rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

down the road if such longer range implications are not addressed. Responsible action in this area will require a more comprehensive approach that considers all stages of the reform process, and makes more specific provision for implementation over the medium term.

- Bank employment and pay operations often have multiple, sometimes conflicting objectives, usually related to fiscal soundness, bureaucratic efficiency, or developmental goals. To the degree possible, such objectives should be made explicit and trade-offs among them clarified from the outset. In cases where the IMF has been active on these issues, it will be important to discuss and coordinate Bank-Fund policy initiatives to ensure that mutual objectives are fully understood and appropriately balanced.

- e Bank operations are being carried out in the absence of adequate information about key pay and employment issues. Basic data on employment numbers and comparative salary scales for central government and parastatal sectors need to be collected and analyzed for reasoned policy formulation. Through an expanded program of research and country and economic sector work, more also needs to be learned about the detailed intricacies of salary and career incentive structures, non-wage allowance systems, supplementary benefit schemes such as social security programs, and labor market absorptive capacity, to name a few important topics for further analysis.

- Although this review has examined only system-wide, central government reforms in pay and employment, there is clearly a large body of experience with such reforms at the sectoral and state enterprise level. A comprehensive review of these experiences will be essential to complete this preliminary picture of the Bank's progress to date in this area.

- No single lending instrument could be identified in this review as the most suitable for bringing about desired changes in pay and employment systems. In fact, the variegated character of country cases and types of pay and employment problems calls for flexibility in operational approach and style. Although SALs have proven an effective mechanism for stimulating reform, one-shot conditionality diminishes the chances for success. The Bank, therefore, needs to consider serial conditionality supported by an institutional infrastructure provided either through specific SAL components, companion technical assistance projects, or mediumterm sectoral support projects with institutional development components.

- A consistent country strategy toward pay and employment issues must be developed at all levels of Bank intervention. These include central state and local government, state enterprises, and individual sectors. Public expenditure reviews provide a minimal framework for cooperation among these levels, but a more systematic methodology for coordination is needed to provide the necessary program coherence.
the likelihood of being satisfied with consumption. In the limiting case of only nonconsumption factors affecting reporting ( _σ_ = 1), the link between consumption and the probability of reporting satisfaction is obscured entirely, and increasing consumption does not change the probability of reporting satisfaction.

And lastly, it is possible that consumption and the likelihood of consumption can be negatively correlated following the onset of a shock. This overly simple framework has assumed that changes in _σ_ and changes in consumption are independent of each other. But there exists the possibility that the onset of a large event could cause both _σ_ and _C_ to co-move in opposite directions. In such a case, non-consumption factors would be weighted more at exactly the time that consumption and welfare might be changing, and one could find that subjective welfare measures move in exactly the _opposite_ direction of traditional consumption-based measures.

One potential example of a shock where both consumption and the weights that individuals place on non-consumption factors could simultaneously change is the onset of violence. Numerous settings demonstrate that consumption declines following the onset of violence;<sup>10</sup> and in such a case, households might suddenly place a higher emphasis on safety in their overall satisfaction with consumption. The key factor in whether satisfaction might actually increase despite consumption following is how large the increased emphasis on non-consumption factors might be relative to the size of the consumption decline.<sup>11</sup><sup>_,_12</sup> Importantly, the increased difficulty for subjective welfare metrics to detect changes in actual consumption is an issue highlighted by previous work (e.g., Ravallion 2013; etc.). However, to our knowledge, the possibility that the individual-specific scales on which subjective welfare questions rely might not be stable over time or in response to strong shocks has not been previously explored.

> 10See Blattman and Miguel (2010) for a survey of these findings.

> 11See appendix 1b for more detail.

> 12Another possibility is that the shock causes consumption and non-consumption factors to change in opposite directions. In such a case, even if the weight of non-consumption factors does not change, the shock could result in an increase in the likelihood an individual reports to be satisfied even if consumption declined. One example of this could be the toppling of an unpopular government. Consumption could decline, but that decline could be overwhelmed by an increase in satisfaction at the change in the political situation (captured by a change in _Si_ ).

