rafmacalaba/datause-encoder-data huggingface dataset:
We employ a small set of firm level control (X) in the estimation - whether the firm is a multi-firm, age of firm (in logs), size of firm (in logs), exporter status, foreign ownership, and whether the firm has a checking or savings account. Sector (ISIC 2 digit level) and survey year fixed effects are also included in the specification.

The main concern of the estimations are omitted variable bias and selection. The key identifying assumption in this analysis is that the experience of extreme dry days in a given year is quasi-random within a given ADM2 unit. A body of empirical climate change literature has exploited random variations in weather to estimate a reduced-form production function-style equation (Dell et al., 2012; Felbermayr et al., 2022; Kotz et al., 2022). Simultaneity bias is less of a concern given that individual firms are unlikely to influence precipitation shocks. To address omitted variable bias concerns, we control for a variety of controls at the firm-level. We also account for time-invariant omitted variables at the ADM1 geographical level through ADM1 fixed effects. Since the number of dry day shocks are exogenous, simultaneity bias is less of a concern. However, we cannot rule out the possibility that firm location selection is endogenous to precipitation shocks. If productive firms move to areas with fewer shocks, then it may appear as though shocks are negatively correlated with firm performance although it is driven to some extent by selection. We try to account for this concern by including controls for determinants of firm productivity. Also, given that our sample only consists of surviving firms, our estimates may understate the true effect if firms are driven out of business due to these negative shocks.

We explore potential channels through which precipitation shocks may affect sales. We achieve this by regressing various variables that have been identified in the literature as plausible channels of climatic shocks and other right-hand side variables defined in equation (1). Finally, a subsample of firms in the Middle East and North Africa, and Europe and Central Asia surveyed around the year 2019 were asked specific questions on green investments and adaptation. We exploit this data to evaluate if firms who experience precipitation shocks are more likely to adopt various green measures.

10
_Table 3_ : Loss-to-harvest ratio across survey rounds and crops in Ethiopia.

||||**Area**<br>**Harvested %**|**Plant**<br>**productivity**<br>**reduction %**|**Kg**|**Kg/Ha**|
|---|---|---|---|---|---|---|
||ESS13|Loss/Harvest Ratio<br>Loss/Harvest Ratio(anyloss)|91.5|18.1|0.27<br>0.28|1.01<br>0.96|
|**Maize**|ESS15|Loss/Harvest Ratio<br>Loss/Harvest Ratio(anyloss)|88.9|25.3|0.61<br>0.58|1.44<br>1.2|
||ESS18|Loss/Harvest Ratio<br>Loss/Harvest Ratio(anyloss)|90.1|18.1|0.36<br>0.34|0.94<br>1.01|
||ESS13|Loss/Harvest Ratio|881|198|0.66|0.66|
|||Loss/Harvest Ratio(anyloss)|.|.|1.79|1.75|
|**Sorhum**|ESS15|Loss/Harvest Ratio|794|40|2.08|2.51|
|**g**||Loss/Harvest Ratio(anyloss)|.||3.32|3.73|
||ESS18|Loss/Harvest Ratio|884|20|0.63|1.61|
|Note: The table|<br>reports the r|Loss/Harvest Ratio(anyloss)<br>atio of estimated losses to self-reported ha|.<br>rvest on maize and sor|<br>ghum plots for the wh|0.53<br>ole sample|1.16<br>and only|

Note: The table reports the ratio of estimated losses to self-reported harvest on maize and sorghum plots for the whole sample and only for plots who have had any loss, by survey round.

Our graphical analysis is further complemented by Table 3. Generally, there is little variation between rounds in the average percent plant productivity reduction and percent area harvested. However, compared to the two other survey rounds, the Ethiopia Socioeconomic Survey 2015/16 (ESS15) clearly stands out as

- More plots had any plant productivity reduction (irrespective of crop)

- More sorghum plots lost some of the planted area

- The average size of losses for maize (plant productivity loss) and sorghum<sup>31</sup> (plant productivity loss and planted area loss) was notably larger.

Conspicuously though, while our losses proxies tell a clear story consistent with the notion of a large drought in 2015/16, this is not supported by average self-reported or crop cut yields. This finding is not unique to our analysis and is extensively discussed in Sohnesen, (2020) who finds that while meteorological data and self-reported shock reports are consistent with a severe drought, this is not supported by vegetation indices and crop yields. In our case, higher than usual reports of area and productivity reductions combined with yet relatively normal realized production leads the loss-to-production ratio to be considerably larger in ESS15 with a few unrealistically large outliers in loss estimates (cases where the loss per hectare exceeds the highest realized yield on any plot in the sample for the respective year).

