the increase in deadweight loss when the monopolist further restricts output to non-SNAP consumers.

# **K Derivations**

## **K.1 Pass-through Formulas**

_<u>b</u>_ Under perfect competition, we can obtain the benefit pass-through elasticity _ερ ≡_<sup>_d_</sup> _db_<sup>_<u>p</u>_</sup> _p_<sup>by</sup> differentiating the equilibrium condition with respect to benefits _b_ . Quantity demanded _Q_<sup>_D_</sup> , the sum of demands by SNAP consumers _Q_<sup>_D,S_</sup> and non-SNAP consumers _Q_<sup>_D,NS_</sup> , is equal to quantity supplied _Q_<sup>_S_</sup> , as follows: 59
# **Table of Contents***

|**I.**|**Intr**|**oduction and Main Findings**|**1**|
|---|---|---|---|
|**II.**|**Stru**<br>|**cture and Performance**<br>|**5**|
||A.|Comparative Development||
||B.|Institutional Structure||
||C.|Asset and Liability Structure||
||D.|General Insurance||
||E.|Motor Insurance||
||F.|Medical Insurance||
||G.|Other General Insurance Lines||
||H.|Life Insurance||
||I.|Insurance Solvency||
|**III.**|**Reg**|**ulation and Supervision**|**20**|
||A.|Regulatory Authority||
||B.|Licensing Criteria||
||C.|Corporate Governance and Internal Controls||
||D.|Underwriting Policies and Technical Provisions||
||E.|Rules on Asset Segregation, Diversification and Valuation||
||F.|Solvency Margin Requirement||
||G.|Reinsurance Controls||
||H.|Code of Market Conduct||
||I.|Off-Site Surveillance and On-Site Inspections||
||J.|Sanctions and Ladder of Compliance||
||K.|Compensation Funds||
||L.|From Reactive to Proactive Risk-Based Supervision||
|**IV.**|**Poli**|**cy Issues and Future Prospects**|**33**|
||A.<br>B.<br>**Refe**|Policy Issues<br>Future Prospects<br>**rences**|**37**|

The paper has benefited from comments and insights provided by officials of the Insurance Commission, the Federation of Insurance Companies, representatives of Jordanian insurers, brokers and consulting firms and colleagues at the World Bank. Special thanks are due to Dana Janbulat of the Insurance Commission for her help in collecting the relevant laws, regulations and data.
the real exchange rate, but will not show up as a wedge between tradables and non-tradables, or as a wedge between consumption and investment. It will also not show up as a distortion to the current account, as the permanent nature of the distortion will leave the saving rate unchanged.

Our ability to identify “demand shifters” that operate by changing the saving rate is limited to the extent that these changes are reflected through changes in investment. For example, in a perfectly integrated small open economy, changes in the saving rate will not change investment, as foreign investors (or foreign investment opportunities) will fill in the gap so that the expected marginal product of capital (including any capital adjustment costs) will be equal to its global equilibrium level. Our approach can therefore hope to identify intertemporal “demand shifters” only when there is some home bias in investment, or when there are some barriers to capital mobility. Similarly, an economy that adjusts its saving primarily through bonds or other non-investment forms of savings is unlikely to show up as “distorted” in our analysis. In this sense, the IMF methodologies can be seen as complementary to our approach, as the MB approach and the ES approach may help identify deviations in the saving rate.

# **5.3 Identifying real exchange rate misalignment based on marginal products**

In their seminal paper, Hsieh and Klenow [2009] estimate a non-structural model of distortions, based on the following idea. Regardless of whether distortions are caused by incomplete access to credit, corruption, or other frictions, the distortions lead to the misallocation of inputs, causing some relatively productive firms to employ too few inputs while less productive firms employ too many or too much inputs. They thus are able to quantify the extent of misallocation by measuring the dispersion in marginal products. Using the Hsieh and Klenow [2009] methodology, we will measure whether there is a _systematic_ dispersion in marginal products between producers of tradable goods and non-tradable goods, and between producers of investment goods and consumption goods.