8
- 22 -

||Average Rate for Property Insurance<br>(Price as basis points of total value insured)|
|---|---|
|Year|Rate<br>% change|
|1990|40|
|1994|130<br>225%|
|1998|70<br>(46%)|

However, while the price adjustment increases were swift, due to the capacity constraints the subsequent reduction and stabilization of prices took years, with such adjustments occurring with substantial lags following the return of insurance capacity to the market.

In 1990, the average rate for property insurance in moderate-risk countries in the East Caribbean was 0.4 percent, meaning that for a $100,000 limit on a property the insured would pay $400. Following Hurricane Andrew in 1992, prices surged, reaching 1.3 percent in 1994. Since 1994, the world catastrophe reinsurance market has improved, and rates declined to an estimated 0.70 percent for 1998 and remained so through 1999. What is clear from this, however, is that the price of insurance tripled for property owners in the East Caribbean, without any change in some of the islands' underlying risks. Many countries suffered no hurricane losses over this period. The price fluctuations reflected changes in the supply of catastrophe reinsurance following disasters in Florida and around the world<sup>6</sup> . It also reflected a changed perception by reinsurers on the potential cost of hurricanes. After Hurricane Andrew, insurers increased their estimates of the potential losses from catastrophes by a factor of 3 to 4 times<sup>7</sup> . The chart below not only takes into account the price increases but also the supply shortages which were reflected in higher retentions of risk by the primary insurers.

> 6For a more detailed analysis of this 'supply crunch', see Froot, Kenneth, "The Limited Financing of Catastrophic Risk", September 1997; and "The Pricing of U.S. Catastrophe Insurance", March 1997; Harvard Business School and National Bureau of Economic Research. Froot shows how increasing reinsurance prices simultaneous with less coverage purchased, inherently reflects a supply shift with reinsurers reducing the availability of catastrophic coverage.

7 There was a severe tightening in world catastrophe reinsurance markets following Hurricane Andrew which hit Florida in August 1992 and generated about US$18 billion in insured losses. In 1993, insurance prices were between 5 and 7 times their historical average, however, since 1995 the market has eased, and during the 1997-98 El Niulo period, the Atlantic hurricane season was abnormally low due to the stronger northern Jet Stream effect which penetrated further eastward into the Atlantic and prevented usual hurricane formations. However, a renewed hazard event frequency (both in the Caribbean and worldwide) could easily cause a reversion to tight conditions.
analysis of household income, expenditure, poverty, and children’s schooling outcomes. It also describes households’ occupational mobility over time. Section 4 presents the trends in assets, net worth, and indebtedness to examine whether microcredit borrowers are trapped in debt. Section 5 presents the impacts of microcredit on household and individual outcomes based on household FE estimates. Section 6 presents the same impacts using alternate estimation techniques. Finally, Section 7 concludes.

# **2. The Long Panel Survey and Its Data Characteristics**

The survey data used in this study were derived from a long panel survey over 20 years. The first survey round, conducted in 1991/92, studied the role of microfinance in the economic and social upliftment of the poor. Carried out jointly by the World Bank and the Bangladesh Institute of Development Studies (BIDS), the survey covered 1,769 households randomly selected from 87 villages in 29 _upazilas_ (rural subdistricts) of Bangladesh.<sup>5</sup> A second survey round, conducted in 1998/99, could not retrace 131 of the 1,769 households from the 1991/92 survey, leaving 1,638 available households, with an attrition rate of 7.4 percent. The 1998/99 survey also included new households from old villages and newly included villages; in total, 2,599 households were surveyed, of which 2,226 were from old villages and 373 from new ones. Among the 2,226 households in old villages, 279 were newly sampled ones and 1,947 were from the old panel surveyed in 1991/92. There were more panel households surveyed in 1998/99 (1,947) than in 1991/92 (1,638) because some from the old panel left after the first survey to form new households.

The households were surveyed a third time in 2010/11 with the help of the Institute of Microfinance (InM). This third survey round tried to revisit all of the households (2,599) surveyed

> 5 An _upazila_ is an administrative unit smaller than a district, consisting of a number of villages.