> 31 Higher losses for sorghum are not in line with the literature on crop drought resilience though and should thus be a reflection of sorghum being planted more often where drought (and thus losses) is/are more severe. This is confirmed by comparing GIS rainfall data to 14-year average rainfall - for maize and sorghum plots, respectively.

17
# Higher Wages, Lower Pay: Public vs. Private Sector Com ensation in Peru <u>p</u>

Andrea Coppola and Oscar Calvo-Gonzalez<sup>**1**</sup> World Bank JEL Classification: J31, J38

## Keywords: public-private sector pay, Peru

> 1 Corresponding author: <u>acoppola@worldbank.org. We would like to thank, without implicating, Barbara Cunha,</u> Julián Messina, Elizaveta Perova, and Jamele Rigolini for guidance on the methodology used in this paper, as well as comments from Jonas Frank and Mariano Lafuente and seminar participants, including Jorge Arrunátegui, Eduardo Beteta, Flor Blanco, Franklin Huaita, Miguel Jaramillo, and José Valdivia. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the World Bank and its affiliated organizations, or those of the Executive Directors of The World Bank or the governments they represent.
“experience to date has shown that as transformed institutions mature, deposits as a percentage of funding liabilities increases.” However, supervision does have a significant impact on outreach. Regulatory supervision is associated with larger average loan sizes, a common proxy for the relative poverty of borrowers, and less lending to women. The finding is consistent with strategic choices by institutions facing high supervision costs to shift away from serving more cost-intensive segments of the population. We also find that supervision is associated with having a higher share of staff concentrated in the head office, a natural response to reporting requirements and formalization.

Mersland and Strøm (2009) also conduct an econometric analysis of the impact of regulation with cross-institution data. In line with our findings on regulation and profitability, they find that regulation does not have a significant impact on financial performance. They do not find evidence for the trade-off with outreach, however. Hartarska and Nadolynk (2007) also show that regulation does not directly affect the performance of microfinance institutions, either in terms of operational self-sustainability (OSS) or outreach. They find that deposit-taking institutions have broader outreach, though, suggesting that regulation may offer an indirect benefit by permitting institutions to expand. The innovation in Cull et al. (2009b) is use of the _MicroBanking Bulletin_ data and, importantly, use of an indicator for on-site supervision.

Taken together, the evidence underscores the need to take more seriously the “regulator’s dilemma,” a notion developed by David Porteous (the studies are available at www.financialaccess.org; see also Jay Rosengard 2010).

# **6. Competition**

Our 2009c study turns to the “industrial organization” of the microfinance sector. We investigate the effects of competition on the profitability and outreach of microfinance institutions, focusing on competition from mainstream commercial banks. Here, we combine the MIX dataset with data on bank penetration from 99 developed and developing countries from Beck, DemirgüçKunt, and Martinez Peria (2007). Missing data for some of the control variables and imperfect overlap between the datasets reduce the final number of observations to 342, from 238 15
# D. CREDIT MARKETS _AND_ INSTITUTIONS

Israel has fairly developed money and capital markets. The markets are strongly dominated by government intervention, but most of the financial transactions are handled by commercial banks, supplemented by a well-developed securities exchange.

# 1. The Banking Systems

Commercial banking in Israel is widely accessible and at the same time highly centralized. There are 29 banks, but the industry is dominated by 5 banking groups which together account for 94 percent of all bank assets. More than 1000 bank branches offer competing service in all parts of the country. The commercial banks also act as major securities investment brokers and most of the highly developed mutual funds industry is owned and m-anaged by the banks.

AFricuJture is additionally served by two specialized banks whose major activity is channeling investment funds. They account however for only 15 percent of total bank credit to agriculture.

In the early 1950s, the number of cooperative credit societies in Israel exceeded My far the number of commercial banks. But they found it hard to compete with the banks and in the long run had to fold, many absorbed by Bank Ha'Poalim, the labor movemernt's bank. By now, despite Israel's deep rooted cooperative tradition, there are only two cooperative financial institutions in operation, and both are small, strictly local, and hardly known to the public.