To illustrate the methodology, consider a simplified environment with two firms, producing two distinct goods, a tradable and a non tradable ( _i ∈{T, NT }_ ). As36
|_which are close_<br>_to the problem_.|• Measurement investments should be targeted at those performance<br>problems that are prioritized by the administration.<br>• Thereafter ensure that any judgements on an intervention’s success<br>or failure are based on credible measures of the problem being<br>fixed and_not_simply on output or process metrics.<br>• Where measures of success relate to whether the intervention is<br>functioning, practitioners should allow flexibility in the<br>implementation of the intervention (where possible) and in the<br>related measurement of its functioning. In this way, implementation<br>strategies can shift if it becomes clear from the collected data that<br>they are not making progress on fixing the problem.|
|---|---|
|3. _Embrace a role_<br>_for qualitative_<br>_data and a_<br>_theory of_<br>_change_.|<br>• Include qualitative data collection as a complement to quantitative<br>data.<br>• This may be as a prelude to future large-scale quantitative<br>instruments, or as perhaps the only available data option for some<br>aspects of public administration in some settings (such as those<br>experiencing sustained violence or natural disasters).<br>• Draw on qualitative methods as a basis for eliciting novel or<br>‘unobserved’ factors driving variation in outcomes.<br>• Tie measurement (both qualitative and quantitative) back to a<br>theory of change. If the implementation of an intervention is not<br>having its intended impacts on the problem, assess whether there<br>|
||are mistaken assumptions regarding the theory of change.|
|4. _Leave room for_<br>_judgment,_<br>_discretion and_<br>_deliberation;_<br>_because not_<br>_everything that_<br>_matters can be_<br>_measured_.|<br>• Consider carefully what you choose to measure, recognizing that<br>whatever you choose will inevitably create incentives to neglect<br>processes and outcomes that cannot be measured.<br>• Actively identify what you cannot (readily) measure that matters,<br>and take it seriously, developing strategies to manage that as well.<br>• This will include identifying those aspects of implementation in the<br>public sector that require inherently discretionary decisions.<br>• Employ strategies that value reasoned judgement, allowing<br>meaningful space for qualitative data inputs and the practical<br>experience of embedded individuals, treating such inputs as having<br>value alongside more quantitative ones.<br>• In the longer term, develop organizational systems that foster<br>“navigation by judgment”. For example: (i) a management structure<br>that delegates high levels of discretion so as to allow those on the<br>ground the space to navigate complex situations; (ii) recruitment<br>strategies that foster high numbers of staff with extensive context-<br>specific knowledge; and (iii) systems of monitoring and learning<br>that encourage the routine evaluation of theory against practice.|

25
of test score of gains.

# **3.4.3 Limitations**

These results are strongly suggestive of the processes outlined in the theory: household and school-level funds are technical substitutes in the production function of test scores and when school-level funding increases, it crowds out private spending within the household. Consequently, such grants have little (if any) impact on the path of test-scores. We are also able to show that the lack of a relationship between test-scores and school-level funding is _not_ because such funds have no effect through the production function—when households are ―surprised‖ and cannot adjust their own expenditures, test-scores increase with school funding.

However, there are a few caveats to the estimates presented from Zambia. First, our household substitution results are only valid for the ―remote‖ rural sample and while we can show that household spending offsets variation in anticipated funding, our test of the hypothesis that it does not respond to unanticipated funding is based on a small sample (only 4 of the 34 linked schools in the remote sample reported any unanticipated funding at all). Second, while standard in the literature, we cannot rule out that the size of the catchment area (used as an instrument for school-level enrollment) could be correlated to returns in the labor market or historical levels of education in the population. These in turn may be directly correlated to educational investments thus biasing downwards our estimates of crowding-out. Third, unanticipated funds could have been targeted in unobservable ways to schools where parental substitution would be less and where test-scores were more likely to increase even in the absence of funding.

While the patterns of observed characteristics and the stability of the results to the use of credible instrumental variables suggest that these are not serious concerns, we cannot fully rule out these alternate explanations. We therefore present results from a field experiment in the Indian state of Andhra Pradesh designed to specifically determine the pattern of crowd-out and we show that the results obtained are virtually identical across these different contexts. The randomized school grants address the second and third caveats above, while the first limitation is addressed by collecting spending data from a large sample of households at multiple points in time (both when the grants were a surprise, and later when they were expected), which allows us to test differential household responses to anticipated and unanticipated funds across a large representative sample of schools.