5
This has important implications for empirical investigations of international trade. Typically, studies use trade data from specific nomenclatures, ensuring consistent comparisons for products in the years in which that nomenclature is used. However, this approach can inadvertently include years marked by major discrepancies due to the nomenclature’s introduction. This is especially significant given that many empirical applications of trade data bypass nomenclature conversions. Some studies have moved toward replication of findings across different nomenclatures to show the robustness of the obtained results.

A notable finding from Figure 5 is that, although only about half of all records are successfully mirrored, a substantial part of the trade value in cross-border transactions can be compared against mirror records, applying equally to both imports and exports. The percentage of trade values verifiable against mirror data is impressively high. This has important implications for reconciliation procedures of trade data. Reconciliation procedures supply a reconciled value for records that can be successfully mirrored, and Figure 5 demonstrates that a considerable volume of global trade is contained in such records. However, caution is still necessary as the large concentration of trade volumes in few reporting countries obscures persistent reporting problems in data quality from some reporters.

The high percentage of trade value with successful mirroring does not necessarily imply accuracy in reporting and absence of discrepancies. To investigate whether discrepancies in reporting have decreased, for each year, we calculate the weighted discrepancy index across all bilateral transactions at the highest level of product aggregation. This will allow us to explore the magnitude and direction of the discrepancy in international trade flows. In addition, we calculate the weighted discrepancy index for successful mirrored records. This will allow us to explore whether the improvements were mostly due to an increasing number of reporters or accompanied by an increase in the quality of reporting on successfully mirrored records. We observe a welcoming trend in which the divergence in recorded values, as indicated by the weighted discrepancy index, is declining. Thus, we note not only an increase in dual recording but also a reduction in the discrepancy level between recorded values. Like the percentage of mirrored records and verifiable trade value, significant differences between reporters remain. Appendix Figure 6 hints at the differences in the quality of reporting from different reporters in the UNSD Comtrade database. Therefore, these differences are explored in detail in the later subsection.

24
**Table A.7. Macro SAM III: Additional calculations (billions of current Guinean francs)**

|**# Item or SAM cell**|**Definition**|**Source/Comment or Formula**|**2016**|
|---|---|---|---|
|_Data inputs to calcu_<br>|_lations for new cells in Macro SAM III (intermedia_<br>|_te data)_<br>||
|1 ginvgdp|Government investment as share of GDP|IMF (2016, p. 34)|0.091|
|2 fdi|Foreign direct investment|IMF (2016, p. 38)|1,933|
|3 fbor|Total foreign borrowing|IMF (2016, p. 38); defined as [sav‐inv,row] minus fdi|7,320|
|4 shrgfbor|Government share in total foreign borrowing|IMF (2016, p. 38); government is all except private short‐term|0.337|
|_New cells in Macro_|_SAM III (not in Macro SAM II)_|||
|5 [com‐prv,inv‐gov]|Government investment|=gdpmp*ginvgdp|5,307|
|6 [com‐prv,inv‐prv]|Private investment|=[com,sav‐inv]‐[c‐prv,inv‐gov]|4,535|
|7 [cap‐hhd,hhd]|Household savings|=[sav‐inv,hhd]|‐4,086|
|8 [cap‐gov,gov]|Government savings|=[sav‐inv,gov]|4,675|
|9 [cap‐row,row]|Foreign savings|=[sav‐inv,row]|9,253|
|10 [inv‐prv,cap‐row]|FDI|=fdi|1,933|
|11 [inv‐prv,cap‐hhd]|Household financing of private investment|=[com‐prv,inv‐prv]‐[inv‐prv,cap‐row]|2,602|
|12 [dstk,cap‐hhd]|Household financing of stock change|=[com‐prv,dstk]|0|
|13 [inv‐gov,cap‐gov]|Government investment financing|=[com‐prv,inv‐gov]|5,307|
|14 [cap‐gov,cap‐row]|Foreign financing of the government|=min{([inv‐gov,cap‐gov]‐[cap‐gov,gov]), fbor*shrgfbor}|632|
|15 [cap‐gov,cap‐hhd]|Household financing of the government|=[inv‐gov,cap‐gov]‐([cap‐gov,gov] + [cap‐gov,cap‐row])|0|
|16 [cap‐hhd,cap‐row]|Foreign financing of household|=([cap‐gov,cap‐hhd] + [inv‐prv,cap‐hhd] + [dstk,cap‐hhd])‐[cap‐hhd,hhd]|6,688|
|*Derivation of the f|ormula for government payment to labor:|||
|Known relationshi|ps:|||
|(1) [com‐prv,act|‐gov]=gintmed/va*[f‐lab,act‐gov]|||
|(2) [com‐gov,gov|]=[f‐lab,act‐gov] + [com‐prv,act‐gov]|||
|Substitutingfor i|ntermediate demand in 2 using1:|||
|[com‐gov,gov]=|[f‐lab,act‐gov] + gintmed/va*[f‐lab,act‐gov]=[f‐la|b,act‐gov]*(1 + gintmed/va)||
|Solving for govern|ment payment to labor:|||
|[f‐lab,act‐gov]=|[com‐gov,gov]/ (1 + gintmed/va)|||