# 2. Bonds<sup>7</sup>

Through most of its history the securities market in Israel was dominated by bonds, which since 1955 have all been linked to the price level or to foreign exchange. Linkage allowed the market to survive inflationary vagaries. Also for most of its history, the bond market 'This subsection draws on the publications of the Supervisor of Banks and on Heth (1966).

7 For the history of Israel's securities market, see Sarnat (1966) and Levy, Smith, Sarnat (1978).

22
**Table 11:** Number of ST households in the 2009-10 expenditure survey

|State|Rural|Urban|Rural + Urban|
|---|---|---|---|
|Uttar Pradesh|46|30|76|
|Maharashtra|468|150|618|
|Bihar|66|21|87|
|Andhra Pradesh|312|76|388|
|West Bengal|230|74|304|
|Tamil Nadu|38|33|71|
|Madhya Pradesh|569|127|696|
|Rajasthan|407|75|482|
|Gujarat|467|81|548|
|Karnataka|153|107|260|
|Orissa|669|149|818|
|Kerala|31|13|44|
|Assam|488|84|572|
|Jharkhand|610|136|746|
|Haryana|13|9|22|
|Punjab|7|12|19|
|Chhattisgarh|520|98|618|
|India(all states)|5359|1323|6682|

# _8.2. Poverty by Social Groups_

We now turn to poverty estimates by social groups. We present statewide poverty ratios at the Tendulkar line for the ST, SC and non-scheduled castes in Table 12 and for the OBC and FC in Table 13. Separate rural and urban poverty estimates at both the Tendulkar and Lakdawala lines for each group are relegated to the appendix. As before, we arrange the states from the largest to the smallest according to population in Tables 12 and 13.

With one exception, Chhattisgarh, the poverty ratio declines for each group in each state between 1993-93 and 2009-10. There is little doubt that rising incomes have helped all social groups nearly everywhere. In the vast majority of the states, we also observe acceleration in the decline in poverty between 2004-05 and 2009-10 compared to between 1993-93 and 2004-05. Reassuringly, the decline in ST and SC poverty has accelerated recently with the gap in poverty rates between them and the non-scheduled castes narrowing.

24
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Table 2 Major Exports in Uganda (1999-2000)

|Main Products|Export <br>1999|Shares<br>2000|
|---|---|---|
|Cofee|60.2|31.2|
|Cotton|3.64|5.5|
|Tea|4.48|9.22|
|Tobacco|3.06|6.69|
|Fish|5.19|7.67|
|Fruits|0.14|0.18|
|Flowers|1.53|2.47|
|Total|77.94|62.93|

Note: Uganda Bureau of Statistics. Compiled by the Uganda Export Promotion Board.

32
misallocation of capital problem, which is reflected in the EXV regression. We hence argue that the evidence is more consistent with the misallocation-of-capital hypothesis.

# **6. Diversification Effects and Economic Development**

In this section, we study the two competing hypotheses by examining the link between diversification effects and the level of economic development of a country. We investigate whether the learning and capital misallocation problems are sensitive to the degree of economic development of the country to which a finn belongs.

# _6.1_ **_Thte learniing-by-doinzg htypothlesis_**

In previous section, using Japan as an example, we argued that learning-by-doing is less costly for firms in more developed countries. Since vertical integration involves more learning than complementary diversification, we should observe that firms in more developed countries benefit more from vertical integration, because they already utilize sophisticated technologies and may have peer firms to learn from. On the other hand, we should not observe such performance difference for complementary diversification, because the degree of required learning is low.

We regress EPM and EXV on diversification variables as well as variables proxying the level of economic development of each of the nine countries. In the first model specification, we use the average per-capita GNP'<sup>3</sup> during 1991-96 (World Bank (1996)) as the proxy for economic development. In an alternative specification, we proxy the level of economic development by using the World Bank classification of countries by income level groups. This income grouping has been used in the studies of La Porta et al (1998) and Fauver et al (1998). As reported by the World Bank, the lower-middle income dummy equals one if the firm is from Indonesia, the Philippines, or Thailand. The high income Dummy equals one if the firm is from Hong Kong, Japan, Singapore or Taiwan. The numeraire is higher-middle income countries (Korea and Malaysia). The results are reported in Table 7.

[Table 7 here] 13We divide the per-capita GNP by 1,000,000 in the regressions.

17