16
34

# TABLE 3

# PARAMETERS OF **THE**

# NIGERIAN AND INDONESIAN RESTRICTED TRANSLOG COST FUNCTIONS

(primary capital and purchased electricity taken as frixed) <!-- Start of picture text --> Variables Restricted Cost Function<br>Dep. Variable:<br>In (C/PI) INDONESIA NIGERIA<br>Nigeria: 1000's of<br>Naira Coefficients Coefficients<br>Indonesia: Rupiah (t-ratios) (t-ratios)<br>Intercept -28.473 -1.4780<br>(-2.081) (-0.804)<br>In (pM/pL) 0.5639 0.3424<br>(1.668) (2.936)<br>In (MC,/pL 0.1421 0.0196<br>(4.109) (4.465)<br>In e, .2.0457 -0.0049<br>(2.027) (-0.019)<br>In Q 1.7270 0.5083<br>(1.947) (1.500)<br>In K 0.2222 0.1900<br>(1.352) (1.778)<br>1X [In eNl 2 0.0308 0.0628<br>(0.286) (1.499)<br>1⁄2h [ 1 n(PM/p)j2 0.0528 0.0914<br>(7.144) (16.837)<br><!-- End of picture text -->
exports from and imports into the European Union.*? Our strategy is to use EU data on imports from developing countries to construct new "mirror" measures of export diversification in those same countries. Although it is true that we thereby measure export diversification vis-a-vis the EU and not the world as a whole, we believe that our measures remain highly relevant since the EU is one of the most important outlets for developing country exports. Moreover, this approach offers two concrete advantages over the use of cross-country export data at the 6-digit level. First, the aggregation problem is reduced (although not eliminated) due to the much greater level of detail in which products are defined and flows recorded. Second, import data from the EU are likely to be more reliable than the corresponding export data from developing countries due in part to stronger governance and customs agency capacity.

Taking 2005 as our base year, we start with a dataset of 470,035 observations across 246 countries and customs areas (including EU members), and 10,753 distinct products.* In this paper, we focus only on the developing country component of that dataset, namely countries that are neither members of the EU-25 nor the OECD. (We return to this definition in the context of robustness checks below.) Our first measure of export diversification, /ines, is a count of the number of 8- digit product lines in which a given country exported to the EU-15 in 2005. It has one observation per country. To provide greater detail, we also construct /ines_cn2 following the same pattern as for Jines, but with counts by 2-digit sector rather than aggregated to the country level. Lines_cn2 therefore has 97 observations per country (the number of 2-digit Chapters in the CN classification). Given that the CN 8-digit classification scheme is inconsistent in the level of detail (i.e., individual "products") it accords each sector, we will need to take care to correct for this when using /ines_cn2 as an indicator of export diversification.

In Table 1, we provide a list of the countries included in our sample divided up according to the quintiles of /ines. On average, they exported 1,138 8-digit product lines to the EU in 2005. <u>~ 3¢e http://fd.comext.eurostat.cec.eu.int/xtweb/.</u> To our knowledge, these data have not previously been used in product variety work. However, Fontagné et al. (1998), Henry de Frahan and Vancauteren (2006), and Manchin (2006) have exploited them in other contexts. 4Although these data are available for a number of years, limited availability of trade and entry cost data means that we must work in a purely cross-sectional context.

6
**Suaind Inflati** - 4

In January 1991 the Soviet government undertook a draconian supply-side policy by declaring large-denomination bank notes to be no longer legal tender. Holders were allowed to exchange them -for smaller bills up to a maximum governed by a worker's monthly salary or a fixed quantity, whichever was less. Household saving accounts were frozen, with individuals allowed to withdraw only 500 rubles per month.' The republics softened these regulations somewhat in subsequent actions. The net impact of this reform on inflation is by no means clear: although money broadly defined is lessened. so also is confidence in these stores of value. The velocity of expenditure almost certainly rose in consequence.