Source: Authors’ elaboration.

‐30‐
plot the shares of total purchases of each industry (HS Section) that are sourced from firms in _Red_ , _Orange_ , and _Green_ zones. Except for the small HS industry 19 (arms and ammunition), there is no noticeable degree of concentration of suppliers from any particular zone.

# 6 ELASTICITY ESTIMATES

In this section, we show the results of the estimation of both firm-level elasticities of substitution across suppliers within a product category, and then across product categories.

## _6.1 Firm-level elasticities of substitution across suppliers_

First, we report OLS estimates in Table 1. In columns (1)-(3), we progressively include stronger sets of fixed effects. In column (1), we include both buyer-by-time and product-by-time fixed effects, as well as the Feenstra term. In column (2), we also include buyer-by-product and seller-byproduct fixed effects. In column (3), we include buyer-by-product-by-time and seller-by-product fixed effects. Notice that in column (3), the former set of fixed effects absorbs the Feenstra term. The implied elasticities exhibit values between 0 _._ 70 and 0 _._ 78 across the different specifications.

To test whether our estimates vary by product aggregation, in columns (4)-(7), the estimates are based on 6-digit and 8-digit HS codes. The elasticities range between 0 _._ 66 and 0 _._ 78, so the estimates do not meaningfully change. Since these elasticities are below 1, these estimates suggest that, at the firm level, suppliers act as complements rather than substitutes for buyers. This is important for aggregate incomes since, from Equation (8) we can see that, once we consider second-order effects, an elasticity of substitution less than 1 implies that the aggregate impacts of negative shocks are amplified.

Nevertheless, as we described previously, the OLS estimates may be contaminated by simultaneous demand and supply shocks that happened during Covid-19. In Table 2, we report 2SLS estimates based on our proposed instruments. We find evidence that inputs across different suppliers of a firm within the same 4-digit HS product category are highly complementary, ranging from 0 _._ 50 − 0 _._ 66, depending on the set of fixed effects and instruments we use.<sup>26</sup> Our preferred specifications are column (3) and (5) with an elasticity of 0 _._ 55, where we use both the seller and the seller-buyer level instrument, essentially deriving variation from both sellers’ production costs and transportation costs. We include buyer-by-time and product-by-time fixed effects that account for time-varying demand shocks and also account for firm entry and exit with the Feenstra (1994)

> 26In Table A11, we show similar patterns for HS-6 and HS-8 categories as described in Section 7.

26
In addition to standard data on household demographics and respondent characteristics, our baseline survey measures financial knowledge, attitudes, and preferences. We measure discount rates in the standard manner, by asking respondents to provide the minimum amount they would be willing to hypothetically accept in one month in lieu of a hypothetical payment of Rs. 350 today. Respondents in our sample report relatively high monthly discount rates; the median is 0.14 and the average is 1.58. We also measure risk aversion by allowing respondents to choose between a certain payment of Rs. 10, or playing a lottery which pays out Rs. 25 or Rs 0 with probability of 1/2 each. 17 percent of our sample chose the safe bet, and these respondents are coded as risk averse.