In 1991 the aggregate fiscal deficit of the members of the Soviet Union reached 26 percent of GDP.<sup>4</sup> Inflation reached previously unimagined rates: 142 percent on retail goods and 236 percent on wholesale products. This intlation induced the government to provide transfers to wage-earners, pensioners and savers to index partially the value of their incomes and financial assets. However, due to a lack of goods and services the households retained the income in forced saving of deposits or currency holding. The real value of household deposits was reduced somewhat by the inflation, while the ratio of currency in circulation to GDP remained nearly constant.

The former republics of the Soviet Union cooperated nearly unanimously on the liberalization of the majority of wholesale and retail prices on 2 January 1992. Those prices not liberalized were nearly all raised by between 300 and 500 percent. Given the repressed inflation of the mble - overhang, prices jumped rapidly in the first three months of 1992 retail prices rose by 600 percent and wholesale prices by over 1000 percent This rise in prices greatly reduced the value of nominal assets like savings deposits and currency: by the end of January 1992 the ratio of money broadly defined to GDP had fallen to less than 20 percent.

- 3 These details are reported in McKinnon (1991, p. 157).

lThe information in these two paragraphs is drawn from World Bank (1992).
<!-- Start of picture text --> 700.00<br>596.70<br>600.00<br>500.00<br>400.00 347.62<br>300.00<br>194.44<br>200.00<br>100.00 4.11 9.79 8.00 26.60 51.86<br>0.00<br>China India South UK USA France Australia Netherland<br>Africa<br><!-- End of picture text --> _Source: Shen and Zhai._ These data imply that, by and large, SOE led Chinese investment, with relatively large size and more focus on oil and gas, mining and other natural resource based sectors, is more likely to compete with traditional European and American investment in Africa. In contrast, private Chinese investors belong more to the group of investors from other emerging markets, such as India and South Africa, featuring numerous small and medium projects concentrated in laborintensive manufacturing, trade and other service activities.

- **_Chinese investment is generally perceived as “positive” in terms of economic impact by most host countries. This is largely thanks to its effect on job creation, a pressing need as perceived by host governments. The overall impact of Chinese investment on local industrialization is mixed, with both praise and concern._**

Four of the five host countries responding to the questions about the economic impact of Chinese investments found the overall impact positive. As seen in Table 1, all five highlighted employment generation to be the top benefit from Chinese investment. To the question about the impact of Chinese investment on local industrialization, the answer is mixed, with three saying “positive” and two saying “negative.” Regarding technology transfer, all responded with “negative.”

**Table 1: African Governments’ Perceptions of the Economic Impact of Chinese Investment**

16
# **Table A.13. Total effect of pensioners by household composition, RLMS 1994-2017**

|**Total number of hh members**|**Number of pensioners**|**Pooled Ordered Logit**|**BUC**|
|---|---|---|---|
|_2_|_1_|-0.056***|0.049*|
|||_(0.01)_|_(0.03)_|
|_3_|_1_|-0.065***|0.033*|
|||_(0.01)_|_(0.02)_|
|_3_|_2_|-0.129***|0.067*|
|||_(0.02)_|_(0.03)_|
|_4_|_1_|-0.071***|0.022|
|||_(0.01)_|_(0.02)_|
|_4_|_2_|-0.142***|0.043|
|||_(0.03)_|_(0.04)_|
|_5_|_1_|-0.076***|0.013|
|||_(0.02)_|_(0.03)_|
|_5_|_2_|-0.152***|0.026|
|||_(0.03)_|_(0.06)_|

**_Note:_** Standard errors in parentheses are calculated using delta-method

48
Figure A6: Specification Charts for z-Score of Total Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .4<br>.2 .2<br>0<br>0<br>-.2<br>* Weather Product * -.2 * Weather Product * -.4<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .2<br>.2 .1<br>0 0<br>-.2<br>-.4 -.1<br>* Weather Product * -.6 * Weather Product * -.2<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .4<br>.1 .2<br>0 0<br>-.1 -.2<br>* Weather Product * * Weather Product *<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

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