Additionally, we measure numeracy skills through a series of eight mathematics questions. The mean score for these mathematics questions is 4.7 out of 8. Almost all respondents could answer a simple addition question (“How much is 4+3?”), but only about 50 percent was able to answer a multiplication question correctly (“What is 3 multiplied by 6?”). Even fewer respondents were able to make percentage calculations correctly (“What is 8 percent of 100?”), with close to half responding “do not know” to this question. Cole, Sampson, and Zia (2011) find similar numeracy levels among households in Indonesia.

Finally, we measure baseline levels of financial literacy based on the following three questions: (1) “If you borrowed Rs. 5,500 and were charged 12 percent interest per month, how much interest would you pay in the first month?”; (2) “Suppose you had Rs. 100 in a savings account and the same amount saved at home, which of the two will yield returns at the end of the year?”; and (3) “Suppose your friend inherits Rs. 10,000 today and his brother inherits Rs. 10,000 three years from now. Who is richer because of the inheritance?” Measured financial literacy is low in our sample, with an average score if 1.6. Similar to the mathematics questions, few respondents (less than 10 percent) were able to calculate interest rates correctly in question 1, and over 60 percent responded “do not know” to this question. In contrast, almost all respondents were aware that a savings account yields positive returns (question 2), but only 58 percent of our sample was able to correctly identify the time value of money (question 3), lower than what Lusardi and Mitchell (2009) find among respondents in the US.

11
Table 7: Gender Gaps in Labor Force Participation by Age (%)

||Age|15-24|Age|25-35|Age|36-50|Age|51-65|
|---|---|---|---|---|---|---|---|---|
||_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|
|Albania|39|65.6|7.1|46.3|22.7|37.9|45.7|62.5|
|Bosnia and Herzegovina|-|51.4|-|33.7|-|36.3|-|43.2|
|Croatia|15.6|44.3|20|23.5|11.7|1.8|29.4|34.9|
|Kosovo|-|61.7|-|64.9|-|68.5|-|80.4|
|Macedonia|41.2|44.3|23.3|27.4|26.7|27.2|49.9|47.3|
|Montenegro|18.5|19.4|29.8|22.1|31.5|32.8|37.8|49.7|
|Serbia|47.1|64.4|29.4|26.3|19.6|24.6|44.3|39.1|
|Turkey|35.8|46.2|59.8|22.9|66.1|57|62.2|60.6|

Table 8: Gender Gaps in Self-Employed by Age

||Age|15-24|Age|25-35|Age|36-50|Age|51-65|
|---|---|---|---|---|---|---|---|---|
||_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|
|Albania|23.7|41.9|19.3|49|25.5|33|44.8|37.3|
|Bosnia and Herzegovina|-|45.5|-|12.2|-|0.9|-|-0.27|
|Croatia|49.2|-7.8|-7.2|9.5|-16.5|-44.6|-24|-77.8|
|Kosovo|-|75.7|-|58.1|-|59.9|-|29.5|
|Macedonia|68|84.5|69.5|61.5|72.4|63.3|74.3|71.9|
|Montenegro|69.3|-|79.6|-|55.1|-|-11.4|-|
|Serbia|71.6|79.9|52.3|67.2|56.8|55.8|43.1|67.9|
|Turkey|78.9|59.9|16.8|63.5|41.3|42.7|60.1|62.1|

there are no female employers at all in these cases. Turkey shows the largest gaps in most groups and years.<sup>11</sup>

# 4.2 Methodology: aggregate costs by age group

To carry out the age group decomposition of the total income loss, we rst need to calculate a counterfactual set of gender gaps for each age group. In particular, to get the fraction of the total income loss due to a particular age group, we compute what would the aggregate gender gap be if all the gender gaps were zero except for that particular age group.

For example, if we had two age groups, the aggregate employer's gap, denoted by _μ_ , would be dened as follows: where _E_ stands for number of employers and _L_ represents the labor force. The subindexes

> 11For Serbia we report an extremely high negative gender gap of -462%. This is due to the fact that there are very few observations for this country-year: only four females and two males report to be employers in that year.

11